UnifiedDistanceMatrix.java
- /*
- * 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.math4.neuralnet.twod.util;
- import org.apache.commons.math4.neuralnet.DistanceMeasure;
- import org.apache.commons.math4.neuralnet.Neuron;
- import org.apache.commons.math4.neuralnet.twod.NeuronSquareMesh2D;
- /**
- * <a href="http://en.wikipedia.org/wiki/U-Matrix">U-Matrix</a>
- * visualization of high-dimensional data projection.
- * 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 additional pixels that lie along a "diagonal" are shared
- * by <em>two</em> pairs of units: their value will be set to the average
- * distance between the units belonging to each of the pairs. The value
- * zero will be stored in the pixel corresponding to the location of a
- * unit of the 2D-map.
- *
- * @since 3.6
- * @see org.apache.commons.math4.neuralnet.twod.NeuronSquareMesh2D.DataVisualization#getUMatrix()
- */
- public class UnifiedDistanceMatrix implements MapVisualization {
- /** Distance. */
- private final DistanceMeasure distance;
- /**
- * @param distance Distance.
- */
- public UnifiedDistanceMatrix(DistanceMeasure distance) {
- this.distance = distance;
- }
- /**
- * 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.
- */
- @Override
- public double[][] computeImage(NeuronSquareMesh2D map) {
- final int numRows = map.getNumberOfRows();
- final int numCols = map.getNumberOfColumns();
- final double[][] uMatrix = new double[numRows * 2 + 1][numCols * 2 + 1];
- // 1.
- // Fill right and bottom slots of each unit's location with the
- // distance between the current unit and each of the two neighbours,
- // respectively.
- 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;
- // Right neighbour.
- neighbour = map.getNeuron(i, j,
- NeuronSquareMesh2D.HorizontalDirection.RIGHT,
- NeuronSquareMesh2D.VerticalDirection.CENTER);
- if (neighbour != null) {
- uMatrix[iR][jR + 1] = distance.applyAsDouble(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.applyAsDouble(current,
- neighbour.getFeatures());
- }
- }
- }
- // 2.
- // Fill the bottom-right slot of each unit's location with the average
- // of the distances between
- // * the current unit and its bottom-right neighbour, and
- // * the bottom-center neighbour and the right neighbour.
- 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 Neuron current = map.getNeuron(i, j);
- final Neuron right = map.getNeuron(i, j,
- NeuronSquareMesh2D.HorizontalDirection.RIGHT,
- NeuronSquareMesh2D.VerticalDirection.CENTER);
- final Neuron bottom = map.getNeuron(i, j,
- NeuronSquareMesh2D.HorizontalDirection.CENTER,
- NeuronSquareMesh2D.VerticalDirection.DOWN);
- final Neuron bottomRight = map.getNeuron(i, j,
- NeuronSquareMesh2D.HorizontalDirection.RIGHT,
- NeuronSquareMesh2D.VerticalDirection.DOWN);
- final double current2BottomRight = bottomRight == null ?
- 0 :
- distance.applyAsDouble(current.getFeatures(),
- bottomRight.getFeatures());
- final double right2Bottom = (right == null ||
- bottom == null) ?
- 0 :
- distance.applyAsDouble(right.getFeatures(),
- bottom.getFeatures());
- // Bottom-right slot.
- uMatrix[iR + 1][jR + 1] = 0.5 * (current2BottomRight + right2Bottom);
- }
- }
- // 3. Copy last row into first row.
- final int lastRow = uMatrix.length - 1;
- uMatrix[0] = uMatrix[lastRow];
- // 4.
- // Copy last column into first column.
- final int lastCol = uMatrix[0].length - 1;
- for (int r = 0; r < lastRow; r++) {
- uMatrix[r][0] = uMatrix[r][lastCol];
- }
- return uMatrix;
- }
- }