jidt/java/source/infodynamics/networkinference/interregional/MeasurementDistributionPerm...

69 lines
2.1 KiB
Java
Executable File

/**
*
*/
package infodynamics.networkinference.interregional;
import infodynamics.utils.MatrixUtils;
import infodynamics.utils.EmpiricalMeasurementDistribution;
/**
* Extends MeasurementDistribution for computations over
* large sets where we generate measures over permutations of subsets.
*
* The member distribution refers to averages over subsets for each permutation.
* The new member distributionForSubsets refers to averages over permutations
* for each subset.
*
* @author Joseph Lizier
*
*/
public class MeasurementDistributionPermutationsOverSubsets extends EmpiricalMeasurementDistribution {
// The true measurements for each subset s
double[] actualValues;
// Distribution over [subsets] then [permutations]
double[][] distributionOverSubsetsAndPermutations;
// The average over permutations for each subset s
double[] avDistributionForSubsets;
/**
*
*/
public MeasurementDistributionPermutationsOverSubsets(int size) {
super(size);
}
public MeasurementDistributionPermutationsOverSubsets
(double[][] theDistribution, double[] actualValues) {
super(theDistribution[0].length);
this.actualValues = actualValues;
this.actualValue = MatrixUtils.mean(actualValues);
distributionOverSubsetsAndPermutations = theDistribution;
int subsets = theDistribution.length;
int permutations = theDistribution[0].length;
// distribution, which is av over permutations, was created in the
// super constructor.
avDistributionForSubsets = new double[subsets];
// distribution will hold the averages for each permutation i
int avValuesFromDistributionGreaterThanActualAvs = 0;
for (int i = 0; i < permutations; i++) {
distribution[i] = MatrixUtils.mean(theDistribution, i);
if (distribution[i] >= actualValue) {
avValuesFromDistributionGreaterThanActualAvs++;
}
}
pValue =
(double) avValuesFromDistributionGreaterThanActualAvs / (double) permutations;
for (int s = 0; s < subsets; s++) {
avDistributionForSubsets[s] = MatrixUtils.mean(theDistribution[s]);
}
}
}