Mercurial > pylearn
annotate doc/v2_planning/plugin_greenlet.py @ 1195:d3ee0d2d03e6
plugin_greenlet draft0
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Sun, 19 Sep 2010 13:05:48 -0400 |
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children | e9bb3340a870 |
rev | line source |
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1195
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plugin_greenlet draft0
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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) |