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annotate examples/linear_classifier.py @ 499:a419edf4e06c

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removed unpicklable nested classes in logistic regression
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 28 Oct 2008 12:57:49 -0400
parents 4060812caa22
children
rev   line source
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1 #! /usr/bin/env python
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2 """
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3 T. Bertin-Mahieux (2008) University of Montreal
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4 bertinmt@iro.umontreal.ca
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5
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6 linear_classifier.py
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7 Simple script that creates a linear_classifier, and
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8 learns the parameters using backpropagation.
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9
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10 This is to illustrate how to use theano/pylearn.
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11 Anyone who knows how to make this script simpler/clearer is welcome to
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12 make the modifications.
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13 """
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14
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15
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16 import os
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17 import sys
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18 import time
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19 import copy
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20 import pickle
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21 import numpy
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22 import numpy as N
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23 import numpy.random as NR
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24 from pylearn import cost
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25 import theano
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26 from theano import tensor as T
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27
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28
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29 def cost_function(*args,**kwargs) :
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30 """ default cost function, quadratic """
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31 return cost.quadratic(*args,**kwargs)
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32
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33
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34 class modelgraph() :
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35 """ class that contains the graph of the model """
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36 lr = T.scalar() # learning rate
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37 inputs = T.matrix() # inputs (one example per line)
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38 true_outputs = T.matrix() # outputs (one example per line)
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39 W = T.matrix() # weights input * W + b= output
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40 b = T.vector() # bias
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41 outputs = T.dot(inputs,W) + b # output, one per line
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42 costs = cost_function(true_outputs,outputs) # costs
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43 g_W = T.grad(costs,W) # gradient of W
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44 g_b = T.grad(costs,b) # gradient of b
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45 new_W = T.sub_inplace(W, lr * g_W) # update inplace of W
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46 new_b = T.sub_inplace(b, lr * g_b) # update inplace of b
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47
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48
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49 class model() :
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50 """
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51 The model!
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52 Contains needed matrices, needed functions, and a link to the model graph.
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53 """
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54
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55 def __init__(self,input_size,output_size) :
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56 """ init matrix and bias, creates the graph, create a dict of compiled functions """
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57 # graph
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58 self.graph = modelgraph()
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59 # weights and bias, saved in self.params
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60 seed = 666
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61 r = NR.RandomState(seed)
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62 W = r.uniform(size = [input_size, output_size], low = -1/N.sqrt(input_size), high = 1/N.sqrt(input_size))
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63 b = numpy.zeros((output_size, ))
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64 self.params = [W,b]
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65 # dictionary of compiled functions
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66 self.func_dict = dict()
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67 # keep some init_infos (may not be necessary)
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68 self.init_params = [input_size,output_size]
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69
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70
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71 def update(self,lr,true_inputs,true_outputs) :
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72 """ does an update of the model, one gradient descent """
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73 # do we already have the proper theano function?
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74 if self.func_dict.has_key('update_func') :
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75 self.func_dict['update_func'](lr,true_inputs,true_outputs,self.params[0],self.params[1])
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76 return
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77 else :
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78 # create the theano function, tell him what are the inputs and outputs)
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79 func = theano.function([self.graph.lr,self.graph.inputs,self.graph.true_outputs,
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80 self.graph.W, self.graph.b],
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81 [self.graph.new_W,self.graph.new_b])
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82 # add function to dictionary, so we don't compile it again
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83 self.func_dict['update_func'] = func
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84 # use this function
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85 func(lr,true_inputs,true_outputs,self.params[0],self.params[1])
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86 return
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87
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88 def costs(self,true_inputs,true_outputs) :
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89 """ get the costs for given examples, don't update """
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90 # do we already have the proper theano function?
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91 if self.func_dict.has_key('costs_func') :
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92 return self.func_dict['costs_func'](true_inputs,true_outputs,self.params[0],self.params[1])
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93 else :
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94 # create the theano function, tell him what are the inputs and outputs)
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95 func = theano.function([self.graph.inputs,self.graph.true_outputs,self.graph.W,self.graph.b],
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96 [self.graph.costs])
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97 # add function to dictionary, se we don't compile it again
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98 self.func_dict['costs_func'] = func
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99 # use this function
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100 return func(true_inputs,true_outputs,self.params[0],self.params[1])
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101
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102 def outputs(self,true_inputs) :
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103 """ get the output for a set of examples (could be called 'predict') """
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104 # do we already have the proper theano function?
