mirror of https://github.com/jlizier/jidt
minor edits
This commit is contained in:
David Shorten
parent
dc226be60a
commit
9a17361cca
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@ -53,13 +53,6 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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*/
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protected Vector<double[]> vectorOfDestinationSpikeTimes = null;
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Vector<double[][]>[] eventTimings = null;
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/**
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* Cache of the timing data for each new observed spiking event for the
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* destination only
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*/
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Vector<double[]> targetEmbeddingsFromSpikes = null;
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Vector<double[]> jointEmbeddingsFromSpikes = null;
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Vector<double[]> targetEmbeddingsFromSamples = null;
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@ -70,67 +63,14 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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protected KdTree kdTreeConditioningAtSpikes = null;
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protected KdTree kdTreeConditioningAtSamples = null;
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Vector<double[][]> destPastAndNextTimings = null;
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/**
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* Cache of the type of event for each new observed spiking event in both the source
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* and destination (i.e. which spiked next)
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*/
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Vector<Integer> eventTypeLocator = null;
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/**
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* Cache for each new observed spiking event of which index it has in the vector
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* of spiking events of the same type
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*/
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Vector<Integer> eventIndexLocator = null;
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/**
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* Cache for each time-series of observed spiking events of how many
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* observations were in that set.
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*/
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Vector<Integer> numEventsPerObservationSet = null;
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/**
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* KdTrees for searching the joint past spaces and time to next spike,
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* for each possibility of which spiked next
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*/
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protected KdTree[] kdTreesJoint = null;
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/**
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* KdTrees for searching the joint past spaces,
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* for each possibility of which spiked next
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*/
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protected KdTree[] kdTreesSourceDestHistories = null;
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/**
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* KdTrees for searching the past destination space and time to next spike
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*/
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protected KdTree kdTreeDestNext = null;
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/**
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* KdTrees for searching the past destination space
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*/
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protected KdTree kdTreeDestHistory = null;
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/**
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* NN searcher for the time to next spike space only, if required
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*/
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protected UnivariateNearestNeighbourSearcher nnSearcherDestTimeToNextSpike = null;
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/**
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* Property name for the number of nearest neighbours to search
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*/
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public static final String KNNS_PROP_NAME = "Knns";
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/**
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* Property name for adjusting the search radius for the next spike such that
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* it does not cover negative times (with respect to the previous spike, being either
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* source or destination spike)
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*/
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public static final String TRIM_TO_POS_PROP_NAME = "TRIM_RANGE_TO_POS_TIMES";
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/**
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* Property name for an amount of random Gaussian noise to be
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* added to the data (default is 1e-8, matching the MILCA toolkit).
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*/
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public static final String PROP_ADD_NOISE = "NOISE_LEVEL_TO_ADD";
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/**
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/**
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* Whether to add an amount of random noise to the incoming data
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*/
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protected boolean addNoise = true;
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@ -193,8 +133,6 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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l = Integer.parseInt(propertyValue);
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} else if (propertyName.equalsIgnoreCase(KNNS_PROP_NAME)) {
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Knns = Integer.parseInt(propertyValue);
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} else if (propertyName.equalsIgnoreCase(TRIM_TO_POS_PROP_NAME)) {
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trimToPosNextSpikeTimes = Boolean.parseBoolean(propertyValue);
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} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
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if (propertyValue.equals("0") ||
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propertyValue.equalsIgnoreCase("false")) {
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@ -225,8 +163,6 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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return Integer.toString(l);
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} else if (propertyName.equalsIgnoreCase(KNNS_PROP_NAME)) {
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return Integer.toString(Knns);
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} else if (propertyName.equalsIgnoreCase(TRIM_TO_POS_PROP_NAME)) {
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return Boolean.toString(trimToPosNextSpikeTimes);
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} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
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return Double.toString(noiseLevel);
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} else {
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@ -266,9 +202,6 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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vectorOfDestinationSpikeTimes.add(destination);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.spiking.TransferEntropyCalculatorSpiking#finaliseAddObservations()
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*/
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@ -285,15 +218,12 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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int timeSeriesIndex = 0;
