From ed4817c7301a942ac17d9a015a90ad8e406dc0e5 Mon Sep 17 00:00:00 2001
From: Gilles Sadowski <gilleseran@gmail.com>
Date: Fri, 26 Jun 2020 18:29:37 +0200
Subject: [PATCH] MATH-1548: Remove methods redundant with functionality
 defined in "NeuronSquareMesh2D".

---
 .../commons/math4/ml/neuralnet/MapUtils.java  | 85 -------------------
 1 file changed, 85 deletions(-)

diff --git a/src/main/java/org/apache/commons/math4/ml/neuralnet/MapUtils.java b/src/main/java/org/apache/commons/math4/ml/neuralnet/MapUtils.java
index 40500b654..b6065bdcd 100644
--- a/src/main/java/org/apache/commons/math4/ml/neuralnet/MapUtils.java
+++ b/src/main/java/org/apache/commons/math4/ml/neuralnet/MapUtils.java
@@ -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