/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against. The array
/// elements can be either of type double (for univariate data) or
/// type double[] (for multivariate data).</param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting, such
/// as regularization constants and additional parameters.</param>
///
/// <example>
/// Please see <see cref="MultivariateNormalDistribution"/>.
/// </example>
///
public void Fit(double[][] observations, double[] weights, NormalOptions options)
{
double[] means;
double[,] cov;
if (weights != null)
{
#if DEBUG
for (int i = 0; i < weights.Length; i++)
{
if (Double.IsNaN(weights[i]) || Double.IsInfinity(weights[i]))
{
throw new ArgumentException("Invalid numbers in the weight vector.", "weights");
}
}
#endif
// Compute weighted mean vector
means = Statistics.Tools.WeightedMean(observations, weights);
// Compute weighted covariance matrix
if (options != null && options.Diagonal)
{
cov = Matrix.Diagonal(Statistics.Tools.WeightedVariance(observations, weights, means));
}
else
{
cov = Statistics.Tools.WeightedCovariance(observations, weights, means);
}
}
else
{
// Compute mean vector
means = Statistics.Tools.Mean(observations);
// Compute covariance matrix
if (options != null && options.Diagonal)
{
cov = Matrix.Diagonal(Statistics.Tools.Variance(observations, means));
}
cov = Statistics.Tools.Covariance(observations, means);
}
CholeskyDecomposition chol = null;
SingularValueDecomposition svd = null;
if (options == null)
{
// We don't have further options. Just attempt a standard
// fitting. If the matrix is not positive semi-definite,
// throw an exception.
chol = new CholeskyDecomposition(cov,
robust: false, lowerTriangular: true);
if (!chol.PositiveDefinite)
{
throw new NonPositiveDefiniteMatrixException("Covariance matrix is not positive "
+ "definite. Try specifying a regularization constant in the fitting options "
+ "(there is an example in the Multivariate Normal Distribution documentation).");
}
// Become the newly fitted distribution.
initialize(means, cov, chol, svd: null);
return;
}
// We have options. In this case, we will either be using the SVD
// or we can add a regularization constant until the covariance
// matrix becomes positive semi-definite.
if (options.Robust)
{
svd = new SingularValueDecomposition(cov, true, true, true);
// No need to apply a regularization constant in this case
// Become the newly fitted distribution.
initialize(means, cov, null, svd);
return;
}
chol = new CholeskyDecomposition(cov,
robust: false, lowerTriangular: true);
// Check if need to add a regularization constant
double regularization = options.Regularization;
if (regularization > 0)
{
int dimension = observations[0].Length;
while (!chol.PositiveDefinite)
{
for (int i = 0; i < dimension; i++)
{
for (int j = 0; j < dimension; j++)
{
if (Double.IsNaN(cov[i, j]) || Double.IsInfinity(cov[i, j]))
{
cov[i, j] = 0.0;
}
}
cov[i, i] += regularization;
}
chol = new CholeskyDecomposition(cov, false, true);
}
}
if (!chol.PositiveDefinite)
{
throw new NonPositiveDefiniteMatrixException("Covariance matrix is not positive "
+ "definite. Try specifying a regularization constant in the fitting options "
+ "(there is an example in the Multivariate Normal Distribution documentation).");
}
// Become the newly fitted distribution.
