package dr.inference.model;
/**
* @author Marc Suchard
*/
public class CompoundSymmetricMatrix extends MatrixParameter {
private Parameter diagonalParameter;
private Parameter offDiagonalParameter;
private boolean asCorrelation = false;
private int dim;
public CompoundSymmetricMatrix(Parameter diagonals, Parameter offDiagonal, boolean asCorrelation) {
super(MATRIX_PARAMETER);
diagonalParameter = diagonals;
offDiagonalParameter = offDiagonal;
addParameter(diagonalParameter);
addParameter(offDiagonal);
dim = diagonalParameter.getDimension();
this.asCorrelation = asCorrelation;
}
public double[] getAttributeValue() {
double[] stats = new double[dim * dim];
int index = 0;
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
stats[index] = getParameterValue(i, j);
index++;
}
}
return stats;
}
public double[] getDiagonals() {
return diagonalParameter.getParameterValues();
}
public double getOffDiagonal() {
return offDiagonalParameter.getParameterValue(0);
}
public double getParameterValue(int row, int col) {
if (row != col) {
if (asCorrelation) {
return offDiagonalParameter.getParameterValue(0) *
Math.sqrt(diagonalParameter.getParameterValue(row) * diagonalParameter.getParameterValue(col));
}
return offDiagonalParameter.getParameterValue(0);
}
return diagonalParameter.getParameterValue(row);
}
public double[][] getParameterAsMatrix() {
final int I = dim;
double[][] parameterAsMatrix = new double[I][I];
for (int i = 0; i < I; i++) {
parameterAsMatrix[i][i] = getParameterValue(i, i);
for (int j = i + 1; j < I; j++) {
parameterAsMatrix[j][i] = parameterAsMatrix[i][j] = getParameterValue(i, j);
}
}
return parameterAsMatrix;
}
public int getColumnDimension() {
return diagonalParameter.getDimension();
}
public int getRowDimension() {
return diagonalParameter.getDimension();
}
}