public void ValidateToString()
{
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
var d = new InverseWishart(1d, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
Assert.AreEqual("InverseWishart(ν = 1, Rows = 2, Columns = 2)", d.ToString());
}
public void HasRandomSourceEvenAfterSetToNull()
{
var d = new InverseWishart(1.0, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
Assert.DoesNotThrow(() => d.RandomSource = null);
Assert.IsNotNull(d.RandomSource);
}
public void HasRandomSourceEvenAfterSetToNull()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.DoesNotThrow(() => d.RandomSource = null);
Assert.IsNotNull(d.RandomSource);
}
public void FailSCreateInverseWishart(double nu, int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
matrix[0, 0] = 0.0;
var d = new InverseWishart(nu, matrix);
}
public void ValidateDensity(double nu, double density)
{
var matrix = Matrix <double> .Build.Dense(1, 1, 1.0);
var x = Matrix <double> .Build.Dense(1, 1, 5.0);
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqualRelative(density, d.Density(x), 16);
}
public void ValidateMode(double nu, int order)
{
var d = new InverseWishart(nu, Matrix <double> .Build.RandomPositiveDefinite(order, 1));
var mode = d.Mode;
for (var i = 0; i < d.Scale.RowCount; i++)
{
for (var j = 0; j < d.Scale.ColumnCount; j++)
{
Assert.AreEqual(d.Scale[i, j] * (1.0 / (nu + d.Scale.RowCount + 1.0)), mode[i, j]);
}
}
}
public void CanGetS()
{
int order = 2;
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(1.0, matrix);
for (int i = 0; i < order; i++)
{
for (int j = 0; j < order; j++)
{
Assert.AreEqual <double>(matrix[i, j], d.S[i, j]);
}
}
}
public void CanCreateInverseWishart(double nu, int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(nu, matrix);
Assert.AreEqual(nu, d.DegreesOfFreedom);
for (var i = 0; i < d.Scale.RowCount; i++)
{
for (var j = 0; j < d.Scale.ColumnCount; j++)
{
Assert.AreEqual(matrix[i, j], d.Scale[i, j]);
}
}
}
public void ValidateMode([Values(0.1, 1.0, 5.0)] double nu, [Values(2, 5)] int order)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order));
var mode = d.Mode;
for (var i = 0; i < d.S.RowCount; i++)
{
for (var j = 0; j < d.S.ColumnCount; j++)
{
Assert.AreEqual(d.S[i, j] * (1.0 / (nu + d.S.RowCount + 1.0)), mode[i, j]);
}
}
}
public void CanGetS()
{
const int Order = 2;
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(Order);
var d = new InverseWishart(1.0, matrix);
for (var i = 0; i < Order; i++)
{
for (var j = 0; j < Order; j++)
{
Assert.AreEqual(matrix[i, j], d.S[i, j]);
}
}
}
public void CanCreateInverseWishart([Values(0.1, 1.0, 5.0)] double nu, [Values(2, 5)] int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(nu, matrix);
Assert.AreEqual(nu, d.Nu);
for (var i = 0; i < d.S.RowCount; i++)
{
for (var j = 0; j < d.S.ColumnCount; j++)
{
Assert.AreEqual(matrix[i, j], d.S[i, j]);
}
}
}
public void CanCreateInverseWishart([Values(0.1, 1.0, 5.0)] double nu, [Values(2, 5)] int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(nu, matrix);
Assert.AreEqual(nu, d.Nu);
for (var i = 0; i < d.S.RowCount; i++)
{
for (var j = 0; j < d.S.ColumnCount; j++)
{
Assert.AreEqual(matrix[i, j], d.S[i, j]);
}
}
}
public void ValidateMean(double nu, int order)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order));
var mean = d.Mean;
for (var i = 0; i < d.Scale.RowCount; i++)
{
for (var j = 0; j < d.Scale.ColumnCount; j++)
{
Assert.AreEqual(d.Scale[i, j] * (1.0 / (nu - d.Scale.RowCount - 1.0)), mean[i, j]);
}
}
}
public void CanCreateInverseWishart(double nu, int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(nu, matrix);
Assert.AreEqual(nu, d.DegreesOfFreedom);
for (var i = 0; i < d.Scale.RowCount; i++)
{
for (var j = 0; j < d.Scale.ColumnCount; j++)
{
Assert.AreEqual(matrix[i, j], d.Scale[i, j]);
}
}
}
public void ValidateDensity([Values(1.0, 2.0, 5.0)] double nu, [Values(0.03228684517430723, 0.018096748360719193, 0.00043049126899076171)] double density)
{
const int Order = 1;
var matrix = new DenseMatrix(Order);
matrix[0, 0] = 1;
var x = new DenseMatrix(Order);
x[0, 0] = 5;
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqual(density, d.Density(x), 16);
}