initialize(means, cov, chol, svd: null);
}
public void AllTests()
{
Matrix A, B, C, Z, O, I, R, S, X, SUB, M, T, SQ, DEF, SOL;
double tmp;
double[] columnwise = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
double[] rowwise = { 1.0, 4.0, 7.0, 10.0, 2.0, 5.0, 8.0, 11.0, 3.0, 6.0, 9.0, 12.0 };
double[][] avals = { new double[] { 1.0, 4.0, 7.0, 10.0 }, new double[] { 2.0, 5.0, 8.0, 11.0 }, new double[] { 3.0, 6.0, 9.0, 12.0 } };
double[][] rankdef = avals;
double[][] tvals = { new double[] { 1.0, 2.0, 3.0 }, new double[] { 4.0, 5.0, 6.0 }, new double[] { 7.0, 8.0, 9.0 }, new double[] { 10.0, 11.0, 12.0 } };
double[][] subavals = { new double[] { 5.0, 8.0, 11.0 }, new double[] { 6.0, 9.0, 12.0 } };
double[][] pvals = { new double[] { 25, -5, 10 }, new double[] { -5, 17, 10 }, new double[] { 10, 10, 62 } };
double[][] ivals = { new double[] { 1.0, 0.0, 0.0, 0.0 }, new double[] { 0.0, 1.0, 0.0, 0.0 }, new double[] { 0.0, 0.0, 1.0, 0.0 } };
double[][] evals = { new double[] { 0.0, 1.0, 0.0, 0.0 }, new double[] { 1.0, 0.0, 2e-7, 0.0 }, new double[] { 0.0, -2e-7, 0.0, 1.0 }, new double[] { 0.0, 0.0, 1.0, 0.0 } };
double[][] square = { new double[] { 166.0, 188.0, 210.0 }, new double[] { 188.0, 214.0, 240.0 }, new double[] { 210.0, 240.0, 270.0 } };
double[][] sqSolution = { new double[] { 13.0 }, new double[] { 15.0 } };
double[][] condmat = { new double[] { 1.0, 3.0 }, new double[] { 7.0, 9.0 } };
int rows = 3, cols = 4;
int invalidld = 5; /* should trigger bad shape for construction with val */
int validld = 3; /* leading dimension of intended test Matrices */
int nonconformld = 4; /* leading dimension which is valid, but nonconforming */
int ib = 1, ie = 2, jb = 1, je = 3; /* index ranges for sub Matrix */
int[] rowindexset = new int[] { 1, 2 };
int[] badrowindexset = new int[] { 1, 3 };
int[] columnindexset = new int[] { 1, 2, 3 };
int[] badcolumnindexset = new int[] { 1, 2, 4 };
double columnsummax = 33.0;
double rowsummax = 30.0;
double sumofdiagonals = 15;
double sumofsquares = 650;
#region Testing constructors and constructor-like methods
// Constructors and constructor-like methods:
// double[], int
// double[,]
// int, int
// int, int, double
// int, int, double[,]
// Create(double[,])
// Random(int,int)
// Identity(int)
try
{
// check that exception is thrown in packed constructor with invalid length
A = new Matrix(columnwise, invalidld);
Assert.Fail("Catch invalid length in packed constructor: exception not thrown for invalid input");
}
catch (ArgumentException) { }
A = new Matrix(columnwise, validld);
B = new Matrix(avals);
tmp = B[0, 0];
avals[0][0] = 0.0;
C = B - A;
avals[0][0] = tmp;
B = Matrix.Create(avals);
tmp = B[0, 0];
avals[0][0] = 0.0;
Assert.AreEqual((tmp - B[0, 0]), 0.0, "Create");
avals[0][0] = columnwise[0];
I = new Matrix(ivals);
NumericAssert.AreAlmostEqual(I, Matrix.Identity(3, 4), "Identity");
#endregion
#region Testing access methods
// Access Methods:
// getColumnDimension()
// getRowDimension()
// getArray()
// getArrayCopy()
// getColumnPackedCopy()
// getRowPackedCopy()
// get(int,int)
// GetMatrix(int,int,int,int)
// GetMatrix(int,int,int[])
// GetMatrix(int[],int,int)
// GetMatrix(int[],int[])
// set(int,int,double)
// SetMatrix(int,int,int,int,Matrix)
// SetMatrix(int,int,int[],Matrix)
// SetMatrix(int[],int,int,Matrix)
// SetMatrix(int[],int[],Matrix)
// Various get methods
B = new Matrix(avals);
Assert.AreEqual(B.RowCount, rows, "getRowDimension");
Assert.AreEqual(B.ColumnCount, cols, "getColumnDimension");
B = new Matrix(avals);
double[][] barray = (Matrix)B;
Assert.AreSame(barray, avals, "getArray");
barray = B.Clone();
