trajs = null;\\n Iterable cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_Iterable_int()\\n {\\n Iterable trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_Iterable_int()\\n {\\n Iterable trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_Iterable_int()\\n {\\n Iterable trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMCMCWithoutAllelicPoN() {\\n final double meanBiasSimulated = 1.2;\\n final double biasVarianceSimulated = 0.04;\\n testMCMC(meanBiasSimulated, biasVarianceSimulated, meanBiasSimulated, biasVarianceSimulated, AllelicPanelOfNormals.EMPTY_PON);\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test public void calcROItest() {\\n DirectMC h = new DirectMC(\\\"AdMailing\\\", 10000.00, 3.00, 2000);\\n Assert.assertEquals(.4286, h.calcROI(), 1);\\n }\",\n \"public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {\\n double p = 0;\\n\\n KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexs in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // this may invoke exactP on small sample sizes which will not terminate in all\\n // cases\\n // p = t.kolmogorovSmirnovTest(this.train_values.get(i),\\n // this.test_values.get(j), false);\\n\\n // this uses approximateP everytime\\n p = t.approximateP(\\n t.kolmogorovSmirnovStatistic(this.train_values.get(i),\\n this.test_values.get(j)),\\n this.train_values.get(i).length,\\n this.test_values.get(j).length);\\n if (p > cutoff) {\\n mwbm.setWeight(i, j, p);\\n }\\n }\\n }\\n }\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"%\\\");\\n discretize0.m_DefaultCols = \\\"%\\\";\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(1);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setAttributeIndices(\\\"\\\");\\n Filter.makeCopy(discretize0);\\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 0;\\n discretize0.setUseBinNumbers(true);\\n discretize0.listOptions();\\n discretize0.makeBinaryTipText();\\n discretize0.getInvertSelection();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-458));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMMI() {\\n System.out.println(\\\"MMI\\\");\\n int a = 0;\\n int m = 0;\\n int expResult = 0;\\n int result = utilsHill.MMI(a, m);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void shouldCalculateProperMoveProbabilities() throws Exception {\\n\\n int actualCity = 0;\\n boolean[] visited = new boolean[] {true, false, false, false};\\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\\n {2, 2, 1, 1},\\n {1, 1, 0, 1},\\n {2, 1, 1, 0}};\\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\\n {3, 0, 0, 0},\\n {2, 0, 0, 0},\\n {1, 0, 0, 0}};\\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\\n// double[][] array = new double[0][0];\\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\\n\\n// assertTrue(probabilities[0] == 0.0);\\n// assertTrue(probabilities[1] == 0.0);\\n// assertTrue(probabilities[2] == 0.5);\\n// assertTrue(probabilities[3] == 0.5);\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n String[] stringArray0 = new String[2];\\n discretize0.m_ClassIndex = 906;\\n discretize0.m_IgnoreClass = true;\\n stringArray0[0] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n stringArray0[1] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setAttributeIndices(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n }\",\n \"private double GetMCRTestSet() \\n\\t{\\n\\t\\tint numErrors = 0;\\n\\t\\t\\n\\t\\tfor(int i = ITrain; i < ITrain+ITest; i++)\\n\\t\\t{\\n\\t\\t\\tPreCompute(i);\\n\\t\\t\\t\\n\\t\\t\\tdouble max_Y_hat_ic = Double.MIN_VALUE;\\n\\t\\t\\tint label_i = -1; \\n\\t\\t\\t\\n\\t\\t\\tfor(int c = 0; c < C; c++)\\n\\t\\t\\t{\\n\\t\\t\\t\\tdouble Y_hat_ic = Predict(i, c);\\n\\t\\t\\t\\t\\n\\n\\t\\t\\t\\tif(lossType == LossTypes.LOGISTIC)\\n\\t\\t\\t\\t\\tY_hat_ic = Sigmoid.Calculate(Y_hat_ic);\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif(Y_hat_ic > max_Y_hat_ic)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tmax_Y_hat_ic = Y_hat_ic; \\n\\t\\t\\t\\t\\tlabel_i = (int)Math.ceil(c);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\tif( nominalLabels.indexOf(Y.get(i)) != label_i ) \\n\\t\\t\\t\\tnumErrors++;\\n\\t\\t}\\n\\t\\t\\n\\t\\treturn (double)numErrors/(double)ITest; \\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.invertSelectionTipText();\\n boolean boolean0 = discretize0.m_UseBinNumbers;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testJuvenilesSpreadFromCellsWithCorrespondingMatureLctFromInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(3, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 6);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 9);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 3);\\n }\",\n \"@Test\\n public void testEstimateC_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"l*{e5SNh\\\");\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 177;\\n discretize0.setUseEqualFrequency(true);\\n double[][] doubleArray0 = new double[5][9];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (double) 177;\\n doubleArray1[1] = (double) 177;\\n doubleArray1[2] = (double) 177;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n doubleArray2[0] = (double) 177;\\n doubleArray2[1] = (double) 177;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[9];\\n doubleArray3[0] = (double) 177;\\n doubleArray3[1] = (double) 177;\\n doubleArray3[2] = (double) 177;\\n doubleArray3[3] = (double) 177;\\n doubleArray3[4] = (double) 177;\\n doubleArray3[5] = (double) 177;\\n doubleArray3[6] = (double) 177;\\n doubleArray3[7] = (double) 177;\\n doubleArray3[8] = (double) 177;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[8];\\n doubleArray4[0] = (double) 177;\\n doubleArray4[1] = (double) 177;\\n doubleArray4[2] = (double) 177;\\n doubleArray4[3] = (double) 177;\\n doubleArray4[4] = (double) 177;\\n doubleArray4[5] = (double) 177;\\n doubleArray4[6] = (double) 177;\\n doubleArray4[7] = (double) 177;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.listOptions();\\n discretize0.setIgnoreClass(true);\\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n discretize0.findNumBinsTipText();\\n discretize0.setInvertSelection(true);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString(511);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 511\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints((-2276));\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test(timeout = 4000)\\n public void test07() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setInvertSelection(true);\\n discretize0.useEqualFrequencyTipText();\\n int int0 = (-501);\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-501));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test08() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\");\\n discretize0.binsTipText();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning(25);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testGetImportanceStats() {\\n System.out.println(\\\"getImportanceStats\\\");\\n MDA instance = new MDA();\\n\\n // make the circles close to force tree to do lots of splits / make it harder\\n ClassificationDataSet train = getHarderC(10000, RandomUtil.getRandom());\\n int good_featres = 2;\\n\\n DecisionTree tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n double[] importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n // categorical features, make space wider b/c we lose resolution\\n train = getHarderC(10000, RandomUtil.getRandom());\\n\\n train.applyTransform(new NumericalToHistogram(train, 7));\\n tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n }\",\n \"private void findAbdomenCM() {\\r\\n \\r\\n if (foundCenterOfMass) return;\\r\\n int xcm = 0, ycm = 0, pixCount = 0;\\r\\n for (int idx= 0, y = 0; y < yDim; y++) {\\r\\n for (int x = 0; x < xDim; x++, idx++) {\\r\\n if (sliceBuffer[idx] == abdomenTissueLabel) {\\r\\n xcm += x;\\r\\n ycm += y;\\r\\n pixCount++;\\r\\n }\\r\\n } // end for (int x = 0; ...)\\r\\n } // end for (int idx = 0, ...)\\r\\n \\r\\n if (pixCount == 0) {\\r\\n// System.err.println(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n MipavUtil.displayError(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n return;\\r\\n }\\r\\n \\r\\n centerOfMass[0] = (short)(xcm / pixCount);\\r\\n centerOfMass[1] = (short)(ycm / pixCount);\\r\\n foundCenterOfMass = true;\\r\\n }\",\n \"final CM computeCM(int[/**/][/**/] votes, Chunk[] chks, boolean local, boolean balance) {\\n CM cm = new CM();\\n int rows = votes.length;\\n int validation_rows = 0;\\n int cmin = (int) _data.vecs()[_classcol].min();\\n\\n // Assemble the votes-per-class into predictions & score each row\\n\\n // Make an empty confusion matrix for this chunk\\n cm._matrix = new long[_N][_N];\\n float preds[] = new float[_N+1];\\n\\n float num_trees = _errorsPerTree.length;\\n\\n // Loop over the rows\\n for( int row = 0; row < rows; row++ ) {\\n\\n // Skip rows with missing response values\\n if (chks[_classcol].isNA0(row)) continue;\\n\\n // The class votes for the i-th row\\n int[] vi = votes[row];\\n\\n // Fill the predictions with the vote counts, keeping the 0th index unchanged\\n for( int v=0; v<_N; v++ ) preds[v+1] = vi[v];\\n\\n float s = doSum(vi);\\n if (s == 0) {\\n cm._skippedRows++;\\n continue;\\n }\\n\\n int result;\\n if (balance) {\\n float[] scored = toProbs(preds.clone(), doSum(vi));\\n double probsum=0;\\n for( int c=1; c 0) : \\\"original fraction should be > 0, but is \\\" + original_fraction + \\\": not using enough training data?\\\";\\n final double oversampled_fraction = _model.modeldist()[c-1];\\n assert(oversampled_fraction > 0) : \\\"oversampled fraction should be > 0, but is \\\" + oversampled_fraction + \\\": not using enough training data?\\\";\\n assert(!Double.isNaN(scored[c]));\\n scored[c] *= original_fraction / oversampled_fraction;\\n probsum += scored[c];\\n }\\n for (int i=1;i compute AUC, draw ROC\\n if(_N == 2 && !local) {\\n float snd = preds[2] / sum;\\n for(int i = 0; i < ModelUtils.DEFAULT_THRESHOLDS.length; i++) {\\n int p = snd >= ModelUtils.DEFAULT_THRESHOLDS[i] ? 1 : 0;\\n _cms[i][cclass][p]++; // Increase matrix\\n }\\n }\\n }\\n\\n // End of loop over rows, return confusion matrix\\n cm._rows=validation_rows;\\n return cm;\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints(3594);\\n Discretize discretize1 = new Discretize();\\n discretize1.attributeIndicesTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"\\\");\\n Discretize discretize2 = new Discretize();\\n // Undeclared exception!\\n try { \\n discretize2.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Before\\n\\tpublic void iniciaDisciplinaConvencional() {\\n\\t\\tList pos = new ArrayList();\\n\\t\\tList pos1 = new ArrayList();\\n\\t\\tpos1.add(\\\"Disciplina 3\\\");\\n\\t\\tpos1.add(\\\"Disciplina 4\\\");\\n\\t\\tpos1.add(\\\"Disciplina 5\\\");\\n\\n\\t\\tminitElimBai_1 = new MinitestesEliminandoBaixa(\\\"Disciplina 1\\\", 5, pos,\\n\\t\\t\\t\\t1);\\n\\t\\tminitElimBai_2 = new MinitestesEliminandoBaixa(\\\"Disciplina 2\\\", 10,\\n\\t\\t\\t\\tpos1, 2);\\n\\t}\",\n \"@Test\\n public void testGetMoveSpeedMultiplier() {\\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\\n }\",\n \"@Test\\n public void test13() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 1));\\n }\",\n \"private void computeMultiClassMCC(ConfusionMatrixElement[] elements) {\\n\\t\\tdouble Sq = matrixTotal * matrixTotal;\\n\\t\\tdouble CS = Arrays.stream(elements).map(n -> n.tp).reduce(0.0d, (n, m) -> n + m) * matrixTotal;\\n\\t\\tdouble t = 0.0d, p = 0.0d, tt = 0.0d, pp = 0.0d, tp = 0.0d;\\n\\t\\t\\n\\t\\tfor (ConfusionMatrixElement e : elements) {\\n\\t\\t\\tt = (e.tp + e.fp); \\n\\t\\t\\tp = (e.tp + e.fn);\\n\\t\\t\\t\\n\\t\\t\\ttt += (t * t);\\n\\t\\t\\tpp += (p * p);\\n\\t\\t\\ttp += (t * p);\\n\\t\\t}\\n\\t\\tmatrixMCC = (CS - tp) / (Math.sqrt(Sq - pp) * Math.sqrt(Sq - tt));\\n\\t}\",\n \"@Test\\n public void testLargeValues() {\\n double x = 123;\\n double dof = 6.4;\\n double nonCent = 100.34;\\n NonCentralChiSquaredDistribution dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.7930769).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 455.038;\\n dof = 12;\\n nonCent = 444.44;\\n\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.4961805).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 999400;\\n dof = 500;\\n nonCent = 1000000;\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.2913029).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n Locale.getISOLanguages();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[3];\\n double[] doubleArray2 = new double[9];\\n doubleArray2[5] = (-163.0);\\n doubleArray2[8] = 1421.4058829;\\n doubleArray0[3] = doubleArray0[2];\\n doubleArray0[4] = doubleArray0[1];\\n discretize0.setOutputFormat();\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1421.4058829;\\n doubleArray3[1] = (-163.0);\\n doubleArray0[6] = doubleArray2;\\n double[] doubleArray4 = new double[0];\\n doubleArray0[7] = doubleArray4;\\n double[] doubleArray5 = new double[6];\\n doubleArray5[2] = (-163.0);\\n doubleArray0[8] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test29() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[3][6];\\n discretize0.setIgnoreClass(false);\\n discretize0.m_CutPoints = doubleArray0;\\n double[] doubleArray1 = new double[4];\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints((-831));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -831\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"c9^n/\\\");\\n discretize0.getCapabilities();\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"@Test\\n\\tpublic void testCollapseByEditDistance() {\\n\\t\\tString cellBC = \\\"ATCAGGGACAGA\\\";\\n\\t\\tString molBC = \\\"CGGGGCTC\\\";\\n\\t\\tint snpPos = 76227022;\\n\\t\\tInterval snpInterval = new Interval(\\\"HUMAN_1\\\", snpPos, snpPos, true, \\\"test\\\");\\n\\t\\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\\n\\t\\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\\n\\t\\tresult.add(p);\\n\\n\\t\\t// let's add another pileup in that's within ed=1 and incorporates an error.\\n\\t\\t// result has only a single base because two UMIs are merged.\\n\\t\\tString molBC2 = \\\"AGGGGCTC\\\";\\n\\t\\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\\n\\t\\tchar[] b2 = { 'G', 'G', 'T' };\\n\\t\\tbyte[] bases2 = new byte[b2.length];\\n\\t\\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\\n\\t\\tbyte[] quals2 = { 37, 37, 10 };\\n\\t\\tp2.setBasesAndQualities(bases2, quals2);\\n\\t\\tresult.add(p2);\\n\\t\\tresult.collapseUMIs(1);\\n\\t\\tList qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 1);\\n\\t\\tAssert.assertEquals(new Byte((byte) 8), qualResult2.get(0));\\n\\t}\",\n \"@Test\\n public void testLogLikelihoodCorrelationMatrix()\\n {\\n IDoubleArray corr = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexes in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // get percentiles\\n double trainvals[] = this.train_values.get(i);\\n double testvals[] = this.test_values.get(j);\\n\\n Arrays.sort(trainvals);\\n Arrays.sort(testvals);\\n\\n // percentiles\\n double train_p;\\n double test_p;\\n double score = 0.0;\\n for (int p = 1; p <= 9; p++) {\\n train_p = trainvals[(int) Math.ceil(trainvals.length * (p / 100))];\\n test_p = testvals[(int) Math.ceil(testvals.length * (p / 100))];\\n\\n if (train_p > test_p) {\\n score += test_p / train_p;\\n }\\n else {\\n score += train_p / test_p;\\n }\\n }\\n\\n if (score > cutoff) {\\n mwbm.setWeight(i, j, score);\\n }\\n }\\n }\\n }\",\n \"public double[] monteCarloPercentiles(){\\n if(!this.monteCarloDone)this.monteCarlo();\\n return this.randomEigenValuesPercentiles;\\n }\",\n \"@Test\\n public void test10() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 3));\\n }\",\n \"@Test\\n public void testMesurerDistanceCoordonnees() {\\n System.out.println(\\\"MesurerDistanceCoordonnees\\\");\\n double lat1 = 0.0;\\n double lon1 = 0.0;\\n double lat2 = 0.0;\\n double lon2 = 0.0;\\n double expResult = 0.0;\\n double result = Utils.MesurerDistanceCoordonnees(lat1, lon1, lat2, lon2);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\\tInvert matching sense of column indexes.\\\");\\n discretize0.setOutputFormat();\\n discretize0.listOptions();\\n Discretize discretize1 = new Discretize();\\n discretize1.setIgnoreClass(false);\\n String[] stringArray0 = new String[2];\\n stringArray0[0] = \\\"\\\\tInvert matching sense of column indexes.\\\";\\n stringArray0[1] = \\\"\\\";\\n Discretize.main(stringArray0);\\n String string0 = discretize1.findNumBinsTipText();\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", string0);\\n \\n discretize1.setInvertSelection(false);\\n discretize0.getBinRangesString(511);\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n \\n discretize1.getFindNumBins();\\n assertEquals(10, discretize1.getBins());\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test096() throws Throwable {\\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\\\"\\\", 1281);\\n assertNull(costMatrix0);\\n }\",\n \"@Test\\n\\tpublic void evalCrispDistanceWithCrisp1(){\\n\\t\\tFuzzySet res = (CrispSet)cs1.distance(cs2);\\n\\t\\tassertTrue(res instanceof CrispSet);\\n\\t\\tCrispSet resCrisp = (CrispSet)res;\\n\\t\\t//Puntos limite\\t\\t\\n\\t\\tassertTrue(resCrisp.getLeftBoundary()==3);\\n\\t\\tassertTrue(resCrisp.getRightBoundary()==6);\\n\\t\\t//Valores extremos\\n\\t\\tassertTrue(resCrisp.getMembershipValue(0)==0);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(3)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4.5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6.01)==0);\\n\\t}\",\n \"@Test\\n public void testJuvenilesSpreadFromOutsideInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 5);\\n }\",\n \"@Test\\n public void pruebaCalculCamiseta() {\\n System.out.println(\\\"prueba Calcul Camiseta\\\");\\n Camiseta camisetaTest = new Camiseta(1);\\n assertEquals(190, (camisetaTest.calculCamiseta(1)),0) ;\\n \\n Camiseta camisetaTestDos = new Camiseta(2);\\n assertEquals(1481.2, (camisetaTestDos.calculCamiseta(7)),0) ;\\n \\n Camiseta camisetaTestTres = new Camiseta(3);\\n assertEquals(1178, (camisetaTestTres.calculCamiseta(4)),0) ;\\n \\n \\n }\",\n \"public int getCostPerMile() {\\n return costPerMile;\\n }\",\n \"@Test()\\n public void testTrainWarmCFastSMO() {\\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\\n\\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\\n warmModel.setC(1);\\n warmModel.train(train);\\n\\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\\n warm.setC(1e4);// too large to train efficently like noraml\\n\\n long start, end;\\n\\n start = System.currentTimeMillis();\\n warm.train(train, warmModel);\\n end = System.currentTimeMillis();\\n long warmTime = (end - start);\\n\\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\\n notWarm.setC(1e4);// too large to train efficently like noraml\\n\\n start = System.currentTimeMillis();\\n notWarm.train(train);\\n end = System.currentTimeMillis();\\n long normTime = (end - start);\\n\\n assertTrue(warmTime < normTime * 0.75);\\n\\n }\",\n \"@Test\\n public void testConnectedComponents()\\n {\\n Collection cc1 = MarkovModel.util.connectedComponents(P1);\\n assertTrue(cc1.containsAll(C1));\\n assertEquals(cc1.size(), C1.size());\\n\\n Collection cc2 = MarkovModel.util.connectedComponents(P2);\\n assertTrue(cc2.containsAll(C2));\\n assertEquals(cc2.size(), C2.size());\\n\\n Collection cc3 = MarkovModel.util.connectedComponents(P3);\\n assertTrue(cc3.containsAll(C3));\\n assertEquals(cc3.size(), C3.size()); \\n }\",\n \"@Test(timeout = 4000)\\n public void test26() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_FindNumBins = true;\\n int[] intArray0 = new int[6];\\n intArray0[0] = (-388);\\n intArray0[1] = (-2121610348);\\n intArray0[2] = 11;\\n intArray0[3] = (-1);\\n intArray0[4] = 1062;\\n intArray0[5] = 70;\\n discretize0.setAttributeIndicesArray(intArray0);\\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\\n discretize0.getOptions();\\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n }\",\n \"@Test\\r\\n\\tpublic void testAllMarkupsElectronics() throws BasePriceMustBePositiveException, \\r\\n\\t NegativePersonCountException, ProductCategoryNotSpecifiedException {\\r\\n\\t\\tNuPackEstimator estimator = new NuPackEstimator(new BigDecimal(\\\"100\\\"), 5, \\\"electronics\\\");\\r\\n\\t assertEquals(estimator.estimateFinalCost(), new BigDecimal(\\\"113.40\\\"));\\r\\n\\t}\",\n \"@Test\\n public void testGetProjectileSpeedMultiplier() {\\n assertEquals(1.3, proj.getProjectileSpeedMultiplier(), 0.001);\\n }\",\n \"public void testConcurrency() throws InterruptedException {\\n initCache();\\n\\n final ArrayList wcos = new ArrayList();\\n final ArrayList test = new ArrayList();\\n final LinkedBlockingQueue idxs = new LinkedBlockingQueue();\\n\\n Collection> tasks = new ArrayList>();\\n\\n for (int i = 0; i < 30000; i++) {\\n wcos.add(getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true));\\n test.add(-1);\\n idxs.put(i);\\n\\n //put and get task\\n tasks.add(new Callable() {\\n\\n public Integer call() throws Exception {\\n //put\\n int i = idxs.take();\\n boolean ok = wmsCache.put(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1, wcos.get(i));\\n\\n //get\\n if (ok) {\\n WMSTile wco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (wco.getCached()) {\\n if (compareWMSObjects(wco, wcos.get(i))) {\\n test.set(i, 1);\\n } else {\\n test.set(i, 0);\\n }\\n }\\n }\\n return i;\\n }\\n });\\n }\\n ExecutorService executorService = Executors.newFixedThreadPool(100);\\n executorService.invokeAll(tasks);\\n\\n //test cache cleaner operated correctly\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test for presence of invalid test comparisons\\n int invalid = 0;\\n int valid = 0;\\n for (int i = 0; i < test.size(); i++) {\\n if (test.get(i) == 0) {\\n invalid++;\\n } else if (test.get(i) == 1) {\\n valid++;\\n }\\n }\\n assertTrue(valid > 0);\\n assertTrue(invalid == 0);\\n }\",\n \"@Test\\n public void testGetCenter() {\\n int expResult = 3;\\n int result = this.dummyDigitSize.getCenter();\\n assertEquals(expResult, result);\\n }\",\n \"@Before\\r\\n\\tpublic void setup() {\\r\\n\\t\\tMockitoAnnotations.initMocks(this);\\r\\n\\t\\tcompute(\\\"12-10-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"10-09-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"11-08-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"03-05-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"09-03-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"01-02-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t}\",\n \"@Test\\n public void testAutocorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testIsRateMatrix()\\n {\\n IDoubleArray K = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isRateMatrix(K);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n doubleArray2[5] = (-236.4995664359);\\n doubleArray2[7] = 1421.4058829;\\n doubleArray0[3] = doubleArray2;\\n double[] doubleArray3 = new double[3];\\n doubleArray3[0] = 0.0;\\n doubleArray3[1] = (-236.4995664359);\\n doubleArray0[4] = doubleArray3;\\n double[] doubleArray4 = new double[21];\\n doubleArray4[0] = 1421.4058829;\\n doubleArray3[2] = (-236.4995664359);\\n doubleArray4[4] = 1.7976931348623157E308;\\n doubleArray0[7] = doubleArray4;\\n discretize0.m_NumBins = 162;\\n doubleArray0[1] = doubleArray4;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getBinRangesString(1);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"public void testSizeManagementWMS() {\\n initCache();\\n \\n ArrayList wcos = new ArrayList();\\n long putSize = 0;\\n long maxSize = 0;\\n int cacheSizeDropCount = 0;\\n for (int i = 0; i < 1000; i++) {\\n WMSTile wco = getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true);\\n putSize += wco.getSize();\\n\\n wcos.add(wco);\\n long size = wmsCache.getSize();\\n\\n if (size < maxSize) {\\n maxSize = size;\\n cacheSizeDropCount++;\\n } else {\\n maxSize = size;\\n }\\n\\n boolean result = wmsCache.put(wco.getQuery(), wco.getColourmode(), PointType.POINT_1, wco);\\n\\n //test if cache is full the put was unsuccessful\\n //allow for cachecleaner to have reduced the size between put and test\\n boolean test = (wmsCache.getSize() + wco.getSize() > wmsCache.getMaxCacheSize()) == !result;\\n if (test == false) {\\n assertTrue((size + wco.getSize() > wmsCache.getMaxCacheSize()) == !result);\\n }\\n\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n }\\n\\n //test size calcuations are operating\\n assertTrue(putSize > 10000);\\n\\n //test cache cleaner was run more than once\\n assertTrue(cacheSizeDropCount > 1);\\n\\n //test cache size is under max\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test gets. Anything that is a placeholder will be null.