Mercurial > pylearn
annotate doc/v2_planning/plugin_JB.py @ 1198:1387771296a8
v2planning adding plugin_JB
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Mon, 20 Sep 2010 02:34:23 -0400 |
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children | 98954d8cb92d |
rev | line source |
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1 """plugin_JB - draft of library architecture using iterators""" |
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2 |
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3 |
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4 """ |
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5 |
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6 - PICKLABLE - algorithms are serializable at all points during execution |
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7 |
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8 - ITERATOR walks through algorithms with fine granularity |
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9 |
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10 - COMPONENTS - library provides components on which programs operate |
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11 |
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12 - ALGORITHMS - library provides algorithms in clean (no hooks) form |
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13 |
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14 - HOOKS - user can insert print / debug logic with search/replace type calls |
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15 e.g. prog.find(CALL(cd1_update)).replace_with(SEQ([CALL(cd1_update), CALL(debugfn)])) |
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16 |
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17 - PRINTING - user can print the 'program code' of an algorithm built from library pieces |
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18 |
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19 - MODULAR EXPERIMENTS - an experiment object with one (or more?) programs and all of the objects referred to by |
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20 those programs. It is the preferred type of object to be serialized. The main components of |
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21 the algorithms should be top-level attributes of the package. This object can be serialized |
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22 and loaded in another process to implement job migration. |
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23 |
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24 - OPTIMIZATION - program can be optimized automatically |
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25 e.g. BUFFER(N, CALL(dataset.next)) can be replaced if dataset.next implements the right |
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26 attribute/protocol for 'bufferable' or something. |
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27 |
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28 e.g. SEQ([a,b,c,d]) can be compiled with Theano if sub-sequence is compatible |
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29 |
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30 - don't need greenlets to get efficiency, the implementations of control flow ops can manage a |
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31 stack or stack tree in the vm (like greenlets do I think) we don't really need |
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32 greenlets/stackless I don't think |
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33 |
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34 """ |
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35 |
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36 __license__ = None |
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37 __copyright__ = None |
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38 |
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39 import copy, sys, cPickle |
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40 |
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41 import numpy |
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42 |
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43 |
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44 ################################################### |
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45 # Virtual Machine for executing programs |
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46 |
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47 class VirtualMachine(object): |
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48 def __init__(self, prog): |
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49 self.prog = prog |
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50 self.started = False |
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51 self.finished=False |
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52 def __iter__(self): |
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53 assert not self.started |
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54 self.prog.start(None) |
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55 self.started = True |
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56 return self |
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57 def next(self): |
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58 if self.finished: |
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59 raise StopIteration() |
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60 r = self.prog.step() |
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61 if r is INCOMPLETE: |
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62 return r |
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63 else: |
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64 self.finished=True |
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65 return r |
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66 def run(self,n_steps=float('inf')): |
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67 i = 0 |
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68 for r in self: |
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69 i += 1 |
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70 if i > n_steps: |
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71 break |
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72 return r |
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73 |
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74 |
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75 #################################################### |
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76 # CONTROL-FLOW CONSTRUCTS |
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77 |
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78 class INCOMPLETE: |
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79 """Return value for Element.step""" |
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80 |
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81 class ELEMENT(object): |
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82 """ |
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83 every execution block has a driver |
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84 |
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85 the driver calls start when entering a new control element |
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86 - this would be called once per e.g. outer loop iteration |
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87 |
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88 the driver calls step to advance the control element |
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89 - which returns INCOMPLETE |
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90 - which returns any other object to indicate completion |
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91 """ |
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92 |
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93 def start(self, arg): |
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94 pass |
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95 def step(self): |
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96 pass |
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97 |
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98 |
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99 class BUFFER_REPEAT(ELEMENT): |
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100 """ |
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101 Accumulate a number of return values into one list / array. |
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102 |
