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annotate doc/v2_planning/arch_src/plugin_JB_main.py @ 1229:515033d4d3bf

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a first draft of layer committee
author Razvan Pascanu <r.pascanu@gmail.com>
date Wed, 22 Sep 2010 19:43:24 -0400
parents 9fac28d80fb7
children
rev   line source
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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, n=1):
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14 rval = self.data[self.pos:self.pos+n]
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15 self.pos += n
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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, n=1):
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32 #TODO: skip the examples that are ommitted in this split
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33 return self.data.next(n)
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34 def init_test(self):
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35 pass
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36 def next_test(self, n=1):
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37 return self.data.next(n)
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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, test_data_reg, test_counter_reg, test_scores_reg):
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44 return REPEAT(self.K, SEQ([
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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 REPEAT(self.test_size(), SEQ([
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50 CALL(self.next_test, _set=test_data_reg),
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51 test]),
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52 counter=test_counter_reg),
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53 CALL(self.store_scores, test_scores_reg)]))
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54
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55 class PCA_Analysis(object):
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56 def __init__(self):
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57 self.clear()
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58
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59 def clear(self):
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60 self.mean = 0
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61 self.eigvecs=0
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62 self.eigvals=0
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63 def analyze(self, X):
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64 self.mean = numpy.mean(X, axis=0)
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65 self.eigvecs=1
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66 self.eigvals=1
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67 def filt(self, X):
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68 return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
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69 def pseudo_inverse(self, Y):
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70 return Y
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71
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72 class Layer(object):
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73 def __init__(self, w):
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74 self.w = w
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75 def filt(self, x):
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76 return self.w*x
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77 def clear(self):
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78 self.w =0
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79
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80 def cd1_update(X, layer, lr):
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81 # update self.layer from observation X
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82 layer.w += X.mean() * lr #TODO: not exactly correct math!
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83
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84
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85 ###############################################################
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86 # Example algorithms written in this control flow mini-language
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87
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88 def main_weave():
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89 # Uses weave to demonstrate the interleaving of two bufferings of a single stream
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90
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91 l = [0]
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92 def f(a):
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93 print l
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94 l[0] += a
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95 return l[0]
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96
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97 print WEAVE(1, [
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98 REPEAT(3,CALL(f,1)),
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99 REPEAT(5,CALL(f,1)),
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100 ]).run()
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101
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102 def main_weave_popen():
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103 # Uses weave and Popen to demonstrate the control of a program with some asynchronous
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104 # parallelism
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105
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106 p = WEAVE(2,[
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107 SEQ([POPEN(['sleep', '5']), PRINT('done 1')]),
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108 SEQ([POPEN(['sleep', '10']), PRINT('done 2')]),
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109 LOOP(SEQ([
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110 CALL(print_obj, 'polling...'),
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111 CALL(time.sleep, 1)]))])
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112 # The LOOP would forever if the WEAVE were not configured to stop after 2 of its elements
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113 # complete.
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114
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115 p.run()
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116 # Note that the program can be run multiple times...
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117 p.run()
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118
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119 def main_spawn():
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120 # illustate the use of SPAWN to drive a set of control programs
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121 # in other processes
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122 data1 = {0:"blah data1"}
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123 data2 = {1:"foo data2"}
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124 p = WEAVE(2,[
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125 SPAWN(data1, REPEAT(3, SEQ([
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126 CALL(importable_fn, data1),
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127 PRINT("hello from 1")]))),
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128 SPAWN(data2, REPEAT(1, SEQ([
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129 CALL(importable_fn, data2),
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130 PRINT("hello from 2")]))),
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131 LOOP(SEQ([
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132 CALL(print_obj, 'polling...'),
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133 CALL(time.sleep, 0.5)]))])
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134 print 'BEFORE'
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135 print data1
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136 print data2
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137 p.run()
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138 print 'AFTER'
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139 print data1
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140 print data2
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141
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142 def main_kfold_dbn():
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143 # Uses many of the control-flow elements to define the k-fold evaluation of a dbn
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144 # The algorithm is not quite right, but the example shows off all of the required
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145 # control-flow elements I think.
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146
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147 # create components
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148 dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
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149 pca = PCA_Analysis()
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150 layer1 = Layer(w=4)
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151 layer2 = Layer(w=3)
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152 kf = KFold(dataset, K=10)
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153 reg = Registers()
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154
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155 pca_batchsize=1000
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156 cd_batchsize = 5
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157 n_cd_updates_layer1 = 10
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158 n_cd_updates_layer2 = 10
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159
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160 # create algorithm
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161
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162 train_pca = SEQ([
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163 CALL(kf.next, pca_batchsize, _set=reg('x')),
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164 CALL(pca.analyze, reg('x'))])
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165
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166 train_layer1 = REPEAT(n_cd_updates_layer1, SEQ([
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167 CALL(kf.next, cd_batchsize, _set=reg('x')),
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168 CALL(pca.filt, reg('x'), _set=reg('x')),
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169 CALL(cd1_update, reg('x'), layer=layer1, lr=.01)]))
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170
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171 train_layer2 = REPEAT(n_cd_updates_layer2, SEQ([
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172 CALL(kf.next, cd_batchsize, _set=reg('x')),
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173 CALL(pca.filt, reg('x'), _set=reg('x')),
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174 CALL(layer1.filt, reg('x'), _set=reg('x')),
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175 CALL(cd1_update, reg('x'), layer=layer2, lr=.01)]))
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176
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177 kfold_prog = kf.prog(
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178 clear = SEQ([ # FRAGMENT 1: this bit is the reset/clear stage
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179 CALL(pca.clear),
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180 CALL(layer1.clear),
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181 CALL(layer2.clear),
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182 ]),
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183 train = SEQ([
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184 train_pca,
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185 WEAVE(1, [ # Silly example of how to do debugging / loggin with WEAVE
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186 train_layer1,
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187 LOOP(PRINT(reg('x')))]),
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188 train_layer2,
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189 ]),
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190 test=SEQ([
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191 CALL(pca.filt, reg('testx'), _set=reg('x')),
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192 CALL(layer1.filt, reg('x'), _set=reg('x')),
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193 CALL(layer2.filt, reg('x'), _set=reg('x')),
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194 CALL(numpy.mean, reg('x'), _set=reg('score'))]),
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195 test_data_reg=reg('testx'),
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196 test_counter_reg=reg('i'),
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197 test_scores_reg=reg('score'))
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198
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199 pkg1 = dict(prog=kfold_prog, kf=kf)
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200 pkg2 = copy.deepcopy(pkg1) # programs can be copied
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201
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202 try:
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203 pkg3 = cPickle.loads(cPickle.dumps(pkg1))
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204 except:
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205 print >> sys.stderr, "pickling doesnt work, but it can be fixed I think"
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206
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207 pkg = pkg2
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208
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209 # running a program updates the variables in its package, but not the other package
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210 pkg['prog'].run()
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211 print pkg['kf'].scores
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212
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213
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214 if __name__ == '__main__':
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215 try:
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216 sys.argv[1]
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217 except:
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218 print """You have to tell which main function to use, try:
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219 - python plugin_JB_main.py 'main_kfold_dbn()'
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220 - python plugin_JB_main.py 'main_weave()'
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221 - python plugin_JB_main.py 'main_weave_popen()'
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222 - python plugin_JB_main.py 'main_spawn()'
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223 """
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224 sys.exit(1)
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225 sys.exit(eval(sys.argv[1]))