annotate trunk/aid/nn/perceptron.d @ 3:314d68bafeff

Backprop and backprop_test added (no testing).
author revcompgeek
date Fri, 11 Apr 2008 18:12:55 -0600
parents 9655c8362b25
children ff92c77006c7
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1 module aid.nn.perceptron;
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3 import aid.nn.outputFunctions;
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4 import aid.misc;
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5 import std.math;
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6 import std.string;
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7
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8
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9 class perceptron {
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10 private int numInputs;
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11 private double[] weights;
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12 private OutputFunction func;
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13 public double learningRate;
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14
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15 /**
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16 * This is the constructor for loading the neural network from a string.
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17 *
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18 * Params:
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19 * savedString = The string that was output from the save function.
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20 *
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21 * Throws:
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22 * Throws an InputException when the string is in the wrong format.
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23 */
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24
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25 public this(char[] savedString){
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26 //TODO: Impliment loading!
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27 throw new Exception("Not implimented.");
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28 }
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29
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30 // This is private because one type of perceptron training requires the use of the sign function.
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31 this(int numInputs, double learningRate=0.3, bool randomize=true,OutputFunction f=null){
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32 this.numInputs = numInputs + 1;
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33 weights.length = numInputs + 1;
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34 func = f;
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35 this.learningRate = learningRate;
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36 if(randomize){
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37 for(int i = 0; i < this.numInputs; i++){
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38 weights[i] = rnd() * 2 - 1;
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39 }
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40 } else {
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41 for(int i = 0; i < this.numInputs; i++){
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42 weights[i] = 0;
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43 }
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44 }
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45 }
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46
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47 /**
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48 * Evaluates the neural network.
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49 *
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50 * Params:
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51 * inputs = The set of inputs to evaluate.
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52 *
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53 * Returns: 1 to indicate true, -1 for false
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54 */
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55
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56 double evaluate(double[] inputs){
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57 if(inputs.length != numInputs-1) throw new InputException("Wrong input length. %d %d");
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58 double total = weights[0];
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59 for(int i = 1; i < numInputs; i++){
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60 total += inputs[i-1] * weights[i];
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61 }
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62 if(func != null) return func(total);
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63 return total;
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64 }
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65
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66 public double[] getWeights(){
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67 return weights.dup;
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68 }
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69
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70 /**
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71 * Trains the neural network. This must be overloaded in a subclass.
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72 *
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73 * Params:
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74 * inputs = The array of inputs to the nerual network.
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75 * targetOutput = The output that the nerual network should give.
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76 */
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77 /* Returns: True if it trained the network, false if not.
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78 */
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79
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80 void train(double[] inputs,double targetOutput){
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81 if(inputs.length != numInputs-1) throw new InputException("Wrong input length.");
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82 double output = evaluate(inputs);
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83 double error = this.learningRate * (targetOutput - output);
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84 weights[0] += error;
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85 for(int i = 1; i < numInputs; i++){
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86 weights[i] += error * inputs[i-1];
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87 }
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88 }
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89
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90 /**
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91 * Calculates the error based on the sum squared error function.
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92 *
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93 * Params:
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94 * inputs = An array of arrays of all testing inputs.
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95 * outputs = An array of all the outputs that the cooresponding inputs should have.
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96 *
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97 * Returns:
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98 * The error value.
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99 */
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100
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101 double getErrorValue(double[][] inputsArray, double[] outputsArray){
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102 double total = 0;
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103 if(inputsArray.length != outputsArray.length) throw new InputException("inputsArray and outputsArray must be the same length");
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104 if(inputsArray.length < 1) throw new InputException("Must have at least 1 training example");
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105 double output,temp;
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106 for(int i = 0; i < inputsArray.length; i++){
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107 output=evaluate(inputsArray[i]);
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108 temp = outputsArray[i] - output;
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109 total += temp*temp;
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110 }
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111 return total*0.5;
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112 }
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113 }
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114
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115 // TODO: Impliment loading and saving of files