/// <summary>
/// Compares 2 batrices and returns true if there are same
/// </summary>
/// <param name="matrix1"></param>
/// <param name="matrix2"></param>
/// <returns></returns>
public bool CompareMatrix(double[][] matrix1,
double[][] matrix2)
{
bool flag = true;
if (matrix1.Length != matrix2.Length)
{
return(false);
}
if (matrix1[0].Length != matrix2[0].Length)
{
return(false);
}
for (int col = 0; col < matrix1.Length; col++)
{
for (int row = 0; row < matrix1[0].Length; row++)
{
if (!SupportFunctions.DoubleCompare(matrix1[col][row],
matrix2[col][row]))
{
flag = false;
break;
}
}
}
return(flag);
}
public void NN_backpropagation_2L_gnb_all_training_samples()
{
initData_dataset_gaussian_naive_bayes_jason_example();
Build2LBackPropagation build =
new Build2LBackPropagation();
build.SetParameters(1);
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < 10; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (SupportFunctions.DoubleCompare(value,
_trainingData[_indexTargetAttribute][row]))
{
count++;
}
}
Assert.AreEqual(count,
10);
}
public void NN_perceptron_multi_all_training_samples_gnb()
{
initData_dataset_gaussian_naive_bayes_jason_example();
BuildMultiUnitPerceptronSGD build =
new BuildMultiUnitPerceptronSGD();
ModelMultiUnitPerceptron model =
(ModelMultiUnitPerceptron)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < 10; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (SupportFunctions.DoubleCompare(value,
_trainingData[_indexTargetAttribute][row]))
{
count++;
}
}
Assert.AreEqual(count,
10);
}
public void Matrix_hessenberg_3_row_test_2()
{
InitData_dataset_3_rows_symmetric_givens_rotation();
double[][] U = null;
double[][] newMatrix =
_mo.Hessenberg(_matrix1, ref U);
//U is null as matrix is already householder
Assert.IsNull(U);
Assert.AreEqual(newMatrix.Length, 3);
Assert.AreEqual(newMatrix[0].Length, 3);
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][0], 6));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][1], 5));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][0], 5));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][1], 1));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][2], 4));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][0], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][1], 4));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][2], 3));
}
public void Matrix_hessenberg_4_row_test_0()
{
InitData_dataset_4_rows_symmetric_hessenberg();
double[][] U = null;
double[][] newMatrix =
_mo.Hessenberg(_matrix1, ref U);
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][0], 4));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][1], 3));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][3], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][0], 3));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][1], 10.0 / 3.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][2], 5.0 / 3.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][3], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][0], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][1], 5.0 / 3.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][2], -33.0 / 25.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][3], -68.0 / 75.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][0], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][1], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][2], -68.0 / 75.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][3], 149.0 / 75.0));
}
public void NN_backpropagation_2L_nb_custom_activation_SigSig_all_training_samples()
{
initData_dataset_naive_bayes_jason_example();
Build2LBackPropagation build =
new Build2LBackPropagation();
build.SetParameters(1);
build.SetActivationFunction(0, new Dasmic.MLLib.Algorithms.NeuralNetwork.Support.ActivationFunction.Sigmoid());
build.SetActivationFunction(1, new Dasmic.MLLib.Algorithms.NeuralNetwork.Support.ActivationFunction.Sigmoid());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < 10; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (SupportFunctions.DoubleCompare(value,
_trainingData[_indexTargetAttribute][row]))
{
count++;
}
}
//Due to random weights
Assert.IsTrue(count >= 8 && count <= 10);
}
public void NN_perceptron_single_sgd_all_training_samples()
{
initData_NN_dataset_linear_subgd_jason_example();
BuildSingleUnitPerceptronSGD build = new BuildSingleUnitPerceptronSGD();
setPrivateVariablesInBuildObject(build);
//Set params
//build.setParameters(1,1,.45);
ModelSingleUnitPerceptron model =
