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Patching conditional MI calculator to properly allow null or empty conditionals if dimension of conditionals is 0

This commit is contained in:
Joseph Lizier 2021-09-06 10:23:31 +10:00
parent aa8148eca9
commit 650159661d
1 changed files with 10 additions and 6 deletions
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16
java/source/infodynamics/measures/continuous/ConditionalMutualInfoMultiVariateCommon.java
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@ -205,12 +205,12 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
* <p>Valid property names, and what their
* values should represent, include:</p>
* <ul>
* <li>{@link #PROP_NORMALISE} - whether to normalise the individual
* <li>{@link #PROP_NORMALISE} - whether to normalise the individual
* variables to mean 0, standard deviation 1
* (true by default, except for child class
* {@link infodynamics.measures.continuous.gaussian.ConditionalMutualInfoCalculatorMultiVariateGaussian}
* for which this property is false and cannot be altered)</li>
* <li>{@link #PROP_ADD_NOISE} -- a standard deviation for an amount of
* <li>{@link #PROP_ADD_NOISE} -- a standard deviation for an amount of
* random Gaussian noise to add to
* each variable, to avoid having neighbourhoods with artificially
* large counts. (We also accept "false" to indicate "0".)
@ -355,16 +355,20 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
public void addObservations(double[] var1, double[] var2,
double[] cond) throws Exception {
if ((dimensionsVar1 != 1) || (dimensionsVar2 != 1) ||
(dimensionsCond != 1)) {
((dimensionsCond != 1) && (dimensionsCond != 0))) {
throw new Exception("The number of dimensions for each variable (having been initialised to " +
dimensionsVar1 + ", " + dimensionsVar2 + " & " +
dimensionsCond + ") can only be 1 when " +
dimensionsCond + ") can only be 1 (or 0 for conditional) when " +
"the univariate addObservations(double[],double[],double[]) and " +
"setObservations(double[],double[],double[]) methods are called");
}
double[][] reshapedConditional = null;
if (dimensionsCond == 1) {
reshapedConditional = MatrixUtils.reshape(cond, cond.length, 1);
}
addObservations(MatrixUtils.reshape(var1, var1.length, 1),
MatrixUtils.reshape(var2, var2.length, 1),
MatrixUtils.reshape(cond, cond.length, 1));
reshapedConditional);
}
@ -592,7 +596,7 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
// instead of just the zeros that we should otherwise get).
miSurrogateCalculator.setProperty(PROP_NORMALISE, "false");
miSurrogateCalculator.setProperty(PROP_ADD_NOISE, "0");
double[] surrogateMeasurements = new double[numPermutationsToCheck];
// Now compute the MI for each set of shuffled data: