public double logPdf(double[] x) {
Matrix W = new Matrix(x, dim, dim);
double logDensity = 0;
// System.err.println("here");
// double det = 0;
// try {
// det = W.determinant();
// } catch (IllegalDimension illegalDimension) {
// illegalDimension.printStackTrace();
// }
// if( det < 0 ) {
// System.err.println("not positive definite");
// return Double.NEGATIVE_INFINITY;
// }
try {
logDensity = Math.log(W.determinant());
logDensity *= -0.5;
logDensity *= df + dim + 1;
Matrix product = S.product(W.inverse());
for (int i = 0; i < dim; i++) logDensity -= 0.5 * product.component(i, i);
} catch (IllegalDimension illegalDimension) {
illegalDimension.printStackTrace();
}
logDensity += logNormalizationConstant;
return logDensity;
}
public void sampleMeans(int maxFull) {
double[][] means = new double[maxFull + 2][2];
// sample mean vector for each cluster
for (int i = 0; i < maxFull + 1; i++) {
// Find all elements in cluster
int ngroup = 0;
for (int ii = 0; ii < assignments.getDimension(); ii++) {
if ((int) assignments.getParameterValue(ii) == i) {
ngroup++;
}
}
if (ngroup != 0) {
double[][] group = new double[ngroup][2];
double[] groupMean = new double[2];
int count = 0;
for (int ii = 0; ii < assignments.getDimension(); ii++) {
if ((int) assignments.getParameterValue(ii) == i) {
group[count][0] = modelLikelihood.getData()[ii][0];
group[count][1] = modelLikelihood.getData()[ii][1];
groupMean[0] += group[count][0];
groupMean[1] += group[count][1];
count += 1;
}
}
groupMean[0] /= ngroup;
groupMean[1] /= ngroup;
double kn = k0 + ngroup;
double vn = v0 + ngroup;
double[][] sumdif = new double[2][2];
for (int jj = 0; jj < ngroup; jj++) {
sumdif[0][0] += (group[jj][0] - groupMean[0]) * (group[jj][0] - groupMean[0]);
sumdif[0][1] += (group[jj][0] - groupMean[0]) * (group[jj][1] - groupMean[1]);
sumdif[1][0] += (group[jj][0] - groupMean[0]) * (group[jj][1] - groupMean[1]);
sumdif[1][1] += (group[jj][1] - groupMean[1]) * (group[jj][1] - groupMean[1]);
}
double[][] TnInv = new double[2][2];
TnInv[0][0] =
T0Inv[0][0]
+ ngroup * (k0 / kn) * (groupMean[0] - m[0]) * (groupMean[0] - m[0])
+ sumdif[0][0];
TnInv[0][1] =
T0Inv[0][1]
+ ngroup * (k0 / kn) * (groupMean[1] - m[1]) * (groupMean[0] - m[0])
+ sumdif[0][1];
TnInv[1][0] =
T0Inv[1][0]
+ ngroup * (k0 / kn) * (groupMean[0] - m[0]) * (groupMean[1] - m[1])
+ sumdif[1][0];
TnInv[1][1] =
T0Inv[1][1]
+ ngroup * (k0 / kn) * (groupMean[1] - m[1]) * (groupMean[1] - m[1])
+ sumdif[1][1];
Matrix Tn = new SymmetricMatrix(TnInv).inverse();
double[] posteriorMean = new double[2];
// compute posterior mean
posteriorMean[0] = (k0 * m[0] + ngroup * groupMean[0]) / (k0 + ngroup);
posteriorMean[1] = (k0 * m[1] + ngroup * groupMean[1]) / (k0 + ngroup);
// compute posterior Precision
double[][] posteriorPrecision =
new WishartDistribution(vn, Tn.toComponents()).nextWishart();
posteriorPrecision[0][0] *= kn;
posteriorPrecision[1][0] *= kn;
posteriorPrecision[0][1] *= kn;
posteriorPrecision[1][1] *= kn;
double[] sample =
new MultivariateNormalDistribution(posteriorMean, posteriorPrecision)
.nextMultivariateNormal();
means[i][0] = sample[0];
means[i][1] = sample[1];
}
}
// Fill in cluster means for each observation
for (int j = 0; j < assignments.getDimension(); j++) {
double[] group = new double[2];
group[0] = means[(int) assignments.getParameterValue(j)][0];
group[1] = means[(int) assignments.getParameterValue(j)][1];
modelLikelihood.setMeans(j, group);
}
}