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Adding unit test for no conditional on CMI Gaussian

This commit is contained in:
Joseph Lizier 2023-10-06 12:29:37 +11:00
parent 4f5d8f894b
commit a554361de9
2 changed files with 38 additions and 3 deletions
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2
java/source/infodynamics/measures/continuous/ActiveInfoStorageCalculatorViaMutualInfo.java
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@ -258,7 +258,7 @@ public class ActiveInfoStorageCalculatorViaMutualInfo implements
* and embedding delay ({@link #TAU_PROP_NAME}). Default is {@link #AUTO_EMBED_METHOD_NONE} meaning
* values are set manually; other accepted values include: {@link #AUTO_EMBED_METHOD_RAGWITZ} for use
* of the Ragwitz criteria and {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS} for using
* the maz bias-corrected AIS criteria (both searching up to {@link #PROP_K_SEARCH_MAX} and
* the max bias-corrected AIS criteria (both searching up to {@link #PROP_K_SEARCH_MAX} and
* {@link #PROP_TAU_SEARCH_MAX}, as outlined by Garland et al. in the references list above).
* Use of any value other than {@link #AUTO_EMBED_METHOD_NONE}
* will lead to any previous settings for k and tau (via e.g. {@link #initialise(int, int)} or

39
java/unittests/infodynamics/measures/continuous/gaussian/ConditionalMutualInfoMultiVariateTester.java
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@ -159,6 +159,40 @@ public class ConditionalMutualInfoMultiVariateTester extends
condMi, 0.0000000001);
}
public void testNoConditional() throws Exception {
ArrayFileReader afr = new ArrayFileReader("demos/data/4ColsPairedOneStepNoisyDependence-1.txt");
double[][] data = afr.getDouble2DMatrix();
double[] source = MatrixUtils.selectColumn(data, 1);
double[] dest = MatrixUtils.selectColumn(data, 2);
// Set up the value we expect from MI:
MutualInfoCalculatorMultiVariateGaussian miCalc =
new MutualInfoCalculatorMultiVariateGaussian();
miCalc.initialise(1, 1);
miCalc.setObservations(source, dest);
double mi = miCalc.computeAverageLocalOfObservations();
// Now compute via CMI calculator with null passed:
ConditionalMutualInfoCalculatorMultiVariateGaussian condMiCalc =
new ConditionalMutualInfoCalculatorMultiVariateGaussian();
condMiCalc.initialise(1, 1, 0);
condMiCalc.setObservations(source, dest, null);
double condMi = condMiCalc.computeAverageLocalOfObservations();
assertEquals(mi, condMi, 0.000001);
// Now compute via CMI calculator with dummy column passed:
condMiCalc.initialise(1, 1, 0);
condMiCalc.setObservations(source, dest, MatrixUtils.selectColumn(data, 3));
condMi = condMiCalc.computeAverageLocalOfObservations();
assertEquals(mi, condMi, 0.000001);
// Now compute via CMI calculator with empty column passed (all as 2D):
condMiCalc.initialise(1, 1, 0);
condMiCalc.setObservations(MatrixUtils.selectColumns(data, 1, 1), MatrixUtils.selectColumns(data, 2, 1), new double[1000][0]);
condMi = condMiCalc.computeAverageLocalOfObservations();
assertEquals(mi, condMi, 0.000001);
}
public void testBiasCorrectionDoesNotChangeAnalyticPValue() throws Exception {
ConditionalMutualInfoCalculatorMultiVariateGaussian cmiCalc =
new ConditionalMutualInfoCalculatorMultiVariateGaussian();
@ -189,7 +223,7 @@ public class ConditionalMutualInfoMultiVariateTester extends
ChiSquareMeasurementDistribution distroBiasCorrected = cmiCalc.computeSignificance();
assertEquals(avBiasCorrected, distroBiasCorrected.actualValue, 0.0000001);
// And now check that the pValues are unchanged whether we bias correct or not:
assertEquals(distroNotBiasCorrected.pValue, distroBiasCorrected.pValue);
assertEquals(distroNotBiasCorrected.pValue, distroBiasCorrected.pValue, 0.0000001);
}
protected int timeStepsDepCheck = 100;
@ -395,7 +429,7 @@ public class ConditionalMutualInfoMultiVariateTester extends
// - both dimensions are copied
double[][] sourceData = rg.generateNormalData(timeStepsDepCheck, dimensions,
0, 1);
double[][] condData = rg.generateNormalData(timeStepsDepCheck, dimensions,
double[][] condData = rg.generateNormalData(timeStepsDepCheck, conditionalDims,
0, 1);
double[][] destData = MatrixUtils.arrayCopy(condData);
@ -447,4 +481,5 @@ public class ConditionalMutualInfoMultiVariateTester extends
condMiCalc.setObservations(MatrixUtils.selectColumns(destData, 0, 1), sourceData, condData);
assertTrue(Double.isInfinite(condMiCalc.computeAverageLocalOfObservations()));
}
}