1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 |
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21 | //Code is based on an implementation from Laurens van der Maaten
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22 |
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23 | /*
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24 | *
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25 | * Copyright (c) 2014, Laurens van der Maaten (Delft University of Technology)
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26 | * All rights reserved.
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27 | *
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28 | * Redistribution and use in source and binary forms, with or without
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29 | * modification, are permitted provided that the following conditions are met:
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30 | * 1. Redistributions of source code must retain the above copyright
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31 | * notice, this list of conditions and the following disclaimer.
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32 | * 2. Redistributions in binary form must reproduce the above copyright
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33 | * notice, this list of conditions and the following disclaimer in the
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34 | * documentation and/or other materials provided with the distribution.
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35 | * 3. All advertising materials mentioning features or use of this software
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36 | * must display the following acknowledgement:
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37 | * This product includes software developed by the Delft University of Technology.
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38 | * 4. Neither the name of the Delft University of Technology nor the names of
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39 | * its contributors may be used to endorse or promote products derived from
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40 | * this software without specific prior written permission.
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41 | *
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42 | * THIS SOFTWARE IS PROVIDED BY LAURENS VAN DER MAATEN ''AS IS'' AND ANY EXPRESS
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43 | * OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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44 | * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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45 | * EVENT SHALL LAURENS VAN DER MAATEN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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46 | * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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47 | * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
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48 | * BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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49 | * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
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50 | * IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
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51 | * OF SUCH DAMAGE.
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52 | *
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53 | */
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54 | #endregion
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55 |
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56 | using System;
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57 | using System.Collections.Generic;
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58 | using System.Linq;
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59 | using HeuristicLab.Analysis;
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60 | using HeuristicLab.Collections;
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61 | using HeuristicLab.Common;
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62 | using HeuristicLab.Core;
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63 | using HeuristicLab.Data;
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64 | using HeuristicLab.Optimization;
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65 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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66 | using HeuristicLab.Random;
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67 |
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68 | namespace HeuristicLab.Algorithms.DataAnalysis {
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69 | [StorableClass]
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70 | public class TSNE<T> : DeepCloneable /*where T : class, IDeepCloneable*/ {
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71 |
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72 | private const string IterationResultName = "Iteration";
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73 | private const string ErrorResultName = "Error";
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74 | private const string ErrorPlotResultName = "ErrorPlot";
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75 | private const string ScatterPlotResultName = "Scatterplot";
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76 | private const string DataResultName = "Projected Data";
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77 |
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78 | #region Properties
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79 | [Storable]
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80 | private IDistance<T> distance;
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81 | [Storable]
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82 | private int maxIter;
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83 | [Storable]
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84 | private int stopLyingIter;
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85 | [Storable]
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86 | private int momSwitchIter;
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87 | [Storable]
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88 | double momentum;
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89 | [Storable]
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90 | private double finalMomentum;
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91 | [Storable]
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92 | private double eta;
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93 | [Storable]
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94 | private IRandom random;
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95 | [Storable]
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96 | private ResultCollection results;
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97 | [Storable]
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98 | private Dictionary<string, List<int>> dataRowLookup;
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99 | [Storable]
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100 | private Dictionary<string, ScatterPlotDataRow> dataRows;
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101 | #endregion
