Mercurial > pylearn

annotate doc/v2_planning/arch_src/plugin_JB_main.py @ 1214:681b5e7e3b81

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a few comments on James version
author Razvan Pascanu <r.pascanu@gmail.com>
date Wed, 22 Sep 2010 10:39:39 -0400
parents 478bb1f8215c
children 9fac28d80fb7
rev   line source
1212
478bb1f8215c plugin_JB - added SPAWN control element and demo program
James Bergstra <bergstrj@iro.umontreal.ca>
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1 """plugin_JB_main - main functions illustrating control flow library"""
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2
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3 from plugin_JB import * #TODO: don't do this
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4
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5
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6 ####################################################
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7 # [Dummy] Components involved in learning algorithms
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8
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9 class Dataset(object):
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10 def __init__(self, data):
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11 self.pos = 0
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12 self.data = data
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13 def next(self):
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14 rval = self.data[self.pos]
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15 self.pos += 1
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16 if self.pos == len(self.data):
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17 self.pos = 0
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18 return rval
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19 def seek(self, pos):
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20 self.pos = pos
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21
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22 class KFold(object):
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23 def __init__(self, data, K):
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24 self.data = data
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25 self.k = -1
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26 self.scores = [None]*K
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27 self.K = K
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28 def next_fold(self):
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29 self.k += 1
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30 self.data.seek(0) # restart the stream
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31 def next(self):
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32 #TODO: skip the examples that are ommitted in this split
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33 return self.data.next()
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34 def init_test(self):
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35 pass
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36 def next_test(self):
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37 return self.data.next()
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38 def test_size(self):
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39 return 5
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40 def store_scores(self, scores):
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41 self.scores[self.k] = scores
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42
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43 def prog(self, clear, train, test):
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44 return REPEAT(self.K, [
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45 CALL(self.next_fold),
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46 clear,
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47 train,
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48 CALL(self.init_test),
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49 BUFFER_REPEAT(self.test_size(),
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50 SEQ([ CALL(self.next_test), test])),
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51 FILT(self.store_scores) ])
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52
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53 class PCA_Analysis(object):
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54 def __init__(self):
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55 self.clear()
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56
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57 def clear(self):
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58 self.mean = 0
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59 self.eigvecs=0
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60 self.eigvals=0
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61 def analyze(self, X):
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62 self.mean = numpy.mean(X, axis=0)
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63 self.eigvecs=1
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64 self.eigvals=1
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65 def filt(self, X):
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66 return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
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67 def pseudo_inverse(self, Y):
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68 return Y
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69
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70 class Layer(object):
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71 def __init__(self, w):
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72 self.w = w
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73 def filt(self, x):
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74 return self.w*x
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75 def clear(self):
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76 self.w =0
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77
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78 def cd1_update(X, layer, lr):
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79 # update self.layer from observation X
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80 layer.w += X.mean() * lr #TODO: not exactly correct math!
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81
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82
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83 ###############################################################
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84 # Example algorithms written in this control flow mini-language
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85
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86 def main_weave():
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87 # Uses weave to demonstrate the interleaving of two bufferings of a single stream
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88
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89 l = [0]
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90 def f(a):
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91 print l
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92 l[0] += a
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93 return l[0]
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94
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95 print WEAVE(1, [
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96 BUFFER_REPEAT(3,CALL(f,1)),
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97 BUFFER_REPEAT(5,CALL(f,1)),
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98 ]).run()
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99
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100 def main_weave_popen():
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101 # Uses weave and Popen to demonstrate the control of a program with some asynchronous
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102 # parallelism
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103
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104 p = WEAVE(2,[
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105 SEQ([POPEN(['sleep', '5']), PRINT('done 1')]),
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106 SEQ([POPEN(['sleep', '10']), PRINT('done 2')]),
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107 LOOP([
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108 CALL(print_obj, 'polling...'),
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109 CALL(time.sleep, 1)])])
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110 # The LOOP would forever if the WEAVE were not configured to stop after 2 of its elements
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111 # complete.