\\n int cachedCount = 0;\\n for (int i = 0; i < wcos.size(); i++) {\\n WMSTile getwco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (getwco.getCached()) {\\n assertTrue(compareWMSObjects(wcos.get(i), getwco));\\n cachedCount++;\\n }\\n }\\n\\n //test if at least 2 objects were cached at the end\\n assertTrue(cachedCount > 1);\\n }\",\n \"@Test\\r\\n public void testCalculateNDM() {\\r\\n System.out.println(\\\"calculateNDM\\\");\\r\\n NDM instance = null;\\r\\n Double expResult = null;\\r\\n //Double result = instance.calculateNDM();\\r\\n //assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n //fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test098() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(0, 0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[0];\\n resultMatrixCSV0.setRowOrder(intArray0);\\n assertArrayEquals(new int[] {}, intArray0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, intArray0.length);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML();\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n resultMatrixHTML0.setMeanPrec((-2615));\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML1 = null;\\n try {\\n resultMatrixHTML1 = new ResultMatrixHTML(2312, (-2214));\\n fail(\\\"Expecting exception: NegativeArraySizeException\\\");\\n \\n } catch(NegativeArraySizeException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testNumCellsColonisedFromOutsideGrid() {\\n CompletelySpatiallyRandomParams params1 = new CompletelySpatiallyRandomParams(0.1, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params1);\\n assertEquals(1, coloniser.getNumCellsColonisedFromOutsideGrid());\\n\\n // if baseRate = 0.5 then 9 * 0.5 = 4.5 ~ 5 cells colonised from outside grid\\n CompletelySpatiallyRandomParams params2 = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params2);\\n assertEquals(5, coloniser.getNumCellsColonisedFromOutsideGrid());\\n }\",\n \"@Test(timeout = 4000)\\n public void test117() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[1];\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String[] stringArray0 = new String[4];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\"[\\\";\\n stringArray0[2] = \\\"*\\\";\\n double[] doubleArray0 = new double[7];\\n doubleArray0[0] = (double) 0;\\n doubleArray0[1] = (double) 2;\\n doubleArray0[2] = (double) 0;\\n doubleArray0[3] = (double) 1;\\n doubleArray0[4] = (double) 2;\\n doubleArray0[5] = (double) 0;\\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex(resultMatrixPlainText0);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertNotNull(resultMatrixLatex0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n String string0 = resultMatrixPlainText0.getSummaryTitle(216);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"i\\\", string0);\\n \\n int[][] intArray1 = new int[8][0];\\n intArray1[0] = intArray0;\\n intArray1[1] = intArray0;\\n intArray1[2] = intArray0;\\n intArray1[3] = intArray0;\\n intArray1[4] = intArray0;\\n intArray1[5] = intArray0;\\n intArray1[6] = intArray0;\\n intArray1[7] = intArray0;\\n resultMatrixLatex0.setSummary(intArray1, intArray1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(8, intArray1.length);\\n \\n ResultMatrixCSV resultMatrixCSV1 = new ResultMatrixCSV();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(resultMatrixCSV1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n int[] intArray2 = resultMatrixCSV1.getColOrder();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNull(intArray2);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n boolean boolean0 = resultMatrixLatex0.getDefaultPrintColNames();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertFalse(boolean0);\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixSignificance0);\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance1.getRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance1.getColCount());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertNotNull(resultMatrixSignificance1);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n \\n String string1 = resultMatrixCSV1.toStringSummary();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(string1);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"-summary data not set-\\\", string1);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n assertFalse(string1.equals((Object)string0));\\n \\n boolean boolean1 = resultMatrixPlainText0.isRowName(1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n int int0 = resultMatrixLatex0.getDefaultColNameWidth();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, int0);\\n }\",\n \"int getN_estimators();\",\n \"@Test(timeout = 4000)\\n public void test048() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixCSV0);\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[][] intArray0 = new int[0][9];\\n // Undeclared exception!\\n try { \\n resultMatrixCSV0.setSummary(intArray0, intArray0);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 0\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testSmallDist() throws Exception {\\n assertTrue(\\n new ImageSimilarity(getFile(\\\"imageSimilarity/with.png\\\"))\\n .calcDistance(ImageIO.read(getFile(\\\"imageSimilarity/without.png\\\")))\\n > 0);\\n }\",\n \"double[] compute_t_Cm (double[][] t_mmnt, double thickness, double camber) {\\n double[] result = new double[15];\\n if (camber >= 0)\\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = ci15_from_javafoil_data(t_mmnt, aoa, thickness, camber);\\n else \\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = -ci15_from_javafoil_data(t_mmnt, -aoa, thickness, camber);\\n return result;\\n }\",\n \"public double[] jacobi_iter(double[] x, double tolerance, int M) {\\n double[][] sContent = {{1.0, 0, 0}, {0, 1.0, 0}, {0, 0, 1.0}};\\n Matrix s = new Matrix(sContent);\\n\\n double[][] tContent = {{0, -0.5, -0.33333333}, {-0.5, 0, -0.25}, {-0.3333333333, -0.25, 0}};\\n Matrix t = new Matrix(tContent);\\n\\n //keep track of the number of iterations\\n\\n int counter = 0;\\n double tol = 10000; //initial\\n double[] tempXVector;\\n Matrix firstFirst;\\n double[] firstPart; //s^-1(-T)(Xn)\\n double[] secondPart; //s^-1(b)\\n String output = \\\"\\\";\\n\\n double tempXVectorNorm;\\n double xVectorNorm;\\n\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"x(0): \\\" + output);\\n while (tol > tolerance && counter < M) {\\n //perform the jacobi iterations\\n firstFirst = s.multiply(t);\\n firstPart = Matrix.matrixVectorMultiply(firstFirst, x);\\n secondPart = Matrix.matrixVectorMultiply(s, bVector);\\n tempXVector = Matrix.plus(firstPart, secondPart);\\n\\n //find norm of vectors and then subtract the\\n tempXVectorNorm = Matrix.norm(tempXVector);\\n xVectorNorm = Matrix.norm(x);\\n tol = tempXVectorNorm - xVectorNorm;\\n tol = Math.abs(tol);\\n counter++;\\n numOfIterations++;\\n x = tempXVector;\\n }\\n\\n output = \\\"\\\";\\n if (counter == M) {\\n System.out.println(\\\"Jacobi Output for x(N): null\\\");\\n System.out.println(\\\"Too many iterations without reaching a accurate answer.\\\");\\n return null;\\n }\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"Jacobi Output for x(N): \\\" + output);\\n System.out.println(\\\"Number of Iterations using Jacobi Algorithm: \\\" + counter + \\\"\\\\n\\\");\\n return x;\\n }\",\n \"@Before\\n public void setup() {\\n threeByTwo = new Matrix(new int[][] {{1, 2, 3}, {2, 5, 6}});\\n compareTwoByThree = new Matrix(new int[][] {{1, 42, 32}, {2, 15, 65}});\\n oneByOne = new Matrix(new int[][] {{4}});\\n rightOneByOne = new Matrix(new int[][] {{8}});\\n twoByFour = new Matrix(new int[][] {{1, 2, 3, 4}, {2, 5, 6, 8}});\\n twoByThree = new Matrix(new int[][] {{4, 5}, {3, 2}, {1, 1}});\\n threeByThree = new Matrix(new int[][] {{4, 5, 6}, {3, 2, 0}, {1, 1, 1}});\\n rightThreeByThree = new Matrix(new int[][] {{1, 2, 3}, {5, 2, 0}, {1, 1, 1}});\\n emptyMatrix = new Matrix(new int[0][0]);\\n // this is the known correct result of multiplying M1 by M2\\n twoByTwoResult = new Matrix(new int[][] {{13, 12}, {29, 26}});\\n threeByThreeResult = new Matrix(new int[][] {{35, 24, 18}, {13, 10, 9}, {7, 5, 4}});\\n oneByOneSum = new Matrix(new int[][] {{12}});\\n oneByOneProduct = new Matrix(new int[][] {{32}});\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[7][2];\\n double[] doubleArray1 = new double[7];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = (-587.14627);\\n doubleArray1[2] = (-2542.40202289275);\\n doubleArray1[3] = (-167.679515);\\n doubleArray1[4] = (-1.0);\\n doubleArray1[5] = 1.0;\\n doubleArray1[6] = (-1.0);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n doubleArray2[0] = (-167.679515);\\n doubleArray2[1] = (-2692.352);\\n doubleArray2[2] = (-167.679515);\\n doubleArray2[3] = (-2542.40202289275);\\n doubleArray2[4] = (-587.14627);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n doubleArray3[0] = (-1.0);\\n doubleArray3[1] = (-1.0);\\n doubleArray3[2] = 1.0;\\n doubleArray3[3] = (-2542.40202289275);\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[7];\\n doubleArray4[0] = (-2692.352);\\n doubleArray4[1] = (-1.0);\\n doubleArray4[2] = (-1.0);\\n doubleArray4[3] = 0.0;\\n doubleArray4[4] = 1.0;\\n doubleArray4[5] = 0.0;\\n doubleArray4[6] = 1.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[1];\\n doubleArray5[0] = (-167.679515);\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[0];\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[1];\\n doubleArray7[0] = (-587.14627);\\n doubleArray0[6] = doubleArray7;\\n discretize0.m_CutPoints = doubleArray0;\\n String string0 = discretize0.invertSelectionTipText();\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\\\", string0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test\\r\\n\\tpublic void testGetReducedCell() {\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 10., 6., 4. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 12.328828005937952, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 7., 11., 5. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 13.96424004376894, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 6.8, -4., -9. },\\r\\n\\t\\t\\t\\tlattice1.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 11.968291440301744, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", -619.2, lattice1.getVolume(), EPS);\\r\\n\\t\\tdouble[] params = lattice1.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] {\\r\\n\\t\\t\\t\\t12.328828005937952, 13.96424004376894, 11.968291440301744,\\r\\n\\t\\t\\t\\t104.34229064539636, 86.89206686722031, 25.025508299339933 },\\r\\n\\t\\t\\t\\tparams, EPS);\\r\\n\\r\\n\\t\\tCMLLattice reducedLattice = lattice1.getReducedCell();\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 3.0, -5.0, -1.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 5.916079783099616, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 3.8, 1.0, -8.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 8.912911982062877, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 10.0, 6.0, 4.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 12.328828005937952, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", 619.2, reducedLattice.getVolume(), EPS);\\r\\n\\t\\tparams = reducedLattice.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] { 5.916079783099616,\\r\\n\\t\\t\\t\\t8.912911982062877, 12.328828005937952, 83.73054962879554,\\r\\n\\t\\t\\t\\t93.14372638090103, 74.15166267765416 }, params, EPS);\\r\\n\\r\\n\\t}\",\n \"@Test\\n\\tpublic void testOptimization() throws Exception {\\n\\t\\tfor(int i = 0; i < 20000000; i++) {\\n\\t\\t\\tint _300s = (int) (Math.random() * 100);\\n\\t\\t\\tint _100s = (int) (Math.random() * 100);\\n\\t\\t\\tint _50s = (int) (Math.random() * 100);\\n\\t\\t\\tint misses = (int) (Math.random() * 100);\\n\\t\\t\\t\\n\\t\\t\\tdouble acc = OsuApiScore.getAccuracy(_300s, _100s, _50s, misses);\\n\\t\\t\\t\\n\\t\\t\\tAccuracyDistribution accuracyDistribution = AccuracyDistribution.closest(\\n\\t\\t\\t\\t\\t_300s + _100s + _50s + misses, misses,\\n\\t\\t\\t\\t\\tacc);\\n\\t\\t\\t\\n\\t\\t\\tdouble rec = OsuApiScore.getAccuracy(accuracyDistribution.getX300(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getX100(), accuracyDistribution.getX50(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getMiss());\\n\\t\\t\\t\\n\\t\\t\\tassertEquals(acc, rec, 0d);\\n\\t\\t}\\n\\t}\",\n \"@Test\\n\\tpublic void testaGetMensagem() {\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 1 eh avaliada com 5 minitestes, eliminando 1 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_1.getMensagem());\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 2 eh avaliada com 10 minitestes, eliminando 2 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_2.getMensagem());\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test35() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertFalse(discretize0.getMakeBinary());\\n \\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n Attribute attribute0 = new Attribute(\\\"invalid CVS revision - not dots!\\\");\\n attribute0.setWeight(0.0);\\n Attribute.typeToString(attribute0);\\n ArrayList arrayList0 = new ArrayList();\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"UR qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 2);\\n\\t\\tAssert.assertEquals(new Byte((byte) 31), qualResult2.get(0));\\n\\t\\tAssert.assertEquals(new Byte((byte) 5), qualResult2.get(1));\\n\\t}\",\n \"@Test\\n public void testQuantile() {\\n System.out.println(\\\"quantile\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(0, instance.quantile(0), 1E-7);\\n assertEquals(0, instance.quantile(0.001), 1E-7);\\n assertEquals(0, instance.quantile(0.01), 1E-7);\\n assertEquals(2, instance.quantile(0.1), 1E-7);\\n assertEquals(3, instance.quantile(0.2), 1E-7);\\n assertEquals(13, instance.quantile(0.9), 1E-7);\\n assertEquals(22, instance.quantile(0.99), 1E-7);\\n assertEquals(30, instance.quantile(0.999), 1E-7);\\n }\",\n \"@Test(timeout = 4000)\\n public void test050() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderPRC((-1));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"public void testGetConformingStimulusLargeScale() throws Exception {\\n long start = System.currentTimeMillis();\\n Stimulus stimulus = new MockStimulus();\\n message.setConformingStimulus(stimulus);\\n for (int i = 0; i < TOTAL_ITERATION; i++) {\\n message.getConformingStimulus();\\n }\\n System.out.println(\\\"Calling \\\" + TOTAL_ITERATION + \\\"times getConformingStimulus method\\\"\\n + \\\" costs:\\\" + (System.currentTimeMillis() - start) + \\\" ms\\\");\\n }\",\n \"@Test\\n public void testLogLikelihood()\\n {\\n IDoubleArray T = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihood(T, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m trajs = null;\n Iterable cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_Iterable_int()\n {\n Iterable trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_Iterable_int()\n {\n Iterable trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCstepping_Iterable_int()\n {\n Iterable trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testMCMCWithoutAllelicPoN() {\n final double meanBiasSimulated = 1.2;\n final double biasVarianceSimulated = 0.04;\n testMCMC(meanBiasSimulated, biasVarianceSimulated, meanBiasSimulated, biasVarianceSimulated, AllelicPanelOfNormals.EMPTY_PON);\n }","@Test\n public void testMetastableMemberships()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.metastableMemberships(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test public void calcROItest() {\n DirectMC h = new DirectMC(\"AdMailing\", 10000.00, 3.00, 2000);\n Assert.assertEquals(.4286, h.calcROI(), 1);\n }","public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {\n double p = 0;\n\n KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();\n for (int i = 0; i < this.train.numAttributes(); i++) {\n for (int j = 0; j < this.test.numAttributes(); j++) {\n // negative infinity counts as not present, we do this so we don't have to map\n // between attribute indexs in weka\n // and the result of the mwbm computation\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\n\n // class attributes are not relevant\n if (this.test.classIndex() == j) {\n continue;\n }\n if (this.train.classIndex() == i) {\n continue;\n }\n\n // this may invoke exactP on small sample sizes which will not terminate in all\n // cases\n // p = t.kolmogorovSmirnovTest(this.train_values.get(i),\n // this.test_values.get(j), false);\n\n // this uses approximateP everytime\n p = t.approximateP(\n t.kolmogorovSmirnovStatistic(this.train_values.get(i),\n this.test_values.get(j)),\n this.train_values.get(i).length,\n this.test_values.get(j).length);\n if (p > cutoff) {\n mwbm.setWeight(i, j, p);\n }\n }\n }\n }","@Test\n public void testCorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable1 = null;\n IDoubleArray observable2 = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize(\"%\");\n FileSystemHandling.createFolder((EvoSuiteFile) null);\n discretize0.findNumBinsTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"%\");\n discretize0.m_DefaultCols = \"%\";\n double[][] doubleArray0 = new double[0][7];\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.getCutPoints(1);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 1\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test03() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setAttributeIndices(\"\");\n Filter.makeCopy(discretize0);\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 0;\n discretize0.setUseBinNumbers(true);\n discretize0.listOptions();\n discretize0.makeBinaryTipText();\n discretize0.getInvertSelection();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-458));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testForwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.forwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testMMI() {\n System.out.println(\"MMI\");\n int a = 0;\n int m = 0;\n int expResult = 0;\n int result = utilsHill.MMI(a, m);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void shouldCalculateProperMoveProbabilities() throws Exception {\n\n int actualCity = 0;\n boolean[] visited = new boolean[] {true, false, false, false};\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\n {2, 2, 1, 1},\n {1, 1, 0, 1},\n {2, 1, 1, 0}};\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\n {3, 0, 0, 0},\n {2, 0, 0, 0},\n {1, 0, 0, 0}};\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\n// double[][] array = new double[0][0];\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\n\n// assertTrue(probabilities[0] == 0.0);\n// assertTrue(probabilities[1] == 0.0);\n// assertTrue(probabilities[2] == 0.5);\n// assertTrue(probabilities[3] == 0.5);\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize(\"^yH*RVC#\\\"|tm,k2XpGN\");\n String[] stringArray0 = new String[2];\n discretize0.m_ClassIndex = 906;\n discretize0.m_IgnoreClass = true;\n stringArray0[0] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n stringArray0[1] = \"^yH*RVC#\\\"|tm,k2XpGN\";\n discretize0.setOptions(stringArray0);\n discretize0.setAttributeIndices(\"^yH*RVC#\\\"|tm,k2XpGN\");\n assertFalse(discretize0.getMakeBinary());\n assertFalse(discretize0.getUseEqualFrequency());\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getFindNumBins());\n }","private double GetMCRTestSet() \n\t{\n\t\tint numErrors = 0;\n\t\t\n\t\tfor(int i = ITrain; i < ITrain+ITest; i++)\n\t\t{\n\t\t\tPreCompute(i);\n\t\t\t\n\t\t\tdouble max_Y_hat_ic = Double.MIN_VALUE;\n\t\t\tint label_i = -1; \n\t\t\t\n\t\t\tfor(int c = 0; c < C; c++)\n\t\t\t{\n\t\t\t\tdouble Y_hat_ic = Predict(i, c);\n\t\t\t\t\n\n\t\t\t\tif(lossType == LossTypes.LOGISTIC)\n\t\t\t\t\tY_hat_ic = Sigmoid.Calculate(Y_hat_ic);\n\t\t\t\t\n\t\t\t\tif(Y_hat_ic > max_Y_hat_ic)\n\t\t\t\t{\n\t\t\t\t\tmax_Y_hat_ic = Y_hat_ic; \n\t\t\t\t\tlabel_i = (int)Math.ceil(c);\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\tif( nominalLabels.indexOf(Y.get(i)) != label_i ) \n\t\t\t\tnumErrors++;\n\t\t}\n\t\t\n\t\treturn (double)numErrors/(double)ITest; \n\t}","@Test(timeout = 4000)\n public void test11() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.invertSelectionTipText();\n boolean boolean0 = discretize0.m_UseBinNumbers;\n // Undeclared exception!\n try { \n discretize0.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testJuvenilesSpreadFromCellsWithCorrespondingMatureLctFromInsideGrid() {\n params = new CompletelySpatiallyRandomParams(3, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params);\n coloniser.updateJuvenilePresenceLayers();\n\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 6);\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 9);\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 3);\n }","@Test\n public void testEstimateC_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize(\"l*{e5SNh\");\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 177;\n discretize0.setUseEqualFrequency(true);\n double[][] doubleArray0 = new double[5][9];\n double[] doubleArray1 = new double[3];\n doubleArray1[0] = (double) 177;\n doubleArray1[1] = (double) 177;\n doubleArray1[2] = (double) 177;\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[2];\n doubleArray2[0] = (double) 177;\n doubleArray2[1] = (double) 177;\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[9];\n doubleArray3[0] = (double) 177;\n doubleArray3[1] = (double) 177;\n doubleArray3[2] = (double) 177;\n doubleArray3[3] = (double) 177;\n doubleArray3[4] = (double) 177;\n doubleArray3[5] = (double) 177;\n doubleArray3[6] = (double) 177;\n doubleArray3[7] = (double) 177;\n doubleArray3[8] = (double) 177;\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[8];\n doubleArray4[0] = (double) 177;\n doubleArray4[1] = (double) 177;\n doubleArray4[2] = (double) 177;\n doubleArray4[3] = (double) 177;\n doubleArray4[4] = (double) 177;\n doubleArray4[5] = (double) 177;\n doubleArray4[6] = (double) 177;\n doubleArray4[7] = (double) 177;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[0];\n doubleArray0[4] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.listOptions();\n discretize0.setIgnoreClass(true);\n String[] stringArray0 = new String[0];\n Discretize.main(stringArray0);\n discretize0.findNumBinsTipText();\n discretize0.setInvertSelection(true);\n // Undeclared exception!\n try { \n discretize0.getBinRangesString(511);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 511\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints((-2276));\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test(timeout = 4000)\n public void test07() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.setInvertSelection(true);\n discretize0.useEqualFrequencyTipText();\n int int0 = (-501);\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning((-501));\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test08() throws Throwable {\n Discretize discretize0 = new Discretize(\"\");\n discretize0.binsTipText();\n // Undeclared exception!\n try { \n discretize0.calculateCutPointsByEqualWidthBinning(25);\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testGetImportanceStats() {\n System.out.println(\"getImportanceStats\");\n MDA instance = new MDA();\n\n // make the circles close to force tree to do lots of splits / make it harder\n ClassificationDataSet train = getHarderC(10000, RandomUtil.getRandom());\n int good_featres = 2;\n\n DecisionTree tree = new DecisionTree();\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\n tree.train(train);\n\n double[] importances = instance.getImportanceStats(tree, train);\n\n // make sure the first 2 features were infered as more important than the\n // others!\n for (int i = good_featres; i < importances.length; i++) {\n for (int j = 0; j < good_featres; j++)\n assertTrue(importances[j] > importances[i]);\n }\n\n // categorical features, make space wider b/c we lose resolution\n train = getHarderC(10000, RandomUtil.getRandom());\n\n train.applyTransform(new NumericalToHistogram(train, 7));\n tree = new DecisionTree();\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\n tree.train(train);\n\n importances = instance.getImportanceStats(tree, train);\n\n // make sure the first 2 features were infered as more important than the\n // others!\n for (int i = good_featres; i < importances.length; i++) {\n for (int j = 0; j < good_featres; j++)\n assertTrue(importances[j] > importances[i]);\n }\n\n }","private void findAbdomenCM() {\r\n \r\n if (foundCenterOfMass) return;\r\n int xcm = 0, ycm = 0, pixCount = 0;\r\n for (int idx= 0, y = 0; y < yDim; y++) {\r\n for (int x = 0; x < xDim; x++, idx++) {\r\n if (sliceBuffer[idx] == abdomenTissueLabel) {\r\n xcm += x;\r\n ycm += y;\r\n pixCount++;\r\n }\r\n } // end for (int x = 0; ...)\r\n } // end for (int idx = 0, ...)\r\n \r\n if (pixCount == 0) {\r\n// System.err.println(\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\");\r\n MipavUtil.displayError(\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\");\r\n return;\r\n }\r\n \r\n centerOfMass[0] = (short)(xcm / pixCount);\r\n centerOfMass[1] = (short)(ycm / pixCount);\r\n foundCenterOfMass = true;\r\n }","final CM computeCM(int[/**/][/**/] votes, Chunk[] chks, boolean local, boolean balance) {\n CM cm = new CM();\n int rows = votes.length;\n int validation_rows = 0;\n int cmin = (int) _data.vecs()[_classcol].min();\n\n // Assemble the votes-per-class into predictions & score each row\n\n // Make an empty confusion matrix for this chunk\n cm._matrix = new long[_N][_N];\n float preds[] = new float[_N+1];\n\n float num_trees = _errorsPerTree.length;\n\n // Loop over the rows\n for( int row = 0; row < rows; row++ ) {\n\n // Skip rows with missing response values\n if (chks[_classcol].isNA0(row)) continue;\n\n // The class votes for the i-th row\n int[] vi = votes[row];\n\n // Fill the predictions with the vote counts, keeping the 0th index unchanged\n for( int v=0; v<_N; v++ ) preds[v+1] = vi[v];\n\n float s = doSum(vi);\n if (s == 0) {\n cm._skippedRows++;\n continue;\n }\n\n int result;\n if (balance) {\n float[] scored = toProbs(preds.clone(), doSum(vi));\n double probsum=0;\n for( int c=1; c 0) : \"original fraction should be > 0, but is \" + original_fraction + \": not using enough training data?