(ModelSingleUnitPerceptron)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < _trainingData[0].Length; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (SupportFunctions.DoubleCompare(value, _trainingData[_indexTargetAttribute][row]))
{
count++;
}
}
Assert.AreEqual(count,
10);
}
public void PCA_new_matrix_3_rows_rank_3_1()
{
InitData_dataset_3_rows_non_symmetric();
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
pca.Rank = 3;
double[][] rotationMatrix = null;
double[] standardDeviation = null;
double[][] newMatrix = pca.GetPrincipleFeatures(_trainingData,
_attributeHeaders, ref standardDeviation, ref rotationMatrix);
Assert.AreEqual(newMatrix.Length, 3);
Assert.AreEqual(newMatrix[0].Length, _trainingData[0].Length);
Assert.IsTrue(SupportFunctions.DoubleCompare(standardDeviation[0], 1.73));
Assert.IsTrue(SupportFunctions.DoubleCompare(standardDeviation[1], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(standardDeviation[2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[0][0], .57));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[0][1], .57));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[0][2], .57));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[1][0], -.81));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[1][1], .40));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[1][2], .40));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[2][0], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[2][1], -.70));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[2][2], .70));
}
public void PCA_new_matrix_3_rows_rank_2_0()
{
InitData_dataset_3_rows_symmetric_hessenberg();
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
pca.Rank = 2;
double[][] rotationMatrix = null;
double[] standardDeviation = null;
double[][] newMatrix = pca.GetPrincipleFeatures(_trainingData,
_attributeHeaders, ref standardDeviation, ref rotationMatrix);
Assert.AreEqual(newMatrix.Length, 2);
Assert.AreEqual(standardDeviation.Length, 2);
Assert.AreEqual(rotationMatrix.Length, 2);
Assert.IsTrue(SupportFunctions.DoubleCompare(standardDeviation[0], 1.26));
Assert.IsTrue(SupportFunctions.DoubleCompare(standardDeviation[1], .85));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[0][0], .29));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[0][1], .60));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[0][2], -.74));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[1][0], -.52));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[1][1], .75));
Assert.IsTrue(SupportFunctions.DoubleCompare(rotationMatrix[1][2], .40));
}
public void Matrix_QR_Householder_3_row_test_0()
{
InitData_dataset_3_rows_symmetric_givens_rotation();
double[][] Q = null;
double[][] R = null;
_mo.QRDecomposition_Householder(_matrix1, ref Q, ref R);
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][0], 7.81));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][1], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][0], 4.48));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][1], 4.68));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[2][0], 2.56));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[2][1], .96));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[2][2], -4.18));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][0], .76));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][1], .64));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][0], .3327));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][1], -.39));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][2], .85));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[2][0], .54));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[2][1], -.65));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[2][2], -.51));
}
public void NN_activation_function_linear()
{
Linear aFunc = new Linear();
double value = aFunc.GetValue(3.0);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 3.00));
}
public void NN_activation_function_sigmoid()
{
Sigmoid aFunc = new Sigmoid();
double value = aFunc.GetValue(3.0);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 0.9526));
}
public void NN_activation_function_hyperbolictangent()
{
HyperbolicTangent aFunc = new HyperbolicTangent();
double value = aFunc.GetValue(3.0);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 0.9950));
}
public void Matrix_QR_Givens_Rotation_2_row_non_symmetric_test_1()
{
InitData_dataset_2_rows_householder();
double[][] Q = null;
double[][] R = null;
_mo.QRDecomposition_Givens_Rotation(_matrix1, ref Q, ref R);
//Verify R is upper triangular
Assert.IsTrue(_mo.IsUpperTriangular(R));
//---Veriy if A = Q.R
