public void TestCenterMatrixOnArithmeticMean()
{
var centeredMatrixA = MahalanobisDistanceCalculator.CenterMatrixOnArithmeticMean(m_matrixA, m_meanArrayA);
var centeredMatrixB = MahalanobisDistanceCalculator.CenterMatrixOnArithmeticMean(m_matrixB, m_meanArrayB);
double[,] testArrayA = { { -1.9, -2.3 }, { -1.9, 0.7 }, { 2.1, 0.7 }, { 3.1, -1.3 }, { 0.1, 2.7 }, { 2.1, -0.3 }, { 1.1, -1.3 }, { 0.1, 1.7 }, { -1.9, 0.7 }, { -2.9, -1.3 } };
double[,] testArrayB = { { -0.2, -0.2 }, { 0.8, -1.2 }, { 1.8, 1.8 }, { -1.2, 0.8 }, { -1.2, -1.2 } };
var testMatrixA = DenseMatrix.OfArray(testArrayA);
var testMatrixB = DenseMatrix.OfArray(testArrayB);
for (var i = 0; i < centeredMatrixA.RowCount; i++)
{
for (var j = 0; j < centeredMatrixA.ColumnCount; j++)
{
Assert.AreEqual(testMatrixA[i, j], centeredMatrixA[i, j], DELTA);
}
}
for (var i = 0; i < centeredMatrixB.RowCount; i++)
{
for (var j = 0; j < centeredMatrixB.ColumnCount; j++)
{
Assert.AreEqual(testMatrixB[i, j], centeredMatrixB[i, j], DELTA);
}
}
}
public void TestCalculateArithmeticMean()
{
var meanA = MahalanobisDistanceCalculator.CalculateArithmeticMean(m_matrixA);
var meanB = MahalanobisDistanceCalculator.CalculateArithmeticMean(m_matrixB);
Assert.AreEqual(3.9, meanA[0], DELTA);
Assert.AreEqual(4.3, meanA[1], DELTA);
Assert.AreEqual(6.2, meanB[0], DELTA);
Assert.AreEqual(5.2, meanB[1], DELTA);
}
public void TestCreateMeanDifferenceMatrix()
{
var meanDifferenceMatrix = MahalanobisDistanceCalculator.CreateMeanDifferenceMatrix(m_meanArrayA, m_meanArrayB);
double[,] testArray = { { -2.3 }, { -0.9 } };
var testMatrix = DenseMatrix.OfArray(testArray);
for (var i = 0; i < meanDifferenceMatrix.RowCount; i++)
{
for (var j = 0; j < meanDifferenceMatrix.ColumnCount; j++)
{
Assert.AreEqual(testMatrix[i, j], meanDifferenceMatrix[i, j], DELTA);
}
}
}
public void TestCreatePooledCovarianceMatrix()
{
var pooledCovarianceMatrix = MahalanobisDistanceCalculator.CreatePooledCovarianceMatrix(m_covarianceMatrixA, m_covarianceMatrixB, m_matrixA, m_matrixB);
double[,] testArray = { { 3.04667, 0.27333 }, { 0.27333, 1.92667 } };
var testMatrix = DenseMatrix.OfArray(testArray);
for (var i = 0; i < pooledCovarianceMatrix.RowCount; i++)
{
for (var j = 0; j < pooledCovarianceMatrix.ColumnCount; j++)
{
Assert.AreEqual(testMatrix[i, j], Math.Round(pooledCovarianceMatrix[i, j], 5), DELTA);
}
}
}
public void TestCreateCovarianceMatrix()
{
var covarianceMatrixA = MahalanobisDistanceCalculator.CreateCovarianceMatrix(m_matrixA, m_meanArrayA);
var covarianceMatrixB = MahalanobisDistanceCalculator.CreateCovarianceMatrix(m_matrixB, m_meanArrayB);
var testMatrixA = m_covarianceMatrixA;
var testMatrixB = m_covarianceMatrixB;
for (var i = 0; i < covarianceMatrixA.RowCount; i++)
{
for (var j = 0; j < covarianceMatrixA.ColumnCount; j++)
{
Assert.AreEqual(testMatrixA[i, j], covarianceMatrixA[i, j], DELTA);
Assert.AreEqual(testMatrixB[i, j], covarianceMatrixB[i, j], DELTA);
}
}
}
public void CalculateMahalanobisDistance()
{
var mahalanobisDistance = MahalanobisDistanceCalculator.CalculateMahalanobisDistance(m_matrixA, m_matrixB);
Assert.AreEqual(1.4104178399830798, mahalanobisDistance);
}