\";\n final double oversampled_fraction = _model.modeldist()[c-1];\n assert(oversampled_fraction > 0) : \"oversampled fraction should be > 0, but is \" + oversampled_fraction + \": not using enough training data?\";\n assert(!Double.isNaN(scored[c]));\n scored[c] *= original_fraction / oversampled_fraction;\n probsum += scored[c];\n }\n for (int i=1;i compute AUC, draw ROC\n if(_N == 2 && !local) {\n float snd = preds[2] / sum;\n for(int i = 0; i < ModelUtils.DEFAULT_THRESHOLDS.length; i++) {\n int p = snd >= ModelUtils.DEFAULT_THRESHOLDS[i] ? 1 : 0;\n _cms[i][cclass][p]++; // Increase matrix\n }\n }\n }\n\n // End of loop over rows, return confusion matrix\n cm._rows=validation_rows;\n return cm;\n }","@Test(timeout = 4000)\n public void test11() throws Throwable {\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints(3594);\n Discretize discretize1 = new Discretize();\n discretize1.attributeIndicesTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"\");\n Discretize discretize2 = new Discretize();\n // Undeclared exception!\n try { \n discretize2.calculateCutPoints();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void testGetMoveSpeedMultiplier() {\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\n }","@Before\n\tpublic void iniciaDisciplinaConvencional() {\n\t\tList pos = new ArrayList();\n\t\tList pos1 = new ArrayList();\n\t\tpos1.add(\"Disciplina 3\");\n\t\tpos1.add(\"Disciplina 4\");\n\t\tpos1.add(\"Disciplina 5\");\n\n\t\tminitElimBai_1 = new MinitestesEliminandoBaixa(\"Disciplina 1\", 5, pos,\n\t\t\t\t1);\n\t\tminitElimBai_2 = new MinitestesEliminandoBaixa(\"Disciplina 2\", 10,\n\t\t\t\tpos1, 2);\n\t}","@Test\n public void test13() {\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 1));\n }","private void computeMultiClassMCC(ConfusionMatrixElement[] elements) {\n\t\tdouble Sq = matrixTotal * matrixTotal;\n\t\tdouble CS = Arrays.stream(elements).map(n -> n.tp).reduce(0.0d, (n, m) -> n + m) * matrixTotal;\n\t\tdouble t = 0.0d, p = 0.0d, tt = 0.0d, pp = 0.0d, tp = 0.0d;\n\t\t\n\t\tfor (ConfusionMatrixElement e : elements) {\n\t\t\tt = (e.tp + e.fp); \n\t\t\tp = (e.tp + e.fn);\n\t\t\t\n\t\t\ttt += (t * t);\n\t\t\tpp += (p * p);\n\t\t\ttp += (t * p);\n\t\t}\n\t\tmatrixMCC = (CS - tp) / (Math.sqrt(Sq - pp) * Math.sqrt(Sq - tt));\n\t}","@Test\n public void testLargeValues() {\n double x = 123;\n double dof = 6.4;\n double nonCent = 100.34;\n NonCentralChiSquaredDistribution dist = new NonCentralChiSquaredDistribution(dof, nonCent);\n assertThat(0.7930769).isCloseTo(dist.getCDF(x), offset(1e-6));\n\n x = 455.038;\n dof = 12;\n nonCent = 444.44;\n\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\n assertThat(0.4961805).isCloseTo(dist.getCDF(x), offset(1e-6));\n\n x = 999400;\n dof = 500;\n nonCent = 1000000;\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\n assertThat(0.2913029).isCloseTo(dist.getCDF(x), offset(1e-6));\n\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize();\n Locale.getISOLanguages();\n double[][] doubleArray0 = new double[9][8];\n double[] doubleArray1 = new double[3];\n double[] doubleArray2 = new double[9];\n doubleArray2[5] = (-163.0);\n doubleArray2[8] = 1421.4058829;\n doubleArray0[3] = doubleArray0[2];\n doubleArray0[4] = doubleArray0[1];\n discretize0.setOutputFormat();\n double[] doubleArray3 = new double[2];\n doubleArray3[0] = 1421.4058829;\n doubleArray3[1] = (-163.0);\n doubleArray0[6] = doubleArray2;\n double[] doubleArray4 = new double[0];\n doubleArray0[7] = doubleArray4;\n double[] doubleArray5 = new double[6];\n doubleArray5[2] = (-163.0);\n doubleArray0[8] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test29() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[3][6];\n discretize0.setIgnoreClass(false);\n discretize0.m_CutPoints = doubleArray0;\n double[] doubleArray1 = new double[4];\n // Undeclared exception!\n try { \n discretize0.getCutPoints((-831));\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // -831\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test01() throws Throwable {\n Discretize discretize0 = new Discretize(\"c9^n/\");\n discretize0.getCapabilities();\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n }","@Test\n\tpublic void testCollapseByEditDistance() {\n\t\tString cellBC = \"ATCAGGGACAGA\";\n\t\tString molBC = \"CGGGGCTC\";\n\t\tint snpPos = 76227022;\n\t\tInterval snpInterval = new Interval(\"HUMAN_1\", snpPos, snpPos, true, \"test\");\n\t\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\n\t\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\n\t\tresult.add(p);\n\n\t\t// let's add another pileup in that's within ed=1 and incorporates an error.\n\t\t// result has only a single base because two UMIs are merged.\n\t\tString molBC2 = \"AGGGGCTC\";\n\t\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\n\t\tchar[] b2 = { 'G', 'G', 'T' };\n\t\tbyte[] bases2 = new byte[b2.length];\n\t\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\n\t\tbyte[] quals2 = { 37, 37, 10 };\n\t\tp2.setBasesAndQualities(bases2, quals2);\n\t\tresult.add(p2);\n\t\tresult.collapseUMIs(1);\n\t\tList qualResult2 = result.getQualities();\n\t\tAssert.assertEquals(qualResult2.size(), 1);\n\t\tAssert.assertEquals(new Byte((byte) 8), qualResult2.get(0));\n\t}","@Test\n public void testLogLikelihoodCorrelationMatrix()\n {\n IDoubleArray corr = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {\n for (int i = 0; i < this.train.numAttributes(); i++) {\n for (int j = 0; j < this.test.numAttributes(); j++) {\n // negative infinity counts as not present, we do this so we don't have to map\n // between attribute indexes in weka\n // and the result of the mwbm computation\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\n\n // class attributes are not relevant\n if (this.test.classIndex() == j) {\n continue;\n }\n if (this.train.classIndex() == i) {\n continue;\n }\n\n // get percentiles\n double trainvals[] = this.train_values.get(i);\n double testvals[] = this.test_values.get(j);\n\n Arrays.sort(trainvals);\n Arrays.sort(testvals);\n\n // percentiles\n double train_p;\n double test_p;\n double score = 0.0;\n for (int p = 1; p <= 9; p++) {\n train_p = trainvals[(int) Math.ceil(trainvals.length * (p / 100))];\n test_p = testvals[(int) Math.ceil(testvals.length * (p / 100))];\n\n if (train_p > test_p) {\n score += test_p / train_p;\n }\n else {\n score += train_p / test_p;\n }\n }\n\n if (score > cutoff) {\n mwbm.setWeight(i, j, score);\n }\n }\n }\n }","public double[] monteCarloPercentiles(){\n if(!this.monteCarloDone)this.monteCarlo();\n return this.randomEigenValuesPercentiles;\n }","@Test\n public void test10() {\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 3));\n }","@Test\n public void testMesurerDistanceCoordonnees() {\n System.out.println(\"MesurerDistanceCoordonnees\");\n double lat1 = 0.0;\n double lon1 = 0.0;\n double lat2 = 0.0;\n double lon2 = 0.0;\n double expResult = 0.0;\n double result = Utils.MesurerDistanceCoordonnees(lat1, lon1, lat2, lon2);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test13() throws Throwable {\n Discretize discretize0 = new Discretize(\"\\tInvert matching sense of column indexes.\");\n discretize0.setOutputFormat();\n discretize0.listOptions();\n Discretize discretize1 = new Discretize();\n discretize1.setIgnoreClass(false);\n String[] stringArray0 = new String[2];\n stringArray0[0] = \"\\tInvert matching sense of column indexes.\";\n stringArray0[1] = \"\";\n Discretize.main(stringArray0);\n String string0 = discretize1.findNumBinsTipText();\n assertEquals(\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\", string0);\n \n discretize1.setInvertSelection(false);\n discretize0.getBinRangesString(511);\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n \n discretize1.getFindNumBins();\n assertEquals(10, discretize1.getBins());\n }","@Test\n public void testMetastableStates()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IIntArray expResult = null;\n IIntArray result = instance.metastableStates(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCstepping_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test096() throws Throwable {\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\"\", 1281);\n assertNull(costMatrix0);\n }","@Test\n\tpublic void evalCrispDistanceWithCrisp1(){\n\t\tFuzzySet res = (CrispSet)cs1.distance(cs2);\n\t\tassertTrue(res instanceof CrispSet);\n\t\tCrispSet resCrisp = (CrispSet)res;\n\t\t//Puntos limite\t\t\n\t\tassertTrue(resCrisp.getLeftBoundary()==3);\n\t\tassertTrue(resCrisp.getRightBoundary()==6);\n\t\t//Valores extremos\n\t\tassertTrue(resCrisp.getMembershipValue(0)==0);\n\t\tassertTrue(resCrisp.getMembershipValue(3)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(4)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(4.5)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(5)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(6)==1);\n\t\tassertTrue(resCrisp.getMembershipValue(6.01)==0);\n\t}","@Test\n public void testJuvenilesSpreadFromOutsideInsideGrid() {\n params = new CompletelySpatiallyRandomParams(0.5, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params);\n coloniser.updateJuvenilePresenceLayers();\n\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 5);\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 5);\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 5);\n }","@Test\n public void pruebaCalculCamiseta() {\n System.out.println(\"prueba Calcul Camiseta\");\n Camiseta camisetaTest = new Camiseta(1);\n assertEquals(190, (camisetaTest.calculCamiseta(1)),0) ;\n \n Camiseta camisetaTestDos = new Camiseta(2);\n assertEquals(1481.2, (camisetaTestDos.calculCamiseta(7)),0) ;\n \n Camiseta camisetaTestTres = new Camiseta(3);\n assertEquals(1178, (camisetaTestTres.calculCamiseta(4)),0) ;\n \n \n }","public int getCostPerMile() {\n return costPerMile;\n }","@Test()\n public void testTrainWarmCFastSMO() {\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\n\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\n warmModel.setC(1);\n warmModel.train(train);\n\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\n warm.setC(1e4);// too large to train efficently like noraml\n\n long start, end;\n\n start = System.currentTimeMillis();\n warm.train(train, warmModel);\n end = System.currentTimeMillis();\n long warmTime = (end - start);\n\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\n notWarm.setC(1e4);// too large to train efficently like noraml\n\n start = System.currentTimeMillis();\n notWarm.train(train);\n end = System.currentTimeMillis();\n long normTime = (end - start);\n\n assertTrue(warmTime < normTime * 0.75);\n\n }","@Test\n public void testConnectedComponents()\n {\n Collection cc1 = MarkovModel.util.connectedComponents(P1);\n assertTrue(cc1.containsAll(C1));\n assertEquals(cc1.size(), C1.size());\n\n Collection cc2 = MarkovModel.util.connectedComponents(P2);\n assertTrue(cc2.containsAll(C2));\n assertEquals(cc2.size(), C2.size());\n\n Collection cc3 = MarkovModel.util.connectedComponents(P3);\n assertTrue(cc3.containsAll(C3));\n assertEquals(cc3.size(), C3.size()); \n }","@Test(timeout = 4000)\n public void test26() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.m_FindNumBins = true;\n int[] intArray0 = new int[6];\n intArray0[0] = (-388);\n intArray0[1] = (-2121610348);\n intArray0[2] = 11;\n intArray0[3] = (-1);\n intArray0[4] = 1062;\n intArray0[5] = 70;\n discretize0.setAttributeIndicesArray(intArray0);\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\n discretize0.getOptions();\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n }","@Test\r\n\tpublic void testAllMarkupsElectronics() throws BasePriceMustBePositiveException, \r\n\t NegativePersonCountException, ProductCategoryNotSpecifiedException {\r\n\t\tNuPackEstimator estimator = new NuPackEstimator(new BigDecimal(\"100\"), 5, \"electronics\");\r\n\t assertEquals(estimator.estimateFinalCost(), new BigDecimal(\"113.40\"));\r\n\t}","@Test\n public void testGetProjectileSpeedMultiplier() {\n assertEquals(1.3, proj.getProjectileSpeedMultiplier(), 0.001);\n }","public void testConcurrency() throws InterruptedException {\n initCache();\n\n final ArrayList wcos = new ArrayList();\n final ArrayList test = new ArrayList();\n final LinkedBlockingQueue idxs = new LinkedBlockingQueue();\n\n Collection> tasks = new ArrayList>();\n\n for (int i = 0; i < 30000; i++) {\n wcos.add(getDefaultWMSCacheObject(\"q\" + i, 50, true));\n test.add(-1);\n idxs.put(i);\n\n //put and get task\n tasks.add(new Callable() {\n\n public Integer call() throws Exception {\n //put\n int i = idxs.take();\n boolean ok = wmsCache.put(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1, wcos.get(i));\n\n //get\n if (ok) {\n WMSTile wco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\n if (wco.getCached()) {\n if (compareWMSObjects(wco, wcos.get(i))) {\n test.set(i, 1);\n } else {\n test.set(i, 0);\n }\n }\n }\n return i;\n }\n });\n }\n ExecutorService executorService = Executors.newFixedThreadPool(100);\n executorService.invokeAll(tasks);\n\n //test cache cleaner operated correctly\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\n\n //test for presence of invalid test comparisons\n int invalid = 0;\n int valid = 0;\n for (int i = 0; i < test.size(); i++) {\n if (test.get(i) == 0) {\n invalid++;\n } else if (test.get(i) == 1) {\n valid++;\n }\n }\n assertTrue(valid > 0);\n assertTrue(invalid == 0);\n }","@Test\n public void testGetCenter() {\n int expResult = 3;\n int result = this.dummyDigitSize.getCenter();\n assertEquals(expResult, result);\n }","@Before\r\n\tpublic void setup() {\r\n\t\tMockitoAnnotations.initMocks(this);\r\n\t\tcompute(\"12-10-19\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"10-09-20\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"11-08-20\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"03-05-17\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"09-03-19\",\"a@gmail.com\", null, 22,20);\r\n\t\tcompute(\"01-02-17\",\"a@gmail.com\", null, 22,20);\r\n\t}","@Test\n public void testAutocorrelation()\n {\n IDoubleArray M = null;\n IDoubleArray observable = null;\n IDoubleArray timepoints = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testIsRateMatrix()\n {\n IDoubleArray K = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n boolean expResult = false;\n boolean result = instance.isRateMatrix(K);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test28() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n doubleArray2[5] = (-236.4995664359);\n doubleArray2[7] = 1421.4058829;\n doubleArray0[3] = doubleArray2;\n double[] doubleArray3 = new double[3];\n doubleArray3[0] = 0.0;\n doubleArray3[1] = (-236.4995664359);\n doubleArray0[4] = doubleArray3;\n double[] doubleArray4 = new double[21];\n doubleArray4[0] = 1421.4058829;\n doubleArray3[2] = (-236.4995664359);\n doubleArray4[4] = 1.7976931348623157E308;\n doubleArray0[7] = doubleArray4;\n discretize0.m_NumBins = 162;\n doubleArray0[1] = doubleArray4;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.getBinRangesString(1);\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","public void testSizeManagementWMS() {\n initCache();\n \n ArrayList wcos = new ArrayList();\n long putSize = 0;\n long maxSize = 0;\n int cacheSizeDropCount = 0;\n for (int i = 0; i < 1000; i++) {\n WMSTile wco = getDefaultWMSCacheObject(\"q\" + i, 50, true);\n putSize += wco.getSize();\n\n wcos.add(wco);\n long size = wmsCache.getSize();\n\n if (size < maxSize) {\n maxSize = size;\n cacheSizeDropCount++;\n } else {\n maxSize = size;\n }\n\n boolean result = wmsCache.put(wco.getQuery(), wco.getColourmode(), PointType.POINT_1, wco);\n\n //test if cache is full the put was unsuccessful\n //allow for cachecleaner to have reduced the size between put and test\n boolean test = (wmsCache.getSize() + wco.getSize() > wmsCache.getMaxCacheSize()) == !result;\n if (test == false) {\n assertTrue((size + wco.getSize() > wmsCache.getMaxCacheSize()) == !result);\n }\n\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\n }\n\n //test size calcuations are operating\n assertTrue(putSize > 10000);\n\n //test cache cleaner was run more than once\n assertTrue(cacheSizeDropCount > 1);\n\n //test cache size is under max\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\n\n //test gets. Anything that is a placeholder will be null.\n int cachedCount = 0;\n for (int i = 0; i < wcos.size(); i++) {\n WMSTile getwco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\n if (getwco.getCached()) {\n assertTrue(compareWMSObjects(wcos.get(i), getwco));\n cachedCount++;\n }\n }\n\n //test if at least 2 objects were cached at the end\n assertTrue(cachedCount > 1);\n }","@Test\r\n public void testCalculateNDM() {\r\n System.out.println(\"calculateNDM\");\r\n NDM instance = null;\r\n Double expResult = null;\r\n //Double result = instance.calculateNDM();\r\n //assertEquals(expResult, result);\r\n // TODO review the generated test code and remove the default call to fail.\r\n //fail(\"The test case is a prototype.\");\r\n }","@Test(timeout = 4000)\n public void test098() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(0, 0);\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(0, resultMatrixCSV0.getColCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(0, resultMatrixCSV0.getRowCount());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertNotNull(resultMatrixCSV0);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[] intArray0 = new int[0];\n resultMatrixCSV0.setRowOrder(intArray0);\n assertArrayEquals(new int[] {}, intArray0);\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(0, resultMatrixCSV0.getColCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(0, resultMatrixCSV0.getRowCount());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(0, intArray0.length);\n \n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML();\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertNotNull(resultMatrixHTML0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n resultMatrixHTML0.setMeanPrec((-2615));\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n ResultMatrixHTML resultMatrixHTML1 = null;\n try {\n resultMatrixHTML1 = new ResultMatrixHTML(2312, (-2214));\n fail(\"Expecting exception: NegativeArraySizeException\");\n \n } catch(NegativeArraySizeException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.experiment.ResultMatrix\", e);\n }\n }","@Test\n public void testNumCellsColonisedFromOutsideGrid() {\n CompletelySpatiallyRandomParams params1 = new CompletelySpatiallyRandomParams(0.1, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params1);\n assertEquals(1, coloniser.getNumCellsColonisedFromOutsideGrid());\n\n // if baseRate = 0.5 then 9 * 0.5 = 4.5 ~ 5 cells colonised from outside grid\n CompletelySpatiallyRandomParams params2 = new CompletelySpatiallyRandomParams(0.5, 0);\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\n juvenileDeciduous, params2);\n assertEquals(5, coloniser.getNumCellsColonisedFromOutsideGrid());\n }","@Test(timeout = 4000)\n public void test117() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertNotNull(resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[] intArray0 = new int[1];\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertNotNull(resultMatrixSignificance0);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n String[] stringArray0 = new String[4];\n stringArray0[0] = \"v\";\n stringArray0[1] = \"[\";\n stringArray0[2] = \"*\";\n double[] doubleArray0 = new double[7];\n doubleArray0[0] = (double) 0;\n doubleArray0[1] = (double) 2;\n doubleArray0[2] = (double) 0;\n doubleArray0[3] = (double) 1;\n doubleArray0[4] = (double) 2;\n doubleArray0[5] = (double) 0;\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertNotNull(resultMatrixPlainText0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex(resultMatrixPlainText0);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertNotNull(resultMatrixLatex0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n \n String string0 = resultMatrixPlainText0.getSummaryTitle(216);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertNotNull(string0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(\"i\", string0);\n \n int[][] intArray1 = new int[8][0];\n intArray1[0] = intArray0;\n intArray1[1] = intArray0;\n intArray1[2] = intArray0;\n intArray1[3] = intArray0;\n intArray1[4] = intArray0;\n intArray1[5] = intArray0;\n intArray1[6] = intArray0;\n intArray1[7] = intArray0;\n resultMatrixLatex0.setSummary(intArray1, intArray1);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(8, intArray1.length);\n \n ResultMatrixCSV resultMatrixCSV1 = new ResultMatrixCSV();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV1.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV1.getColCount());\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\n assertEquals(\"CSV\", resultMatrixCSV1.getDisplayName());\n assertTrue(resultMatrixCSV1.getPrintRowNames());\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV1.showAverageTipText());\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV1.globalInfo());\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV1.countWidthTipText());\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV1.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\n assertFalse(resultMatrixCSV1.getShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV1.meanPrecTipText());\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV1.printRowNamesTipText());\n assertEquals(1, resultMatrixCSV1.getRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateRowNamesTipText());\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV1.meanWidthTipText());\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV1.showStdDevTipText());\n assertFalse(resultMatrixCSV1.getShowStdDev());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV1.significanceWidthTipText());\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV1.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV1.rowNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV1.printColNamesTipText());\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\n assertFalse(resultMatrixCSV1.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV1.removeFilterNameTipText());\n assertNotNull(resultMatrixCSV1);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\n \n int[] intArray2 = resultMatrixCSV1.getColOrder();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV1.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV1.getColCount());\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\n assertEquals(\"CSV\", resultMatrixCSV1.getDisplayName());\n assertTrue(resultMatrixCSV1.getPrintRowNames());\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV1.showAverageTipText());\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV1.globalInfo());\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV1.countWidthTipText());\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV1.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\n assertFalse(resultMatrixCSV1.getShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV1.meanPrecTipText());\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV1.printRowNamesTipText());\n assertEquals(1, resultMatrixCSV1.getRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateRowNamesTipText());\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV1.meanWidthTipText());\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV1.showStdDevTipText());\n assertFalse(resultMatrixCSV1.getShowStdDev());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV1.significanceWidthTipText());\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV1.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV1.rowNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV1.printColNamesTipText());\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\n assertFalse(resultMatrixCSV1.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV1.removeFilterNameTipText());\n assertNull(intArray2);\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\n \n boolean boolean0 = resultMatrixLatex0.getDefaultPrintColNames();\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertFalse(boolean0);\n \n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixSignificance0);\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance0.colNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance0.printColNamesTipText());\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance0.showAverageTipText());\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance0.countWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance0.