double[][] T = _mo.Multiply(Q, R);
Assert.IsTrue(_mo.CompareMatrix(_matrix1, T));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][0], 5));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][1], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][0], 2.4));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][1], -3.2));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][0], .6));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][1], .8));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][0], -.8));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][1], .6));
}
public void Matrix_QR_Givens_Rotation_2_row_symmetric_test_2()
{
InitData_dataset_2_rows_symmetric_eigenValue();
double[][] Q = null;
double[][] R = null;
_mo.QRDecomposition_Givens_Rotation(_matrix1, ref Q, ref R);
//Verify R is upper triangular
Assert.IsTrue(_mo.IsUpperTriangular(R));
//---Veriy if A = Q.R
double[][] T = _mo.Multiply(Q, R);
Assert.IsTrue(_mo.CompareMatrix(_matrix1, T));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][0], 2.23));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[0][1], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][0], 1.78));
Assert.IsTrue(SupportFunctions.DoubleCompare(R[1][1], 1.34));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][0], .89));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[0][1], .44));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][0], -.44));
Assert.IsTrue(SupportFunctions.DoubleCompare(Q[1][1], .89));
}
public void Statictics_CoVariance_1()
{
InitData_dataset_pca_example();
double value = Dispersion.CoVarianceSample(_trainingData[0], _trainingData[1]);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, .6154));
}
public void Matrix_hessenberg_4_row_test_1()
{
InitData_dataset_4_rows_symmetric_hessenberg();
//Convert Matrix 1 to Hessenberg
_matrix1[0][2] = 0;
_matrix1[0][3] = 0;
_matrix1[1][3] = 0;
double[][] U = null;
double[][] newMatrix =
_mo.Hessenberg(_matrix1, ref U);
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][0], 4));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][1], 1));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[0][3], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][0], 1));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][1], 2));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][2], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[1][3], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][0], -2));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][1], 0));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][2], 3));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[2][3], -2));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][0], 2));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][1], 1));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][2], -2));
Assert.IsTrue(SupportFunctions.DoubleCompare(newMatrix[3][3], -1));
}
public void NN_backpropagation_generic_std_3L_gnb_all_training_samples()
{
initData_dataset_gaussian_naive_bayes_jason_example();
BuildGenericBackPropagationStandard build =
new BuildGenericBackPropagationStandard();
build.SetParameters(1, 2, .5, 1500);
//build.SetNumberOfHiddenLayers(2);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.AddHiddenLayer(1, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Sigmoid());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < 10; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (SupportFunctions.DoubleCompare(value,
_trainingData[_indexTargetAttribute][row]))
{
count++;
}
}
//Due to random weights
Assert.IsTrue(count >= 5);
}
public void Statistics_Pdf_Example_3()
{
DistributionNormal snd =
new DistributionNormal();
double value = snd.ProbabilityDensityFunction(4, 0, 1);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, .0001));
}
public void Statictics_CoVariance_matrix_2_cols_1()
{
InitData_dataset_pca_example();
double[][] matrix = Dispersion.CovarianceMatrixSample(_trainingData);
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][0], .6165));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][1], .6154));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][0], .6154));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][1], .7165));
}
public void Statictics_CoVariance_Matrix_2_cols_0()
{
double[][] data = new double[2][];
data[0] = ta;
data[1] = ta2;
double[][] matrix = Dispersion.CovarianceMatrixSample(data);
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][0], 52.36));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][1], 21.12));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][0], 21.12));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][1], 55.75));