Assert.AreNotSame(barray, avals, "getArrayCopy");
NumericAssert.AreAlmostEqual(new Matrix(barray), B, "getArrayCopy II");
// double[] bpacked = B.ColumnPackedCopy;
// try
// {
// check(bpacked, columnwise);
// try_success("getColumnPackedCopy... ", "");
// }
// catch (System.SystemException e)
// {
// errorCount = try_failure(errorCount, "getColumnPackedCopy... ", "data not successfully (deep) copied by columns");
// System.Console.Out.WriteLine(e.Message);
// }
// bpacked = B.RowPackedCopy;
// try
// {
// check(bpacked, rowwise);
// try_success("getRowPackedCopy... ", "");
// }
// catch (System.SystemException e)
// {
// errorCount = try_failure(errorCount, "getRowPackedCopy... ", "data not successfully (deep) copied by rows");
// System.Console.Out.WriteLine(e.Message);
// }
try
{
tmp = B[B.RowCount, B.ColumnCount - 1];
Assert.Fail("get(int,int): OutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
tmp = B[B.RowCount - 1, B.ColumnCount];
Assert.Fail("get(int,int): OutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
Assert.AreEqual(B[B.RowCount - 1, B.ColumnCount - 1], avals[B.RowCount - 1][B.ColumnCount - 1], "get(int,int)");
SUB = new Matrix(subavals);
try
{
M = B.GetMatrix(ib, ie + B.RowCount + 1, jb, je);
Assert.Fail("GetMatrix(int,int,int,int): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
M = B.GetMatrix(ib, ie, jb, je + B.ColumnCount + 1);
Assert.Fail("GetMatrix(int,int,int,int): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
M = B.GetMatrix(ib, ie, jb, je);
NumericAssert.AreAlmostEqual(SUB, M, "GetMatrix(int,int,int,int)");
try
{
M = B.GetMatrix(ib, ie, badcolumnindexset);
Assert.Fail("GetMatrix(int,int,int[]): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
M = B.GetMatrix(ib, ie + B.RowCount + 1, columnindexset);
Assert.Fail("GetMatrix(int,int,int[]): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
M = B.GetMatrix(ib, ie, columnindexset);
NumericAssert.AreAlmostEqual(SUB, M, "GetMatrix(int,int,int[])");
try
{
M = B.GetMatrix(badrowindexset, jb, je);
Assert.Fail("GetMatrix(int[],int,int): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
M = B.GetMatrix(rowindexset, jb, je + B.ColumnCount + 1);
Assert.Fail("GetMatrix(int[],int,int): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
M = B.GetMatrix(rowindexset, jb, je);
NumericAssert.AreAlmostEqual(SUB, M, "GetMatrix(int[],int,int)");
try
{
M = B.GetMatrix(badrowindexset, columnindexset);
Assert.Fail("GetMatrix(int[],int[]): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
M = B.GetMatrix(rowindexset, badcolumnindexset);
Assert.Fail("GetMatrix(int[],int[]): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
M = B.GetMatrix(rowindexset, columnindexset);
NumericAssert.AreAlmostEqual(SUB, M, "GetMatrix(int[],int[])");
// Various set methods:
try
{
B[B.RowCount, B.ColumnCount - 1] = 0.0;
Assert.Fail("set(int,int,double): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
B[B.RowCount - 1, B.ColumnCount] = 0.0;
Assert.Fail("set(int,int,double): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
B[ib, jb] = 0.0;
tmp = B[ib, jb];
NumericAssert.AreAlmostEqual(tmp, 0.0, "set(int,int,double)");
M = new Matrix(2, 3, 0.0);
try
{
B.SetMatrix(ib, ie + B.RowCount + 1, jb, je, M);
Assert.Fail("SetMatrix(int,int,int,int,Matrix): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
B.SetMatrix(ib, ie, jb, je + B.ColumnCount + 1, M);
Assert.Fail("SetMatrix(int,int,int,int,Matrix): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
B.SetMatrix(ib, ie, jb, je, M);
NumericAssert.AreAlmostEqual(M - B.GetMatrix(ib, ie, jb, je), M, "SetMatrix(int,int,int,int,Matrix)");
B.SetMatrix(ib, ie, jb, je, SUB);
try
{