public void CanGetS()
{
const int Order = 2;
var matrix = Matrix <double> .Build.RandomPositiveDefinite(Order, 1);
var d = new InverseWishart(1.0, matrix);
for (var i = 0; i < Order; i++)
{
for (var j = 0; j < Order; j++)
{
Assert.AreEqual(matrix[i, j], d.Scale[i, j]);
}
}
}
public void CanCreateInverseWishart(double nu, int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(nu, matrix);
Assert.AreEqual<double>(nu, d.Nu);
for (int i = 0; i < d.S.RowCount; i++)
{
for (int j = 0; j < d.S.ColumnCount; j++)
{
Assert.AreEqual(matrix[i, j], d.S[i, j]);
}
}
}
public void ValidateDensity(double nu, double density)
{
const int Order = 1;
var matrix = new DenseMatrix(Order);
matrix[0, 0] = 1;
var x = new DenseMatrix(Order);
x[0, 0] = 5;
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqualRelative(density, d.Density(x), 16);
}
public void CanCreateInverseWishart(double nu, int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(nu, matrix);
Assert.AreEqual <double>(nu, d.Nu);
for (int i = 0; i < d.S.RowCount; i++)
{
for (int j = 0; j < d.S.ColumnCount; j++)
{
Assert.AreEqual(matrix[i, j], d.S[i, j]);
}
}
}
public void ValidateDensity(double nu, double density)
{
int order = 1;
var matrix = new DenseMatrix(order);
matrix[0, 0] = 1;
var X = new DenseMatrix(order);
X[0, 0] = 5;
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqual(density, d.Density(X), 16);
}
public void ValidateVariance([Values(0.1, 1.0, 5.0)] double nu, [Values(2, 5)] int order)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order));
var variance = d.Variance;
for (var i = 0; i < d.S.RowCount; i++)
{
for (var j = 0; j < d.S.ColumnCount; j++)
{
var num1 = ((nu - d.S.RowCount + 1) * d.S[i, j] * d.S[i, j]) + ((nu - d.S.RowCount - 1) * d.S[i, i] * d.S[j, j]);
var num2 = (nu - d.S.RowCount) * (nu - d.S.RowCount - 1) * (nu - d.S.RowCount - 1) * (nu - d.S.RowCount - 3);
Assert.AreEqual(num1 / num2, variance[i, j]);
}
}
}
public void ValidateVariance(double nu, int order)
{
var d = new InverseWishart(nu, Matrix <double> .Build.RandomPositiveDefinite(order, 1));
var variance = d.Variance;
for (var i = 0; i < d.Scale.RowCount; i++)
{
for (var j = 0; j < d.Scale.ColumnCount; j++)
{
var num1 = ((nu - d.Scale.RowCount + 1) * d.Scale[i, j] * d.Scale[i, j]) + ((nu - d.Scale.RowCount - 1) * d.Scale[i, i] * d.Scale[j, j]);
var num2 = (nu - d.Scale.RowCount) * (nu - d.Scale.RowCount - 1) * (nu - d.Scale.RowCount - 1) * (nu - d.Scale.RowCount - 3);
Assert.AreEqual(num1 / num2, variance[i, j]);
}
}
}
public void CanGetNu([Values(1.0, 2.0, 5.0)] double nu)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.AreEqual(nu, d.Nu);
}
public void FailSCreateInverseWishart(double nu, int order)
{
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
matrix[0, 0] = 0.0;
var d = new InverseWishart(nu, matrix);
}
public void FailSetRandomSourceWithNullReference()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
d.RandomSource = null;
}
public void CanGetNu(double nu)
{
var d = new InverseWishart(nu, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
Assert.AreEqual(nu, d.DegreesOfFreedom);
}
public void CanGetS()
{
int order = 2;
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order);
var d = new InverseWishart(1.0, matrix);
for (int i = 0; i < order; i++)
{
for (int j = 0; j < order; j++)
{
Assert.AreEqual<double>(matrix[i, j], d.S[i, j]);
}
}
}
public void CanGetS()
{
const int Order = 2;
var matrix = MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(Order);
var d = new InverseWishart(1.0, matrix);
for (var i = 0; i < Order; i++)
{
for (var j = 0; j < Order; j++)
{
Assert.AreEqual(matrix[i, j], d.S[i, j]);
}
}
}
public void HasRandomSourceEvenAfterSetToNull()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.DoesNotThrow(() => d.RandomSource = null);
Assert.IsNotNull(d.RandomSource);
}
public void CanSampleStatic()
{
InverseWishart.Sample(new Random(), 1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
}
public void CanSample()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
d.Sample();
}
public void CanSetRandomSource()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
d.RandomSource = new Random();
}
public void CanSampleStatic()