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102 |
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103 | #region Stopping
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104 | public volatile bool Running; // TODO
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105 | #endregion
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106 |
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107 | #region HLConstructors & Cloning
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108 | [StorableConstructor]
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109 | protected TSNE(bool deserializing) { }
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110 | protected TSNE(TSNE<T> original, Cloner cloner) : base(original, cloner) {
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111 | distance = cloner.Clone(original.distance);
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112 | maxIter = original.maxIter;
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113 | stopLyingIter = original.stopLyingIter;
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114 | momSwitchIter = original.momSwitchIter;
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115 | momentum = original.momentum;
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116 | finalMomentum = original.finalMomentum;
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117 | eta = original.eta;
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118 | random = cloner.Clone(random);
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119 | results = cloner.Clone(results);
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120 | dataRowLookup = original.dataRowLookup.ToDictionary(entry => entry.Key, entry => entry.Value.Select(x => x).ToList());
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121 | dataRows = original.dataRows.ToDictionary(entry => entry.Key, entry => cloner.Clone(entry.Value));
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122 | }
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123 | public override IDeepCloneable Clone(Cloner cloner) { return new TSNE<T>(this, cloner); }
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124 | public TSNE(IDistance<T> distance, IRandom random, ResultCollection results = null, int maxIter = 1000, int stopLyingIter = 250, int momSwitchIter = 250, double momentum = .5, double finalMomentum = .8, double eta = 200.0, Dictionary<string, List<int>> dataRowLookup = null, Dictionary<string, ScatterPlotDataRow> dataRows = null) {
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125 | this.distance = distance;
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126 | this.maxIter = maxIter;
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127 | this.stopLyingIter = stopLyingIter;
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128 | this.momSwitchIter = momSwitchIter;
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129 | this.momentum = momentum;
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130 | this.finalMomentum = finalMomentum;
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131 | this.eta = eta;
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132 | this.random = random;
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133 | this.results = results;
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134 | this.dataRowLookup = dataRowLookup;
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135 | if (dataRows != null) this.dataRows = dataRows;
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136 | else { this.dataRows = new Dictionary<string, ScatterPlotDataRow>(); }
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137 | }
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138 | #endregion
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139 |
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140 | public double[,] Run(T[] data, int newDimensions, double perplexity, double theta) {
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141 | var currentMomentum = momentum;
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142 | var noDatapoints = data.Length;
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143 | if (noDatapoints - 1 < 3 * perplexity) throw new ArgumentException("Perplexity too large for the number of data points!");
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144 | SetUpResults(data);
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145 | Running = true;
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146 | var exact = Math.Abs(theta) < double.Epsilon;
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147 | var newData = new double[noDatapoints, newDimensions];
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148 | var dY = new double[noDatapoints, newDimensions];
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149 | var uY = new double[noDatapoints, newDimensions];
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150 | var gains = new double[noDatapoints, newDimensions];
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151 | for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < newDimensions; j++) gains[i, j] = 1.0;
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152 | double[,] p = null;
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153 | int[] rowP = null;
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154 | int[] colP = null;
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155 | double[] valP = null;
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156 | var rand = new NormalDistributedRandom(random, 0, 1);
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157 |
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158 | //Calculate Similarities
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159 | if (exact) p = CalculateExactSimilarites(data, perplexity);
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160 | else CalculateApproximateSimilarities(data, perplexity, out rowP, out colP, out valP);
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161 |
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162 | // Lie about the P-values
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163 | if (exact) for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < noDatapoints; j++) p[i, j] *= 12.0;
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164 | else for (var i = 0; i < rowP[noDatapoints]; i++) valP[i] *= 12.0;
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165 |
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166 | // Initialize solution (randomly)
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167 | for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < newDimensions; j++) newData[i, j] = rand.NextDouble() * .0001; // TODO const
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168 |
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169 | // Perform main training loop
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170 | for (var iter = 0; iter < maxIter && Running; iter++) {
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171 | if (exact) ComputeExactGradient(p, newData, noDatapoints, newDimensions, dY);
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172 | else ComputeApproximateGradient(rowP, colP, valP, newData, noDatapoints, newDimensions, dY, theta);
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173 | // Update gains
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174 | for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < newDimensions; j++) gains[i, j] = Math.Sign(dY[i, j]) != Math.Sign(uY[i, j]) ? gains[i, j] + .2 : gains[i, j] * .8;
