Adding unit tests for MI Kernel and Gaussian calculators, to check that their average calculations do not change across calls to compute the statistical significance (where we clone the calculator to handle the surrogates).

java/unittests/infodynamics/measures/continuous/gaussian/MutualInfoMultiVariateTester.java

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+package infodynamics.measures.continuous.gaussian;
+
+import junit.framework.TestCase;
+import infodynamics.utils.EmpiricalMeasurementDistribution;
+import infodynamics.utils.RandomGenerator;
+
+public class MutualInfoMultiVariateTester extends TestCase {
+
+	public void testComputeSignificanceDoesntAlterAverage() throws Exception {
+		
+		MutualInfoCalculatorMultiVariateGaussian miCalc =
+				new MutualInfoCalculatorMultiVariateGaussian();
+		
+		int dimensions = 2;
+		int timeSteps = 100;
+		
+		miCalc.initialise(dimensions, dimensions);
+		
+		// generate some random data
+		RandomGenerator rg = new RandomGenerator();
+		double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] destData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		
+		miCalc.setObservations(sourceData, destData);
+		
+		//miCalc.setDebug(true);
+		double mi = miCalc.computeAverageLocalOfObservations();
+		//miCalc.setDebug(false);
+		//double[] miLocal = miCalc.computeLocalOfPreviousObservations();
+		
+		System.out.printf("Average was %.5f\n", mi);
+		
+		// Now look at statistical significance tests
+		int[][] newOrderings = rg.generateDistinctRandomPerturbations(
+				timeSteps, 100);
+
+		EmpiricalMeasurementDistribution measDist =
+				miCalc.computeSignificance(newOrderings);
+		
+		System.out.printf("pValue of sig test was %.3f\n", measDist.pValue);
+		
+		// And compute the average value again to check that it's consistent:
+		for (int i = 0; i < 10; i++) {
+			double averageCheck1 = miCalc.computeAverageLocalOfObservations();
+			assertEquals(mi, averageCheck1);
+		}
+	}
+}

java/unittests/infodynamics/measures/continuous/kernel/MutualInfoMultiVariateTester.java

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+package infodynamics.measures.continuous.kernel;
+
+import junit.framework.TestCase;
+import infodynamics.utils.RandomGenerator;
+
+public class MutualInfoMultiVariateTester extends TestCase {
+
+	public void testComputeSignificanceDoesntAlterAverage() throws Exception {
+		
+		MutualInfoCalculatorMultiVariateKernel miCalc =
+				new MutualInfoCalculatorMultiVariateKernel();
+		
+		int dimensions = 2;
+		int timeSteps = 100;
+		double kernelWidth = 1;
+		
+		miCalc.setProperty(
+				MutualInfoCalculatorMultiVariateKernel.NORMALISE_PROP_NAME,
+				"true");
+		miCalc.initialise(dimensions, dimensions, kernelWidth);
+		
+		// generate some random data
+		RandomGenerator rg = new RandomGenerator();
+		double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] destData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		
+		miCalc.setObservations(sourceData, destData);
+		
+		//miCalc.setDebug(true);
+		double mi = miCalc.computeAverageLocalOfObservations();
+		//miCalc.setDebug(false);
+		//double[] miLocal = miCalc.computeLocalOfPreviousObservations();
+		
+		System.out.printf("Average was %.5f\n", mi);
+		
+		// Now look at statistical significance tests
+		int[][] newOrderings = rg.generateDistinctRandomPerturbations(
+				timeSteps, 100);
+
+		miCalc.computeSignificance(newOrderings);
+		
+		// And compute the average value again to check that it's consistent:
+		for (int i = 0; i < 10; i++) {
+			double averageCheck1 = miCalc.computeAverageLocalOfObservations();
+			assertEquals(mi, averageCheck1);
+		}
+	}
+}