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Removed old code.
author revcompgeek
date Tue, 06 May 2008 22:20:26 -0600
parents ff92c77006c7
children
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ff92c77006c7 Added support for reading training examples from files.
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1 /**
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2 * perceptron.d
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3 * Holds the simple perceptron class.
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4 */
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5
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6 module aid.nn.perceptron;
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7
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8 import aid.nn.outputFunctions;
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9 import aid.misc;
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10 import std.math;
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11 import std.string;
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12
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13
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14 class perceptron {
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15 private int numInputs;
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16 private double[] weights;
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17 private OutputFunction func;
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18 public double learningRate;
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19
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20 /**
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21 * This is the constructor for loading the neural network from a string.
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22 *
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23 * Params:
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24 * savedString = The string that was output from the save function.
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25 *
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26 * Throws:
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27 * Throws an InputException when the string is in the wrong format.
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28 */
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29
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30 public this(char[] savedString){
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31 //TODO: Impliment loading!
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32 throw new Exception("Not implimented.");
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33 }
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34
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35 // This is private because one type of perceptron training requires the use of the sign function.
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36 this(int numInputs, double learningRate=0.3, bool randomize=true,OutputFunction f=null){
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37 this.numInputs = numInputs + 1;
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38 weights.length = numInputs + 1;
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39 func = f;
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40 this.learningRate = learningRate;
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41 if(randomize){
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42 for(int i = 0; i < this.numInputs; i++){
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43 weights[i] = rnd() * 2 - 1;
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44 }
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45 } else {
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46 for(int i = 0; i < this.numInputs; i++){
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47 weights[i] = 0;
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48 }
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49 }
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50 }
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51
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52 /**
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53 * Evaluates the neural network.
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54 *
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55 * Params:
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56 * inputs = The set of inputs to evaluate.
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57 *
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58 * Returns: 1 to indicate true, -1 for false
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59 */
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60
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61 double evaluate(double[] inputs){
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62 if(inputs.length != numInputs-1) throw new InputException("Wrong input length. %d %d");
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63 double total = weights[0];
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64 for(int i = 1; i < numInputs; i++){
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65 total += inputs[i-1] * weights[i];
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66 }
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67 if(func != null) return func(total);
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68 return total;
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69 }
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70
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71 public double[] getWeights(){
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72 return weights.dup;
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73 }
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74
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75 /**
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76 * Trains the neural network. This must be overloaded in a subclass.
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77 *
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78 * Params:
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79 * inputs = The array of inputs to the nerual network.
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80 * targetOutput = The output that the nerual network should give.
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81 */
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82 /* Returns: True if it trained the network, false if not.
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83 */
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84
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85 void train(double[] inputs,double targetOutput){
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86 if(inputs.length != numInputs-1) throw new InputException("Wrong input length.");
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87 double output = evaluate(inputs);
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88 double error = this.learningRate * (targetOutput - output);
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89 weights[0] += error;
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90 for(int i = 1; i < numInputs; i++){
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91 weights[i] += error * inputs[i-1];
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92 }
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93 }
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94
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95 /**
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96 * Calculates the error based on the sum squared error function.
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97 *
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98 * Params:
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99 * inputs = An array of arrays of all testing inputs.
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100 * outputs = An array of all the outputs that the cooresponding inputs should have.
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101 *
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102 * Returns:
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103 * The error value.
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104 */
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105
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106 double getErrorValue(double[][] inputsArray, double[] outputsArray){
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107 double total = 0;
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108 if(inputsArray.length != outputsArray.length) throw new InputException("inputsArray and outputsArray must be the same length");
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109 if(inputsArray.length < 1) throw new InputException("Must have at least 1 training example");
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110 double output,temp;
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111 for(int i = 0; i < inputsArray.length; i++){
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112 output=evaluate(inputsArray[i]);
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113 temp = outputsArray[i] - output;
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114 total += temp*temp;
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115 }
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116 return total*0.5;
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117 }
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118 }
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119
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120 // TODO: Impliment loading and saving of files