mirror of https://github.com/jlizier/jidt
Making indenting style consistent through file
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@ -402,7 +402,7 @@ public class TransferEntropyCalculatorKraskov
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autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY) ||
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autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS) ||
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autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS_DEST_ONLY) ||
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autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE)) {
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autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE)) {
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// Use a Kraskov AIS calculator to embed both time-series individually:
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ActiveInfoStorageCalculatorKraskov aisCalc = new ActiveInfoStorageCalculatorKraskov();
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@ -473,54 +473,54 @@ public class TransferEntropyCalculatorKraskov
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System.out.println("Starting embedding of source:");
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}
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// Instantiate a new calculator to optimize the embedding parameters
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TransferEntropyCalculatorKraskov teEmbeddingCalc =
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new TransferEntropyCalculatorKraskov();
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// Instantiate a new calculator to optimize the embedding parameters
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TransferEntropyCalculatorKraskov teEmbeddingCalc =
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new TransferEntropyCalculatorKraskov();
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// Set all properties of the current calculator except embedding method
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for (String key : props.keySet()) {
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teEmbeddingCalc.setProperty(key, props.get(key));
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}
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teEmbeddingCalc.setProperty(PROP_AUTO_EMBED_METHOD, AUTO_EMBED_METHOD_NONE);
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// Set all properties of the current calculator except embedding method
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for (String key : props.keySet()) {
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teEmbeddingCalc.setProperty(key, props.get(key));
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}
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teEmbeddingCalc.setProperty(PROP_AUTO_EMBED_METHOD, AUTO_EMBED_METHOD_NONE);
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double bestTE = Double.NEGATIVE_INFINITY;
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int l_candidate_best = 1;
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int l_tau_candidate_best = 1;
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double bestTE = Double.NEGATIVE_INFINITY;
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int l_candidate_best = 1;
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int l_tau_candidate_best = 1;
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// Iterate over all possible embeddings
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for (int l_candidate = 1; l_candidate <= k_search_max; l_candidate++) {
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for (int l_tau_candidate = 1; l_tau_candidate <= tau_search_max; l_tau_candidate++) {
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// Iterate over all possible embeddings
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for (int l_candidate = 1; l_candidate <= k_search_max; l_candidate++) {
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for (int l_tau_candidate = 1; l_tau_candidate <= tau_search_max; l_tau_candidate++) {
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teEmbeddingCalc.initialise(k, k_tau, l_candidate, l_tau_candidate, delay);
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teEmbeddingCalc.startAddObservations();
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teEmbeddingCalc.initialise(k, k_tau, l_candidate, l_tau_candidate, delay);
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teEmbeddingCalc.startAddObservations();
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Iterator<double[]> destIterator = vectorOfDestinationTimeSeries.iterator();
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for (double[] source : vectorOfSourceTimeSeries) {
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double[] dest = destIterator.next();
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teEmbeddingCalc.addObservations(source, dest);
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}
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teEmbeddingCalc.finaliseAddObservations();
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double thisTE = teEmbeddingCalc.computeAverageLocalOfObservations();
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Iterator<double[]> destIterator = vectorOfDestinationTimeSeries.iterator();
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for (double[] source : vectorOfSourceTimeSeries) {
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double[] dest = destIterator.next();
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teEmbeddingCalc.addObservations(source, dest);
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}
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teEmbeddingCalc.finaliseAddObservations();
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double thisTE = teEmbeddingCalc.computeAverageLocalOfObservations();
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if (debug) {
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System.out.printf("TE for l=%d, l_tau=%d is %.3f\n",
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l_candidate, l_tau_candidate, thisTE);
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}
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if (debug) {
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System.out.printf("TE for l=%d, l_tau=%d is %.3f\n",
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l_candidate, l_tau_candidate, thisTE);
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}
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if (thisTE > bestTE) {
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// This parameter setting is the best so far:
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bestTE = thisTE;
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l_candidate_best = l_candidate;
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l_tau_candidate_best = l_tau_candidate;
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}
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if (l_candidate == 1) {
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// tau is irrelevant, so no point testing other values
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break;
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}
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}
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}
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l = l_candidate_best;
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l_tau = l_tau_candidate_best;
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if (thisTE > bestTE) {
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// This parameter setting is the best so far:
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bestTE = thisTE;
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l_candidate_best = l_candidate;
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l_tau_candidate_best = l_tau_candidate;
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}
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if (l_candidate == 1) {
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// tau is irrelevant, so no point testing other values
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break;
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}
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}
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}
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l = l_candidate_best;
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l_tau = l_tau_candidate_best;
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if (debug) {
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System.out.printf("Embedding parameters for source set to l=%d,l_tau=%d\n",
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l, l_tau);
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