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175 | for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < newDimensions; j++) if (gains[i, j] < .01) gains[i, j] = .01;
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176 | // Perform gradient update (with momentum and gains)
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177 | for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < newDimensions; j++) uY[i, j] = currentMomentum * uY[i, j] - eta * gains[i, j] * dY[i, j];
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178 | for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < newDimensions; j++) newData[i, j] = newData[i, j] + uY[i, j];
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179 | // Make solution zero-mean
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180 | ZeroMean(newData);
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181 | // Stop lying about the P-values after a while, and switch momentum
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182 | if (iter == stopLyingIter) {
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183 | if (exact) for (var i = 0; i < noDatapoints; i++) for (var j = 0; j < noDatapoints; j++) p[i, j] /= 12.0;
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184 | else for (var i = 0; i < rowP[noDatapoints]; i++) valP[i] /= 12.0;
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185 | }
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186 | if (iter == momSwitchIter) currentMomentum = finalMomentum;
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187 |
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188 | Analyze(exact, iter, p, rowP, colP, valP, newData, noDatapoints, newDimensions, theta);
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189 | }
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190 | return newData;
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191 | }
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192 |
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193 | #region helpers
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194 |
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195 | private void SetUpResults(IReadOnlyCollection<T> data) {
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196 | if (dataRowLookup == null) dataRowLookup = new Dictionary<string, List<int>> { { "Data", Enumerable.Range(0, data.Count).ToList() } };
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197 | if (results == null) return;
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198 |
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199 | if (!results.ContainsKey(IterationResultName)) results.Add(new Result(IterationResultName, new IntValue(0)));
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200 | else ((IntValue)results[IterationResultName].Value).Value = 0;
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201 |
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202 | if (!results.ContainsKey(ErrorResultName)) results.Add(new Result(ErrorResultName, new DoubleValue(0)));
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203 | else ((DoubleValue)results[ErrorResultName].Value).Value = 0;
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204 |
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205 | if (!results.ContainsKey(ErrorPlotResultName)) results.Add(new Result(ErrorPlotResultName, new DataTable(ErrorPlotResultName, "Development of errors during gradient descent")));
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206 | else results[ErrorPlotResultName].Value = new DataTable(ErrorPlotResultName, "Development of errors during gradient descent");
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207 |
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208 | var plot = results[ErrorPlotResultName].Value as DataTable;
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209 | if (plot == null) throw new ArgumentException("could not create/access error data table in results collection");
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210 |
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211 | if (!plot.Rows.ContainsKey("errors")) plot.Rows.Add(new DataRow("errors"));
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212 | plot.Rows["errors"].Values.Clear();
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213 |
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214 | results.Add(new Result(ScatterPlotResultName, "Plot of the projected data", new ScatterPlot(DataResultName, "")));
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215 | results.Add(new Result(DataResultName, "Projected Data", new DoubleMatrix()));
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216 |
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217 | }
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218 | private void Analyze(bool exact, int iter, double[,] p, int[] rowP, int[] colP, double[] valP, double[,] newData, int noDatapoints, int newDimensions, double theta) {
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219 | if (results == null) return;
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220 | var plot = results[ErrorPlotResultName].Value as DataTable;
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221 | if (plot == null) throw new ArgumentException("Could not create/access error data table in results collection. Was it removed by some effect?");
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222 | var errors = plot.Rows["errors"].Values;
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223 | var c = exact
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224 | ? EvaluateErrorExact(p, newData, noDatapoints, newDimensions)
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225 | : EvaluateErrorApproximate(rowP, colP, valP, newData, theta);
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226 | errors.Add(c);
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227 | ((IntValue)results[IterationResultName].Value).Value = iter + 1;
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228 | ((DoubleValue)results[ErrorResultName].Value).Value = errors.Last();
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229 |
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230 | var ndata = Normalize(newData);
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231 | results[DataResultName].Value = new DoubleMatrix(ndata);
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232 | var splot = results[ScatterPlotResultName].Value as ScatterPlot;
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233 | FillScatterPlot(ndata, splot);
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234 |
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235 |
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236 | }
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237 | private void FillScatterPlot(double[,] lowDimData, ScatterPlot plot) {
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238 | foreach (var rowName in dataRowLookup.Keys) {
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239 | if (!plot.Rows.ContainsKey(rowName))
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240 | plot.Rows.Add(dataRows.ContainsKey(rowName) ? dataRows[rowName] : new ScatterPlotDataRow(rowName, "", new List<Point2D<double>>()));
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241 | plot.Rows[rowName].Points.Replace(dataRowLookup[rowName].Select(i => new Point2D<double>(lowDimData[i, 0], lowDimData[i, 1])));
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242 | }
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243 | }