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance0.removeFilterNameTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance0.rowNameWidthTipText());\n assertFalse(resultMatrixSignificance0.getPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateColNamesTipText());\n assertFalse(resultMatrixSignificance0.getShowStdDev());\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\n assertEquals(1, resultMatrixSignificance0.getRowCount());\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance0.globalInfo());\n assertFalse(resultMatrixSignificance0.getShowAverage());\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance0.stdDevWidthTipText());\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\n assertEquals(\"Significance only\", resultMatrixSignificance0.getDisplayName());\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance0.showStdDevTipText());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance0.meanPrecTipText());\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance0.enumerateRowNamesTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance0.printRowNamesTipText());\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\n assertEquals(1, resultMatrixSignificance0.getColCount());\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance0.meanWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixSignificance1.showStdDevTipText());\n assertEquals(1, resultMatrixSignificance1.getRowCount());\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixSignificance1.meanWidthTipText());\n assertFalse(resultMatrixSignificance1.getPrintColNames());\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\n assertFalse(resultMatrixSignificance1.getShowStdDev());\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixSignificance1.removeFilterNameTipText());\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixSignificance1.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixSignificance1.significanceWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixSignificance1.countWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixSignificance1.printRowNamesTipText());\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixSignificance1.stdDevPrecTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixSignificance1.rowNameWidthTipText());\n assertEquals(1, resultMatrixSignificance1.getColCount());\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixSignificance1.enumerateRowNamesTipText());\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixSignificance1.colNameWidthTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixSignificance1.stdDevWidthTipText());\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\n assertFalse(resultMatrixSignificance1.getShowAverage());\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixSignificance1.meanPrecTipText());\n assertEquals(\"Only outputs the significance indicators. Can be used for spotting patterns.\", resultMatrixSignificance1.globalInfo());\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\n assertEquals(\"Significance only\", resultMatrixSignificance1.getDisplayName());\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixSignificance1.showAverageTipText());\n assertNotNull(resultMatrixSignificance1);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\n \n String string1 = resultMatrixCSV1.toStringSummary();\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV1.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\n assertEquals(1, resultMatrixCSV1.getColCount());\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\n assertEquals(\"CSV\", resultMatrixCSV1.getDisplayName());\n assertTrue(resultMatrixCSV1.getPrintRowNames());\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV1.showAverageTipText());\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV1.globalInfo());\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV1.countWidthTipText());\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV1.stdDevWidthTipText());\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\n assertFalse(resultMatrixCSV1.getShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV1.meanPrecTipText());\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV1.printRowNamesTipText());\n assertEquals(1, resultMatrixCSV1.getRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateRowNamesTipText());\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV1.meanWidthTipText());\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV1.showStdDevTipText());\n assertFalse(resultMatrixCSV1.getShowStdDev());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV1.significanceWidthTipText());\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV1.stdDevPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV1.rowNameWidthTipText());\n assertEquals(0, resultMatrixCSV1.getCountWidth());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV1.printColNamesTipText());\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV1.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\n assertFalse(resultMatrixCSV1.getPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV1.removeFilterNameTipText());\n assertNotNull(string1);\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(\"-summary data not set-\", string1);\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\n assertFalse(string1.equals((Object)string0));\n \n boolean boolean1 = resultMatrixPlainText0.isRowName(1);\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertFalse(boolean1);\n assertTrue(boolean1 == boolean0);\n \n int int0 = resultMatrixLatex0.getDefaultColNameWidth();\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixPlainText0.meanWidthTipText());\n assertEquals(1, resultMatrixPlainText0.getRowCount());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixPlainText0.stdDevWidthTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixPlainText0.significanceWidthTipText());\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateRowNamesTipText());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixPlainText0.showStdDevTipText());\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixPlainText0.countWidthTipText());\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixPlainText0.colNameWidthTipText());\n assertEquals(\"Generates the output as plain text (for fixed width fonts).\", resultMatrixPlainText0.globalInfo());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixPlainText0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixPlainText0.showAverageTipText());\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixPlainText0.stdDevPrecTipText());\n assertEquals(1, resultMatrixPlainText0.getColCount());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixPlainText0.printRowNamesTipText());\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixPlainText0.meanPrecTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixPlainText0.removeFilterNameTipText());\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixPlainText0.printColNamesTipText());\n assertTrue(resultMatrixPlainText0.getPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\n assertFalse(resultMatrixPlainText0.getShowStdDev());\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixPlainText0.rowNameWidthTipText());\n assertFalse(resultMatrixPlainText0.getShowAverage());\n assertEquals(\"Plain Text\", resultMatrixPlainText0.getDisplayName());\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\n assertEquals(5, resultMatrixLatex0.getCountWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixLatex0.rowNameWidthTipText());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixLatex0.countWidthTipText());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateRowNamesTipText());\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixLatex0.stdDevPrecTipText());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixLatex0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixLatex0.removeFilterNameTipText());\n assertEquals(\"Generates the matrix output in LaTeX-syntax.\", resultMatrixLatex0.globalInfo());\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\n assertTrue(resultMatrixLatex0.getPrintRowNames());\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixLatex0.printColNamesTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixLatex0.significanceWidthTipText());\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixLatex0.stdDevWidthTipText());\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\n assertFalse(resultMatrixLatex0.getShowAverage());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixLatex0.meanWidthTipText());\n assertFalse(resultMatrixLatex0.getShowStdDev());\n assertEquals(\"LaTeX\", resultMatrixLatex0.getDisplayName());\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixLatex0.showStdDevTipText());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixLatex0.colNameWidthTipText());\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\n assertEquals(1, resultMatrixLatex0.getColCount());\n assertTrue(resultMatrixLatex0.getPrintColNames());\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixLatex0.printRowNamesTipText());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixLatex0.showAverageTipText());\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixLatex0.getRowCount());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixLatex0.meanPrecTipText());\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(0, int0);\n }","int getN_estimators();","@Test(timeout = 4000)\n public void test048() throws Throwable {\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertNotNull(resultMatrixCSV0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n \n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixCSV0);\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\n assertFalse(resultMatrixCSV0.getShowStdDev());\n assertFalse(resultMatrixCSV0.getShowAverage());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixCSV0.significanceWidthTipText());\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixCSV0.showStdDevTipText());\n assertEquals(1, resultMatrixCSV0.getRowCount());\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixCSV0.stdDevPrecTipText());\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixCSV0.printColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\n assertEquals(0, resultMatrixCSV0.getCountWidth());\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixCSV0.removeFilterNameTipText());\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\n assertFalse(resultMatrixCSV0.getPrintColNames());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateColNamesTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixCSV0.meanPrecTipText());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixCSV0.rowNameWidthTipText());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixCSV0.printRowNamesTipText());\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\n assertTrue(resultMatrixCSV0.getPrintRowNames());\n assertEquals(\"CSV\", resultMatrixCSV0.getDisplayName());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixCSV0.showAverageTipText());\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\n assertEquals(\"Generates the matrix in CSV ('comma-separated values') format.\", resultMatrixCSV0.globalInfo());\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixCSV0.countWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixCSV0.colNameWidthTipText());\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\n assertEquals(1, resultMatrixCSV0.getColCount());\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixCSV0.enumerateRowNamesTipText());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixCSV0.stdDevWidthTipText());\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixCSV0.meanWidthTipText());\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\n assertFalse(resultMatrixHTML0.getPrintColNames());\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\n assertEquals(1, resultMatrixHTML0.getRowCount());\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\n assertEquals(\"The width of the standard deviation (0 = optimal).\", resultMatrixHTML0.stdDevWidthTipText());\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\n assertEquals(\"The width of the counts (0 = optimal).\", resultMatrixHTML0.countWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\n assertEquals(\"The width of the mean (0 = optimal).\", resultMatrixHTML0.meanWidthTipText());\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\n assertEquals(\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateRowNamesTipText());\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\n assertEquals(\"Whether to display the standard deviation column.\", resultMatrixHTML0.showStdDevTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\n assertEquals(\"HTML\", resultMatrixHTML0.getDisplayName());\n assertEquals(\"The maximum width of the column names (0 = optimal).\", resultMatrixHTML0.colNameWidthTipText());\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\n assertEquals(\"Generates the matrix output as HTML.\", resultMatrixHTML0.globalInfo());\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\n assertEquals(\"Whether to output column names or just numbers representing them.\", resultMatrixHTML0.printColNamesTipText());\n assertEquals(\"The number of decimals after the decimal point for the standard deviation.\", resultMatrixHTML0.stdDevPrecTipText());\n assertEquals(1, resultMatrixHTML0.getColCount());\n assertEquals(\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\", resultMatrixHTML0.enumerateColNamesTipText());\n assertTrue(resultMatrixHTML0.getPrintRowNames());\n assertEquals(\"Whether to show the row with averages.\", resultMatrixHTML0.showAverageTipText());\n assertEquals(\"Whether to remove the classname package prefixes from the filter names in datasets.\", resultMatrixHTML0.removeFilterNameTipText());\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\n assertEquals(\"Whether to output row names or just numbers representing them.\", resultMatrixHTML0.printRowNamesTipText());\n assertEquals(0, resultMatrixHTML0.getCountWidth());\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\n assertEquals(\"The number of decimals after the decimal point for the mean.\", resultMatrixHTML0.meanPrecTipText());\n assertEquals(\"The width of the significance indicator (0 = optimal).\", resultMatrixHTML0.significanceWidthTipText());\n assertFalse(resultMatrixHTML0.getShowAverage());\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\n assertEquals(\"The maximum width for the row names (0 = optimal).\", resultMatrixHTML0.rowNameWidthTipText());\n assertFalse(resultMatrixHTML0.getShowStdDev());\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\n assertNotNull(resultMatrixHTML0);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\n \n int[][] intArray0 = new int[0][9];\n // Undeclared exception!\n try { \n resultMatrixCSV0.setSummary(intArray0, intArray0);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 0\n //\n verifyException(\"weka.experiment.ResultMatrix\", e);\n }\n }","@Test\n public void testSmallDist() throws Exception {\n assertTrue(\n new ImageSimilarity(getFile(\"imageSimilarity/with.png\"))\n .calcDistance(ImageIO.read(getFile(\"imageSimilarity/without.png\")))\n > 0);\n }","double[] compute_t_Cm (double[][] t_mmnt, double thickness, double camber) {\n double[] result = new double[15];\n if (camber >= 0)\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \n result[i] = ci15_from_javafoil_data(t_mmnt, aoa, thickness, camber);\n else \n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \n result[i] = -ci15_from_javafoil_data(t_mmnt, -aoa, thickness, camber);\n return result;\n }","@Test\r\n\tpublic void testGetReducedCell() {\r\n\t\tCMLVector3Test.assertEquals(\"a\", new double[] { 10., 6., 4. }, lattice1\r\n\t\t\t\t.getCMLVector3(0), EPS);\r\n\t\tAssert.assertEquals(\"alen\", 12.328828005937952, lattice1.getCMLVector3(\r\n\t\t\t\t0).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"b\", new double[] { 7., 11., 5. }, lattice1\r\n\t\t\t\t.getCMLVector3(1), EPS);\r\n\t\tAssert.assertEquals(\"blen\", 13.96424004376894, lattice1\r\n\t\t\t\t.getCMLVector3(1).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"c\", new double[] { 6.8, -4., -9. },\r\n\t\t\t\tlattice1.getCMLVector3(2), EPS);\r\n\t\tAssert.assertEquals(\"clen\", 11.968291440301744, lattice1.getCMLVector3(\r\n\t\t\t\t2).getLength(), EPS);\r\n\t\tAssert.assertEquals(\"vol\", -619.2, lattice1.getVolume(), EPS);\r\n\t\tdouble[] params = lattice1.getCellParameters();\r\n\t\tDoubleTestBase.assertEquals(\"params\", new double[] {\r\n\t\t\t\t12.328828005937952, 13.96424004376894, 11.968291440301744,\r\n\t\t\t\t104.34229064539636, 86.89206686722031, 25.025508299339933 },\r\n\t\t\t\tparams, EPS);\r\n\r\n\t\tCMLLattice reducedLattice = lattice1.getReducedCell();\r\n\t\tCMLVector3Test.assertEquals(\"a\", new double[] { 3.0, -5.0, -1.0 },\r\n\t\t\t\treducedLattice.getCMLVector3(0), EPS);\r\n\t\tAssert.assertEquals(\"alen\", 5.916079783099616, reducedLattice\r\n\t\t\t\t.getCMLVector3(0).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"b\", new double[] { 3.8, 1.0, -8.0 },\r\n\t\t\t\treducedLattice.getCMLVector3(1), EPS);\r\n\t\tAssert.assertEquals(\"blen\", 8.912911982062877, reducedLattice\r\n\t\t\t\t.getCMLVector3(1).getLength(), EPS);\r\n\t\tCMLVector3Test.assertEquals(\"c\", new double[] { 10.0, 6.0, 4.0 },\r\n\t\t\t\treducedLattice.getCMLVector3(2), EPS);\r\n\t\tAssert.assertEquals(\"clen\", 12.328828005937952, reducedLattice\r\n\t\t\t\t.getCMLVector3(2).getLength(), EPS);\r\n\t\tAssert.assertEquals(\"vol\", 619.2, reducedLattice.getVolume(), EPS);\r\n\t\tparams = reducedLattice.getCellParameters();\r\n\t\tDoubleTestBase.assertEquals(\"params\", new double[] { 5.916079783099616,\r\n\t\t\t\t8.912911982062877, 12.328828005937952, 83.73054962879554,\r\n\t\t\t\t93.14372638090103, 74.15166267765416 }, params, EPS);\r\n\r\n\t}","public double[] jacobi_iter(double[] x, double tolerance, int M) {\n double[][] sContent = {{1.0, 0, 0}, {0, 1.0, 0}, {0, 0, 1.0}};\n Matrix s = new Matrix(sContent);\n\n double[][] tContent = {{0, -0.5, -0.33333333}, {-0.5, 0, -0.25}, {-0.3333333333, -0.25, 0}};\n Matrix t = new Matrix(tContent);\n\n //keep track of the number of iterations\n\n int counter = 0;\n double tol = 10000; //initial\n double[] tempXVector;\n Matrix firstFirst;\n double[] firstPart; //s^-1(-T)(Xn)\n double[] secondPart; //s^-1(b)\n String output = \"\";\n\n double tempXVectorNorm;\n double xVectorNorm;\n\n for (double elem : x) {\n output += elem + \", \";\n }\n System.out.println(\"x(0): \" + output);\n while (tol > tolerance && counter < M) {\n //perform the jacobi iterations\n firstFirst = s.multiply(t);\n firstPart = Matrix.matrixVectorMultiply(firstFirst, x);\n secondPart = Matrix.matrixVectorMultiply(s, bVector);\n tempXVector = Matrix.plus(firstPart, secondPart);\n\n //find norm of vectors and then subtract the\n tempXVectorNorm = Matrix.norm(tempXVector);\n xVectorNorm = Matrix.norm(x);\n tol = tempXVectorNorm - xVectorNorm;\n tol = Math.abs(tol);\n counter++;\n numOfIterations++;\n x = tempXVector;\n }\n\n output = \"\";\n if (counter == M) {\n System.out.println(\"Jacobi Output for x(N): null\");\n System.out.println(\"Too many iterations without reaching a accurate answer.\");\n return null;\n }\n for (double elem : x) {\n output += elem + \", \";\n }\n System.out.println(\"Jacobi Output for x(N): \" + output);\n System.out.println(\"Number of Iterations using Jacobi Algorithm: \" + counter + \"\\n\");\n return x;\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[7][2];\n double[] doubleArray1 = new double[7];\n doubleArray1[0] = 0.0;\n doubleArray1[1] = (-587.14627);\n doubleArray1[2] = (-2542.40202289275);\n doubleArray1[3] = (-167.679515);\n doubleArray1[4] = (-1.0);\n doubleArray1[5] = 1.0;\n doubleArray1[6] = (-1.0);\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[5];\n doubleArray2[0] = (-167.679515);\n doubleArray2[1] = (-2692.352);\n doubleArray2[2] = (-167.679515);\n doubleArray2[3] = (-2542.40202289275);\n doubleArray2[4] = (-587.14627);\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[4];\n doubleArray3[0] = (-1.0);\n doubleArray3[1] = (-1.0);\n doubleArray3[2] = 1.0;\n doubleArray3[3] = (-2542.40202289275);\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[7];\n doubleArray4[0] = (-2692.352);\n doubleArray4[1] = (-1.0);\n doubleArray4[2] = (-1.0);\n doubleArray4[3] = 0.0;\n doubleArray4[4] = 1.0;\n doubleArray4[5] = 0.0;\n doubleArray4[6] = 1.0;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[1];\n doubleArray5[0] = (-167.679515);\n doubleArray0[4] = doubleArray5;\n double[] doubleArray6 = new double[0];\n doubleArray0[5] = doubleArray6;\n double[] doubleArray7 = new double[1];\n doubleArray7[0] = (-587.14627);\n doubleArray0[6] = doubleArray7;\n discretize0.m_CutPoints = doubleArray0;\n String string0 = discretize0.invertSelectionTipText();\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals(\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\", string0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Before\n public void setup() {\n threeByTwo = new Matrix(new int[][] {{1, 2, 3}, {2, 5, 6}});\n compareTwoByThree = new Matrix(new int[][] {{1, 42, 32}, {2, 15, 65}});\n oneByOne = new Matrix(new int[][] {{4}});\n rightOneByOne = new Matrix(new int[][] {{8}});\n twoByFour = new Matrix(new int[][] {{1, 2, 3, 4}, {2, 5, 6, 8}});\n twoByThree = new Matrix(new int[][] {{4, 5}, {3, 2}, {1, 1}});\n threeByThree = new Matrix(new int[][] {{4, 5, 6}, {3, 2, 0}, {1, 1, 1}});\n rightThreeByThree = new Matrix(new int[][] {{1, 2, 3}, {5, 2, 0}, {1, 1, 1}});\n emptyMatrix = new Matrix(new int[0][0]);\n // this is the known correct result of multiplying M1 by M2\n twoByTwoResult = new Matrix(new int[][] {{13, 12}, {29, 26}});\n threeByThreeResult = new Matrix(new int[][] {{35, 24, 18}, {13, 10, 9}, {7, 5, 4}});\n oneByOneSum = new Matrix(new int[][] {{12}});\n oneByOneProduct = new Matrix(new int[][] {{32}});\n }","@Test\n\tpublic void testOptimization() throws Exception {\n\t\tfor(int i = 0; i < 20000000; i++) {\n\t\t\tint _300s = (int) (Math.random() * 100);\n\t\t\tint _100s = (int) (Math.random() * 100);\n\t\t\tint _50s = (int) (Math.random() * 100);\n\t\t\tint misses = (int) (Math.random() * 100);\n\t\t\t\n\t\t\tdouble acc = OsuApiScore.getAccuracy(_300s, _100s, _50s, misses);\n\t\t\t\n\t\t\tAccuracyDistribution accuracyDistribution = AccuracyDistribution.closest(\n\t\t\t\t\t_300s + _100s + _50s + misses, misses,\n\t\t\t\t\tacc);\n\t\t\t\n\t\t\tdouble rec = OsuApiScore.getAccuracy(accuracyDistribution.getX300(),\n\t\t\t\t\taccuracyDistribution.getX100(), accuracyDistribution.getX50(),\n\t\t\t\t\taccuracyDistribution.getMiss());\n\t\t\t\n\t\t\tassertEquals(acc, rec, 0d);\n\t\t}\n\t}","@Test(timeout = 4000)\n public void test35() throws Throwable {\n Discretize discretize0 = new Discretize();\n assertFalse(discretize0.getMakeBinary());\n \n double[][] doubleArray0 = new double[8][8];\n double[] doubleArray1 = new double[3];\n doubleArray1[2] = 1421.4058829;\n doubleArray0[1] = doubleArray1;\n double[] doubleArray2 = new double[9];\n doubleArray2[1] = 1.7976931348623157E308;\n doubleArray2[2] = 0.0;\n Attribute attribute0 = new Attribute(\"invalid CVS revision - not dots!\");\n attribute0.setWeight(0.0);\n Attribute.typeToString(attribute0);\n ArrayList arrayList0 = new ArrayList();\n arrayList0.add(attribute0);\n Instances instances0 = new Instances(\"UR qualResult2 = result.getQualities();\n\t\tAssert.assertEquals(qualResult2.size(), 2);\n\t\tAssert.assertEquals(new Byte((byte) 31), qualResult2.get(0));\n\t\tAssert.assertEquals(new Byte((byte) 5), qualResult2.get(1));\n\t}","@Test\n\tpublic void testaGetMensagem() {\n\t\tAssert.assertEquals(\n\t\t\t\t\"Erro no getMensagem()\",\n\t\t\t\t\"Disciplina 1 eh avaliada com 5 minitestes, eliminando 1 dos mais baixos.\",\n\t\t\t\tminitElimBai_1.getMensagem());\n\t\tAssert.assertEquals(\n\t\t\t\t\"Erro no getMensagem()\",\n\t\t\t\t\"Disciplina 2 eh avaliada com 10 minitestes, eliminando 2 dos mais baixos.