MATH-1270 SOFM visualization: Unified distance matrix.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/5e28d11b Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/5e28d11b Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/5e28d11b Branch: refs/heads/MATH_3_X Commit: 5e28d11b17c3e1aae2d344cef70aee18ef05a43b Parents: 4ac5a9d Author: Gilles <er...@apache.org> Authored: Fri Sep 11 00:49:55 2015 +0200 Committer: Gilles <er...@apache.org> Committed: Fri Sep 11 00:49:55 2015 +0200 ---------------------------------------------------------------------- .../twod/util/UnifiedDistanceMatrix.java | 202 +++++++++++++++++++ 1 file changed, 202 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/5e28d11b/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/UnifiedDistanceMatrix.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/UnifiedDistanceMatrix.java b/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/UnifiedDistanceMatrix.java new file mode 100644 index 0000000..3f56260 --- /dev/null +++ b/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/UnifiedDistanceMatrix.java @@ -0,0 +1,202 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.commons.math3.ml.neuralnet.twod.util; + +import java.util.Collection; +import org.apache.commons.math3.ml.neuralnet.Neuron; +import org.apache.commons.math3.ml.neuralnet.Network; +import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D; +import org.apache.commons.math3.ml.distance.DistanceMeasure; + +/** + * <a href="http://en.wikipedia.org/wiki/U-Matrix";>U-Matrix</a> + * visualization of high-dimensional data projection. + */ +public class UnifiedDistanceMatrix implements MapVisualization { + /** Whether to show distance between each pair of neighbouring units. */ + private final boolean individualDistances; + /** Distance. */ + private final DistanceMeasure distance; + + /** + * @param boolean individualDistances. If {@code true}, the 8 individual + * inter-units distances will be {@link #computeImage(NeuronSquareMesh2D) + * computed}. They will be stored in additional pixels around each of + * the original units of the 2D-map. The value zero will be stored in the + * pixel corresponding to the location of a unit of the 2D-map. + * If {@code false}, only the average distance between a unit and all its + * neighbours will be computed (and stored in the pixel corresponding to + * that unit of the 2D-map). In that case, the number of neighbours taken + * into account depends on the network's + * {@link org.apache.commons.math3.ml.neuralnet.SquareNeighbourhood + * neighbourhood type}. + * @param distance Distance. + */ + public UnifiedDistanceMatrix(boolean individualDistances, + DistanceMeasure distance) { + this.individualDistances = individualDistances; + this.distance = distance; + } + + /** {@inheritDoc} */ + public double[][] computeImage(NeuronSquareMesh2D map) { + if (individualDistances) { + return individualDistances(map); + } else { + return averageDistances(map); + } + } + + /** + * Computes the distances between a unit of the map and its + * neighbours. + * The image will contain more pixels than the number of neurons + * in the given {@code map} because each neuron has 8 neighbours. + * The value zero will be stored in the pixels corresponding to + * the location of a map unit. + * + * @param map Map. + * @return an image representing the individual distances. + */ + private double[][] individualDistances(NeuronSquareMesh2D map) { + final int numRows = map.getNumberOfRows(); + final int numCols = map.getNumberOfColumns(); + + final double[][] uMatrix = new double[numRows * 2 + 1][numCols * 2 + 1]; + + for (int i = 0; i < numRows; i++) { + // Current unit's row index in result image. + final int iR = 2 * i + 1; + + for (int j = 0; j < numCols; j++) { + // Current unit's column index in result image. + final int jR = 2 * j + 1; + + final double[] current = map.getNeuron(i, j).getFeatures(); + Neuron neighbour; + + // Top-left neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.LEFT, + NeuronSquareMesh2D.VerticalDirection.UP); + if (neighbour != null) { + uMatrix[iR - 1][jR - 1] = distance.compute(current, + neighbour.getFeatures()); + } + + // Top-center neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.CENTER, + NeuronSquareMesh2D.VerticalDirection.UP); + if (neighbour != null) { + uMatrix[iR - 1][jR] = distance.compute(current, + neighbour.getFeatures()); + } + + // Top-right neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.RIGHT, + NeuronSquareMesh2D.VerticalDirection.UP); + if (neighbour != null) { + uMatrix[iR - 1][jR + 1] = distance.compute(current, + neighbour.getFeatures()); + } + + // Left neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.LEFT, + NeuronSquareMesh2D.VerticalDirection.CENTER); + if (neighbour != null) { + uMatrix[iR][jR - 1] = distance.compute(current, + neighbour.getFeatures()); + } + + // Right neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.RIGHT, + NeuronSquareMesh2D.VerticalDirection.CENTER); + if (neighbour != null) { + uMatrix[iR][jR + 1] = distance.compute(current, + neighbour.getFeatures()); + } + + // Bottom-left neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.LEFT, + NeuronSquareMesh2D.VerticalDirection.DOWN); + if (neighbour != null) { + uMatrix[iR + 1][jR - 1] = distance.compute(current, + neighbour.getFeatures()); + } + + // Bottom-center neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.CENTER, + NeuronSquareMesh2D.VerticalDirection.DOWN); + if (neighbour != null) { + uMatrix[iR + 1][jR] = distance.compute(current, + neighbour.getFeatures()); + } + + // Bottom-right neighbour. + neighbour = map.getNeuron(i, j, + NeuronSquareMesh2D.HorizontalDirection.RIGHT, + NeuronSquareMesh2D.VerticalDirection.DOWN); + if (neighbour != null) { + uMatrix[iR + 1][jR + 1] = distance.compute(current, + neighbour.getFeatures()); + } + } + } + + return uMatrix; + } + + /** + * Computes the distances between a unit of the map and its neighbours. + * + * @param map Map. + * @return an image representing the average distances. + */ + private double[][] averageDistances(NeuronSquareMesh2D map) { + 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; + } +}
