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annotate doc/v2_planning/plugin_greenlet.py @ 1196:e9bb3340a870

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plugin_greenlet draft0
author James Bergstra <bergstrj@iro.umontreal.ca>
date Sun, 19 Sep 2010 13:06:16 -0400
parents d3ee0d2d03e6
children a60b3472c4ba
rev   line source
1195
d3ee0d2d03e6 plugin_greenlet draft0
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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1 """plugin_greenlet - draft of library architecture using greenlets"""
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2
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3 __license__ = None
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4 __copyright__ = None
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5
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6 import copy, sys
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7
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8 import numpy
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9 from greenlet import greenlet
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10
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11 def vm_unpack(incoming):
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12 # can't reliably distinguish between a kwargs-only switch and a switch with one dict
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13 # argument
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14 if incoming is None:
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15 rval = (), {}
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16 if isinstance(incoming, dict):
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17 rval = (), incoming
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18 elif isinstance(incoming, tuple):
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19 if (len(incoming)==2
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20 and isinstance(incoming[0], tuple)
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21 and isinstance(incoming[1], dict)):
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22 rval = incoming
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23 else:
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24 rval = incoming, {}
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25 else:
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26 rval = (incoming,), {}
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27 #print 'unpack', incoming, rval
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28 return rval[0][0], rval[0][1], rval[0][2:], rval[1]
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29
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30 def unpack_from_vm(incoming):
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31 assert isinstance(incoming, tuple)
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32 assert len(incoming)==4
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33 return incoming
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34
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35 def vm_run(prog, *args, **kwargs):
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36
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37 def vm_loop(gr, dest, a, kw):
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38 while True:
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39 if gr == 'return':
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40 return a, kw
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41 #print 'vm_loop gr=',gr,'args=',a, 'kwargs=', kw
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42 gr, dest, a, kw = gr.switch(vm, gr, dest, a, kw)
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43 #print 'gmain incoming', incoming
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44 vm = greenlet(vm_loop)
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45
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46 return vm.switch(prog, 'return', args, kwargs)
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47
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48
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49 def seq(glets):
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50 return repeat(1, glets)
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51
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52 def repeat(N, glets):
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53 def repeat_task(vm, gself, dest, args, kwargs):
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54 while True:
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55 for i in xrange(N):
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56 for glet in glets:
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57 #print 'repeat_task_i dest=%(dest)s args=%(args)s, kw=%(kwargs)s'%locals()
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58 # jump to task `glet`
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59 # with instructions to report results back to this loop `g`
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60 _vm, _gself, _dest, args, kwargs = vm.switch(glet, gself, args, kwargs)
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61 assert _gself is gself
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62 assert _dest is None # instructions can't tell us where to jump
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63 vm, gself, dest, args, kwargs = vm.switch(dest, None, args, kwargs)
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64 return greenlet(repeat_task)
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65
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66 def choose(which, options):
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67 raise NotImplementedError()
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68
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69 def weave(threads):
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70 raise NotImplementedError()
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71
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72 def service(fn):
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73 """
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74 Create a greenlet whose first argument is the return-jump location.
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75
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76 fn must accept as the first positional argument this greenlet itself, which can be used as
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77 the return-jump location for internal greenlet switches (ideally using gswitch).