\",\n\t\t\t\tminitElimBai_2.getMensagem());\n\t}","@Test(timeout = 4000)\n public void test050() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate(\"\");\n Evaluation evaluation0 = new Evaluation(instances0);\n evaluation0.areaUnderPRC((-1));\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\n }","@Test\n public void testQuantile() {\n System.out.println(\"quantile\");\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\n instance.rand();\n assertEquals(0, instance.quantile(0), 1E-7);\n assertEquals(0, instance.quantile(0.001), 1E-7);\n assertEquals(0, instance.quantile(0.01), 1E-7);\n assertEquals(2, instance.quantile(0.1), 1E-7);\n assertEquals(3, instance.quantile(0.2), 1E-7);\n assertEquals(13, instance.quantile(0.9), 1E-7);\n assertEquals(22, instance.quantile(0.99), 1E-7);\n assertEquals(30, instance.quantile(0.999), 1E-7);\n }","public void testGetConformingStimulusLargeScale() throws Exception {\n long start = System.currentTimeMillis();\n Stimulus stimulus = new MockStimulus();\n message.setConformingStimulus(stimulus);\n for (int i = 0; i < TOTAL_ITERATION; i++) {\n message.getConformingStimulus();\n }\n System.out.println(\"Calling \" + TOTAL_ITERATION + \"times getConformingStimulus method\"\n + \" costs:\" + (System.currentTimeMillis() - start) + \" ms\");\n }","@Test\n public void testLogLikelihood()\n {\n IDoubleArray T = null;\n IDoubleArray C = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n double expResult = 0.0;\n double result = instance.logLikelihood(T, C);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap words)\r\n{\r\n int t=0;\r\n double LearningRates[] = {1, 0.1, 0.01};\r\n double Margins[] = {1, 0.1, 0.01};\r\n\r\n\tint minInt =0; // Set To Your Desired Min Value\r\n int maxInt =2;\r\n\tRandom rand = new Random();\r\n\t\r\n \tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\r\n\r\n \tdouble LearningRate = LearningRates[randomNum];\r\n \t\r\nfor(int m=0;m trajs = null;\\n Iterable cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_3args_2()\\n {\\n IIntArray traj = null;\\n Iterable cores = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCmilestoning_Iterable_int()\\n {\\n Iterable trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateC_Iterable_int()\\n {\\n Iterable trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateT()\\n {\\n \\n IDoubleArray counts = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateT(counts);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_Iterable_int()\\n {\\n Iterable trajs = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMCMCWithoutAllelicPoN() {\\n final double meanBiasSimulated = 1.2;\\n final double biasVarianceSimulated = 0.04;\\n testMCMC(meanBiasSimulated, biasVarianceSimulated, meanBiasSimulated, biasVarianceSimulated, AllelicPanelOfNormals.EMPTY_PON);\\n }\",\n \"@Test\\n public void testMetastableMemberships()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.metastableMemberships(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test public void calcROItest() {\\n DirectMC h = new DirectMC(\\\"AdMailing\\\", 10000.00, 3.00, 2000);\\n Assert.assertEquals(.4286, h.calcROI(), 1);\\n }\",\n \"public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {\\n double p = 0;\\n\\n KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexs in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // this may invoke exactP on small sample sizes which will not terminate in all\\n // cases\\n // p = t.kolmogorovSmirnovTest(this.train_values.get(i),\\n // this.test_values.get(j), false);\\n\\n // this uses approximateP everytime\\n p = t.approximateP(\\n t.kolmogorovSmirnovStatistic(this.train_values.get(i),\\n this.test_values.get(j)),\\n this.train_values.get(i).length,\\n this.test_values.get(j).length);\\n if (p > cutoff) {\\n mwbm.setWeight(i, j, p);\\n }\\n }\\n }\\n }\",\n \"@Test\\n public void testCorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable1 = null;\\n IDoubleArray observable2 = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.correlation(M, observable1, observable2, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"%\\\");\\n FileSystemHandling.createFolder((EvoSuiteFile) null);\\n discretize0.findNumBinsTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"%\\\");\\n discretize0.m_DefaultCols = \\\"%\\\";\\n double[][] doubleArray0 = new double[0][7];\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints(1);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 1\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test03() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setAttributeIndices(\\\"\\\");\\n Filter.makeCopy(discretize0);\\n discretize0.setDesiredWeightOfInstancesPerInterval(2.0);\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 0;\\n discretize0.setUseBinNumbers(true);\\n discretize0.listOptions();\\n discretize0.makeBinaryTipText();\\n discretize0.getInvertSelection();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-458));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testForwardCommittor()\\n {\\n IDoubleArray M = null;\\n IIntArray A = null;\\n IIntArray B = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.forwardCommittor(M, A, B);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testMMI() {\\n System.out.println(\\\"MMI\\\");\\n int a = 0;\\n int m = 0;\\n int expResult = 0;\\n int result = utilsHill.MMI(a, m);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void shouldCalculateProperMoveProbabilities() throws Exception {\\n\\n int actualCity = 0;\\n boolean[] visited = new boolean[] {true, false, false, false};\\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\\n {2, 2, 1, 1},\\n {1, 1, 0, 1},\\n {2, 1, 1, 0}};\\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\\n {3, 0, 0, 0},\\n {2, 0, 0, 0},\\n {1, 0, 0, 0}};\\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\\n// double[][] array = new double[0][0];\\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\\n\\n// assertTrue(probabilities[0] == 0.0);\\n// assertTrue(probabilities[1] == 0.0);\\n// assertTrue(probabilities[2] == 0.5);\\n// assertTrue(probabilities[3] == 0.5);\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n String[] stringArray0 = new String[2];\\n discretize0.m_ClassIndex = 906;\\n discretize0.m_IgnoreClass = true;\\n stringArray0[0] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n stringArray0[1] = \\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\";\\n discretize0.setOptions(stringArray0);\\n discretize0.setAttributeIndices(\\\"^yH*RVC#\\\\\\\"|tm,k2XpGN\\\");\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getFindNumBins());\\n }\",\n \"private double GetMCRTestSet() \\n\\t{\\n\\t\\tint numErrors = 0;\\n\\t\\t\\n\\t\\tfor(int i = ITrain; i < ITrain+ITest; i++)\\n\\t\\t{\\n\\t\\t\\tPreCompute(i);\\n\\t\\t\\t\\n\\t\\t\\tdouble max_Y_hat_ic = Double.MIN_VALUE;\\n\\t\\t\\tint label_i = -1; \\n\\t\\t\\t\\n\\t\\t\\tfor(int c = 0; c < C; c++)\\n\\t\\t\\t{\\n\\t\\t\\t\\tdouble Y_hat_ic = Predict(i, c);\\n\\t\\t\\t\\t\\n\\n\\t\\t\\t\\tif(lossType == LossTypes.LOGISTIC)\\n\\t\\t\\t\\t\\tY_hat_ic = Sigmoid.Calculate(Y_hat_ic);\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif(Y_hat_ic > max_Y_hat_ic)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tmax_Y_hat_ic = Y_hat_ic; \\n\\t\\t\\t\\t\\tlabel_i = (int)Math.ceil(c);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\tif( nominalLabels.indexOf(Y.get(i)) != label_i ) \\n\\t\\t\\t\\tnumErrors++;\\n\\t\\t}\\n\\t\\t\\n\\t\\treturn (double)numErrors/(double)ITest; \\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.invertSelectionTipText();\\n boolean boolean0 = discretize0.m_UseBinNumbers;\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testJuvenilesSpreadFromCellsWithCorrespondingMatureLctFromInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(3, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 6);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 9);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 3);\\n }\",\n \"@Test\\n public void testEstimateC_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateC(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"l*{e5SNh\\\");\\n boolean boolean0 = true;\\n discretize0.m_ClassIndex = 177;\\n discretize0.setUseEqualFrequency(true);\\n double[][] doubleArray0 = new double[5][9];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[0] = (double) 177;\\n doubleArray1[1] = (double) 177;\\n doubleArray1[2] = (double) 177;\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[2];\\n doubleArray2[0] = (double) 177;\\n doubleArray2[1] = (double) 177;\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[9];\\n doubleArray3[0] = (double) 177;\\n doubleArray3[1] = (double) 177;\\n doubleArray3[2] = (double) 177;\\n doubleArray3[3] = (double) 177;\\n doubleArray3[4] = (double) 177;\\n doubleArray3[5] = (double) 177;\\n doubleArray3[6] = (double) 177;\\n doubleArray3[7] = (double) 177;\\n doubleArray3[8] = (double) 177;\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[8];\\n doubleArray4[0] = (double) 177;\\n doubleArray4[1] = (double) 177;\\n doubleArray4[2] = (double) 177;\\n doubleArray4[3] = (double) 177;\\n doubleArray4[4] = (double) 177;\\n doubleArray4[5] = (double) 177;\\n doubleArray4[6] = (double) 177;\\n doubleArray4[7] = (double) 177;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[0];\\n doubleArray0[4] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.listOptions();\\n discretize0.setIgnoreClass(true);\\n String[] stringArray0 = new String[0];\\n Discretize.main(stringArray0);\\n discretize0.findNumBinsTipText();\\n discretize0.setInvertSelection(true);\\n // Undeclared exception!\\n try { \\n discretize0.getBinRangesString(511);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 511\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test19() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints((-2276));\\n assertFalse(discretize0.getFindNumBins());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Test(timeout = 4000)\\n public void test07() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.setInvertSelection(true);\\n discretize0.useEqualFrequencyTipText();\\n int int0 = (-501);\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning((-501));\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test08() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\");\\n discretize0.binsTipText();\\n // Undeclared exception!\\n try { \\n discretize0.calculateCutPointsByEqualWidthBinning(25);\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testGetImportanceStats() {\\n System.out.println(\\\"getImportanceStats\\\");\\n MDA instance = new MDA();\\n\\n // make the circles close to force tree to do lots of splits / make it harder\\n ClassificationDataSet train = getHarderC(10000, RandomUtil.getRandom());\\n int good_featres = 2;\\n\\n DecisionTree tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n double[] importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n // categorical features, make space wider b/c we lose resolution\\n train = getHarderC(10000, RandomUtil.getRandom());\\n\\n train.applyTransform(new NumericalToHistogram(train, 7));\\n tree = new DecisionTree();\\n tree.setPruningMethod(TreePruner.PruningMethod.NONE);\\n tree.train(train);\\n\\n importances = instance.getImportanceStats(tree, train);\\n\\n // make sure the first 2 features were infered as more important than the\\n // others!\\n for (int i = good_featres; i < importances.length; i++) {\\n for (int j = 0; j < good_featres; j++)\\n assertTrue(importances[j] > importances[i]);\\n }\\n\\n }\",\n \"private void findAbdomenCM() {\\r\\n \\r\\n if (foundCenterOfMass) return;\\r\\n int xcm = 0, ycm = 0, pixCount = 0;\\r\\n for (int idx= 0, y = 0; y < yDim; y++) {\\r\\n for (int x = 0; x < xDim; x++, idx++) {\\r\\n if (sliceBuffer[idx] == abdomenTissueLabel) {\\r\\n xcm += x;\\r\\n ycm += y;\\r\\n pixCount++;\\r\\n }\\r\\n } // end for (int x = 0; ...)\\r\\n } // end for (int idx = 0, ...)\\r\\n \\r\\n if (pixCount == 0) {\\r\\n// System.err.println(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n MipavUtil.displayError(\\\"findAbdomenCM(): No pixels with abdomenTissueLabel in segmented image\\\");\\r\\n return;\\r\\n }\\r\\n \\r\\n centerOfMass[0] = (short)(xcm / pixCount);\\r\\n centerOfMass[1] = (short)(ycm / pixCount);\\r\\n foundCenterOfMass = true;\\r\\n }\",\n \"final CM computeCM(int[/**/][/**/] votes, Chunk[] chks, boolean local, boolean balance) {\\n CM cm = new CM();\\n int rows = votes.length;\\n int validation_rows = 0;\\n int cmin = (int) _data.vecs()[_classcol].min();\\n\\n // Assemble the votes-per-class into predictions & score each row\\n\\n // Make an empty confusion matrix for this chunk\\n cm._matrix = new long[_N][_N];\\n float preds[] = new float[_N+1];\\n\\n float num_trees = _errorsPerTree.length;\\n\\n // Loop over the rows\\n for( int row = 0; row < rows; row++ ) {\\n\\n // Skip rows with missing response values\\n if (chks[_classcol].isNA0(row)) continue;\\n\\n // The class votes for the i-th row\\n int[] vi = votes[row];\\n\\n // Fill the predictions with the vote counts, keeping the 0th index unchanged\\n for( int v=0; v<_N; v++ ) preds[v+1] = vi[v];\\n\\n float s = doSum(vi);\\n if (s == 0) {\\n cm._skippedRows++;\\n continue;\\n }\\n\\n int result;\\n if (balance) {\\n float[] scored = toProbs(preds.clone(), doSum(vi));\\n double probsum=0;\\n for( int c=1; c 0) : \\\"original fraction should be > 0, but is \\\" + original_fraction + \\\": not using enough training data?\\\";\\n final double oversampled_fraction = _model.modeldist()[c-1];\\n assert(oversampled_fraction > 0) : \\\"oversampled fraction should be > 0, but is \\\" + oversampled_fraction + \\\": not using enough training data?\\\";\\n assert(!Double.isNaN(scored[c]));\\n scored[c] *= original_fraction / oversampled_fraction;\\n probsum += scored[c];\\n }\\n for (int i=1;i compute AUC, draw ROC\\n if(_N == 2 && !local) {\\n float snd = preds[2] / sum;\\n for(int i = 0; i < ModelUtils.DEFAULT_THRESHOLDS.length; i++) {\\n int p = snd >= ModelUtils.DEFAULT_THRESHOLDS[i] ? 1 : 0;\\n _cms[i][cclass][p]++; // Increase matrix\\n }\\n }\\n }\\n\\n // End of loop over rows, return confusion matrix\\n cm._rows=validation_rows;\\n return cm;\\n }\",\n \"@Test(timeout = 4000)\\n public void test11() throws Throwable {\\n FileSystemHandling.setPermissions((EvoSuiteFile) null, true, true, false);\\n Discretize discretize0 = new Discretize();\\n discretize0.getCutPoints(3594);\\n Discretize discretize1 = new Discretize();\\n discretize1.attributeIndicesTipText();\\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \\\"\\\");\\n Discretize discretize2 = new Discretize();\\n // Undeclared exception!\\n try { \\n discretize2.calculateCutPoints();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test\\n public void testGetMoveSpeedMultiplier() {\\n assertEquals(1, proj.getMoveSpeedMultiplier(), 0.001);\\n }\",\n \"@Before\\n\\tpublic void iniciaDisciplinaConvencional() {\\n\\t\\tList pos = new ArrayList();\\n\\t\\tList pos1 = new ArrayList();\\n\\t\\tpos1.add(\\\"Disciplina 3\\\");\\n\\t\\tpos1.add(\\\"Disciplina 4\\\");\\n\\t\\tpos1.add(\\\"Disciplina 5\\\");\\n\\n\\t\\tminitElimBai_1 = new MinitestesEliminandoBaixa(\\\"Disciplina 1\\\", 5, pos,\\n\\t\\t\\t\\t1);\\n\\t\\tminitElimBai_2 = new MinitestesEliminandoBaixa(\\\"Disciplina 2\\\", 10,\\n\\t\\t\\t\\tpos1, 2);\\n\\t}\",\n \"@Test\\n public void test13() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 1));\\n }\",\n \"private void computeMultiClassMCC(ConfusionMatrixElement[] elements) {\\n\\t\\tdouble Sq = matrixTotal * matrixTotal;\\n\\t\\tdouble CS = Arrays.stream(elements).map(n -> n.tp).reduce(0.0d, (n, m) -> n + m) * matrixTotal;\\n\\t\\tdouble t = 0.0d, p = 0.0d, tt = 0.0d, pp = 0.0d, tp = 0.0d;\\n\\t\\t\\n\\t\\tfor (ConfusionMatrixElement e : elements) {\\n\\t\\t\\tt = (e.tp + e.fp); \\n\\t\\t\\tp = (e.tp + e.fn);\\n\\t\\t\\t\\n\\t\\t\\ttt += (t * t);\\n\\t\\t\\tpp += (p * p);\\n\\t\\t\\ttp += (t * p);\\n\\t\\t}\\n\\t\\tmatrixMCC = (CS - tp) / (Math.sqrt(Sq - pp) * Math.sqrt(Sq - tt));\\n\\t}\",\n \"@Test\\n public void testLargeValues() {\\n double x = 123;\\n double dof = 6.4;\\n double nonCent = 100.34;\\n NonCentralChiSquaredDistribution dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.7930769).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 455.038;\\n dof = 12;\\n nonCent = 444.44;\\n\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.4961805).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n x = 999400;\\n dof = 500;\\n nonCent = 1000000;\\n dist = new NonCentralChiSquaredDistribution(dof, nonCent);\\n assertThat(0.2913029).isCloseTo(dist.getCDF(x), offset(1e-6));\\n\\n }\",\n \"@Test(timeout = 4000)\\n public void test02() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n Locale.getISOLanguages();\\n double[][] doubleArray0 = new double[9][8];\\n double[] doubleArray1 = new double[3];\\n double[] doubleArray2 = new double[9];\\n doubleArray2[5] = (-163.0);\\n doubleArray2[8] = 1421.4058829;\\n doubleArray0[3] = doubleArray0[2];\\n doubleArray0[4] = doubleArray0[1];\\n discretize0.setOutputFormat();\\n double[] doubleArray3 = new double[2];\\n doubleArray3[0] = 1421.4058829;\\n doubleArray3[1] = (-163.0);\\n doubleArray0[6] = doubleArray2;\\n double[] doubleArray4 = new double[0];\\n doubleArray0[7] = doubleArray4;\\n double[] doubleArray5 = new double[6];\\n doubleArray5[2] = (-163.0);\\n doubleArray0[8] = doubleArray5;\\n discretize0.m_CutPoints = doubleArray0;\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test29() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[3][6];\\n discretize0.setIgnoreClass(false);\\n discretize0.m_CutPoints = doubleArray0;\\n double[] doubleArray1 = new double[4];\\n // Undeclared exception!\\n try { \\n discretize0.getCutPoints((-831));\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // -831\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"@Test(timeout = 4000)\\n public void test01() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"c9^n/\\\");\\n discretize0.getCapabilities();\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseBinNumbers());\\n assertFalse(discretize0.getUseEqualFrequency());\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n }\",\n \"@Test\\n\\tpublic void testCollapseByEditDistance() {\\n\\t\\tString cellBC = \\\"ATCAGGGACAGA\\\";\\n\\t\\tString molBC = \\\"CGGGGCTC\\\";\\n\\t\\tint snpPos = 76227022;\\n\\t\\tInterval snpInterval = new Interval(\\\"HUMAN_1\\\", snpPos, snpPos, true, \\\"test\\\");\\n\\t\\tSampleGenotypeProbabilities result = new SampleGenotypeProbabilities(snpInterval, cellBC);\\n\\t\\tSNPUMIBasePileup p = getPileUpFromFile(cellBC, molBC);\\n\\t\\tresult.add(p);\\n\\n\\t\\t// let's add another pileup in that's within ed=1 and incorporates an error.\\n\\t\\t// result has only a single base because two UMIs are merged.\\n\\t\\tString molBC2 = \\\"AGGGGCTC\\\";\\n\\t\\tSNPUMIBasePileup p2 = getPileUpFromFile(cellBC, molBC2);\\n\\t\\tchar[] b2 = { 'G', 'G', 'T' };\\n\\t\\tbyte[] bases2 = new byte[b2.length];\\n\\t\\tStringUtil.charsToBytes(b2, 0, b2.length, bases2, 0);\\n\\t\\tbyte[] quals2 = { 37, 37, 10 };\\n\\t\\tp2.setBasesAndQualities(bases2, quals2);\\n\\t\\tresult.add(p2);\\n\\t\\tresult.collapseUMIs(1);\\n\\t\\tList qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 1);\\n\\t\\tAssert.assertEquals(new Byte((byte) 8), qualResult2.get(0));\\n\\t}\",\n \"@Test\\n public void testLogLikelihoodCorrelationMatrix()\\n {\\n IDoubleArray corr = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihoodCorrelationMatrix(corr, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {\\n for (int i = 0; i < this.train.numAttributes(); i++) {\\n for (int j = 0; j < this.test.numAttributes(); j++) {\\n // negative infinity counts as not present, we do this so we don't have to map\\n // between attribute indexes in weka\\n // and the result of the mwbm computation\\n mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);\\n\\n // class attributes are not relevant\\n if (this.test.classIndex() == j) {\\n continue;\\n }\\n if (this.train.classIndex() == i) {\\n continue;\\n }\\n\\n // get percentiles\\n double trainvals[] = this.train_values.get(i);\\n double testvals[] = this.test_values.get(j);\\n\\n Arrays.sort(trainvals);\\n Arrays.sort(testvals);\\n\\n // percentiles\\n double train_p;\\n double test_p;\\n double score = 0.0;\\n for (int p = 1; p <= 9; p++) {\\n train_p = trainvals[(int) Math.ceil(trainvals.length * (p / 100))];\\n test_p = testvals[(int) Math.ceil(testvals.length * (p / 100))];\\n\\n if (train_p > test_p) {\\n score += test_p / train_p;\\n }\\n else {\\n score += train_p / test_p;\\n }\\n }\\n\\n if (score > cutoff) {\\n mwbm.setWeight(i, j, score);\\n }\\n }\\n }\\n }\",\n \"public double[] monteCarloPercentiles(){\\n if(!this.monteCarloDone)this.monteCarlo();\\n return this.randomEigenValuesPercentiles;\\n }\",\n \"@Test\\n public void test10() {\\n Assert.assertEquals(1, MedianOfThreeNumbers.median(1, 1, 3));\\n }\",\n \"@Test\\n public void testMesurerDistanceCoordonnees() {\\n System.out.println(\\\"MesurerDistanceCoordonnees\\\");\\n double lat1 = 0.0;\\n double lon1 = 0.0;\\n double lat2 = 0.0;\\n double lon2 = 0.0;\\n double expResult = 0.0;\\n double result = Utils.MesurerDistanceCoordonnees(lat1, lon1, lat2, lon2);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test13() throws Throwable {\\n Discretize discretize0 = new Discretize(\\\"\\\\tInvert matching sense of column indexes.\\\");\\n discretize0.setOutputFormat();\\n discretize0.listOptions();\\n Discretize discretize1 = new Discretize();\\n discretize1.setIgnoreClass(false);\\n String[] stringArray0 = new String[2];\\n stringArray0[0] = \\\"\\\\tInvert matching sense of column indexes.\\\";\\n stringArray0[1] = \\\"\\\";\\n Discretize.main(stringArray0);\\n String string0 = discretize1.findNumBinsTipText();\\n assertEquals(\\\"Optimize number of equal-width bins using leave-one-out. Doesn't work for equal-frequency binning\\\", string0);\\n \\n discretize1.setInvertSelection(false);\\n discretize0.getBinRangesString(511);\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n \\n discretize1.getFindNumBins();\\n assertEquals(10, discretize1.getBins());\\n }\",\n \"@Test\\n public void testMetastableStates()\\n {\\n IDoubleArray M = null;\\n int nstates = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IIntArray expResult = null;\\n IIntArray result = instance.metastableStates(M, nstates);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testEstimateCstepping_IIntArray_int()\\n {\\n IIntArray traj = null;\\n int lag = 0;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.estimateCstepping(traj, lag);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test096() throws Throwable {\\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\\\"\\\", 1281);\\n assertNull(costMatrix0);\\n }\",\n \"@Test\\n\\tpublic void evalCrispDistanceWithCrisp1(){\\n\\t\\tFuzzySet res = (CrispSet)cs1.distance(cs2);\\n\\t\\tassertTrue(res instanceof CrispSet);\\n\\t\\tCrispSet resCrisp = (CrispSet)res;\\n\\t\\t//Puntos limite\\t\\t\\n\\t\\tassertTrue(resCrisp.getLeftBoundary()==3);\\n\\t\\tassertTrue(resCrisp.getRightBoundary()==6);\\n\\t\\t//Valores extremos\\n\\t\\tassertTrue(resCrisp.getMembershipValue(0)==0);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(3)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(4.5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(5)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6)==1);\\n\\t\\tassertTrue(resCrisp.getMembershipValue(6.01)==0);\\n\\t}\",\n \"@Test\\n public void testJuvenilesSpreadFromOutsideInsideGrid() {\\n params = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params);\\n coloniser.updateJuvenilePresenceLayers();\\n\\n assertTrue(countJuvenilesInGrid(juvenilePine) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileOak) >= 5);\\n assertTrue(countJuvenilesInGrid(juvenileDeciduous) >= 5);\\n }\",\n \"@Test\\n public void pruebaCalculCamiseta() {\\n System.out.println(\\\"prueba Calcul Camiseta\\\");\\n Camiseta camisetaTest = new Camiseta(1);\\n assertEquals(190, (camisetaTest.calculCamiseta(1)),0) ;\\n \\n Camiseta camisetaTestDos = new Camiseta(2);\\n assertEquals(1481.2, (camisetaTestDos.calculCamiseta(7)),0) ;\\n \\n Camiseta camisetaTestTres = new Camiseta(3);\\n assertEquals(1178, (camisetaTestTres.calculCamiseta(4)),0) ;\\n \\n \\n }\",\n \"public int getCostPerMile() {\\n return costPerMile;\\n }\",\n \"@Test()\\n public void testTrainWarmCFastSMO() {\\n ClassificationDataSet train = FixedProblems.getHalfCircles(250, RandomUtil.getRandom(), 0.1, 0.2);\\n\\n PlattSMO warmModel = new PlattSMO(new LinearKernel(1));\\n warmModel.setC(1);\\n warmModel.train(train);\\n\\n PlattSMO warm = new PlattSMO(new LinearKernel(1));\\n warm.setC(1e4);// too large to train efficently like noraml\\n\\n long start, end;\\n\\n start = System.currentTimeMillis();\\n warm.train(train, warmModel);\\n end = System.currentTimeMillis();\\n long warmTime = (end - start);\\n\\n PlattSMO notWarm = new PlattSMO(new LinearKernel(1));\\n notWarm.setC(1e4);// too large to train efficently like noraml\\n\\n start = System.currentTimeMillis();\\n notWarm.train(train);\\n end = System.currentTimeMillis();\\n long normTime = (end - start);\\n\\n assertTrue(warmTime < normTime * 0.75);\\n\\n }\",\n \"@Test\\n public void testConnectedComponents()\\n {\\n Collection cc1 = MarkovModel.util.connectedComponents(P1);\\n assertTrue(cc1.containsAll(C1));\\n assertEquals(cc1.size(), C1.size());\\n\\n Collection cc2 = MarkovModel.util.connectedComponents(P2);\\n assertTrue(cc2.containsAll(C2));\\n assertEquals(cc2.size(), C2.size());\\n\\n Collection cc3 = MarkovModel.util.connectedComponents(P3);\\n assertTrue(cc3.containsAll(C3));\\n assertEquals(cc3.size(), C3.size()); \\n }\",\n \"@Test(timeout = 4000)\\n public void test26() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n discretize0.m_FindNumBins = true;\\n int[] intArray0 = new int[6];\\n intArray0[0] = (-388);\\n intArray0[1] = (-2121610348);\\n intArray0[2] = 11;\\n intArray0[3] = (-1);\\n intArray0[4] = 1062;\\n intArray0[5] = 70;\\n discretize0.setAttributeIndicesArray(intArray0);\\n discretize0.setDesiredWeightOfInstancesPerInterval((-1149.15832593));\\n discretize0.getOptions();\\n assertEquals((-1149.15832593), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n }\",\n \"@Test\\r\\n\\tpublic void testAllMarkupsElectronics() throws BasePriceMustBePositiveException, \\r\\n\\t NegativePersonCountException, ProductCategoryNotSpecifiedException {\\r\\n\\t\\tNuPackEstimator estimator = new NuPackEstimator(new BigDecimal(\\\"100\\\"), 5, \\\"electronics\\\");\\r\\n\\t assertEquals(estimator.estimateFinalCost(), new BigDecimal(\\\"113.40\\\"));\\r\\n\\t}\",\n \"@Test\\n public void testGetProjectileSpeedMultiplier() {\\n assertEquals(1.3, proj.getProjectileSpeedMultiplier(), 0.001);\\n }\",\n \"public void testConcurrency() throws InterruptedException {\\n initCache();\\n\\n final ArrayList wcos = new ArrayList();\\n final ArrayList test = new ArrayList();\\n final LinkedBlockingQueue idxs = new LinkedBlockingQueue();\\n\\n Collection> tasks = new ArrayList>();\\n\\n for (int i = 0; i < 30000; i++) {\\n wcos.add(getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true));\\n test.add(-1);\\n idxs.put(i);\\n\\n //put and get task\\n tasks.add(new Callable() {\\n\\n public Integer call() throws Exception {\\n //put\\n int i = idxs.take();\\n boolean ok = wmsCache.put(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1, wcos.get(i));\\n\\n //get\\n if (ok) {\\n WMSTile wco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (wco.getCached()) {\\n if (compareWMSObjects(wco, wcos.get(i))) {\\n test.set(i, 1);\\n } else {\\n test.set(i, 0);\\n }\\n }\\n }\\n return i;\\n }\\n });\\n }\\n ExecutorService executorService = Executors.newFixedThreadPool(100);\\n executorService.invokeAll(tasks);\\n\\n //test cache cleaner operated correctly\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test for presence of invalid test comparisons\\n int invalid = 0;\\n int valid = 0;\\n for (int i = 0; i < test.size(); i++) {\\n if (test.get(i) == 0) {\\n invalid++;\\n } else if (test.get(i) == 1) {\\n valid++;\\n }\\n }\\n assertTrue(valid > 0);\\n assertTrue(invalid == 0);\\n }\",\n \"@Test\\n public void testGetCenter() {\\n int expResult = 3;\\n int result = this.dummyDigitSize.getCenter();\\n assertEquals(expResult, result);\\n }\",\n \"@Before\\r\\n\\tpublic void setup() {\\r\\n\\t\\tMockitoAnnotations.initMocks(this);\\r\\n\\t\\tcompute(\\\"12-10-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"10-09-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"11-08-20\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"03-05-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"09-03-19\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t\\tcompute(\\\"01-02-17\\\",\\\"a@gmail.com\\\", null, 22,20);\\r\\n\\t}\",\n \"@Test\\n public void testAutocorrelation()\\n {\\n IDoubleArray M = null;\\n IDoubleArray observable = null;\\n IDoubleArray timepoints = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n IDoubleArray expResult = null;\\n IDoubleArray result = instance.autocorrelation(M, observable, timepoints);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test\\n public void testIsRateMatrix()\\n {\\n IDoubleArray K = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n boolean expResult = false;\\n boolean result = instance.isRateMatrix(K);\\n assertEquals(expResult, result);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"@Test(timeout = 4000)\\n public void test28() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n doubleArray2[5] = (-236.4995664359);\\n doubleArray2[7] = 1421.4058829;\\n doubleArray0[3] = doubleArray2;\\n double[] doubleArray3 = new double[3];\\n doubleArray3[0] = 0.0;\\n doubleArray3[1] = (-236.4995664359);\\n doubleArray0[4] = doubleArray3;\\n double[] doubleArray4 = new double[21];\\n doubleArray4[0] = 1421.4058829;\\n doubleArray3[2] = (-236.4995664359);\\n doubleArray4[4] = 1.7976931348623157E308;\\n doubleArray0[7] = doubleArray4;\\n discretize0.m_NumBins = 162;\\n doubleArray0[1] = doubleArray4;\\n discretize0.m_CutPoints = doubleArray0;\\n discretize0.getBinRangesString(1);\\n // Undeclared exception!