}
public void Regression_linear_multivariable_web_example_single_row_0()
{
InitData_dataset_regression_web_example();
BuildLinearMultiVariable lm = new BuildLinearMultiVariable();
ModelLinearMultiVariableBase mb = (ModelLinearMultiVariableBase)lm.BuildModel(_trainingData,
_attributeHeaders, _indexTargetAttribute);
double[] data = GetSingleTrainingRowDataForTest(0);
double value = mb.RunModelForSingleData(data);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 249.98));
}
public void Regression_linear_multivariable_jason_simple()
{
Init_dataset_jason_linear_regression();
BuildLinearMultiVariable lm = new BuildLinearMultiVariable();
ModelLinearMultiVariableBase mb = (ModelLinearMultiVariableBase)lm.BuildModel(_trainingData,
_attributeHeaders, _indexTargetAttribute);
double[] data = { 1 };
double value = mb.RunModelForSingleData(data);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 1.2));
}
public void Regression_linear_check_model_jason_input_row_2()
{
Init_dataset_jason_linear_regression();
BuildLinearSimple lm = new BuildLinearSimple();
Dasmic.MLLib.Algorithms.Regression.ModelBase mb = (Dasmic.MLLib.Algorithms.Regression.ModelBase)lm.BuildModel(_trainingData,
_attributeHeaders, _indexTargetAttribute);
double[] data = GetSingleTrainingRowDataForTest(2);
double value = mb.RunModelForSingleData(
data);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 3.59));
}
public void Matrix_inverse_determinant_0_3_row_test()
{
double[][] matrixA = new double[3][];
matrixA[0] = new double[] { 1, 4, 7 };
matrixA[1] = new double[] { 2, 5, 8 };
matrixA[2] = new double[] { 3, 6, 9 };
MatrixOperations mo = new MatrixOperations();
double[][] matrixI =
mo.Inverse(matrixA);
//Check Det
Assert.IsFalse(SupportFunctions.DoubleCompare(matrixI[0][0], -11.0 / 12.0));
}
public void Regression_linear_multivariable_jason_simple_rmse()
{
Init_dataset_jason_linear_regression();
BuildLinearMultiVariable lm = new BuildLinearMultiVariable();
ModelLinearMultiVariableBase mb = (ModelLinearMultiVariableBase)lm.BuildModel(_trainingData,
_attributeHeaders, _indexTargetAttribute);
//Refill the trainingData array since it gets messed up
Init_dataset_jason_linear_regression();
double value = mb.GetModelRMSE(_trainingData);
Assert.IsTrue(SupportFunctions.DoubleCompare(value,
0.69));
}
public void Regression_linear_gd_check_rmse_power_input()
{
initData_Power();
BuildLinearSGD lm = new BuildLinearSGD();
Dasmic.MLLib.Algorithms.Regression.ModelBase mb = (Dasmic.MLLib.Algorithms.Regression.ModelBase)
lm.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
double value = mb.GetModelRMSE(_trainingData);
//Some issues were seen earlier maybe with parrelization
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 3.18));
}
public void Regression_linear_gd_check_rmse_power_param()
{
initData_Power();
BuildLinearSGD lm = new BuildLinearSGD();
lm.SetParameters(.02, 20);
Dasmic.MLLib.Algorithms.Regression.ModelBase mb = (Dasmic.MLLib.Algorithms.Regression.ModelBase)
lm.BuildModel(_trainingData,
_attributeHeaders,
_indexTargetAttribute);
double value = mb.GetModelRMSE(_trainingData);
Assert.IsTrue(SupportFunctions.DoubleCompare(value, 3.16));
}
public void Statictics_ZeroCentered_2_rows_0()
{
InitData_dataset_pca_example();
double[][] matrix = Dispersion.GetZeroCenteredData(_trainingData, false);
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][0], .69));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][1], -1.31));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][2], .39));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][3], .09));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[0][4], 1.29));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][0], .49));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][1], -1.21));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][2], .99));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][3], .29));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrix[1][4], 1.09));
}
public void Matrix_inverse_2_row_test()
{
double[][] matrixA = new double[2][];
matrixA[0] = new double[] { 1, 2 };
matrixA[1] = new double[] { 3, 4 };
MatrixOperations mo = new MatrixOperations();
double[][] matrixI =
mo.Inverse(matrixA);
//Check Det
Assert.IsTrue(SupportFunctions.DoubleCompare(matrixI[0][0], -2.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrixI[0][1], 1.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrixI[1][0], 3.0 / 2.0));
Assert.IsTrue(SupportFunctions.DoubleCompare(matrixI[1][1], -1.0 / 2.0));
}