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244 | private static double[,] Normalize(double[,] data) {
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245 | var max = new double[data.GetLength(1)];
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246 | var min = new double[data.GetLength(1)];
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247 | var res = new double[data.GetLength(0), data.GetLength(1)];
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248 | for (var i = 0; i < max.Length; i++) max[i] = min[i] = data[0, i];
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249 | for (var i = 0; i < data.GetLength(0); i++)
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250 | for (var j = 0; j < data.GetLength(1); j++) {
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251 | var v = data[i, j];
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252 | max[j] = Math.Max(max[j], v);
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253 | min[j] = Math.Min(min[j], v);
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254 | }
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255 | for (var i = 0; i < data.GetLength(0); i++) {
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256 | for (var j = 0; j < data.GetLength(1); j++) {
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257 | res[i, j] = (data[i, j] - (max[j] + min[j]) / 2) / (max[j] - min[j]);
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258 | }
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259 | }
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260 | return res;
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261 | }
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262 | private void CalculateApproximateSimilarities(T[] data, double perplexity, out int[] rowP, out int[] colP, out double[] valP) {
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263 | // Compute asymmetric pairwise input similarities
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264 | ComputeGaussianPerplexity(data, data.Length, out rowP, out colP, out valP, perplexity, (int)(3 * perplexity));
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265 | // Symmetrize input similarities
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266 | int[] sRowP, symColP;
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267 | double[] sValP;
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268 | SymmetrizeMatrix(rowP, colP, valP, out sRowP, out symColP, out sValP);
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269 | rowP = sRowP;
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270 | colP = symColP;
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271 | valP = sValP;
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272 | var sumP = .0;
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273 | for (var i = 0; i < rowP[data.Length]; i++) sumP += valP[i];
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274 | for (var i = 0; i < rowP[data.Length]; i++) valP[i] /= sumP;
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275 | }
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276 | private double[,] CalculateExactSimilarites(T[] data, double perplexity) {
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277 | // Compute similarities
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278 | var p = new double[data.Length, data.Length];
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279 | ComputeGaussianPerplexity(data, data.Length, p, perplexity);
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280 | // Symmetrize input similarities
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281 | for (var n = 0; n < data.Length; n++) {
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282 | for (var m = n + 1; m < data.Length; m++) {
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283 | p[n, m] += p[m, n];
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284 | p[m, n] = p[n, m];
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285 | }
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286 | }
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287 | var sumP = .0;
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288 | for (var i = 0; i < data.Length; i++) for (var j = 0; j < data.Length; j++) sumP += p[i, j];
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289 | for (var i = 0; i < data.Length; i++) for (var j = 0; j < data.Length; j++) p[i, j] /= sumP;
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290 | return p;
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291 | }
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292 |
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293 | private void ComputeGaussianPerplexity(IReadOnlyList<T> x, int n, out int[] rowP, out int[] colP, out double[] valP, double perplexity, int k) {
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294 | if (perplexity > k) throw new ArgumentException("Perplexity should be lower than K!");
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295 |
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296 | // Allocate the memory we need
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297 | rowP = new int[n + 1];
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298 | colP = new int[n * k];
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299 | valP = new double[n * k];
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300 | var curP = new double[n - 1];
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301 | rowP[0] = 0;
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302 | for (var i = 0; i < n; i++) rowP[i + 1] = rowP[i] + k;
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303 |
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304 | var objX = new List<IndexedItem<T>>();
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305 | for (var i = 0; i < n; i++) objX.Add(new IndexedItem<T>(i, x[i]));
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306 |
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307 | // Build ball tree on data set
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308 | var tree = new VantagePointTree<IndexedItem<T>>(new IndexedItemDistance<T>(distance), objX); // do we really want to re-create the tree on each call?
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309 |
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310 | // Loop over all points to find nearest neighbors
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311 | for (var i = 0; i < n; i++) {
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312 | IList<IndexedItem<T>> indices;
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313 | IList<double> distances;
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314 |
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315 | // Find nearest neighbors
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316 | tree.Search(objX[i], k + 1, out indices, out distances);
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317 |
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318 | // Initialize some variables for binary search
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319 | var found = false;
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320 | var beta = 1.0;
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321 | var minBeta = double.MinValue;
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322 | var maxBeta = double.MaxValue;
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323 | const double tol = 1e-5; // TODO: why 1e-5?