{
InverseWishart.Sample(new System.Random(0), 1.0, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
}
public void ValidateMean(double nu, int order)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order));
var mean = d.Mean;
for (var i = 0; i < d.Scale.RowCount; i++)
{
for (var j = 0; j < d.Scale.ColumnCount; j++)
{
Assert.AreEqual(d.Scale[i, j] * (1.0 / (nu - d.Scale.RowCount - 1.0)), mean[i, j]);
}
}
}
public void ValidateDensity(double nu, double density)
{
const int Order = 1;
var matrix = new DenseMatrix(Order);
matrix[0, 0] = 1;
var x = new DenseMatrix(Order);
x[0, 0] = 5;
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqualRelative(density, d.Density(x), 16);
}
public void ValidateToString()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.AreEqual("InverseWishart(Nu = 1, Rows = 2, Columns = 2)", d.ToString());
}
public void ValidateVariance([Values(0.1, 1.0, 5.0)] double nu, [Values(2, 5)] int order)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order));
var variance = d.Variance;
for (var i = 0; i < d.S.RowCount; i++)
{
for (var j = 0; j < d.S.ColumnCount; j++)
{
var num1 = ((nu - d.S.RowCount + 1) * d.S[i, j] * d.S[i, j]) + ((nu - d.S.RowCount - 1) * d.S[i, i] * d.S[j, j]);
var num2 = (nu - d.S.RowCount) * (nu - d.S.RowCount - 1) * (nu - d.S.RowCount - 1) * (nu - d.S.RowCount - 3);
Assert.AreEqual(num1 / num2, variance[i, j]);
}
}
}
public void FailSampleStatic()
{
Assert.Throws <ArgumentOutOfRangeException>(() => InverseWishart.Sample(new Random(), -1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2)));
}
public void CanGetNu([Values(1.0, 2.0, 5.0)] double nu)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.AreEqual(nu, d.Nu);
}
public void CanGetNu(double nu)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.AreEqual(nu, d.DegreesOfFreedom);
}
public void HasRandomSource()
{
var d = new InverseWishart(1.0, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
Assert.IsNotNull(d.RandomSource);
}
public void CanSample()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
d.Sample();
}
public void ValidateToString()
{
var d = new InverseWishart(1d, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
Assert.AreEqual("InverseWishart(ν = 1, Rows = 2, Columns = 2)", d.ToString());
}
public void FailSetRandomSourceWithNullReference()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.Throws<ArgumentNullException>(() => d.RandomSource = null);
}
public void ValidateDensity(double nu, double density)
{
int order = 1;
var matrix = new DenseMatrix(order);
matrix[0, 0] = 1;
var X = new DenseMatrix(order);
X[0, 0] = 5;
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqual(density, d.Density(X), 16);
}
public void HasRandomSource()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.IsNotNull(d.RandomSource);
}
public void ValidateDensity([Values(1.0, 2.0, 5.0)] double nu, [Values(0.03228684517430723, 0.018096748360719193, 0.00043049126899076171)] double density)
{
const int Order = 1;
var matrix = new DenseMatrix(Order);
matrix[0, 0] = 1;
var x = new DenseMatrix(Order);
x[0, 0] = 5;
var d = new InverseWishart(nu, matrix);
AssertHelpers.AlmostEqual(density, d.Density(x), 16);
}
public void ValidateMode([Values(0.1, 1.0, 5.0)] double nu, [Values(2, 5)] int order)
{
var d = new InverseWishart(nu, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(order));
var mode = d.Mode;
for (var i = 0; i < d.S.RowCount; i++)
{
for (var j = 0; j < d.S.ColumnCount; j++)
{
Assert.AreEqual(d.S[i, j] * (1.0 / (nu + d.S.RowCount + 1.0)), mode[i, j]);
}
}
}
public void CanSample()
{
var d = new InverseWishart(1.0, Matrix <double> .Build.RandomPositiveDefinite(2, 1));
d.Sample();
}
public void ValidateToString()
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
Assert.AreEqual("InverseWishart(Nu = 1, Rows = 2, Columns = 2)", d.ToString());
}
public void FailSampleStatic()
{
Assert.That(() => InverseWishart.Sample(new System.Random(0), -1.0, Matrix <double> .Build.RandomPositiveDefinite(2, 1)), Throws.ArgumentException);
}
public void CanSetNu(double nu)
{
var d = new InverseWishart(1.0, MatrixLoader.GenerateRandomPositiveDefiniteDenseMatrix(2));
d.Nu = nu;
}
