natas_hw
/
jidt
mirror of https://github.com/jlizier/jidt
0
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Implementing local TE contributions for the spiking estimator

This commit is contained in:
Joseph Lizier 2025-03-21 16:08:39 +11:00
parent 912bdf94e1
commit efd3943697
1 changed files with 57 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

71
java/source/infodynamics/measures/spiking/integration/TransferEntropyCalculatorSpikingIntegration.java
View File

@ -697,19 +697,51 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
return new distanceAndNumPoints(maxDistance, i);
}
@Override
public double computeAverageLocalOfObservations() throws Exception {
return computeAverageLocalOfObservations(kdTreeJointAtSpikes, jointEmbeddingsFromSpikes);
/**
* Data structure to store the interim results from a TE calculation
*/
protected class TERateResults {
protected double[] localContributions;
protected double meanRate;
protected double totalTime;
protected int numTargetSpikes;
protected TERateResults(double meanRate, double[] localValues, int numTargetSpikes, double totalTime) {
this.meanRate = meanRate;
this.localContributions = localValues;
this.numTargetSpikes = numTargetSpikes;
this.totalTime = totalTime;
}
}
@Override
public double computeAverageLocalOfObservations() throws Exception {
TERateResults teRateResults = computeAverageLocalOfObservations(kdTreeJointAtSpikes, jointEmbeddingsFromSpikes);
return teRateResults.meanRate;
}
/*
* We take the actual joint tree at spikes (along with the associated embeddings) as an argument, as we will need to swap these out when
* computing surrogates.
*/
public double computeAverageLocalOfObservations(KdTree actualKdTreeJointAtSpikes, Vector<double[]> actualJointEmbeddingsFromSpikes) throws Exception {
public TERateResults computeAverageLocalOfObservations(KdTree actualKdTreeJointAtSpikes, Vector<double[]> actualJointEmbeddingsFromSpikes) throws Exception {
double currentSum = 0;
int numTargetSpikesToSkip = MatrixUtils.max(destPastIntervals);
double[] localValues;
int indexInLocals = 0;
if (processTimeLengths.size() == 1) {
// Pad the local values for the first spikes (which we can't compute a TE contribution for) to zero
localValues = new double[conditioningEmbeddingsFromSpikes.size() + numTargetSpikesToSkip];
indexInLocals = numTargetSpikesToSkip;
} else {
// Don't pad with zeros, would need to properly return locals for each separate spike train
// TODO Do this separately for each separate spike train when we implement in a similar fashion
// for regular time series estimators
localValues = new double[conditioningEmbeddingsFromSpikes.size()];
}
for (int i = 0; i < conditioningEmbeddingsFromSpikes.size(); i++) {
double radiusJointSpikes = actualKdTreeJointAtSpikes.findKNearestNeighbours(Knns, i).poll().norms[0];
@ -812,11 +844,12 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
radiusConditioningSamples = Math.sqrt(radiusConditioningSamples);
}
currentSum += (MathsUtils.digamma(kJointSpikes) - MathsUtils.digamma(kJointSamples) +
localValues[indexInLocals] = (MathsUtils.digamma(kJointSpikes) - MathsUtils.digamma(kJointSamples) +
((numDestPastIntervals + numCondPastIntervals + numSourcePastIntervals) * (-Math.log(radiusJointSpikes) + Math.log(radiusJointSamples))) -
MathsUtils.digamma(kConditioningSpikes) + MathsUtils.digamma(kConditioningSamples) +
+ ((numDestPastIntervals + numCondPastIntervals) * (Math.log(radiusConditioningSpikes) - Math.log(radiusConditioningSamples))));
if (Double.isNaN(currentSum)) {
+ ((numDestPastIntervals + numCondPastIntervals) * (Math.log(radiusConditioningSpikes) - Math.log(radiusConditioningSamples))));
currentSum += localValues[indexInLocals];
if (Double.isNaN(localValues[indexInLocals])) {
for (double[] embed : jointEmbeddingsFromSamples) {
System.out.println(Arrays.toString(embed));
}
@ -829,7 +862,9 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
timeSum += time;
}
currentSum /= timeSum;
return currentSum;
TERateResults teRateResults = new TERateResults(currentSum, localValues, conditioningEmbeddingsFromSpikes.size(), timeSum);
return teRateResults;
}
@ -920,13 +955,21 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
}
KdTree conditionallyPermutedKdTreeJointFromSpikes = new KdTree(arrayedConditionallyPermutedJointEmbeddingsFromSpikes);
conditionallyPermutedKdTreeJointFromSpikes.setNormType(normType);
surrogateTEValues[permutationNumber] = computeAverageLocalOfObservations(conditionallyPermutedKdTreeJointFromSpikes,
conditionallyPermutedJointEmbeddingsFromSpikes);
TERateResults teRateResults = computeAverageLocalOfObservations(conditionallyPermutedKdTreeJointFromSpikes,
conditionallyPermutedJointEmbeddingsFromSpikes);
surrogateTEValues[permutationNumber] = teRateResults.meanRate;
}
return new EmpiricalMeasurementDistribution(surrogateTEValues, estimatedValue);
}
public class SpikingTELocalValues implements SpikingLocalInformationValues {
public double[] contributionsAtEachSpike;
public SpikingTELocalValues(double[] contributionsAtEachSpike) {
this.contributionsAtEachSpike = contributionsAtEachSpike;
}
}
/*
* (non-Javadoc)
*
@ -935,8 +978,8 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
*/
@Override
public SpikingLocalInformationValues computeLocalOfPreviousObservations() throws Exception {
// TODO Auto-generated method stub
return null;
TERateResults teRateResults = computeAverageLocalOfObservations(kdTreeJointAtSpikes, jointEmbeddingsFromSpikes);
return new SpikingTELocalValues(teRateResults.localContributions);
}