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324 |
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325 | // Iterate until we found a good perplexity
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326 | var iter = 0; double sumP = 0;
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327 | while (!found && iter < 200) {
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328 |
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329 | // Compute Gaussian kernel row
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330 | for (var m = 0; m < k; m++) curP[m] = Math.Exp(-beta * distances[m + 1]);
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331 |
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332 | // Compute entropy of current row
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333 | sumP = double.Epsilon;
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334 | for (var m = 0; m < k; m++) sumP += curP[m];
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335 | var h = .0;
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336 | for (var m = 0; m < k; m++) h += beta * (distances[m + 1] * curP[m]);
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337 | h = h / sumP + Math.Log(sumP);
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338 |
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339 | // Evaluate whether the entropy is within the tolerance level
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340 | var hdiff = h - Math.Log(perplexity);
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341 | if (hdiff < tol && -hdiff < tol) {
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342 | found = true;
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343 | } else {
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344 | if (hdiff > 0) {
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345 | minBeta = beta;
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346 | if (maxBeta.IsAlmost(double.MaxValue) || maxBeta.IsAlmost(double.MinValue))
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347 | beta *= 2.0;
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348 | else
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349 | beta = (beta + maxBeta) / 2.0;
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350 | } else {
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351 | maxBeta = beta;
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352 | if (minBeta.IsAlmost(double.MinValue) || minBeta.IsAlmost(double.MaxValue))
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353 | beta /= 2.0;
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354 | else
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355 | beta = (beta + minBeta) / 2.0;
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356 | }
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357 | }
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358 |
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359 | // Update iteration counter
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360 | iter++;
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361 | }
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362 |
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363 | // Row-normalize current row of P and store in matrix
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364 | for (var m = 0; m < k; m++) curP[m] /= sumP;
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365 | for (var m = 0; m < k; m++) {
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366 | colP[rowP[i] + m] = indices[m + 1].Index;
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367 | valP[rowP[i] + m] = curP[m];
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368 | }
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369 | }
|
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370 | }