B.SetMatrix(ib, ie + B.RowCount + 1, columnindexset, M);
Assert.Fail("SetMatrix(int,int,int[],Matrix): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
B.SetMatrix(ib, ie, badcolumnindexset, M);
Assert.Fail("SetMatrix(int,int,int[],Matrix): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
B.SetMatrix(ib, ie, columnindexset, M);
NumericAssert.AreAlmostEqual(M - B.GetMatrix(ib, ie, columnindexset), M, "SetMatrix(int,int,int[],Matrix)");
B.SetMatrix(ib, ie, jb, je, SUB);
try
{
B.SetMatrix(rowindexset, jb, je + B.ColumnCount + 1, M);
Assert.Fail("SetMatrix(int[],int,int,Matrix): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
B.SetMatrix(badrowindexset, jb, je, M);
Assert.Fail("SetMatrix(int[],int,int,Matrix): IndexOutOfBoundsException expected but not thrown II");
}
catch (IndexOutOfRangeException) { }
B.SetMatrix(rowindexset, jb, je, M);
NumericAssert.AreAlmostEqual(M - B.GetMatrix(rowindexset, jb, je), M, "SetMatrix(int[],int,int,Matrix)");
B.SetMatrix(ib, ie, jb, je, SUB);
try
{
B.SetMatrix(rowindexset, badcolumnindexset, M);
Assert.Fail("SetMatrix(int[],int[],Matrix): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
try
{
B.SetMatrix(badrowindexset, columnindexset, M);
Assert.Fail("SetMatrix(int[],int[],Matrix): IndexOutOfBoundsException expected but not thrown");
}
catch (IndexOutOfRangeException) { }
B.SetMatrix(rowindexset, columnindexset, M);
NumericAssert.AreAlmostEqual(M - B.GetMatrix(rowindexset, columnindexset), M, "SetMatrix(int[],int[],Matrix)");
#endregion
#region Testing array-like methods
// Array-like methods:
// Subtract
// SubtractEquals
// Add
// AddEquals
// ArrayLeftDivide
// ArrayLeftDivideEquals
// ArrayRightDivide
// ArrayRightDivideEquals
// arrayTimes
// ArrayMultiplyEquals
// uminus
S = new Matrix(columnwise, nonconformld);
R = Matrix.Random(A.RowCount, A.ColumnCount);
A = R;
try
{
S = A - S;
Assert.Fail("Subtract conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
Assert.AreEqual((A - R).Norm1(), 0.0, "Subtract: difference of identical Matrices is nonzero,\nSubsequent use of Subtract should be suspect");
A = R.Clone();
A.Subtract(R);
Z = new Matrix(A.RowCount, A.ColumnCount);
try
{
A.Subtract(S);
Assert.Fail("SubtractEquals conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
Assert.AreEqual((A - Z).Norm1(), 0.0, "SubtractEquals: difference of identical Matrices is nonzero,\nSubsequent use of Subtract should be suspect");
A = R.Clone();
B = Matrix.Random(A.RowCount, A.ColumnCount);
C = A - B;
try
{
S = A + S;
Assert.Fail("Add conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
NumericAssert.AreAlmostEqual(C + B, A, "Add");
C = A - B;
C.Add(B);
try
{
A.Add(S);
Assert.Fail("AddEquals conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
NumericAssert.AreAlmostEqual(C, A, "AddEquals");
A = ((Matrix)R.Clone());
A.UnaryMinus();
NumericAssert.AreAlmostEqual(A + R, Z, "UnaryMinus");
A = (Matrix)R.Clone();
O = new Matrix(A.RowCount, A.ColumnCount, 1.0);
try
{
Matrix.ArrayDivide(A, S);
Assert.Fail("ArrayRightDivide conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
C = Matrix.ArrayDivide(A, R);
NumericAssert.AreAlmostEqual(C, O, "ArrayRightDivide");
try
{
A.ArrayDivide(S);
Assert.Fail("ArrayRightDivideEquals conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
A.ArrayDivide(R);
NumericAssert.AreAlmostEqual(A, O, "ArrayRightDivideEquals");
A = (Matrix)R.Clone();
B = Matrix.Random(A.RowCount, A.ColumnCount);
try
{
S = Matrix.ArrayMultiply(A, S);
Assert.Fail("arrayTimes conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
C = Matrix.ArrayMultiply(A, B);
C.ArrayDivide(B);
NumericAssert.AreAlmostEqual(C, A, "arrayTimes");
try
{