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78 """
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79 def service_loop(vm, gself, dest, args, kwargs):
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80 while True:
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81 #print 'service calling', fn.__name__, args, kwargs
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82 t = fn(vm, gself, *args, **kwargs)
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83 #TODO consider a protocol for returning args, kwargs
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84 if t is None:
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85 _vm,_gself,dest, args, kwargs = vm.switch(dest, None, (), {})
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86 else:
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87 _vm,_gself,dest, args, kwargs = vm.switch(dest, None, (t,), {})
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88
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89 assert gself is _gself
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90 return greenlet(service_loop)
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91
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92 ####################################################
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93
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94 class Dataset(object):
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95 def __init__(self, data):
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96 self.pos = 0
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97 self.data = data
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98 def next(self, vm, gself):
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99 rval = self.data[self.pos]
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100 self.pos += 1
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101 if self.pos == len(self.data):
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102 self.pos = 0
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103 return rval
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104
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105 class PCA_Analysis(object):
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106 def __init__(self):
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107 self.mean = 0
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108 self.eigvecs=0
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109 self.eigvals=0
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110 def analyze(self, me, X):
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111 self.mean = X.mean(axis=0)
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112 self.eigvecs=1
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113 self.eigvals=1
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114 def filt(self,me, X):
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115 return (self.X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
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116 def pseudo_inverse(self, Y):
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117 return Y
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118
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119 class Layer(object):
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120 def __init__(self, w):
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121 self.w = w
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122 def filt(self, x):
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123 return self.w*x
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124
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125 def batches(src, N):
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126 # src is a service
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127 def rval(me):
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128 print 'batches src=', src, 'me=', me
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129 return numpy.asarray([gswitch(src, me)[0][0] for i in range(N)])
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130 return rval
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131
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132 def print_obj(vm, gself, obj):
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133 print obj
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134 def no_op(*args, **kwargs):
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135 pass
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136
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137 def build_pca_trainer(data_src, pca_module, N):
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138 return greenlet(
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139 batches(
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140 N=5,
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141 src=inf_data,
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142 dest=flow(pca_module.analyze,
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143 dest=layer1_trainer)))
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144
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145 def main():
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146 dataset = Dataset(numpy.random.RandomState(123).randn(10,2))
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147
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148 prog=repeat(3, [service(dataset.next),service(print_obj)])
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149 vm_run(prog)
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150 vm_run(prog)
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151
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152
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153 def main_arch():
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154
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155 # create components
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156 dataset = Dataset(numpy.random.RandomState(123).randn(10,2))
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157 pca_module = PCA_Analysis()
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158 layer1 = Layer(w=4)
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159 layer2 = Layer(w=3)
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160 kf = KFold(dataset, K=10)
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161
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162 # create algorithm
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163
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164 train_pca = seq([ np_batch(kf.next, 1000), pca.analyze])
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165 train_layer1 = repeat(100, [kf.next, pca.filt, cd1_update(layer1, lr=.01)])
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166
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167 algo = repeat(10, [
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168 KFold.step,
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169 seq([train_pca,
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170 train_layer1,
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171 train_layer2,
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172 train_classifier,
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173 save_classifier,
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174 test_classifier]),
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175 KFold.set_score])
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176
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177 gswitch(algo)
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178
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179
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180 def main1():
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181 dataset = Dataset(numpy.random.RandomState(123).randn(10,2))
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182 pca_module = PCA_Analysis()
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183
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184 # pca
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185 next_data = service(dataset.next)
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186 b5 = service(batches(src=next_data, N=5))
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187 print_pca_analyze = flow(pca_module.analyze, dest=sink(print_obj))
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188
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189 # layer1_training
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190 layer1_training = driver(
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191 fn=cd1_trainer(layer1),
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192 srcs=[],
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193 )
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194
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195 gswitch(b5, print_pca_analyze)
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196
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197 if __name__ == '__main__':
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198 sys.exit(main())
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199
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200
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201
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202 def flow(fn, dest):
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203 def rval(*args, **kwargs):
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204 while True:
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205 print 'flow calling', fn.__name__, args, kwargs
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206 t = fn(g, *args, **kwargs)
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207 args, kwargs = gswitch(dest, t)
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208 g = greenlet(rval)
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209 return g
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210
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211 def sink(fn):
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212 def rval(*args, **kwargs):
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213 return fn(g, *args, **kwargs)
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214 g = greenlet(rval)
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215 return g
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216
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217 def consumer(fn, src):
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218 def rval(*args, **kwargs):
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219 while True:
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220 fn(gswitch(src, *args, **kwargs))
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221 return greenlet(rval)