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105 if self.func_dict.has_key('outputs_func') :
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106 return self.func_dict['outputs_func'](true_inputs,self.params[0],self.params[1])
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107 else :
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108 # create the theano function, tell him what are the inputs and outputs)
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109 func = theano.function([self.graph.inputs, self.graph.W, self.graph.b],
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110 [self.graph.outputs])
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111 # add function to dictionary, se we don't compile it again
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112 self.func_dict['outputs_func'] = func
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113 # use this function
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114 return func(true_inputs,self.params[0],self.params[1])
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115
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116 def __getitem__(self,inputs) :
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117 """ for simplicity, we can use the model this way: predictions = model[inputs] """
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118 return self.outputs(inputs)
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119
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120 def __getstate__(self) :
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121 """
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122 To save/copy the model, used by pickle.dump() and by copy.deepcopy().
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123 @return a dictionnary with the params (matrix + bias)
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124 """
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125 d = dict()
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126 d['params'] = self.params
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127 d['init_params'] = self.init_params
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128 return d
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129
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130 def __setstate__(self,d) :
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131 """
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132 Get the dictionary created by __getstate__(), use it to recreate the model.
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133 """
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134 self.params = d['params']
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135 self.init_params = d['init_params']
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136 self.graph = modelgraph() # we did not save the model graph
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137
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138 def __str__(self) :
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139 """ returns a string representing the model """
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140 res = "Linear regressor, input size =",str(self.init_params[0])
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141 res += ", output size =", str(self.init_params[1])
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142 return res
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143
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144 def __equal__(self,other) :
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145 """
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146 Compares the model based on the params.
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147 @return True if the params are the same, False otherwise
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148 """
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149 # class
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150 if not isinstance(other,model) :
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151 return False
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152 # input size
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153 if self.params[0].shape[0] != other.params[0].shape[0] :
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154 return False
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155 # output size
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156 if self.params[0].shape[1] != other.params[0].shape[1] :
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157 return False
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158 # actual values
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159 if not (self.params[0] == other.params[0]).all():
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160 return False
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161 if not (self.params[1] == other.params[1]).all():
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162 return False
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163 # all good
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164 return True
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165
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166
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167 def die_with_usage() :
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168 """ help menu """
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169 print 'simple script to illustrate how to use theano/pylearn'
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170 print 'to launch:'
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171 print ' python linear_classifier.py -launch'
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172 sys.exit(0)
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173
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174
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175
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176 #************************************************************
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177 # main
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178
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179 if __name__ == '__main__' :
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180
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181 if len(sys.argv) < 2 :
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182 die_with_usage()
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183
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184 # print create data
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185 inputs = numpy.array([[.1,.2],
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186 [.2,.8],
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187 [.9,.3],
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188 [.6,.5]])
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189 outputs = numpy.array([[0],
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190 [0],
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191 [1],
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192 [1]])
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193 assert inputs.shape[0] == outputs.shape[0]
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194
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195 # create model
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196 m = model(2,1)
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197
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198 # predict
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199 print 'prediction before training:'
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200 print m[inputs]
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201
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202 # update it for 100 iterations
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203 for k in range(50) :
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204 m.update(.1,inputs,outputs)
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205
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206 # predict
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207 print 'prediction after training:'
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208 print m[inputs]
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209
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210 # show points
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211 import pylab as P
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212 colors = outputs.flatten().tolist()
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213 x = inputs[:,0]
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214 y = inputs[:,1]
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215 P.plot(x[numpy.where(outputs==0)[0]],y[numpy.where(outputs==0)[0]],'r+')
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216 P.plot(x[numpy.where(outputs==1)[0]],y[numpy.where(outputs==1)[0]],'b+')
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217 # decision line
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218 p1 = (.5 - m.params[1] * 1.) / m.params[0][1,0] # abs = 0
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219 p2 = (.5 - m.params[1] * 1.) / m.params[0][0,0] # ord = 0
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220 P.plot((0,p2[0],2*p2[0]),(p1[0],0,-p1[0]),'g-')
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221 # show
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222 P.axis([-1,2,-1,2])
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223 P.show()
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224