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Add KDTrees to computeLocalUsingObservations in KSG mixed calc.

This commit is contained in:
Pedro Martinez Mediano 2017-08-16 19:28:24 +01:00
parent 641d17ec24
commit bfe8156584
1 changed files with 19 additions and 50 deletions
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69
java/source/infodynamics/measures/mixed/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java
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@ -604,58 +604,27 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu
continuousNewStates = MatrixUtils.normaliseIntoNewArray(
continuousNewStates, means, stds);
}
// Thanks to Rosalind Wang for picking up the bug in not using two different N's here
int N_newObservations = continuousNewStates.length; // number of new observations we're evaluating
int N_samplesForPdfs = continuousData.length; // number of observations we're using to compute the PDFs
double[] locals = new double[N_newObservations];
// Don't need the 1/k correction here because the conditional entropy term
// is taken over the continuous space only. The correction is (m-1)/k
// for an entropy over m subspaces.
// double fixedPartOfLocals = MathsUtils.digamma(k) - 1.0/(double)k +
// MathsUtils.digamma(N);
// Instead do:
double fixedPartOfLocals = digammaK + digammaN; // Use N_samplesForPdfs here because that's what would be in denominator of probability functions
double testSum = 0.0;
if (debug) {
System.out.printf("digamma(k)=%.3f + digamma(N)=%.3f\n",
digammaK, digammaN);
}
double avNx = 0;
double avNy = 0;
for (int t = 0; t < N_newObservations; t++) {
// Compute eps_x and eps_y for this time step:
// First get x norms to all points in the previously
// given observations.
double[] norms = new double[continuousData.length];
for (int t2 = 0; t2 < continuousData.length; t2++) {
// Compute norm in the continuous space
norms[t2] = normCalculator.norm(continuousNewStates[t], continuousData[t2]);
}
// Then find the k closest neighbours in the same discrete bin
double eps_x = MatrixUtils.kthMinSubjectTo(norms, k, discreteData, discreteNewStates[t]);
double fixedPartOfLocals = digammaK + MathsUtils.digamma(totalObservations); // Use N_samplesForPdfs here because that's what would be in denominator of probability functions
double testSum = 0.0, avNx = 0.0, avNy = 0.0;
double[] locals = new double[discreteNewStates.length];
for (int t = 0; t < discreteNewStates.length; t++) {
int b = discreteNewStates[t];
double[][] x = new double[][] {continuousNewStates[t]};
double eps_x = kdTreeBins[b].findKNearestNeighbours(k, x).poll().distance;
int n_x = kdTreeJoint.countPointsWithinR(x, eps_x, true);
int n_y = counts[b];
// Count the number of points whose x distance is less
// than or equal to eps_x
int n_x = 0;
for (int t2 = 0; t2 < continuousData.length; t2++) {
if (norms[t2] <= eps_x) {
n_x++;
}
}
// n_y is number of points in that discrete bin
int n_y = counts[discreteData[t]];
avNx += n_x;
avNy += n_y;
// Now compute the local value:
locals[t] = fixedPartOfLocals -
MathsUtils.digamma(n_x) - MathsUtils.digamma(n_y);
if (debug) {
if (debug) {
testSum += locals[t];
avNx += n_x;
avNy += n_y;
if (dimensions == 1) {
System.out.printf("t=%d: x=%.3f, eps_x=%.3f, n_x=%d, n_y=%d, local=%.3f, running total = %.5f\n",
t, continuousNewStates[t][0], eps_x, n_x, n_y, locals[t], testSum);
@ -663,11 +632,11 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu
System.out.printf("t=%d: eps_x=%.3f, n_x=%d, n_y=%d, local=%.3f, running total = %.5f\n",
t, eps_x, n_x, n_y, locals[t], testSum);
}
}
}
}
}
if (debug) {
avNx /= (double)N_newObservations;
avNy /= (double)N_newObservations;
avNx /= (double) discreteNewStates.length;
avNy /= (double) discreteNewStates.length;
System.out.printf("Average n_x=%.3f, Average n_y=%.3f\n", avNx, avNy);
}