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(); } }