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annotate trunk/aid/nn/perceptron.d @ 2:9655c8362b25

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Added the Perceptron class and the perceptron_test testing program.
author revcompgeek
date Sat, 05 Apr 2008 23:41:30 -0600
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children 314d68bafeff
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9655c8362b25 Added the Perceptron class and the perceptron_test testing program.
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1 module aid.nn.perceptron;
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2
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3 import std.random;
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4 import std.math;
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5 import std.string;
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6
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7 double rnd(){ // The function that should be included in every math library!
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8 return (cast(double)rand())/uint.max;
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9 }
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10
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11 class InputException : Exception {
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12 this(char[] message){
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13 super(message);
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14 }
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15 }
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16
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17 // The output functions
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18
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19 alias double function(double) OutputFunction;
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20
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21 double sign(double y){
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22 if(y>0) return 1;
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23 return -1;
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24 }
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25
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26 double sigmoid(double x){
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27 return 1/(1+exp(-x));
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28 }
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29
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30 double tanh(double x){
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31 return cast(double)tanh(cast(real)x);
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32 }
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33
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34 // End output functions
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35
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36
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37 class perceptron {
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38 private int numInputs;
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39 private double[] weights;
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40 private OutputFunction func;
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41 public double learningRate;
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42
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43 /**
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44 * This is the constructor for loading the neural network from a string.
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45 *
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46 * Params:
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47 * savedString = The string that was output from the save function.
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48 *
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49 * Throws:
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50 * Throws an InputException when the string is in the wrong format.
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51 */
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52
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53 public this(char[] savedString){
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54 //TODO: Impliment loading!
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55 throw new Exception("Not implimented.");
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56 }
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57
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58 // This is private because one type of perceptron training requires the use of the sign function.
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59 this(int numInputs, double learningRate=0.3, bool randomize=true,OutputFunction f=null){
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60 this.numInputs = numInputs + 1;
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61 weights.length = numInputs + 1;
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62 func = f;
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63 this.learningRate = learningRate;
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64 if(randomize){
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65 for(int i = 0; i < this.numInputs; i++){
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66 weights[i] = rnd() * 2 - 1;
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67 }
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68 } else {
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69 for(int i = 0; i < this.numInputs; i++){
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70 weights[i] = 0;
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71 }
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72 }
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73 }
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74
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75 /**
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76 * Evaluates the neural network.
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77 *
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78 * Params:
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79 * inputs = The set of inputs to evaluate.
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80 *
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81 * Returns: 1 to indicate true, -1 for false
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82 */
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83
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84 double evaluate(double[] inputs){
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85 if(inputs.length != numInputs-1) throw new InputException("Wrong input length. %d %d");
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86 double total = weights[0];
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87 for(int i = 1; i < numInputs; i++){
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88 total += inputs[i-1] * weights[i];
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89 }
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90 if(func != null) return func(total);
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91 return total;
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92 }
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93
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94 public double[] getWeights(){
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95 return weights.dup;
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96 }
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97
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98 /**
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99 * Trains the neural network. This must be overloaded in a subclass.
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100 *
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101 * Params:
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102 * inputs = The array of inputs to the nerual network.
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103 * targetOutput = The output that the nerual network should give.
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104 */
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105 /* Returns: True if it trained the network, false if not.
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106 */
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107
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108 void train(double[] inputs,double targetOutput){
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109 if(inputs.length != numInputs-1) throw new InputException("Wrong input length.");
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110 double output = evaluate(inputs);
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111 double error = this.learningRate * (targetOutput - output);
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112 weights[0] += error;
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113 for(int i = 1; i < numInputs; i++){
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114 weights[i] += error * inputs[i-1];
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115 }
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116 }
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117
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118 /**
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119 * Calculates the error based on the sum squared error function.
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120 *
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121 * Params:
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122 * inputs = An array of arrays of all testing inputs.
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123 * outputs = An array of all the outputs that the cooresponding inputs should have.
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124 *
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125 * Returns:
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126 * The error value.
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127 */
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128
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129 double getErrorValue(double[][] inputsArray, double[] outputsArray){
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130 double total = 0;
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131 if(inputsArray.length != outputsArray.length) throw new InputException("inputsArray and outputsArray must be the same length");
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132 if(inputsArray.length < 1) throw new InputException("Must have at least 1 training example");
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133 double output,temp;
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134 for(int i = 0; i < inputsArray.length; i++){
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135 output=evaluate(inputsArray[i]);
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136 temp = outputsArray[i] - output;
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137 total += temp*temp;
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138 }
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139 return total*0.5;
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140 }
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141 }
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142