mirror of https://github.com/jlizier/jidt
87 lines
2.9 KiB
Java
Executable File
87 lines
2.9 KiB
Java
Executable File
package infodynamics.measures.continuous;
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import junit.framework.TestCase;
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import infodynamics.utils.MatrixUtils;
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import infodynamics.utils.RandomGenerator;
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public abstract class TransferEntropyMultiVariateTester extends TestCase {
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/**
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* Confirm that the local values average correctly back to the average value
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*
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* @param teCalc a pre-constructed TransferEntropyCalculatorMultiVariate object
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* @param dimensions number of dimensions for the source and dest data to use
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* @param timeSteps number of time steps for the random data
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* @param k history length for the TE calculator to use
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*/
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public void testLocalsAverageCorrectly(TransferEntropyCalculatorMultiVariate teCalc,
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int dimensions, int timeSteps, int k)
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throws Exception {
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teCalc.initialise(k, dimensions, dimensions);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
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0, 1);
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double[][] destData = rg.generateNormalData(timeSteps, dimensions,
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0, 1);
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teCalc.setObservations(sourceData, destData);
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//teCalc.setDebug(true);
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double te = teCalc.computeAverageLocalOfObservations();
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//teCalc.setDebug(false);
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double[] teLocal = teCalc.computeLocalOfPreviousObservations();
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System.out.printf("Average was %.5f\n", te);
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assertEquals(te, MatrixUtils.mean(teLocal, k, timeSteps-k), 0.00001);
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}
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/**
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* Confirm that significance testing doesn't alter the average that
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* would be returned.
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*
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* @param teCalc a pre-constructed TransferEntropyCalculatorMultiVariate object
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* @param dimensions number of dimensions for the source and dest data to use
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* @param timeSteps number of time steps for the random data
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* @param k history length for the TE calculator to use
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* @throws Exception
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*/
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public void testComputeSignificanceDoesntAlterAverage(TransferEntropyCalculatorMultiVariate teCalc,
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int dimensions, int timeSteps, int k) throws Exception {
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teCalc.initialise(k, dimensions, dimensions);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
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0, 1);
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double[][] destData = rg.generateNormalData(timeSteps, dimensions,
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0, 1);
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teCalc.setObservations(sourceData, destData);
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//teCalc.setDebug(true);
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double te = teCalc.computeAverageLocalOfObservations();
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//teCalc.setDebug(false);
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//double[] teLocal = teCalc.computeLocalOfPreviousObservations();
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System.out.printf("Average was %.5f\n", te);
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// Now look at statistical significance tests
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int[][] newOrderings = rg.generateDistinctRandomPerturbations(
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timeSteps - k, 100);
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teCalc.computeSignificance(newOrderings);
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// And compute the average value again to check that it's consistent:
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for (int i = 0; i < 10; i++) {
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double averageCheck1 = teCalc.computeAverageLocalOfObservations();
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assertEquals(te, averageCheck1);
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}
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}
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}
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