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371 | private void ComputeGaussianPerplexity(T[] x, int n, double[,] p, double perplexity) {
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372 | // Compute the distance matrix
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373 | var dd = ComputeDistances(x);
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374 |
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375 | // Compute the Gaussian kernel row by row
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376 | for (var i = 0; i < n; i++) {
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377 | // Initialize some variables
|
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378 | var found = false;
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---|
379 | var beta = 1.0;
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380 | var minBeta = -double.MaxValue;
|
---|
381 | var maxBeta = double.MaxValue;
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382 | const double tol = 1e-5;
|
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383 | double sumP = 0;
|
---|
384 |
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385 | // Iterate until we found a good perplexity
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386 | var iter = 0;
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---|
387 | while (!found && iter < 200) { // TODO constant
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388 |
|
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389 | // Compute Gaussian kernel row
|
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390 | for (var m = 0; m < n; m++) p[i, m] = Math.Exp(-beta * dd[i][m]);
|
---|
391 | p[i, i] = double.Epsilon;
|
---|
392 |
|
---|
393 | // Compute entropy of current row
|
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394 | sumP = double.Epsilon;
|
---|
395 | for (var m = 0; m < n; m++) sumP += p[i, m];
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---|
396 | var h = 0.0;
|
---|
397 | for (var m = 0; m < n; m++) h += beta * (dd[i][m] * p[i, m]);
|
---|
398 | h = h / sumP + Math.Log(sumP);
|
---|
399 |
|
---|
400 | // Evaluate whether the entropy is within the tolerance level
|
---|
401 | var hdiff = h - Math.Log(perplexity);
|
---|
402 | if (hdiff < tol && -hdiff < tol) {
|
---|
403 | found = true;
|
---|
404 | } else {
|
---|
405 | if (hdiff > 0) {
|
---|
406 | minBeta = beta;
|
---|
407 | if (maxBeta.IsAlmost(double.MaxValue) || maxBeta.IsAlmost(double.MinValue))
|
---|
408 | beta *= 2.0;
|
---|
409 | else
|
---|
410 | beta = (beta + maxBeta) / 2.0;
|
---|
411 | } else {
|
---|
412 | maxBeta = beta;
|
---|
413 | if (minBeta.IsAlmost(double.MinValue) || minBeta.IsAlmost(double.MaxValue))
|
---|
414 | beta /= 2.0;
|
---|
415 | else
|
---|
416 | beta = (beta + minBeta) / 2.0;
|
---|
417 | }
|
---|
418 | }
|
---|
419 |
|
---|
420 | // Update iteration counter
|
---|
421 | iter++;
|
---|
422 | }
|
---|
423 |
|
---|
424 | // Row normalize P
|
---|
425 | for (var m = 0; m < n; m++) p[i, m] /= sumP;
|
---|
426 | }
|
---|
427 | }
|
---|
428 |
|
---|
429 | private double[][] ComputeDistances(T[] x) {
|
---|
430 | return x.Select(m => x.Select(n => distance.Get(m, n)).ToArray()).ToArray();
|
---|
431 | }
|
---|
432 |
|
---|
433 | private static void ComputeExactGradient(double[,] p, double[,] y, int n, int d, double[,] dC) {
|
---|
434 |
|
---|
435 | // Make sure the current gradient contains zeros
|
---|
436 | for (var i = 0; i < n; i++) for (var j = 0; j < d; j++) dC[i, j] = 0.0;
|
---|
437 |
|
---|
438 | // Compute the squared Euclidean distance matrix
|
---|
439 | var dd = new double[n, n];
|
---|
440 | ComputeSquaredEuclideanDistance(y, n, d, dd);
|
---|
441 |
|
---|
442 | // Compute Q-matrix and normalization sum
|
---|
443 | var q = new double[n, n];
|
---|
444 | var sumQ = .0;
|
---|
445 | for (var n1 = 0; n1 < n; n1++) {
|
---|
446 | for (var m = 0; m < n; m++) {
|
---|
447 | if (n1 == m) continue;
|
---|
448 | q[n1, m] = 1 / (1 + dd[n1, m]);
|
---|
449 | sumQ += q[n1, m];
