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MATH-1548: Remove methods redundant with functionality defined in "NeuronSquareMesh2D".

This commit is contained in:
Gilles Sadowski 2020-06-26 18:29:37 +02:00
parent 28e5b802fe
commit ed4817c730
1 changed files with 0 additions and 85 deletions
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85
src/main/java/org/apache/commons/math4/ml/neuralnet/MapUtils.java
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@ -17,13 +17,9 @@
package org.apache.commons.math4.ml.neuralnet;
import java.util.Collection;
import java.util.HashMap;
import java.util.List;
import org.apache.commons.math4.exception.NoDataException;
import org.apache.commons.math4.ml.distance.DistanceMeasure;
import org.apache.commons.math4.ml.neuralnet.twod.NeuronSquareMesh2D;
/**
* Utilities for network maps.
@ -36,87 +32,6 @@ public class MapUtils {
*/
private MapUtils() {}
/**
* Computes the <a href="http://en.wikipedia.org/wiki/U-Matrix">
* U-matrix</a> of a two-dimensional map.
*
* @param map Network.
* @param distance Function to use for computing the average
* distance from a neuron to its neighbours.
* @return the matrix of average distances.
*/
public static double[][] computeU(NeuronSquareMesh2D map,
DistanceMeasure distance) {
final int numRows = map.getNumberOfRows();
final int numCols = map.getNumberOfColumns();
final double[][] uMatrix = new double[numRows][numCols];
final Network net = map.getNetwork();
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numCols; j++) {
final Neuron neuron = map.getNeuron(i, j);
final Collection<Neuron> neighbours = net.getNeighbours(neuron);
final double[] features = neuron.getFeatures();
double d = 0;
int count = 0;
for (Neuron n : neighbours) {
++count;
d += distance.compute(features, n.getFeatures());
}
uMatrix[i][j] = d / count;
}
}
return uMatrix;
}
/**
* Computes the "hit" histogram of a two-dimensional map.
*
* @param data Feature vectors.
* @param map Network.
* @param distance Function to use for determining the best matching unit.
* @return the number of hits for each neuron in the map.
*/
public static int[][] computeHitHistogram(Iterable<double[]> data,
NeuronSquareMesh2D map,
DistanceMeasure distance) {
final HashMap<Neuron, Integer> hit = new HashMap<>();
final MapRanking rank = new MapRanking(map.getNetwork(), distance);
for (double[] f : data) {
final Neuron best = rank.rank(f, 1).get(0);
final Integer count = hit.get(best);
if (count == null) {
hit.put(best, 1);
} else {
hit.put(best, count + 1);
}
}
// Copy the histogram data into a 2D map.
final int numRows = map.getNumberOfRows();
final int numCols = map.getNumberOfColumns();
final int[][] histo = new int[numRows][numCols];
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numCols; j++) {
final Neuron neuron = map.getNeuron(i, j);
final Integer count = hit.get(neuron);
if (count == null) {
histo[i][j] = 0;
} else {
histo[i][j] = count;
}
}
}
return histo;
}
/**
* Computes the quantization error.
* The quantization error is the average distance between a feature vector