\\n try { \\n discretize0.setOutputFormat();\\n fail(\\\"Expecting exception: NullPointerException\\\");\\n \\n } catch(NullPointerException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.filters.unsupervised.attribute.Discretize\\\", e);\\n }\\n }\",\n \"public void testSizeManagementWMS() {\\n initCache();\\n \\n ArrayList wcos = new ArrayList();\\n long putSize = 0;\\n long maxSize = 0;\\n int cacheSizeDropCount = 0;\\n for (int i = 0; i < 1000; i++) {\\n WMSTile wco = getDefaultWMSCacheObject(\\\"q\\\" + i, 50, true);\\n putSize += wco.getSize();\\n\\n wcos.add(wco);\\n long size = wmsCache.getSize();\\n\\n if (size < maxSize) {\\n maxSize = size;\\n cacheSizeDropCount++;\\n } else {\\n maxSize = size;\\n }\\n\\n boolean result = wmsCache.put(wco.getQuery(), wco.getColourmode(), PointType.POINT_1, wco);\\n\\n //test if cache is full the put was unsuccessful\\n //allow for cachecleaner to have reduced the size between put and test\\n boolean test = (wmsCache.getSize() + wco.getSize() > wmsCache.getMaxCacheSize()) == !result;\\n if (test == false) {\\n assertTrue((size + wco.getSize() > wmsCache.getMaxCacheSize()) == !result);\\n }\\n\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n }\\n\\n //test size calcuations are operating\\n assertTrue(putSize > 10000);\\n\\n //test cache cleaner was run more than once\\n assertTrue(cacheSizeDropCount > 1);\\n\\n //test cache size is under max\\n assertTrue(wmsCache.getSize() <= wmsCache.getMaxCacheSize());\\n\\n //test gets. Anything that is a placeholder will be null.\\n int cachedCount = 0;\\n for (int i = 0; i < wcos.size(); i++) {\\n WMSTile getwco = wmsCache.get(wcos.get(i).getQuery(), wcos.get(i).getColourmode(), PointType.POINT_1);\\n if (getwco.getCached()) {\\n assertTrue(compareWMSObjects(wcos.get(i), getwco));\\n cachedCount++;\\n }\\n }\\n\\n //test if at least 2 objects were cached at the end\\n assertTrue(cachedCount > 1);\\n }\",\n \"@Test\\r\\n public void testCalculateNDM() {\\r\\n System.out.println(\\\"calculateNDM\\\");\\r\\n NDM instance = null;\\r\\n Double expResult = null;\\r\\n //Double result = instance.calculateNDM();\\r\\n //assertEquals(expResult, result);\\r\\n // TODO review the generated test code and remove the default call to fail.\\r\\n //fail(\\\"The test case is a prototype.\\\");\\r\\n }\",\n \"@Test(timeout = 4000)\\n public void test098() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV(0, 0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[0];\\n resultMatrixCSV0.setRowOrder(intArray0);\\n assertArrayEquals(new int[] {}, intArray0);\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(0, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(0, resultMatrixCSV0.getRowCount());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(0, intArray0.length);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML();\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n resultMatrixHTML0.setMeanPrec((-2615));\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML1 = null;\\n try {\\n resultMatrixHTML1 = new ResultMatrixHTML(2312, (-2214));\\n fail(\\\"Expecting exception: NegativeArraySizeException\\\");\\n \\n } catch(NegativeArraySizeException e) {\\n //\\n // no message in exception (getMessage() returned null)\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testNumCellsColonisedFromOutsideGrid() {\\n CompletelySpatiallyRandomParams params1 = new CompletelySpatiallyRandomParams(0.1, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params1);\\n assertEquals(1, coloniser.getNumCellsColonisedFromOutsideGrid());\\n\\n // if baseRate = 0.5 then 9 * 0.5 = 4.5 ~ 5 cells colonised from outside grid\\n CompletelySpatiallyRandomParams params2 = new CompletelySpatiallyRandomParams(0.5, 0);\\n coloniser = new CompletelySpatiallyRandomColoniser(landCoverType, juvenilePine, juvenileOak,\\n juvenileDeciduous, params2);\\n assertEquals(5, coloniser.getNumCellsColonisedFromOutsideGrid());\\n }\",\n \"@Test(timeout = 4000)\\n public void test117() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[] intArray0 = new int[1];\\n ResultMatrixSignificance resultMatrixSignificance0 = new ResultMatrixSignificance();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertNotNull(resultMatrixSignificance0);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n String[] stringArray0 = new String[4];\\n stringArray0[0] = \\\"v\\\";\\n stringArray0[1] = \\\"[\\\";\\n stringArray0[2] = \\\"*\\\";\\n double[] doubleArray0 = new double[7];\\n doubleArray0[0] = (double) 0;\\n doubleArray0[1] = (double) 2;\\n doubleArray0[2] = (double) 0;\\n doubleArray0[3] = (double) 1;\\n doubleArray0[4] = (double) 2;\\n doubleArray0[5] = (double) 0;\\n ResultMatrixPlainText resultMatrixPlainText0 = new ResultMatrixPlainText();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(resultMatrixPlainText0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n ResultMatrixLatex resultMatrixLatex0 = new ResultMatrixLatex(resultMatrixPlainText0);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertNotNull(resultMatrixLatex0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n \\n String string0 = resultMatrixPlainText0.getSummaryTitle(216);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertNotNull(string0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"i\\\", string0);\\n \\n int[][] intArray1 = new int[8][0];\\n intArray1[0] = intArray0;\\n intArray1[1] = intArray0;\\n intArray1[2] = intArray0;\\n intArray1[3] = intArray0;\\n intArray1[4] = intArray0;\\n intArray1[5] = intArray0;\\n intArray1[6] = intArray0;\\n intArray1[7] = intArray0;\\n resultMatrixLatex0.setSummary(intArray1, intArray1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(8, intArray1.length);\\n \\n ResultMatrixCSV resultMatrixCSV1 = new ResultMatrixCSV();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(resultMatrixCSV1);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n int[] intArray2 = resultMatrixCSV1.getColOrder();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNull(intArray2);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n \\n boolean boolean0 = resultMatrixLatex0.getDefaultPrintColNames();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertFalse(boolean0);\\n \\n ResultMatrixSignificance resultMatrixSignificance1 = new ResultMatrixSignificance(resultMatrixSignificance0);\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance0.colNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance0.getRemoveFilterName());\\n assertEquals(1, resultMatrixSignificance0.getVisibleColCount());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance0.printColNamesTipText());\\n assertEquals(0, resultMatrixSignificance0.getCountWidth());\\n assertFalse(resultMatrixSignificance0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixSignificance0.getColNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultMeanWidth());\\n assertEquals(40, resultMatrixSignificance0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getMeanWidth());\\n assertTrue(resultMatrixSignificance0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance0.showAverageTipText());\\n assertEquals(2, resultMatrixSignificance0.getDefaultMeanPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance0.countWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultStdDevWidth());\\n assertTrue(resultMatrixSignificance0.getEnumerateColNames());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance0.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getSignificanceWidth());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance0.removeFilterNameTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance0.rowNameWidthTipText());\\n assertFalse(resultMatrixSignificance0.getPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateColNamesTipText());\\n assertFalse(resultMatrixSignificance0.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance0.getMeanPrec());\\n assertEquals(0, resultMatrixSignificance0.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixSignificance0.getRowCount());\\n assertFalse(resultMatrixSignificance0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance0.globalInfo());\\n assertFalse(resultMatrixSignificance0.getShowAverage());\\n assertFalse(resultMatrixSignificance0.getDefaultRemoveFilterName());\\n assertTrue(resultMatrixSignificance0.getDefaultEnumerateColNames());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance0.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance0.getStdDevWidth());\\n assertEquals(40, resultMatrixSignificance0.getRowNameWidth());\\n assertEquals(0, resultMatrixSignificance0.getDefaultColNameWidth());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance0.getDisplayName());\\n assertEquals(2, resultMatrixSignificance0.getDefaultStdDevPrec());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance0.showStdDevTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance0.meanPrecTipText());\\n assertTrue(resultMatrixSignificance0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance0.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance0.getDefaultShowAverage());\\n assertEquals(2, resultMatrixSignificance0.getStdDevPrec());\\n assertEquals(1, resultMatrixSignificance0.getColCount());\\n assertEquals(1, resultMatrixSignificance0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance0.meanWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getSignificanceWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixSignificance1.showStdDevTipText());\\n assertEquals(1, resultMatrixSignificance1.getRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixSignificance1.getStdDevPrec());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixSignificance1.meanWidthTipText());\\n assertFalse(resultMatrixSignificance1.getPrintColNames());\\n assertFalse(resultMatrixSignificance1.getEnumerateRowNames());\\n assertFalse(resultMatrixSignificance1.getShowStdDev());\\n assertEquals(2, resultMatrixSignificance1.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixSignificance1.removeFilterNameTipText());\\n assertFalse(resultMatrixSignificance1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixSignificance1.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixSignificance1.significanceWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixSignificance1.getDefaultMeanPrec());\\n assertEquals(1, resultMatrixSignificance1.getVisibleColCount());\\n assertTrue(resultMatrixSignificance1.getPrintRowNames());\\n assertEquals(40, resultMatrixSignificance1.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixSignificance1.getDefaultSignificanceWidth());\\n assertEquals(0, resultMatrixSignificance1.getCountWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixSignificance1.countWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixSignificance1.printRowNamesTipText());\\n assertFalse(resultMatrixSignificance1.getRemoveFilterName());\\n assertEquals(40, resultMatrixSignificance1.getRowNameWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowStdDev());\\n assertFalse(resultMatrixSignificance1.getDefaultRemoveFilterName());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultMeanWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixSignificance1.stdDevPrecTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultColNameWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixSignificance1.rowNameWidthTipText());\\n assertEquals(1, resultMatrixSignificance1.getColCount());\\n assertEquals(0, resultMatrixSignificance1.getColNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixSignificance1.enumerateRowNamesTipText());\\n assertTrue(resultMatrixSignificance1.getEnumerateColNames());\\n assertEquals(0, resultMatrixSignificance1.getMeanWidth());\\n assertFalse(resultMatrixSignificance1.getDefaultShowAverage());\\n assertEquals(1, resultMatrixSignificance1.getVisibleRowCount());\\n assertTrue(resultMatrixSignificance1.getDefaultEnumerateColNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixSignificance1.colNameWidthTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixSignificance1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixSignificance1.getDefaultCountWidth());\\n assertFalse(resultMatrixSignificance1.getShowAverage());\\n assertFalse(resultMatrixSignificance1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixSignificance1.meanPrecTipText());\\n assertEquals(\\\"Only outputs the significance indicators. Can be used for spotting patterns.\\\", resultMatrixSignificance1.globalInfo());\\n assertEquals(2, resultMatrixSignificance1.getDefaultStdDevPrec());\\n assertEquals(\\\"Significance only\\\", resultMatrixSignificance1.getDisplayName());\\n assertEquals(0, resultMatrixSignificance1.getStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixSignificance1.showAverageTipText());\\n assertNotNull(resultMatrixSignificance1);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertFalse(resultMatrixSignificance1.equals((Object)resultMatrixSignificance0));\\n \\n String string1 = resultMatrixCSV1.toStringSummary();\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV1.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getDefaultStdDevWidth());\\n assertEquals(1, resultMatrixCSV1.getColCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixCSV1.getColNameWidth());\\n assertEquals(1, resultMatrixCSV1.getVisibleColCount());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV1.getDisplayName());\\n assertTrue(resultMatrixCSV1.getPrintRowNames());\\n assertEquals(2, resultMatrixCSV1.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV1.showAverageTipText());\\n assertTrue(resultMatrixCSV1.getEnumerateColNames());\\n assertEquals(0, resultMatrixCSV1.getMeanWidth());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV1.globalInfo());\\n assertEquals(0, resultMatrixCSV1.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV1.countWidthTipText());\\n assertEquals(25, resultMatrixCSV1.getDefaultRowNameWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV1.stdDevWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getStdDevWidth());\\n assertFalse(resultMatrixCSV1.getShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultRemoveFilterName());\\n assertEquals(0, resultMatrixCSV1.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV1.meanPrecTipText());\\n assertEquals(2, resultMatrixCSV1.getDefaultStdDevPrec());\\n assertTrue(resultMatrixCSV1.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV1.printRowNamesTipText());\\n assertEquals(1, resultMatrixCSV1.getRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateRowNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getStdDevPrec());\\n assertFalse(resultMatrixCSV1.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV1.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV1.getRemoveFilterName());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV1.meanWidthTipText());\\n assertFalse(resultMatrixCSV1.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV1.getVisibleRowCount());\\n assertEquals(0, resultMatrixCSV1.getDefaultCountWidth());\\n assertTrue(resultMatrixCSV1.getDefaultEnumerateColNames());\\n assertEquals(25, resultMatrixCSV1.getRowNameWidth());\\n assertFalse(resultMatrixCSV1.getDefaultEnumerateRowNames());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV1.showStdDevTipText());\\n assertFalse(resultMatrixCSV1.getShowStdDev());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV1.significanceWidthTipText());\\n assertFalse(resultMatrixCSV1.getDefaultPrintColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV1.stdDevPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV1.rowNameWidthTipText());\\n assertEquals(0, resultMatrixCSV1.getCountWidth());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV1.printColNamesTipText());\\n assertEquals(2, resultMatrixCSV1.getMeanPrec());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV1.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV1.getSignificanceWidth());\\n assertFalse(resultMatrixCSV1.getPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV1.removeFilterNameTipText());\\n assertNotNull(string1);\\n assertNotSame(resultMatrixCSV1, resultMatrixCSV0);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(\\\"-summary data not set-\\\", string1);\\n assertFalse(resultMatrixCSV1.equals((Object)resultMatrixCSV0));\\n assertFalse(string1.equals((Object)string0));\\n \\n boolean boolean1 = resultMatrixPlainText0.isRowName(1);\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertFalse(boolean1);\\n assertTrue(boolean1 == boolean0);\\n \\n int int0 = resultMatrixLatex0.getDefaultColNameWidth();\\n assertFalse(resultMatrixPlainText0.getEnumerateRowNames());\\n assertEquals(2, resultMatrixPlainText0.getMeanPrec());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintRowNames());\\n assertEquals(2, resultMatrixPlainText0.getStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getDefaultShowAverage());\\n assertEquals(0, resultMatrixPlainText0.getSignificanceWidth());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixPlainText0.meanWidthTipText());\\n assertEquals(1, resultMatrixPlainText0.getRowCount());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixPlainText0.stdDevWidthTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixPlainText0.significanceWidthTipText());\\n assertEquals(5, resultMatrixPlainText0.getCountWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultEnumerateColNames());\\n assertEquals(1, resultMatrixPlainText0.getVisibleRowCount());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateRowNamesTipText());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixPlainText0.showStdDevTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixPlainText0.getDefaultMeanWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultColNameWidth());\\n assertEquals(0, resultMatrixPlainText0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixPlainText0.countWidthTipText());\\n assertEquals(25, resultMatrixPlainText0.getDefaultRowNameWidth());\\n assertFalse(resultMatrixPlainText0.getDefaultRemoveFilterName());\\n assertFalse(resultMatrixPlainText0.getRemoveFilterName());\\n assertEquals(1, resultMatrixPlainText0.getVisibleColCount());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixPlainText0.colNameWidthTipText());\\n assertEquals(\\\"Generates the output as plain text (for fixed width fonts).\\\", resultMatrixPlainText0.globalInfo());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixPlainText0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixPlainText0.getDefaultStdDevWidth());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixPlainText0.showAverageTipText());\\n assertTrue(resultMatrixPlainText0.getPrintRowNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixPlainText0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixPlainText0.getColCount());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixPlainText0.printRowNamesTipText());\\n assertEquals(5, resultMatrixPlainText0.getDefaultCountWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultMeanPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixPlainText0.meanPrecTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixPlainText0.removeFilterNameTipText());\\n assertFalse(resultMatrixPlainText0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixPlainText0.printColNamesTipText());\\n assertTrue(resultMatrixPlainText0.getPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getStdDevWidth());\\n assertEquals(2, resultMatrixPlainText0.getDefaultStdDevPrec());\\n assertFalse(resultMatrixPlainText0.getShowStdDev());\\n assertTrue(resultMatrixPlainText0.getEnumerateColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixPlainText0.rowNameWidthTipText());\\n assertFalse(resultMatrixPlainText0.getShowAverage());\\n assertEquals(\\\"Plain Text\\\", resultMatrixPlainText0.getDisplayName());\\n assertEquals(0, resultMatrixPlainText0.getMeanWidth());\\n assertTrue(resultMatrixPlainText0.getDefaultPrintColNames());\\n assertEquals(0, resultMatrixPlainText0.getColNameWidth());\\n assertEquals(25, resultMatrixPlainText0.getRowNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleRowCount());\\n assertFalse(resultMatrixLatex0.getDefaultRemoveFilterName());\\n assertEquals(5, resultMatrixLatex0.getCountWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixLatex0.rowNameWidthTipText());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixLatex0.countWidthTipText());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixLatex0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixLatex0.getDefaultRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultColNameWidth());\\n assertEquals(1, resultMatrixLatex0.getVisibleColCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixLatex0.stdDevPrecTipText());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixLatex0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultStdDevWidth());\\n assertEquals(2, resultMatrixLatex0.getMeanPrec());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixLatex0.removeFilterNameTipText());\\n assertEquals(\\\"Generates the matrix output in LaTeX-syntax.