|
---|
450 | }
|
---|
451 | }
|
---|
452 |
|
---|
453 | // Perform the computation of the gradient
|
---|
454 | for (var n1 = 0; n1 < n; n1++) {
|
---|
455 | for (var m = 0; m < n; m++) {
|
---|
456 | if (n1 == m) continue;
|
---|
457 | var mult = (p[n1, m] - q[n1, m] / sumQ) * q[n1, m];
|
---|
458 | for (var d1 = 0; d1 < d; d1++) {
|
---|
459 | dC[n1, d1] += (y[n1, d1] - y[m, d1]) * mult;
|
---|
460 | }
|
---|
461 | }
|
---|
462 | }
|
---|
463 | }
|
---|
464 | private static void ComputeSquaredEuclideanDistance(double[,] x, int n, int d, double[,] dd) {
|
---|
465 | var dataSums = new double[n];
|
---|
466 | for (var i = 0; i < n; i++) {
|
---|
467 | for (var j = 0; j < d; j++) {
|
---|
468 | dataSums[i] += x[i, j] * x[i, j];
|
---|
469 | }
|
---|
470 | }
|
---|
471 | for (var i = 0; i < n; i++) {
|
---|
472 | for (var m = 0; m < n; m++) {
|
---|
473 | dd[i, m] = dataSums[i] + dataSums[m];
|
---|
474 | }
|
---|
475 | }
|
---|
476 | for (var i = 0; i < n; i++) {
|
---|
477 | dd[i, i] = 0.0;
|
---|
478 | for (var m = i + 1; m < n; m++) {
|
---|
479 | dd[i, m] = 0.0;
|
---|
480 | for (var j = 0; j < d; j++) {
|
---|
481 | dd[i, m] += (x[i, j] - x[m, j]) * (x[i, j] - x[m, j]);
|
---|
482 | }
|
---|
483 | dd[m, i] = dd[i, m];
|
---|
484 | }
|
---|
485 | }
|
---|
486 | }
|
---|
487 | private static void ComputeApproximateGradient(int[] rowP, int[] colP, double[] valP, double[,] y, int n, int d, double[,] dC, double theta) {
|
---|
488 | var tree = new SpacePartitioningTree(y);
|
---|
489 | double[] sumQ = { 0 };
|
---|
490 | var posF = new double[n, d];
|
---|
491 | var negF = new double[n, d];
|
---|
492 | tree.ComputeEdgeForces(rowP, colP, valP, n, posF);
|
---|
493 | var row = new double[d];
|
---|
494 | for (var n1 = 0; n1 < n; n1++) {
|
---|
495 | Buffer.BlockCopy(negF, (sizeof(double) * n1 * d), row, 0, d);
|
---|
496 | tree.ComputeNonEdgeForces(n1, theta, row, sumQ);
|
---|
497 | }
|
---|
498 |
|
---|
499 | // Compute final t-SNE gradient
|
---|
500 | for (var i = 0; i < n; i++)
|
---|
501 | for (var j = 0; j < d; j++) {
|
---|
502 | dC[i, j] = (posF[i, j] - negF[i, j]) / sumQ[0]; // TODO: check parenthesis
|
---|
503 | }
|
---|
504 | }
|
---|
505 |
|
---|
506 | private static double EvaluateErrorExact(double[,] p, double[,] y, int n, int d) {
|
---|
507 | // Compute the squared Euclidean distance matrix
|
---|
508 | var dd = new double[n, n];
|
---|
509 | var q = new double[n, n];
|
---|
510 | ComputeSquaredEuclideanDistance(y, n, d, dd);
|
---|
511 |
|
---|
512 | // Compute Q-matrix and normalization sum
|
---|
513 | var sumQ = double.Epsilon;
|
---|
514 | for (var n1 = 0; n1 < n; n1++) {
|
---|
515 | for (var m = 0; m < n; m++) {
|
---|
516 | if (n1 != m) {
|
---|
517 | q[n1, m] = 1 / (1 + dd[n1, m]);
|
---|
518 | sumQ += q[n1, m];
|
---|
519 | } else q[n1, m] = double.Epsilon;
|
---|
520 | }
|
---|
521 | }
|
---|
522 | for (var i = 0; i < n; i++) for (var j = 0; j < n; j++) q[i, j] /= sumQ;
|
---|
523 |
|
---|
524 | // Sum t-SNE error
|
---|
525 | var c = .0;
|
---|
526 | for (var i = 0; i < n; i++)
|
---|
527 | for (var j = 0; j < n; j++) {
|
---|
528 | c += p[i, j] * Math.Log((p[i, j] + float.Epsilon) / (q[i, j] + float.Epsilon));
|
---|
529 | }
|
---|
530 | return c;
|
---|
531 | }
|
---|
532 | private static double EvaluateErrorApproximate(IReadOnlyList<int> rowP, IReadOnlyList<int> colP, IReadOnlyList<double> valP, double[,] y, double theta) {
|
---|
533 | // Get estimate of normalization term
|
---|
534 | var n = y.GetLength(0);
|
---|
535 | var d = y.GetLength(1);
|
---|
536 | var tree = new SpacePartitioningTree(y);
|
---|
537 | var buff = new double[d];
|
---|
538 | double[] sumQ = { 0 };
|
---|
539 | for (var i = 0; i < n; i++) tree.ComputeNonEdgeForces(i, theta, buff, sumQ);
|
---|
540 |
|
---|
541 | // Loop over all edges to compute t-SNE error
|
---|
542 | var c = .0;
|
---|
543 | for (var k = 0; k < n; k++) {
|
---|
544 | for (var i = rowP[k]; i < rowP[k + 1]; i++) {
|
---|
545 | var q = .0;