A.ArrayMultiply(S);
Assert.Fail("ArrayMultiplyEquals conformance check: nonconformance not raised");
}
catch (ArgumentException) { }
A.ArrayMultiply(B);
A.ArrayDivide(B);
NumericAssert.AreAlmostEqual(A, R, "ArrayMultiplyEquals");
#endregion
#region Testing linear algebra methods
// LA methods:
// Transpose
// Multiply
// Condition
// Rank
// Determinant
// trace
// Norm1
// norm2
// normF
// normInf
// Solve
// solveTranspose
// Inverse
// chol
// Eigen
// lu
// qr
// svd
A = new Matrix(columnwise, 3);
T = new Matrix(tvals);
T = Matrix.Transpose(A);
NumericAssert.AreAlmostEqual(Matrix.Transpose(A), T, "Transpose");
NumericAssert.AreAlmostEqual(A.Norm1(), columnsummax, "Norm1");
NumericAssert.AreAlmostEqual(A.NormInf(), rowsummax, "NormInf");
NumericAssert.AreAlmostEqual(A.NormF(), Math.Sqrt(sumofsquares), "NormF");
NumericAssert.AreAlmostEqual(A.Trace(), sumofdiagonals, "Trace");
NumericAssert.AreAlmostEqual(A.GetMatrix(0, A.RowCount - 1, 0, A.RowCount - 1).Determinant(), 0.0, "Determinant");
SQ = new Matrix(square);
NumericAssert.AreAlmostEqual(A * Matrix.Transpose(A), SQ, "Multiply(Matrix)");
NumericAssert.AreAlmostEqual(0.0 * A, Z, "Multiply(double)");
A = new Matrix(columnwise, 4);
QRDecomposition QR = A.QRDecomposition;
R = QR.R;
NumericAssert.AreAlmostEqual(A, QR.Q * R, "QRDecomposition");
SingularValueDecomposition SVD = A.SingularValueDecomposition;
NumericAssert.AreAlmostEqual(A, SVD.LeftSingularVectors * (SVD.S * Matrix.Transpose(SVD.RightSingularVectors)), "SingularValueDecomposition");
DEF = new Matrix(rankdef);
NumericAssert.AreAlmostEqual(DEF.Rank(), Math.Min(DEF.RowCount, DEF.ColumnCount) - 1, "Rank");
B = new Matrix(condmat);
SVD = B.SingularValueDecomposition;
double[] singularvalues = SVD.SingularValues;
NumericAssert.AreAlmostEqual(B.Condition(), singularvalues[0] / singularvalues[Math.Min(B.RowCount, B.ColumnCount) - 1], "Condition");
int n = A.ColumnCount;
A = A.GetMatrix(0, n - 1, 0, n - 1);
A[0, 0] = 0.0;
LUDecomposition LU = A.LUDecomposition;
NumericAssert.AreAlmostEqual(A.GetMatrix(LU.Pivot, 0, n - 1), LU.L * LU.U, "LUDecomposition");
X = A.Inverse();
NumericAssert.AreAlmostEqual(A * X, Matrix.Identity(3, 3), "Inverse");
O = new Matrix(SUB.RowCount, 1, 1.0);
SOL = new Matrix(sqSolution);
SQ = SUB.GetMatrix(0, SUB.RowCount - 1, 0, SUB.RowCount - 1);
NumericAssert.AreAlmostEqual(SQ.Solve(SOL), O, "Solve");
A = new Matrix(pvals);
CholeskyDecomposition Chol = A.CholeskyDecomposition;
Matrix L = Chol.TriangularFactor;
NumericAssert.AreAlmostEqual(A, L * Matrix.Transpose(L), "CholeskyDecomposition");
X = Chol.Solve(Matrix.Identity(3, 3));
NumericAssert.AreAlmostEqual(A * X, Matrix.Identity(3, 3), "CholeskyDecomposition Solve");
EigenvalueDecomposition Eig = A.EigenvalueDecomposition;
Matrix D = Eig.BlockDiagonal;
Matrix V = Eig.EigenVectors;
NumericAssert.AreAlmostEqual(A * V, V * D, "EigenvalueDecomposition (symmetric)");
A = new Matrix(evals);
Eig = A.EigenvalueDecomposition;
D = Eig.BlockDiagonal;
V = Eig.EigenVectors;
NumericAssert.AreAlmostEqual(A * V, V * D, "EigenvalueDecomposition (nonsymmetric)");
#endregion
}
/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against. The array
/// elements can be either of type double (for univariate data) or
/// type double[] (for multivariate data).</param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting, such
/// as regularization constants and additional parameters.</param>
///
/// <remarks>
/// Although both double[] and double[][] arrays are supported,
/// providing a double[] for a multivariate distribution or a
/// double[][] for a univariate distribution may have a negative
/// impact in performance.