\\\", resultMatrixLatex0.globalInfo());\\n assertEquals(0, resultMatrixLatex0.getSignificanceWidth());\\n assertTrue(resultMatrixLatex0.getPrintRowNames());\\n assertEquals(2, resultMatrixLatex0.getDefaultMeanPrec());\\n assertFalse(resultMatrixLatex0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixLatex0.printColNamesTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixLatex0.significanceWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getStdDevWidth());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixLatex0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixLatex0.getStdDevPrec());\\n assertFalse(resultMatrixLatex0.getShowAverage());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixLatex0.meanWidthTipText());\\n assertFalse(resultMatrixLatex0.getShowStdDev());\\n assertEquals(\\\"LaTeX\\\", resultMatrixLatex0.getDisplayName());\\n assertTrue(resultMatrixLatex0.getDefaultEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getEnumerateColNames());\\n assertTrue(resultMatrixLatex0.getDefaultPrintRowNames());\\n assertEquals(0, resultMatrixLatex0.getMeanWidth());\\n assertEquals(25, resultMatrixLatex0.getRowNameWidth());\\n assertEquals(0, resultMatrixLatex0.getColNameWidth());\\n assertEquals(0, resultMatrixLatex0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixLatex0.showStdDevTipText());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixLatex0.colNameWidthTipText());\\n assertEquals(0, resultMatrixLatex0.getDefaultCountWidth());\\n assertFalse(resultMatrixLatex0.getEnumerateRowNames());\\n assertFalse(resultMatrixLatex0.getRemoveFilterName());\\n assertFalse(resultMatrixLatex0.getDefaultShowAverage());\\n assertEquals(1, resultMatrixLatex0.getColCount());\\n assertTrue(resultMatrixLatex0.getPrintColNames());\\n assertFalse(resultMatrixLatex0.getDefaultShowStdDev());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixLatex0.printRowNamesTipText());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixLatex0.showAverageTipText());\\n assertEquals(2, resultMatrixLatex0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixLatex0.getRowCount());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixLatex0.meanPrecTipText());\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(0, int0);\\n }\",\n \"int getN_estimators();\",\n \"@Test(timeout = 4000)\\n public void test048() throws Throwable {\\n ResultMatrixCSV resultMatrixCSV0 = new ResultMatrixCSV();\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertNotNull(resultMatrixCSV0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n \\n ResultMatrixHTML resultMatrixHTML0 = new ResultMatrixHTML(resultMatrixCSV0);\\n assertEquals(2, resultMatrixCSV0.getStdDevPrec());\\n assertFalse(resultMatrixCSV0.getShowStdDev());\\n assertFalse(resultMatrixCSV0.getShowAverage());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixCSV0.significanceWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getStdDevWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixCSV0.showStdDevTipText());\\n assertEquals(1, resultMatrixCSV0.getRowCount());\\n assertEquals(25, resultMatrixCSV0.getRowNameWidth());\\n assertTrue(resultMatrixCSV0.getEnumerateColNames());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixCSV0.stdDevPrecTipText());\\n assertTrue(resultMatrixCSV0.getDefaultEnumerateColNames());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixCSV0.printColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getMeanWidth());\\n assertEquals(0, resultMatrixCSV0.getColNameWidth());\\n assertEquals(0, resultMatrixCSV0.getCountWidth());\\n assertFalse(resultMatrixCSV0.getDefaultPrintColNames());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixCSV0.removeFilterNameTipText());\\n assertEquals(0, resultMatrixCSV0.getSignificanceWidth());\\n assertEquals(2, resultMatrixCSV0.getMeanPrec());\\n assertFalse(resultMatrixCSV0.getPrintColNames());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateColNamesTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultColNameWidth());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixCSV0.meanPrecTipText());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixCSV0.rowNameWidthTipText());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixCSV0.printRowNamesTipText());\\n assertFalse(resultMatrixCSV0.getDefaultRemoveFilterName());\\n assertEquals(2, resultMatrixCSV0.getDefaultMeanPrec());\\n assertTrue(resultMatrixCSV0.getPrintRowNames());\\n assertEquals(\\\"CSV\\\", resultMatrixCSV0.getDisplayName());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixCSV0.showAverageTipText());\\n assertEquals(2, resultMatrixCSV0.getDefaultStdDevPrec());\\n assertEquals(1, resultMatrixCSV0.getVisibleColCount());\\n assertEquals(\\\"Generates the matrix in CSV ('comma-separated values') format.\\\", resultMatrixCSV0.globalInfo());\\n assertEquals(0, resultMatrixCSV0.getDefaultStdDevWidth());\\n assertEquals(0, resultMatrixCSV0.getDefaultSignificanceWidth());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixCSV0.countWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultMeanWidth());\\n assertFalse(resultMatrixCSV0.getDefaultShowAverage());\\n assertFalse(resultMatrixCSV0.getDefaultEnumerateRowNames());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixCSV0.colNameWidthTipText());\\n assertEquals(0, resultMatrixCSV0.getDefaultCountWidth());\\n assertFalse(resultMatrixCSV0.getEnumerateRowNames());\\n assertEquals(1, resultMatrixCSV0.getColCount());\\n assertEquals(25, resultMatrixCSV0.getDefaultRowNameWidth());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixCSV0.enumerateRowNamesTipText());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixCSV0.stdDevWidthTipText());\\n assertTrue(resultMatrixCSV0.getDefaultPrintRowNames());\\n assertFalse(resultMatrixCSV0.getDefaultShowStdDev());\\n assertFalse(resultMatrixCSV0.getRemoveFilterName());\\n assertEquals(1, resultMatrixCSV0.getVisibleRowCount());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixCSV0.meanWidthTipText());\\n assertFalse(resultMatrixHTML0.getEnumerateRowNames());\\n assertFalse(resultMatrixHTML0.getDefaultShowAverage());\\n assertFalse(resultMatrixHTML0.getPrintColNames());\\n assertEquals(2, resultMatrixHTML0.getMeanPrec());\\n assertEquals(0, resultMatrixHTML0.getSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getRowCount());\\n assertEquals(0, resultMatrixHTML0.getDefaultCountWidth());\\n assertFalse(resultMatrixHTML0.getDefaultRemoveFilterName());\\n assertEquals(\\\"The width of the standard deviation (0 = optimal).\\\", resultMatrixHTML0.stdDevWidthTipText());\\n assertEquals(2, resultMatrixHTML0.getStdDevPrec());\\n assertEquals(\\\"The width of the counts (0 = optimal).\\\", resultMatrixHTML0.countWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultSignificanceWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleRowCount());\\n assertFalse(resultMatrixHTML0.getDefaultShowStdDev());\\n assertEquals(\\\"The width of the mean (0 = optimal).\\\", resultMatrixHTML0.meanWidthTipText());\\n assertTrue(resultMatrixHTML0.getDefaultPrintRowNames());\\n assertEquals(\\\"Whether to enumerate the row names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateRowNamesTipText());\\n assertFalse(resultMatrixHTML0.getDefaultEnumerateRowNames());\\n assertEquals(0, resultMatrixHTML0.getDefaultMeanWidth());\\n assertEquals(\\\"Whether to display the standard deviation column.\\\", resultMatrixHTML0.showStdDevTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultColNameWidth());\\n assertEquals(\\\"HTML\\\", resultMatrixHTML0.getDisplayName());\\n assertEquals(\\\"The maximum width of the column names (0 = optimal).\\\", resultMatrixHTML0.colNameWidthTipText());\\n assertEquals(0, resultMatrixHTML0.getDefaultStdDevWidth());\\n assertEquals(\\\"Generates the matrix output as HTML.\\\", resultMatrixHTML0.globalInfo());\\n assertFalse(resultMatrixHTML0.getRemoveFilterName());\\n assertEquals(\\\"Whether to output column names or just numbers representing them.\\\", resultMatrixHTML0.printColNamesTipText());\\n assertEquals(\\\"The number of decimals after the decimal point for the standard deviation.\\\", resultMatrixHTML0.stdDevPrecTipText());\\n assertEquals(1, resultMatrixHTML0.getColCount());\\n assertEquals(\\\"Whether to enumerate the column names (prefixing them with '(x)', with 'x' being the index).\\\", resultMatrixHTML0.enumerateColNamesTipText());\\n assertTrue(resultMatrixHTML0.getPrintRowNames());\\n assertEquals(\\\"Whether to show the row with averages.\\\", resultMatrixHTML0.showAverageTipText());\\n assertEquals(\\\"Whether to remove the classname package prefixes from the filter names in datasets.\\\", resultMatrixHTML0.removeFilterNameTipText());\\n assertEquals(2, resultMatrixHTML0.getDefaultMeanPrec());\\n assertEquals(\\\"Whether to output row names or just numbers representing them.\\\", resultMatrixHTML0.printRowNamesTipText());\\n assertEquals(0, resultMatrixHTML0.getCountWidth());\\n assertEquals(1, resultMatrixHTML0.getVisibleColCount());\\n assertEquals(25, resultMatrixHTML0.getDefaultRowNameWidth());\\n assertEquals(2, resultMatrixHTML0.getDefaultStdDevPrec());\\n assertEquals(\\\"The number of decimals after the decimal point for the mean.\\\", resultMatrixHTML0.meanPrecTipText());\\n assertEquals(\\\"The width of the significance indicator (0 = optimal).\\\", resultMatrixHTML0.significanceWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowAverage());\\n assertTrue(resultMatrixHTML0.getDefaultEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getStdDevWidth());\\n assertFalse(resultMatrixHTML0.getDefaultPrintColNames());\\n assertEquals(\\\"The maximum width for the row names (0 = optimal).\\\", resultMatrixHTML0.rowNameWidthTipText());\\n assertFalse(resultMatrixHTML0.getShowStdDev());\\n assertTrue(resultMatrixHTML0.getEnumerateColNames());\\n assertEquals(0, resultMatrixHTML0.getMeanWidth());\\n assertEquals(25, resultMatrixHTML0.getRowNameWidth());\\n assertEquals(0, resultMatrixHTML0.getColNameWidth());\\n assertNotNull(resultMatrixHTML0);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(1, ResultMatrix.SIGNIFICANCE_WIN);\\n assertEquals(2, ResultMatrix.SIGNIFICANCE_LOSS);\\n assertEquals(0, ResultMatrix.SIGNIFICANCE_TIE);\\n \\n int[][] intArray0 = new int[0][9];\\n // Undeclared exception!\\n try { \\n resultMatrixCSV0.setSummary(intArray0, intArray0);\\n fail(\\\"Expecting exception: ArrayIndexOutOfBoundsException\\\");\\n \\n } catch(ArrayIndexOutOfBoundsException e) {\\n //\\n // 0\\n //\\n verifyException(\\\"weka.experiment.ResultMatrix\\\", e);\\n }\\n }\",\n \"@Test\\n public void testSmallDist() throws Exception {\\n assertTrue(\\n new ImageSimilarity(getFile(\\\"imageSimilarity/with.png\\\"))\\n .calcDistance(ImageIO.read(getFile(\\\"imageSimilarity/without.png\\\")))\\n > 0);\\n }\",\n \"double[] compute_t_Cm (double[][] t_mmnt, double thickness, double camber) {\\n double[] result = new double[15];\\n if (camber >= 0)\\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = ci15_from_javafoil_data(t_mmnt, aoa, thickness, camber);\\n else \\n for (int aoa = -28, i = 0; aoa <= 28; aoa += 4, i++) \\n result[i] = -ci15_from_javafoil_data(t_mmnt, -aoa, thickness, camber);\\n return result;\\n }\",\n \"@Test\\r\\n\\tpublic void testGetReducedCell() {\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 10., 6., 4. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 12.328828005937952, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 7., 11., 5. }, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 13.96424004376894, lattice1\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 6.8, -4., -9. },\\r\\n\\t\\t\\t\\tlattice1.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 11.968291440301744, lattice1.getCMLVector3(\\r\\n\\t\\t\\t\\t2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", -619.2, lattice1.getVolume(), EPS);\\r\\n\\t\\tdouble[] params = lattice1.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] {\\r\\n\\t\\t\\t\\t12.328828005937952, 13.96424004376894, 11.968291440301744,\\r\\n\\t\\t\\t\\t104.34229064539636, 86.89206686722031, 25.025508299339933 },\\r\\n\\t\\t\\t\\tparams, EPS);\\r\\n\\r\\n\\t\\tCMLLattice reducedLattice = lattice1.getReducedCell();\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"a\\\", new double[] { 3.0, -5.0, -1.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(0), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"alen\\\", 5.916079783099616, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(0).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"b\\\", new double[] { 3.8, 1.0, -8.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(1), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"blen\\\", 8.912911982062877, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(1).getLength(), EPS);\\r\\n\\t\\tCMLVector3Test.assertEquals(\\\"c\\\", new double[] { 10.0, 6.0, 4.0 },\\r\\n\\t\\t\\t\\treducedLattice.getCMLVector3(2), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"clen\\\", 12.328828005937952, reducedLattice\\r\\n\\t\\t\\t\\t.getCMLVector3(2).getLength(), EPS);\\r\\n\\t\\tAssert.assertEquals(\\\"vol\\\", 619.2, reducedLattice.getVolume(), EPS);\\r\\n\\t\\tparams = reducedLattice.getCellParameters();\\r\\n\\t\\tDoubleTestBase.assertEquals(\\\"params\\\", new double[] { 5.916079783099616,\\r\\n\\t\\t\\t\\t8.912911982062877, 12.328828005937952, 83.73054962879554,\\r\\n\\t\\t\\t\\t93.14372638090103, 74.15166267765416 }, params, EPS);\\r\\n\\r\\n\\t}\",\n \"public double[] jacobi_iter(double[] x, double tolerance, int M) {\\n double[][] sContent = {{1.0, 0, 0}, {0, 1.0, 0}, {0, 0, 1.0}};\\n Matrix s = new Matrix(sContent);\\n\\n double[][] tContent = {{0, -0.5, -0.33333333}, {-0.5, 0, -0.25}, {-0.3333333333, -0.25, 0}};\\n Matrix t = new Matrix(tContent);\\n\\n //keep track of the number of iterations\\n\\n int counter = 0;\\n double tol = 10000; //initial\\n double[] tempXVector;\\n Matrix firstFirst;\\n double[] firstPart; //s^-1(-T)(Xn)\\n double[] secondPart; //s^-1(b)\\n String output = \\\"\\\";\\n\\n double tempXVectorNorm;\\n double xVectorNorm;\\n\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"x(0): \\\" + output);\\n while (tol > tolerance && counter < M) {\\n //perform the jacobi iterations\\n firstFirst = s.multiply(t);\\n firstPart = Matrix.matrixVectorMultiply(firstFirst, x);\\n secondPart = Matrix.matrixVectorMultiply(s, bVector);\\n tempXVector = Matrix.plus(firstPart, secondPart);\\n\\n //find norm of vectors and then subtract the\\n tempXVectorNorm = Matrix.norm(tempXVector);\\n xVectorNorm = Matrix.norm(x);\\n tol = tempXVectorNorm - xVectorNorm;\\n tol = Math.abs(tol);\\n counter++;\\n numOfIterations++;\\n x = tempXVector;\\n }\\n\\n output = \\\"\\\";\\n if (counter == M) {\\n System.out.println(\\\"Jacobi Output for x(N): null\\\");\\n System.out.println(\\\"Too many iterations without reaching a accurate answer.\\\");\\n return null;\\n }\\n for (double elem : x) {\\n output += elem + \\\", \\\";\\n }\\n System.out.println(\\\"Jacobi Output for x(N): \\\" + output);\\n System.out.println(\\\"Number of Iterations using Jacobi Algorithm: \\\" + counter + \\\"\\\\n\\\");\\n return x;\\n }\",\n \"@Test(timeout = 4000)\\n public void test05() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n double[][] doubleArray0 = new double[7][2];\\n double[] doubleArray1 = new double[7];\\n doubleArray1[0] = 0.0;\\n doubleArray1[1] = (-587.14627);\\n doubleArray1[2] = (-2542.40202289275);\\n doubleArray1[3] = (-167.679515);\\n doubleArray1[4] = (-1.0);\\n doubleArray1[5] = 1.0;\\n doubleArray1[6] = (-1.0);\\n doubleArray0[0] = doubleArray1;\\n double[] doubleArray2 = new double[5];\\n doubleArray2[0] = (-167.679515);\\n doubleArray2[1] = (-2692.352);\\n doubleArray2[2] = (-167.679515);\\n doubleArray2[3] = (-2542.40202289275);\\n doubleArray2[4] = (-587.14627);\\n doubleArray0[1] = doubleArray2;\\n double[] doubleArray3 = new double[4];\\n doubleArray3[0] = (-1.0);\\n doubleArray3[1] = (-1.0);\\n doubleArray3[2] = 1.0;\\n doubleArray3[3] = (-2542.40202289275);\\n doubleArray0[2] = doubleArray3;\\n double[] doubleArray4 = new double[7];\\n doubleArray4[0] = (-2692.352);\\n doubleArray4[1] = (-1.0);\\n doubleArray4[2] = (-1.0);\\n doubleArray4[3] = 0.0;\\n doubleArray4[4] = 1.0;\\n doubleArray4[5] = 0.0;\\n doubleArray4[6] = 1.0;\\n doubleArray0[3] = doubleArray4;\\n double[] doubleArray5 = new double[1];\\n doubleArray5[0] = (-167.679515);\\n doubleArray0[4] = doubleArray5;\\n double[] doubleArray6 = new double[0];\\n doubleArray0[5] = doubleArray6;\\n double[] doubleArray7 = new double[1];\\n doubleArray7[0] = (-587.14627);\\n doubleArray0[6] = doubleArray7;\\n discretize0.m_CutPoints = doubleArray0;\\n String string0 = discretize0.invertSelectionTipText();\\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\\n assertFalse(discretize0.getUseBinNumbers());\\n assertEquals(10, discretize0.getBins());\\n assertFalse(discretize0.getMakeBinary());\\n assertEquals(\\\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\\\", string0);\\n assertFalse(discretize0.getFindNumBins());\\n assertFalse(discretize0.getUseEqualFrequency());\\n }\",\n \"@Before\\n public void setup() {\\n threeByTwo = new Matrix(new int[][] {{1, 2, 3}, {2, 5, 6}});\\n compareTwoByThree = new Matrix(new int[][] {{1, 42, 32}, {2, 15, 65}});\\n oneByOne = new Matrix(new int[][] {{4}});\\n rightOneByOne = new Matrix(new int[][] {{8}});\\n twoByFour = new Matrix(new int[][] {{1, 2, 3, 4}, {2, 5, 6, 8}});\\n twoByThree = new Matrix(new int[][] {{4, 5}, {3, 2}, {1, 1}});\\n threeByThree = new Matrix(new int[][] {{4, 5, 6}, {3, 2, 0}, {1, 1, 1}});\\n rightThreeByThree = new Matrix(new int[][] {{1, 2, 3}, {5, 2, 0}, {1, 1, 1}});\\n emptyMatrix = new Matrix(new int[0][0]);\\n // this is the known correct result of multiplying M1 by M2\\n twoByTwoResult = new Matrix(new int[][] {{13, 12}, {29, 26}});\\n threeByThreeResult = new Matrix(new int[][] {{35, 24, 18}, {13, 10, 9}, {7, 5, 4}});\\n oneByOneSum = new Matrix(new int[][] {{12}});\\n oneByOneProduct = new Matrix(new int[][] {{32}});\\n }\",\n \"@Test\\n\\tpublic void testOptimization() throws Exception {\\n\\t\\tfor(int i = 0; i < 20000000; i++) {\\n\\t\\t\\tint _300s = (int) (Math.random() * 100);\\n\\t\\t\\tint _100s = (int) (Math.random() * 100);\\n\\t\\t\\tint _50s = (int) (Math.random() * 100);\\n\\t\\t\\tint misses = (int) (Math.random() * 100);\\n\\t\\t\\t\\n\\t\\t\\tdouble acc = OsuApiScore.getAccuracy(_300s, _100s, _50s, misses);\\n\\t\\t\\t\\n\\t\\t\\tAccuracyDistribution accuracyDistribution = AccuracyDistribution.closest(\\n\\t\\t\\t\\t\\t_300s + _100s + _50s + misses, misses,\\n\\t\\t\\t\\t\\tacc);\\n\\t\\t\\t\\n\\t\\t\\tdouble rec = OsuApiScore.getAccuracy(accuracyDistribution.getX300(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getX100(), accuracyDistribution.getX50(),\\n\\t\\t\\t\\t\\taccuracyDistribution.getMiss());\\n\\t\\t\\t\\n\\t\\t\\tassertEquals(acc, rec, 0d);\\n\\t\\t}\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test35() throws Throwable {\\n Discretize discretize0 = new Discretize();\\n assertFalse(discretize0.getMakeBinary());\\n \\n double[][] doubleArray0 = new double[8][8];\\n double[] doubleArray1 = new double[3];\\n doubleArray1[2] = 1421.4058829;\\n doubleArray0[1] = doubleArray1;\\n double[] doubleArray2 = new double[9];\\n doubleArray2[1] = 1.7976931348623157E308;\\n doubleArray2[2] = 0.0;\\n Attribute attribute0 = new Attribute(\\\"invalid CVS revision - not dots!\\\");\\n attribute0.setWeight(0.0);\\n Attribute.typeToString(attribute0);\\n ArrayList arrayList0 = new ArrayList();\\n arrayList0.add(attribute0);\\n Instances instances0 = new Instances(\\\"UR qualResult2 = result.getQualities();\\n\\t\\tAssert.assertEquals(qualResult2.size(), 2);\\n\\t\\tAssert.assertEquals(new Byte((byte) 31), qualResult2.get(0));\\n\\t\\tAssert.assertEquals(new Byte((byte) 5), qualResult2.get(1));\\n\\t}\",\n \"@Test\\n\\tpublic void testaGetMensagem() {\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 1 eh avaliada com 5 minitestes, eliminando 1 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_1.getMensagem());\\n\\t\\tAssert.assertEquals(\\n\\t\\t\\t\\t\\\"Erro no getMensagem()\\\",\\n\\t\\t\\t\\t\\\"Disciplina 2 eh avaliada com 10 minitestes, eliminando 2 dos mais baixos.\\\",\\n\\t\\t\\t\\tminitElimBai_2.getMensagem());\\n\\t}\",\n \"@Test(timeout = 4000)\\n public void test050() throws Throwable {\\n TestInstances testInstances0 = new TestInstances();\\n Instances instances0 = testInstances0.generate(\\\"\\\");\\n Evaluation evaluation0 = new Evaluation(instances0);\\n evaluation0.areaUnderPRC((-1));\\n assertEquals(0.0, evaluation0.SFPriorEntropy(), 0.01);\\n }\",\n \"@Test\\n public void testQuantile() {\\n System.out.println(\\\"quantile\\\");\\n NegativeBinomialDistribution instance = new NegativeBinomialDistribution(3, 0.3);\\n instance.rand();\\n assertEquals(0, instance.quantile(0), 1E-7);\\n assertEquals(0, instance.quantile(0.001), 1E-7);\\n assertEquals(0, instance.quantile(0.01), 1E-7);\\n assertEquals(2, instance.quantile(0.1), 1E-7);\\n assertEquals(3, instance.quantile(0.2), 1E-7);\\n assertEquals(13, instance.quantile(0.9), 1E-7);\\n assertEquals(22, instance.quantile(0.99), 1E-7);\\n assertEquals(30, instance.quantile(0.999), 1E-7);\\n }\",\n \"public void testGetConformingStimulusLargeScale() throws Exception {\\n long start = System.currentTimeMillis();\\n Stimulus stimulus = new MockStimulus();\\n message.setConformingStimulus(stimulus);\\n for (int i = 0; i < TOTAL_ITERATION; i++) {\\n message.getConformingStimulus();\\n }\\n System.out.println(\\\"Calling \\\" + TOTAL_ITERATION + \\\"times getConformingStimulus method\\\"\\n + \\\" costs:\\\" + (System.currentTimeMillis() - start) + \\\" ms\\\");\\n }\",\n \"@Test\\n public void testLogLikelihood()\\n {\\n IDoubleArray T = null;\\n IDoubleArray C = null;\\n MarkovModelUtilities instance = new MarkovModelUtilities();\\n double expResult = 0.0;\\n double result = instance.logLikelihood(T, C);\\n assertEquals(expResult, result, 0.0);\\n // TODO review the generated test code and remove the default call to fail.\\n fail(\\\"The test case is a prototype.\\\");\\n }\",\n \"public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap words)\\r\\n{\\r\\n int t=0;\\r\\n double LearningRates[] = {1, 0.1, 0.01};\\r\\n double Margins[] = {1, 0.1, 0.01};\\r\\n\\r\\n\\tint minInt =0; // Set To Your Desired Min Value\\r\\n int maxInt =2;\\r\\n\\tRandom rand = new Random();\\r\\n\\t\\r\\n \\tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\\r\\n\\r\\n \\tdouble LearningRate = LearningRates[randomNum];\\r\\n \\t\\r\\nfor(int m=0;m trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["@Test\n public void testEstimateT()\n {\n \n IDoubleArray counts = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateT(counts);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCstepping_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCstepping(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_Iterable_int()\n {\n Iterable trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_1()\n {\n Iterable trajs = null;\n Iterable cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateCmilestoning_3args_2()\n {\n IIntArray traj = null;\n Iterable cores = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(traj, cores, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testForwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.forwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize(\"l*{e5SNh\");\n boolean boolean0 = true;\n discretize0.m_ClassIndex = 177;\n discretize0.setUseEqualFrequency(true);\n double[][] doubleArray0 = new double[5][9];\n double[] doubleArray1 = new double[3];\n doubleArray1[0] = (double) 177;\n doubleArray1[1] = (double) 177;\n doubleArray1[2] = (double) 177;\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[2];\n doubleArray2[0] = (double) 177;\n doubleArray2[1] = (double) 177;\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[9];\n doubleArray3[0] = (double) 177;\n doubleArray3[1] = (double) 177;\n doubleArray3[2] = (double) 177;\n doubleArray3[3] = (double) 177;\n doubleArray3[4] = (double) 177;\n doubleArray3[5] = (double) 177;\n doubleArray3[6] = (double) 177;\n doubleArray3[7] = (double) 177;\n doubleArray3[8] = (double) 177;\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[8];\n doubleArray4[0] = (double) 177;\n doubleArray4[1] = (double) 177;\n doubleArray4[2] = (double) 177;\n doubleArray4[3] = (double) 177;\n doubleArray4[4] = (double) 177;\n doubleArray4[5] = (double) 177;\n doubleArray4[6] = (double) 177;\n doubleArray4[7] = (double) 177;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[0];\n doubleArray0[4] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n discretize0.listOptions();\n discretize0.setIgnoreClass(true);\n String[] stringArray0 = new String[0];\n Discretize.main(stringArray0);\n discretize0.findNumBinsTipText();\n discretize0.setInvertSelection(true);\n // Undeclared exception!