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112
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113 p.run()
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114 # Note that the program can be run multiple times...
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115 p.run()
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116
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117 def main_spawn():
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118 # illustate the use of SPAWN to drive a set of control programs
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119 # in other processes
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120 data1 = {0:"blah data1"}
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121 data2 = {1:"foo data2"}
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122 p = WEAVE(2,[
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123 SPAWN(data1, REPEAT(3, [
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124 CALL(importable_fn, data1),
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125 PRINT("hello from 1")])),
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126 SPAWN(data2, REPEAT(1, [
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127 CALL(importable_fn, data2),
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128 PRINT("hello from 2")])),
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129 LOOP([
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130 CALL(print_obj, 'polling...'),
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131 CALL(time.sleep, 0.5)])])
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132 print 'BEFORE'
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133 print data1
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134 print data2
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135 p.run()
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136 print 'AFTER'
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137 print data1
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138 print data2
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139
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140 def main_kfold_dbn():
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141 # Uses many of the control-flow elements to define the k-fold evaluation of a dbn
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142 # The algorithm is not quite right, but the example shows off all of the required
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143 # control-flow elements I think.
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144
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145 # create components
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146 dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
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147 pca = PCA_Analysis()
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148 layer1 = Layer(w=4)
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149 layer2 = Layer(w=3)
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150 kf = KFold(dataset, K=10)
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151
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152 pca_batchsize=1000
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153 cd_batchsize = 5
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154 n_cd_updates_layer1 = 10
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155 n_cd_updates_layer2 = 10
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156
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157 # create algorithm
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158
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159 train_pca = SEQ([
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160 BUFFER_REPEAT(pca_batchsize, CALL(kf.next)),
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161 FILT(pca.analyze)])
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162
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163 train_layer1 = REPEAT(n_cd_updates_layer1, [
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164 BUFFER_REPEAT(cd_batchsize, CALL(kf.next)),
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165 FILT(pca.filt),
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166 FILT(cd1_update, layer=layer1, lr=.01)])
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167
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168 train_layer2 = REPEAT(n_cd_updates_layer2, [
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169 BUFFER_REPEAT(cd_batchsize, CALL(kf.next)),
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170 FILT(pca.filt),
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171 FILT(layer1.filt),
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172 FILT(cd1_update, layer=layer2, lr=.01)])
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173
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174 kfold_prog = kf.prog(
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175 clear = SEQ([ # FRAGMENT 1: this bit is the reset/clear stage
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176 CALL(pca.clear),
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177 CALL(layer1.clear),
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178 CALL(layer2.clear),
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179 ]),
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180 train = SEQ([
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181 train_pca,
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182 WEAVE(1, [ # Silly example of how to do debugging / loggin with WEAVE
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183 train_layer1,
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184 LOOP(CALL(print_obj_attr, layer1, 'w'))]),
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185 train_layer2,
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186 ]),
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187 test=SEQ([
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188 FILT(pca.filt), # may want to allow this SEQ to be
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189 FILT(layer1.filt), # optimized into a shorter one that
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190 FILT(layer2.filt), # compiles these calls together with
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191 FILT(numpy.mean)])) # Theano
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192
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193 pkg1 = dict(prog=kfold_prog, kf=kf)
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194 pkg2 = copy.deepcopy(pkg1) # programs can be copied
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195
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196 try:
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197 pkg3 = cPickle.loads(cPickle.dumps(pkg1))
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198 except:
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199 print >> sys.stderr, "pickling doesnt work, but it can be fixed I think"
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200
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201 pkg = pkg2
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202
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203 # running a program updates the variables in its package, but not the other package
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204 pkg['prog'].run()
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205 print pkg['kf'].scores
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206
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207
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208 if __name__ == '__main__':
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209 sys.exit(eval(sys.argv[1]))