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for (double[] destSpikeTimes : vectorOfDestinationSpikeTimes) {
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double[] sourceSpikeTimes = sourceIterator.next();
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timeSeriesIndex++;
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processEventsFromSpikingTimeSeries(sourceSpikeTimes, destSpikeTimes,
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timeSeriesIndex, eventTimings, destPastAndNextTimings,
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eventTypeLocator, eventIndexLocator, numEventsPerObservationSet,
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targetEmbeddingsFromSpikes, jointEmbeddingsFromSpikes,
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targetEmbeddingsFromSamples, jointEmbeddingsFromSamples);
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}
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// Convert the vectors to arrays so that they can be put in the trees
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double[][] arrayedTargetEmbeddingsFromSpikes = new double[targetEmbeddingsFromSpikes.size()][k];
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double[][] arrayedJointEmbeddingsFromSpikes = new double[targetEmbeddingsFromSpikes.size()][k + l];
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for (int i = 0; i < targetEmbeddingsFromSpikes.size(); i++) {
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@ -371,38 +301,33 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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destSpikeTimes[most_recent_dest_index];
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jointPast[k] = pointsAtWhichToMakeEmbeddings[embedding_point_index] -
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sourceSpikeTimes[most_recent_source_index];
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if (addNoise) {
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destPast[0] += random.nextGaussian()*noiseLevel;
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jointPast[0] += random.nextGaussian()*noiseLevel;
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jointPast[k] += random.nextGaussian()*noiseLevel;
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}
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for (int i = 1; i < k; i++) {
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destPast[i] = destSpikeTimes[most_recent_dest_index - i + 1] -
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destSpikeTimes[most_recent_dest_index - i];
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jointPast[i] = destSpikeTimes[most_recent_dest_index - i + 1] -
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destSpikeTimes[most_recent_dest_index - i];
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if (addNoise) {
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destPast[i] += random.nextGaussian()*noiseLevel;
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jointPast[i] += random.nextGaussian()*noiseLevel;
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}
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}
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for (int i = 1; i < l; i++) {
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jointPast[k + i] = sourceSpikeTimes[most_recent_source_index - i + 1] -
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sourceSpikeTimes[most_recent_source_index - i];
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if (addNoise) {
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jointPast[k + i] += random.nextGaussian()*noiseLevel;
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}
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}
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if (addNoise) {
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for (int i = 0; i < k; i++) {
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destPast[i] += random.nextGaussian()*noiseLevel;
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}
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for (int i = 0; i < l; i++) {
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jointPast[i] += random.nextGaussian()*noiseLevel;
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}
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}
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targetEmbeddings.add(destPast);
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jointEmbeddings.add(jointPast);
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}
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}
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protected void processEventsFromSpikingTimeSeries(double[] sourceSpikeTimes, double[] destSpikeTimes,
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int timeSeriesIndex, Vector<double[][]>[] eventTimings,
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Vector<double[][]> destPastAndNextTimings, Vector<Integer> eventTypeLocator,
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Vector<Integer> eventIndexLocator, Vector<Integer> numEventsPerObservationSet,
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Vector<double[]> targetEmbeddingsFromSpikes, Vector<double[]> jointEmbeddingsFromSpikes,
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Vector<double[]> targetEmbeddingsFromSamples, Vector<double[]> jointEmbeddingsFromSamples)
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throws Exception {
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@ -412,18 +337,14 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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Arrays.sort(sourceSpikeTimes);
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Arrays.sort(destSpikeTimes);
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// New
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int NUM_SAMPLES = sourceSpikeTimes.length;
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double sample_lower_bound = Arrays.stream(sourceSpikeTimes).min().getAsDouble();
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double sample_upper_bound = Arrays.stream(sourceSpikeTimes).max().getAsDouble();
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double[] randomSampleTimes = new double[NUM_SAMPLES];
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double[] randomSampleTimes = new double[sourceSpikeTimes.length];
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Random rand = new Random();
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for (int i = 0; i < randomSampleTimes.length; i++) {
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randomSampleTimes[i] = sample_lower_bound + rand.nextDouble() * (sample_upper_bound - sample_lower_bound);
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}
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Arrays.sort(randomSampleTimes);
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// End New
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makeEmbeddingsAtPoints(destSpikeTimes, sourceSpikeTimes, destSpikeTimes, targetEmbeddingsFromSpikes, jointEmbeddingsFromSpikes);
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makeEmbeddingsAtPoints(randomSampleTimes, sourceSpikeTimes, destSpikeTimes, targetEmbeddingsFromSamples, jointEmbeddingsFromSamples);
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@ -475,9 +396,9 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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currentSum += ((k + l) * (- Math.log(radiusJointSpikes) + Math.log(radiusJointSamples))
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+ k * (Math.log(radiusConditioningSpikes) - Math.log(radiusConditioningSamples)));
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}
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// Normalise by time
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currentSum /= (vectorOfDestinationSpikeTimes.elementAt(0)[vectorOfDestinationSpikeTimes.elementAt(0).length - 1]
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- vectorOfDestinationSpikeTimes.elementAt(0)[0]);
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return currentSum;
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}
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@ -527,5 +448,4 @@ public class TransferEntropyCalculatorSpikingIntegration implements
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// TODO Auto-generated method stub
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return 0;
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}
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}
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