|
---|
546 | for (var j = 0; j < d; j++) buff[j] = y[k, j];
|
---|
547 | for (var j = 0; j < d; j++) buff[j] -= y[colP[i], j];
|
---|
548 | for (var j = 0; j < d; j++) q += buff[j] * buff[j];
|
---|
549 | q = 1.0 / (1.0 + q) / sumQ[0];
|
---|
550 | c += valP[i] * Math.Log((valP[i] + float.Epsilon) / (q + float.Epsilon));
|
---|
551 | }
|
---|
552 | }
|
---|
553 | return c;
|
---|
554 | }
|
---|
555 | private static void SymmetrizeMatrix(IReadOnlyList<int> rowP, IReadOnlyList<int> colP, IReadOnlyList<double> valP, out int[] symRowP, out int[] symColP, out double[] symValP) {
|
---|
556 |
|
---|
557 | // Count number of elements and row counts of symmetric matrix
|
---|
558 | var n = rowP.Count - 1;
|
---|
559 | var rowCounts = new int[n];
|
---|
560 | for (var j = 0; j < n; j++) {
|
---|
561 | for (var i = rowP[j]; i < rowP[j + 1]; i++) {
|
---|
562 |
|
---|
563 | // Check whether element (col_P[i], n) is present
|
---|
564 | var present = false;
|
---|
565 | for (var m = rowP[colP[i]]; m < rowP[colP[i] + 1]; m++) {
|
---|
566 | if (colP[m] == j) present = true;
|
---|
567 | }
|
---|
568 | if (present) rowCounts[j]++;
|
---|
569 | else {
|
---|
570 | rowCounts[j]++;
|
---|
571 | rowCounts[colP[i]]++;
|
---|
572 | }
|
---|
573 | }
|
---|
574 | }
|
---|
575 | var noElem = 0;
|
---|
576 | for (var i = 0; i < n; i++) noElem += rowCounts[i];
|
---|
577 |
|
---|
578 | // Allocate memory for symmetrized matrix
|
---|
579 | symRowP = new int[n + 1];
|
---|
580 | symColP = new int[noElem];
|
---|
581 | symValP = new double[noElem];
|
---|
582 |
|
---|
583 | // Construct new row indices for symmetric matrix
|
---|
584 | symRowP[0] = 0;
|
---|
585 | for (var i = 0; i < n; i++) symRowP[i + 1] = symRowP[i] + rowCounts[i];
|
---|
586 |
|
---|
587 | // Fill the result matrix
|
---|
588 | var offset = new int[n];
|
---|
589 | for (var j = 0; j < n; j++) {
|
---|
590 | for (var i = rowP[j]; i < rowP[j + 1]; i++) { // considering element(n, colP[i])
|
---|
591 |
|
---|
592 | // Check whether element (col_P[i], n) is present
|
---|
593 | var present = false;
|
---|
594 | for (var m = rowP[colP[i]]; m < rowP[colP[i] + 1]; m++) {
|
---|
595 | if (colP[m] != j) continue;
|
---|
596 | present = true;
|
---|
597 | if (j > colP[i]) continue; // make sure we do not add elements twice
|
---|
598 | symColP[symRowP[j] + offset[j]] = colP[i];
|
---|
599 | symColP[symRowP[colP[i]] + offset[colP[i]]] = j;
|
---|
600 | symValP[symRowP[j] + offset[j]] = valP[i] + valP[m];
|
---|
601 | symValP[symRowP[colP[i]] + offset[colP[i]]] = valP[i] + valP[m];
|
---|
602 | }
|
---|
603 |
|
---|
604 | // If (colP[i], n) is not present, there is no addition involved
|
---|
605 | if (!present) {
|
---|
606 | symColP[symRowP[j] + offset[j]] = colP[i];
|
---|
607 | symColP[symRowP[colP[i]] + offset[colP[i]]] = j;
|
---|
608 | symValP[symRowP[j] + offset[j]] = valP[i];
|
---|
609 | symValP[symRowP[colP[i]] + offset[colP[i]]] = valP[i];
|
---|
610 | }
|
---|
611 |
|
---|
612 | // Update offsets
|
---|
613 | if (present && (j > colP[i])) continue;
|
---|
614 | offset[j]++;
|
---|
615 | if (colP[i] != j) offset[colP[i]]++;
|
---|
616 | }
|
---|
617 | }
|
---|
618 |
|
---|
619 | // Divide the result by two
|
---|
620 | for (var i = 0; i < noElem; i++) symValP[i] /= 2.0;
|
---|
621 | }
|
---|
622 | private static void ZeroMean(double[,] x) {
|
---|
623 | // Compute data mean
|
---|
624 | var n = x.GetLength(0);
|
---|
625 | var d = x.GetLength(1);
|
---|
626 | var mean = new double[d];
|
---|
627 | for (var i = 0; i < n; i++) {
|
---|
628 | for (var j = 0; j < d; j++) {
|
---|
629 | mean[j] += x[i, j];
|
---|
630 | }
|
---|
631 | }
|
---|
632 | for (var i = 0; i < d; i++) {
|
---|
633 | mean[i] /= n;
|
---|
634 | }
|
---|
635 | // Subtract data mean
|
---|
636 | for (var i = 0; i < n; i++) {
|
---|
637 | for (var j = 0; j < d; j++) {
|
---|
638 | x[i, j] -= mean[j];
|
---|
639 | }
|
---|
640 | }
|
---|
641 | }
|
---|
642 | #endregion
|
---|
643 | }
|
---|
644 | }
|
---|