/// </remarks>
///
public override void Fit(double[][] observations, double[] weights, IFittingOptions options)
{
double[] means;
double[,] cov;
NormalOptions opt = options as NormalOptions;
if (weights != null)
{
#if DEBUG
double sum = 0;
for (int i = 0; i < weights.Length; i++)
{
if (Double.IsNaN(weights[i]) || Double.IsInfinity(weights[i]))
{
throw new Exception("Invalid numbers in the weight vector.");
}
sum += weights[i];
}
if (Math.Abs(sum - 1.0) > 1e-10)
{
throw new Exception("Weights do not sum to one.");
}
#endif
// Compute weighted mean vector
means = Statistics.Tools.Mean(observations, weights);
// Compute weighted covariance matrix
if (opt != null && opt.Diagonal)
{
cov = Matrix.Diagonal(Statistics.Tools.WeightedVariance(observations, weights, means));
}
else
{
cov = Statistics.Tools.WeightedCovariance(observations, weights, means);
}
}
else
{
// Compute mean vector
means = Statistics.Tools.Mean(observations);
// Compute covariance matrix
if (opt != null && opt.Diagonal)
{
cov = Matrix.Diagonal(Statistics.Tools.Variance(observations, means));
}
cov = Statistics.Tools.Covariance(observations, means);
}
CholeskyDecomposition chol = new CholeskyDecomposition(cov, false, true);
if (opt != null)
{
// Parse optional estimation options
double regularization = opt.Regularization;
if (regularization > 0)
{
int dimension = observations[0].Length;
while (!chol.PositiveDefinite)
{
for (int i = 0; i < dimension; i++)
{
for (int j = 0; j < dimension; j++)
{
if (Double.IsNaN(cov[i, j]) || Double.IsInfinity(cov[i, j]))
{
cov[i, j] = 0.0;
}
}
cov[i, i] += regularization;
}
chol = new CholeskyDecomposition(cov, false, true);
}
}
}
if (!chol.PositiveDefinite)
{
throw new NonPositiveDefiniteMatrixException("Covariance matrix is not positive "
+ "definite. Try specifying a regularization constant in the fitting options.");
}
// Become the newly fitted distribution.
initialize(means, cov, chol);
}
/// <summary>Cholesky decomposition.</summary>
protected static double[,] chol(double[,] a)
{
var chol = new CholeskyDecomposition(a);
return(chol.LeftTriangularFactor);
}
public object[,] Compute()
{
int[] indices = new int[n];
for (int i = 0; i < n; ++i)
{
indices[i] = i;
}
bestScore = double.MinValue;
int[] bestPermutation = indices;
pivotIndex = 0;
swapIndex = 0;
iteration = 0;
int[] permutation = null;
while (Permute(bestPermutation, out permutation))
{
double[,] permutedData = new double[r, r];
for (int i = 0; i < n; ++i)
{
for (int j = 0; j <= i; ++j)
{
permutedData[i, j] = permutedData[j, i] = InputCorrelationConfiguration[permutation[i], permutation[j]];
}
}
for (int j = 0; j < n; ++j)
{
permutedData[n, j] = permutedData[j, n] = InputCorrelationConfiguration[n, permutation[j]];
}
permutedData[n, n] = InputCorrelationConfiguration[n, n];
double[,] cholesky = new CholeskyDecomposition(permutedData).LeftTriangularFactor;
double score = Math.Pow(cholesky[r - 1, pivotIndex], 2);
if (score > bestScore)
{
bestScore = score;
bestPermutation = permutation.ToArray();
}
iteration++;
}
optimalCorrelationConfiguration = new double[r, r];
for (int i = 0; i < n; ++i)
{
for (int j = 0; j <= i; ++j)
{
optimalCorrelationConfiguration[i, j] = optimalCorrelationConfiguration[j, i] = InputCorrelationConfiguration[bestPermutation[i], bestPermutation[j]];
}
}
for (int j = 0; j < n; ++j)
{
optimalCorrelationConfiguration[n, j] = optimalCorrelationConfiguration[j, n] = InputCorrelationConfiguration[n, bestPermutation[j]];
}
optimalCorrelationConfiguration[n, n] = InputCorrelationConfiguration[n, n];
OptimalCholeskyDecomposition = new CholeskyDecomposition(optimalCorrelationConfiguration).LeftTriangularFactor;
object[,] ret = new object[r + 1, r + 1];
ret[0, 0] = "";
for (int i = 0; i < n; ++i)
{
OptimalLabels[i] = InputLabels[bestPermutation[i]];
ret[0, i + 1] = ret[i + 1, 0] = OptimalLabels[i];
for (int j = 0; j < r; ++j)
{
ret[i + 1, j + 1] = OptimalCholeskyDecomposition[i, j];
}
}
OptimalLabels[n] = InputLabels[n];
ret[0, n + 1] = ret[n + 1, 0] = OptimalLabels[n];
for (int j = 0; j < r; ++j)
{
ret[n + 1, j + 1] = OptimalCholeskyDecomposition[n, j];
}
return(ret);
}
public CholeskyWrapper(CholeskyDecomposition ch)
{
_ch = ch;
}