\n try { \n discretize0.getBinRangesString(511);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 511\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n\tpublic void testRepeatibleDynamics() throws IOException, PropertiesException {\n\n\t\t\n\t\t// INSTANTIATE benchmark \n\t\tProperties props = PropertiesUtil.setpointProperties(new File (\"src/main/resources/sim.properties\"));\n\t\tSetPointGenerator lg = new SetPointGenerator (props);\n\t\tList externalDrivers = new ArrayList();\n\t\texternalDrivers.add(lg);\n\t\tIndustrialBenchmarkDynamics d = new IndustrialBenchmarkDynamics (props, externalDrivers);\n\t\tRandom actionRand = new Random(System.currentTimeMillis());\n \n // 1) do 100000 random steps, in order to initialize dynamics\n\t\tfinal ActionDelta action = new ActionDelta(0.001f, 0.001f, 0.001f); \n\t\tfor (int i=0; i atoms = cluster.getAtoms();\r\n Atom a;\r\n int numAtoms = atoms.size();\r\n double potentialEnergy;\r\n double trialPotential;\r\n int i;\r\n\r\n //Increment time\r\n totalTime += currentStep;\r\n\r\n //Calculate force on each atom\r\n cluster.calculateForces();\r\n\r\n //Get energy of current configuration\r\n HashMap energies = cluster.getEnergies();\r\n potentialEnergy = 0.0;\r\n for(String eName : energies.keySet())\r\n potentialEnergy += energies.get(eName).doubleValue();\r\n\r\n //Save current atom positions and advance atoms to trial positions\r\n for(i = 0; i < numAtoms; i++) {\r\n a = atoms.get(i);\r\n\r\n savedLocations[i].x = a.location.x;\r\n savedLocations[i].y = a.location.y;\r\n savedLocations[i].z = a.location.z;\r\n\r\n //Compute trial locations\r\n a.location.x += a.force.x * currentStep;\r\n a.location.y += a.force.y * currentStep;\r\n a.location.z += a.force.z * currentStep;\r\n }\r\n\r\n //Get energy of the trial configuration\r\n energies = cluster.getEnergies();\r\n trialPotential = 0.0;\r\n for(String eName : energies.keySet())\r\n trialPotential += energies.get(eName).doubleValue();\r\n\r\n //Decide whether to keep trial configuration based on convergence\r\n if(trialPotential < potentialEnergy) {\r\n //Keep positions and check for convergence\r\n if(Math.abs((potentialEnergy - trialPotential) / potentialEnergy) < convergenceCriterion)\r\n isConverged = true;\r\n\r\n currentStep *= 1.2;\r\n } else {\r\n //Restore old positions\r\n for(i = 0; i < numAtoms; i++) {\r\n a = atoms.get(i);\r\n\r\n a.location.x = savedLocations[i].x;\r\n a.location.y = savedLocations[i].y;\r\n a.location.z = savedLocations[i].z;\r\n }\r\n\r\n currentStep *= 0.5;\r\n }\r\n }","@Test\n public void testMetastableStates()\n {\n IDoubleArray M = null;\n int nstates = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IIntArray expResult = null;\n IIntArray result = instance.metastableStates(M, nstates);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testBackwardCommittor()\n {\n IDoubleArray M = null;\n IIntArray A = null;\n IIntArray B = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.backwardCommittor(M, A, B);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n public void testEstimateC_IIntArray_int()\n {\n IIntArray traj = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateC(traj, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test\n void makeCoffee() {\n Assertions.assertFalse(machine.makeCoffee());\n\n //try with cup\n Assertions.assertDoesNotThrow(() -> {\n machine.positionCup();\n });\n\n Assertions.assertThrows(Exception.class, () -> {\n machine.positionCup();\n });\n\n\n Assertions.assertTrue(machine.makeCoffee());\n\n //check if correct amount as been subtracted\n Assertions.assertEquals(machine.getCurrentWater(), 1000 - machine.waterPerCup);\n Assertions.assertEquals(machine.getCurrentBeans(), 800 - machine.beansPerCup);\n\n //reset\n machine.fillWater();\n machine.fillBeans();\n machine.setCupPositioned(true);\n\n //test over boundary\n Assertions.assertTrue(machine.makeCoffee());\n Assertions.assertTrue(machine.makeCoffee());\n\n //set only water to boundary values and below\n machine.setCurrentWater(0);\n Assertions.assertFalse(machine.makeCoffee());\n machine.setCurrentWater(machine.waterPerCup);\n Assertions.assertTrue(machine.makeCoffee());\n\n //reset water\n machine.fillWater();\n\n //set only beans to boundary value and below\n machine.setCurrentBeans(0);\n Assertions.assertFalse(machine.makeCoffee());\n machine.setCurrentBeans(machine.beansPerCup);\n Assertions.assertTrue(machine.makeCoffee());\n\n\n }","public void checkMovementZMinimum(ITestContext context) throws Exception {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=40;\n String disResult=oa.clickDistanceMinus(clickTimes);\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n\n clickTimes=5;\n disResult=oa.clickDistanceMinus(clickTimes);\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n\n clickTimes=6;\n disResult=oa.clickDistanceMinus(clickTimes);//click times: 51\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-5\",\"distance is not same as expected result\");\n\n embededCompareResult(higher,middle,lower, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n disResult=oa.clickDistanceReset();\n embededScreenShot(screenWithinCase+\"_reset_\"+disResult,\"click Z reset(1 times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"0\",\"distance is not same as expected result\");\n }\n Reporter.testEnd();\n }","public static void runMarginPerceptron(Data[] data, int index,String testFileName,HashMap words)\r\n{\r\n int t=0;\r\n double LearningRates[] = {1, 0.1, 0.01};\r\n double Margins[] = {1, 0.1, 0.01};\r\n\r\n\tint minInt =0; // Set To Your Desired Min Value\r\n int maxInt =2;\r\n\tRandom rand = new Random();\r\n\t\r\n \tint randomNum = rand.nextInt((maxInt - minInt) + 1) + minInt;\r\n\r\n \tdouble LearningRate = LearningRates[randomNum];\r\n \t\r\nfor(int m=0;m machine.positionCup());\n\n Assertions.assertTrue(machine.removeCup());\n Assertions.assertFalse(machine.isCupPositioned());\n machine.setCupPositioned(true);\n Assertions.assertTrue(machine.isCupPositioned());\n\n }","public ModelParams optimize() throws Exception {\n\t\tint iterCount = 0;\n\t\tdivergence = KLDivergenceCalculator.calculate(cState, tState);\n\t\tSystem.out.println(\"KL Divergence : \" + divergence);\n\t\t// ((DivergenceChart) BeanFinder\n\t\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\n\t\t// iterCount, divergence);\n\t\tdouble delta[] = null;\n\t\tdouble theta[] = data.getParams().getParams();\n\t\tdouble newDivergence = 0.0d;\n\t\tdouble initialStep = data.getInitialStepSize();\n\t\tboolean optimized = false;\n\t\tdo {\n\t\t\tdelta = computeGradient(theta);\n\t\t\tfor (double step = initialStep; iterCount < data.getMaxIterations(); step /= 10) {\n\t\t\t\tSystem.out.println(\"Step size : \" + step);\n\t\t\t\tSystem.out.println(\"KL divergence before taking a step : \"\n\t\t\t\t\t\t+ KLDivergenceCalculator.calculate(cState, tState));\n\t\t\t\tfor (int i = 0; i < delta.length; i++) {\n\t\t\t\t\tif (Double.isNaN(delta[i]))\n\t\t\t\t\t\tSystem.out.print(\"delta is NAN!!\" + delta[i] + \", \");\n\t\t\t\t\ttheta[i] -= step * delta[i];\n\t\t\t\t\tSystem.out.print((theta[i]) + \", \");\n\t\t\t\t}\n\t\t\t\tSystem.out.println();\n\t\t\t\tgetNewState(theta);\n\t\t\t\titerCount++;\n\t\t\t\tnewDivergence = KLDivergenceCalculator\n\t\t\t\t\t\t.calculate(cState, tState);\n\t\t\t\tSystem.out.println(\"KL Divergence : \" + newDivergence);\n\t\t\t\t// ((DivergenceChart) BeanFinder\n\t\t\t\t// .findBean(MetaConstants.BEAN_DIVERGENCE_CHART)).update(\n\t\t\t\t// iterCount, newDivergence);\n\t\t\t\tif (newDivergence < divergence) {\n\t\t\t\t\tdivergence = newDivergence;\n\t\t\t\t\tinitialStep = step;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t// before taking the next step size, reset theta to what it was\n\t\t\t\tfor (int i = 0; i < delta.length; i++)\n\t\t\t\t\ttheta[i] += step * delta[i];\n\t\t\t\tgetNewState(theta); // Added for checking. Not required.\n\t\t\t}\n\t\t\toptimized = optimized(theta);\n\t\t} while (!optimized && iterCount < data.getMaxIterations());\n\t\tModelParams optimParams = null;\n\t\tif (optimized) {\n\t\t\toptimParams = new ModelParams();\n\t\t\toptimParams.setParams(theta);\n\t\t}\n\t\treturn optimParams;\n\t}","@Test\n public void testEstimateCmilestoning_Iterable_int()\n {\n Iterable trajs = null;\n int lag = 0;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n IDoubleArray expResult = null;\n IDoubleArray result = instance.estimateCmilestoning(trajs, lag);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test05() throws Throwable {\n Discretize discretize0 = new Discretize();\n double[][] doubleArray0 = new double[7][2];\n double[] doubleArray1 = new double[7];\n doubleArray1[0] = 0.0;\n doubleArray1[1] = (-587.14627);\n doubleArray1[2] = (-2542.40202289275);\n doubleArray1[3] = (-167.679515);\n doubleArray1[4] = (-1.0);\n doubleArray1[5] = 1.0;\n doubleArray1[6] = (-1.0);\n doubleArray0[0] = doubleArray1;\n double[] doubleArray2 = new double[5];\n doubleArray2[0] = (-167.679515);\n doubleArray2[1] = (-2692.352);\n doubleArray2[2] = (-167.679515);\n doubleArray2[3] = (-2542.40202289275);\n doubleArray2[4] = (-587.14627);\n doubleArray0[1] = doubleArray2;\n double[] doubleArray3 = new double[4];\n doubleArray3[0] = (-1.0);\n doubleArray3[1] = (-1.0);\n doubleArray3[2] = 1.0;\n doubleArray3[3] = (-2542.40202289275);\n doubleArray0[2] = doubleArray3;\n double[] doubleArray4 = new double[7];\n doubleArray4[0] = (-2692.352);\n doubleArray4[1] = (-1.0);\n doubleArray4[2] = (-1.0);\n doubleArray4[3] = 0.0;\n doubleArray4[4] = 1.0;\n doubleArray4[5] = 0.0;\n doubleArray4[6] = 1.0;\n doubleArray0[3] = doubleArray4;\n double[] doubleArray5 = new double[1];\n doubleArray5[0] = (-167.679515);\n doubleArray0[4] = doubleArray5;\n double[] doubleArray6 = new double[0];\n doubleArray0[5] = doubleArray6;\n double[] doubleArray7 = new double[1];\n doubleArray7[0] = (-587.14627);\n doubleArray0[6] = doubleArray7;\n discretize0.m_CutPoints = doubleArray0;\n String string0 = discretize0.invertSelectionTipText();\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals(\"Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.\", string0);\n assertFalse(discretize0.getFindNumBins());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test\r\n public void testAverageacceleration() {\r\n System.out.println(\"Average Acceleration\");\r\n \r\n // Test case one.\r\n System.out.println(\"Test case #1\"); \r\n \r\n double distance = 200;\r\n long time = 16;\r\n \r\n SceneControl instance = new SceneControl();\r\n \r\n double expResult = 0.78125;\r\n double result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case two.\r\n System.out.println(\"Test case #2\"); \r\n \r\n distance = 80;\r\n time = 200;\r\n \r\n \r\n expResult = .002;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case three.\r\n System.out.println(\"Test case #3\"); \r\n \r\n distance = 0;\r\n time = 15;\r\n \r\n \r\n expResult = 0;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case four.\r\n System.out.println(\"Test case #4\"); \r\n \r\n distance = 12;\r\n time = 1;\r\n \r\n \r\n expResult = 12;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case five.\r\n System.out.println(\"Test case #5\"); \r\n \r\n distance = 1;\r\n time = 1;\r\n \r\n \r\n expResult = 1;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case six.\r\n System.out.println(\"Test case #6\"); \r\n \r\n distance = 3;\r\n time = 8;\r\n \r\n \r\n expResult = .046875;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n // Test case seven.\r\n System.out.println(\"Test case #7\"); \r\n \r\n distance = 200;\r\n time = 1;\r\n \r\n \r\n expResult = 200;\r\n result = instance.averageAcceleration(distance, time);\r\n assertEquals(expResult, result, -1);\r\n \r\n \r\n \r\n }","@Test\n public void testGetSetTemperaturaExteriorMinima() {\n System.out.println(\"getSetTemperaturaExteriorMinima\");\n double expResult = 10.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExteriorMinima(expResult);\n double result = instance.getTemperaturaExteriorMinima();\n assertEquals(expResult, result, 0.0);\n }","public void checkMovementZReset(ITestContext context) {\n Reporter.testStart();\n String caseFolder=context.getName()+\"/\";\n String importFile=context.getCurrentXmlTest().getParameter(\"importFile\");\n String expectationFile=context.getCurrentXmlTest().getParameter(\"expectationFile\");\n String methodName=Thread.currentThread().getStackTrace()[1].getMethodName();\n String screenWithinCase=caseFolder+methodName;\n MeshViewPage meshViewPage=new MeshViewPage(getWebDriverWrapper());\n if(meshViewPage.isThePage()){\n\n uploadDCM(meshViewPage,importFile);\n\n OrientationAdjustment oa= meshViewPage.clickOrientationAdjustment();\n Reporter.log(\"click Orientation Adjustment tab on left bar\");\n String text=\"0.1\";\n Reporter.log(\"select Movement step \"+text);\n Assert.assertEquals(oa.selectMovementStep(text),text,\"Cann't select \"+text);\n\n Integer clickTimes=30;\n String disResult=oa.clickDistanceMinus(clickTimes);\n embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z minus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-3\",\"distance is not same as expected result\");\n\n clickTimes=10;\n disResult=oa.clickDistancePlus(clickTimes);\n String lower=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-2\",\"distance is not same as expected result\");\n\n disResult=oa.clickDistancePlus(clickTimes);\n String middle=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z plus(\"+clickTimes.toString()+\" times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"-1\",\"distance is not same as expected result\");\n\n disResult=oa.clickDistanceReset();\n String higher=embededScreenShot(screenWithinCase+\"_distance_\"+disResult,\"click Z reset(1 times), get distance is \"+disResult);\n Assert.assertEquals(disResult,\"0\",\"distance is not same as expected result\");\n\n embededCompareResult(lower,middle,higher, IComFolder.RESULT_ACTUAL_FOLDER +caseFolder, IComFolder.RESULT_EXPECTATION_FOLDER+expectationFile);\n\n }\n Reporter.testEnd();\n }","private static void testTakeStep(Drunkard drunkard, int testStepTimes, int testStepSize){\n\t for (int i = 0; i < testStepTimes; i++)\n\t{\n\t ImPoint pointA = drunkard.getCurrentLoc(); \n\t\tdrunkard.takeStep();\n\t\tImPoint pointB = drunkard.getCurrentLoc();\n\t\t\n\t\tif (pointA.getX()-pointB.getX() != 0 && pointA.getY()-pointB.getY() != 0)\n\t\t{\n\t\t\tSystem.out.println(\"Not a valid compass direction!\");\n\t\t}\n\t\telse if (Math.sqrt((pointA.getX() - pointB.getX())*(pointA.getX() - pointB.getX()) + (pointA.getY() - pointB.getY())*(pointA.getY() - pointB.getY())) != testStepSize)\n\t\t{\n\t\t\tSystem.out.println(\"took step to (\" + pointB.getX() + \",\" + pointB.getY() + \"), FAILED, not a valid step\");\n\t\t}\n\t\telse\n\t\t{\n\t\t\tSystem.out.println(\"took step to (\" + pointB.getX() + \",\" + pointB.getY() + \"), SUCCEEDED\");\n\t\t}\n\t}\n\t System.out.println(); \n\t}","@Test\n public void testIsTransitionMatrix()\n {\n IDoubleArray T = null;\n MarkovModelUtilities instance = new MarkovModelUtilities();\n boolean expResult = false;\n boolean result = instance.isTransitionMatrix(T);\n assertEquals(expResult, result);\n // TODO review the generated test code and remove the default call to fail.\n fail(\"The test case is a prototype.\");\n }","@Test(timeout = 4000)\n public void test072() throws Throwable {\n TestInstances testInstances0 = new TestInstances();\n Instances instances0 = testInstances0.generate((String) null);\n Evaluation evaluation0 = new Evaluation(instances0);\n double[] doubleArray0 = new double[7];\n evaluation0.updateMargins(doubleArray0, 3, (-1));\n assertEquals(0.0, evaluation0.SFEntropyGain(), 0.01);\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize();\n discretize0.getCutPoints((-2276));\n assertFalse(discretize0.getFindNumBins());\n assertEquals(10, discretize0.getBins());\n assertFalse(discretize0.getMakeBinary());\n assertEquals((-1.0), discretize0.getDesiredWeightOfInstancesPerInterval(), 0.01);\n assertFalse(discretize0.getUseBinNumbers());\n assertFalse(discretize0.getUseEqualFrequency());\n }","@Test\n public void testGiveChange() throws Exception {\n System.out.println(\"giveChange\");\n //CashRegister instance = new CashRegister();\n assertEquals(0.0, instance.giveChange(), EPSILON);\n instance.scanItem(0.55);\n instance.scanItem(1.27);\n double expResult = 0.18;\n instance.collectPayment(Money.QUARTER, 8);\n double result = instance.giveChange();\n assertEquals(expResult, result, EPSILON);\n \n }","@Test\n public void testCalcMedAmount() {\n \n //Test case 1\n \n System.out.println(\"calcMedAmount Test Case 1 - Dose is correct\");\n \n //Input Values for Test Case 1\n \n double weightInPounds = 100;\n double numberOfPills = 1;\n \n AntidoteControl instance = new AntidoteControl();\n \n double expResult = 227;\n double result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 2\n \n System.out.println(\"calcMedAmount Test Case 2 Invalid weight\");\n \n //Input Values for Test Case 2\n \n weightInPounds = 0;\n numberOfPills = 1;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = -1;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 3\n \n System.out.println(\"calcMedAmount Test Case 3 Underdose\");\n \n //Input Values for Test Case 3\n \n weightInPounds = 10001;\n numberOfPills = 0;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = -2;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n \n \n //Test case 4\n \n System.out.println(\"calcMedAmount Test Case 4 Overdose\");\n \n //Input Values for Test Case 4\n \n weightInPounds = 100;\n numberOfPills = 4;\n \n // AntidoteControl instance = new AntidoteControl();\n \n expResult = 227;\n result = AntidoteControl.calcMedAmount(weightInPounds);\n assertEquals(expResult, result, 0.0);\n // TODO review the generated test code and remove the default call to fail.\n //fail(\"The test case is a prototype.\");\n }","int calculateMST();","@Test\n public void transferCheckingtoSavingsTest(){\n assertTrue(userC.transfer(150.00, userS));\n }","@Test(timeout = 4000)\n public void test02() throws Throwable {\n Discretize discretize0 = new Discretize();\n Locale.getISOLanguages();\n double[][] doubleArray0 = new double[9][8];\n double[] doubleArray1 = new double[3];\n double[] doubleArray2 = new double[9];\n doubleArray2[5] = (-163.0);\n doubleArray2[8] = 1421.4058829;\n doubleArray0[3] = doubleArray0[2];\n doubleArray0[4] = doubleArray0[1];\n discretize0.setOutputFormat();\n double[] doubleArray3 = new double[2];\n doubleArray3[0] = 1421.4058829;\n doubleArray3[1] = (-163.0);\n doubleArray0[6] = doubleArray2;\n double[] doubleArray4 = new double[0];\n doubleArray0[7] = doubleArray4;\n double[] doubleArray5 = new double[6];\n doubleArray5[2] = (-163.0);\n doubleArray0[8] = doubleArray5;\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.setOutputFormat();\n fail(\"Expecting exception: NullPointerException\");\n \n } catch(NullPointerException e) {\n //\n // no message in exception (getMessage() returned null)\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test(timeout = 4000)\n public void test096() throws Throwable {\n CostMatrix costMatrix0 = Evaluation.handleCostOption(\"\", 1281);\n assertNull(costMatrix0);\n }","@Test(timeout = 4000)\n public void test19() throws Throwable {\n Discretize discretize0 = new Discretize(\"%\");\n FileSystemHandling.createFolder((EvoSuiteFile) null);\n discretize0.findNumBinsTipText();\n FileSystemHandling.appendStringToFile((EvoSuiteFile) null, \"%\");\n discretize0.m_DefaultCols = \"%\";\n double[][] doubleArray0 = new double[0][7];\n discretize0.m_CutPoints = doubleArray0;\n // Undeclared exception!\n try { \n discretize0.getCutPoints(1);\n fail(\"Expecting exception: ArrayIndexOutOfBoundsException\");\n \n } catch(ArrayIndexOutOfBoundsException e) {\n //\n // 1\n //\n verifyException(\"weka.filters.unsupervised.attribute.Discretize\", e);\n }\n }","@Test\n public void shouldCalculateProperMoveProbabilities() throws Exception {\n\n int actualCity = 0;\n boolean[] visited = new boolean[] {true, false, false, false};\n int[][] firstCriterium = new int[][] {{0, 2, 1, 2},\n {2, 2, 1, 1},\n {1, 1, 0, 1},\n {2, 1, 1, 0}};\n int[][] secondCriterium = new int[][] {{0, 3, 2, 1},\n {3, 0, 0, 0},\n {2, 0, 0, 0},\n {1, 0, 0, 0}};\n// final AttractivenessCalculator attractivenessCalculator = new AttractivenessCalculator(1.0, 0.0, 0.0, 0.0, 0.0);\n// double[][] array = new double[0][0];\n// final double[] probabilities = attractivenessCalculator.movesProbability(actualCity, visited, array, firstCriterium, secondCriterium, null, 0.0, 0.0);\n\n// assertTrue(probabilities[0] == 0.0);\n// assertTrue(probabilities[1] == 0.0);\n// assertTrue(probabilities[2] == 0.5);\n// assertTrue(probabilities[3] == 0.5);\n }","public void testMainLBGFSCachedSemiMarkov() throws Exception {\n\t\tConrad.main(new String[] {\"train\", \"test/input/zeroOrderLBFGSCachedSemiMarkov.xml\", \"test/input/zeroOrderTestShortMax.txt\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\"});\n\t\tConrad.main(new String[] {\"test\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\", \"test/input/zeroOrderTest.txt\", \"test/working/zeroOrderMainLBGFSCachedSemiMarkovPredicted.txt\"});\n\t\tConrad.main(new String[] {\"test\", \"test/working/zeroLBGFSModelCachedSemiMarkov.ser\", \"test/input/zeroOrderTest2.txt\", \"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\"});\n\t\tassertFilesMatch(\"test/output/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\", \"test/working/zeroOrder2MainLBGFSCachedSemiMarkovPredicted.txt\");\n\t}","@Test\n public void testGetSetTemperaturaExterior() {\n System.out.println(\"getSetTemperaturaExterior\");\n double expResult = 20.0;\n Simulacao simulacao = new Simulacao();\n simulacao.setSala(sala);\n AlterarTemperaturasMeioController instance = new AlterarTemperaturasMeioController(simulacao);\n instance.setTemperaturaExterior(expResult);\n double result = instance.getTemperaturaExterior();\n assertEquals(expResult, result, 0.0);\n }","@Test\r\n public void gallonsCauldronHolds() {\r\n \r\n System.out.println(\"gallonsCauldronHolds\");\r\n \r\n //Test #1\r\n System.out.println(\"Test case #1\"); \r\n \r\n double diameter = 50;\r\n double depth = 25;\r\n \r\n SceneControl instance = new SceneControl();\r\n \r\n double expResult = 212.500;\r\n double result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #2\r\n System.out.println(\"Test case #2\");\r\n \r\n diameter = 15;\r\n depth = -12;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #3\r\n System.out.println(\"Test case #3\");\r\n \r\n diameter = -5;\r\n depth = 20;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #4\r\n System.out.println(\"Test case #4\");\r\n \r\n diameter = 0;\r\n depth = 30;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #5\r\n System.out.println(\"Test case #5\");\r\n \r\n diameter = 14;\r\n depth = 0;\r\n \r\n expResult = -1;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #6\r\n System.out.println(\"Test case #6\");\r\n \r\n diameter = 1;\r\n depth = 12;\r\n \r\n expResult = .041;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n //Test #7\r\n System.out.println(\"Test case #7\");\r\n \r\n diameter = 20;\r\n depth = 1;\r\n \r\n expResult = 1.36;\r\n result = instance.gallonsCauldronHolds(diameter, depth);\r\n \r\n assertEquals(expResult, result, 0.0001);\r\n \r\n }","@Test\n public void testInbreedingCoeffForMultiallelicVC() {\n VariantContext test1 = makeVC(\"1\",Arrays.asList(Aref,T,C),\n makeG(\"s1\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s2\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s3\", T, C, 7099, 2530, 7099, 3056, 0, 14931),\n makeG(\"s4\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s5\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s6\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s7\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s8\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s9\", T, T, 7099, 2530, 0, 7099, 366, 3056),\n makeG(\"s10\", Aref, T, 2530, 0, 7099, 366, 3056, 14931));\n\n final AS_InbreedingCoeff testClass1 = new AS_InbreedingCoeff();\n final double ICresult1 = testClass1.calculateIC(test1, T);\n Assert.assertEquals(ICresult1, -0.4285714, DELTA_PRECISION, \"Pass\");\n final double ICresult1b = testClass1.calculateIC(test1, C);\n Assert.assertEquals(ICresult1b, -0.05263, DELTA_PRECISION, \"Pass\");\n\n //make sure that hets with different alternate alleles all get counted\n VariantContext test2 = makeVC(\"2\", Arrays.asList(Aref,T,C),\n makeG(\"s1\", Aref, C, 4878, 1623, 11297, 0, 7970, 8847),\n makeG(\"s2\", Aref, T, 2530, 0, 7099, 366, 3056, 14931),\n makeG(\"s3\", Aref, T, 3382, 0, 6364, 1817, 5867, 12246),\n makeG(\"s4\", Aref, T, 2488, 0, 9110, 3131, 9374, 12505),\n makeG(\"s5\", Aref, C, 4530, 2006, 18875, 0, 6847, 23949),\n makeG(\"s6\", Aref, T, 5325, 0, 18692, 389, 16014, 24570),\n makeG(\"s7\", Aref, T, 2936, 0, 29743, 499, 21979, 38630),\n makeG(\"s8\", Aref, T, 6902, 0, 8976, 45, 5844, 9061),\n makeG(\"s9\", Aref, T, 5732, 0, 10876, 6394, 11408, 17802),\n makeG(\"s10\", Aref, T, 2780, 0, 25045, 824, 23330, 30939));\n\n final AS_InbreedingCoeff testClass2 = new AS_InbreedingCoeff();\n final double ICresult2 = testClass2.calculateIC(test2, T);\n Assert.assertEquals(ICresult2, -0.666666, DELTA_PRECISION, \"Pass\");\n final double ICresult2b = testClass2.calculateIC(test2, C);\n Assert.assertEquals(ICresult2b, -0.111129, DELTA_PRECISION, \"Pass\");\n }","public void test_setClippingIIII() {\n}","@Test\n\tvoid testCalculateMarkTillSemesterSuccess3() {\n\t\n\t\tString semesterName = \"20193\";\n\t\t\n\t\tUser user = userService.findByUsername(\"B17DCCN123\");\n\t\tStudent student = user.getStudent();\n\t\tList resultsTillSemester = resultService.findResultTillSemester(student.getId(),\n\t\t\t\tsemesterName);\n\t\t\n\t\tString tktMark = \"\";\n\t\t\n\t\tfor (StudentResult result : resultsTillSemester) {\n\t\t\tSubject subject = result.getStudentRegister().getCourse().getSubject();\n\t\t\tif (subject.getName().equals(\"Toán kinh tế\")) {\n\t\t\t\ttktMark = result.getMarkToChar();\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\t\n\t\tList