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103 The source of return values `src` is a control element that will be restarted repeatedly in |
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104 order to fulfil the requiement of gathering N samples. |
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105 |
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106 TODO: support accumulating of tuples of arrays |
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107 """ |
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108 def __init__(self, N, src, storage=None): |
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109 """ |
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110 TODO: use preallocated `storage` |
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111 """ |
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112 self.N = N |
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113 self.n = 0 |
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114 self.src = src |
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115 self.storage = storage |
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116 self.src.start(None) |
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117 if self.storage != None: |
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118 raise NotImplementedError() |
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119 def start(self, arg): |
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120 self.buf = [None] * self.N |
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121 self.n = 0 |
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122 self.finished = False |
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123 def step(self): |
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124 assert not self.finished |
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125 r = self.src.step() |
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126 if r is INCOMPLETE: |
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127 return r |
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128 self.src.start(None) # restart our stream |
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129 self.buf[self.n] = r |
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130 self.n += 1 |
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131 if self.n == self.N: |
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132 self.finished = True |
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133 return self.buf |
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134 else: |
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135 return INCOMPLETE |
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136 assert 0 |
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137 |
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138 class CALL(ELEMENT): |
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139 """ |
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140 Control flow terminal - call a python function or method. |
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141 |
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142 Returns the return value of the call. |
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143 """ |
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144 def __init__(self, fn, *args, **kwargs): |
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145 self.fn = fn |
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146 self.args = args |
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147 self.kwargs=kwargs |
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148 self.use_start_arg = kwargs.pop('use_start_arg', False) |
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149 def start(self, arg): |
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150 self.start_arg = arg |
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151 self.finished = False |
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152 return self |
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153 def step(self): |
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154 assert not self.finished |
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155 self.finished = True |
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156 if self.use_start_arg: |
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157 if self.args: |
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158 raise TypeError('cant get positional args both ways') |
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159 return self.fn(self.start_arg, **self.kwargs) |
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160 else: |
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161 return self.fn(*self.args, **self.kwargs) |
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162 def __getstate__(self): |
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163 rval = self.__dict__ |
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164 if type(self.fn) is type(self.step): #instancemethod |
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165 fn = rval.pop('fn') |
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166 rval['i fn'] = fn.im_func, fn.im_self, fn.im_class |
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167 return rval |
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168 def __setstate__(self, dct): |
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169 if 'i fn' in dct: |
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170 dct['fn'] = type(self.step)(*dct.pop('i fn')) |
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171 self.__dict__.update(dct) |
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172 |
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173 def FILT(*args, **kwargs): |
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174 return CALL(use_start_arg=True, *args, **kwargs) |
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175 |
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176 def CHOOSE(which, options): |
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177 """ |
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178 Execute one out of a number of optional control flow paths |
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179 """ |
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180 raise NotImplementedError() |
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181 |
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182 def LOOP(elements): |
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183 #TODO: implement a true infinite loop |
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184 try: |
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185 iter(elements) |
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186 return REPEAT(sys.maxint, elements) |
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187 except TypeError: |
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188 return REPEAT(sys.maxint, [elements]) |
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189 |
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190 class REPEAT(ELEMENT): |
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191 def __init__(self, N, elements, pass_rvals=False): |
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192 self.N = N |
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193 self.elements = elements |
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194 self.pass_rvals = pass_rvals |
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195 #TODO: check for N being callable |
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196 def start(self, arg): |
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197 self.n = 0 #loop iteration |
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198 self.idx = 0 #element idx |
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199 self.finished = False |
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200 self.elements[0].start(arg) |
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201 def step(self): |
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202 assert not self.finished |
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203 r = self.elements[self.idx].step() |
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204 if r is INCOMPLETE: |
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205 return INCOMPLETE |
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206 self.idx += 1 |
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207 if self.idx < len(self.elements): |
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208 self.elements[self.idx].start(r) |
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209 return INCOMPLETE |
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210 self.n += 1 |
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211 if self.n < self.N: |
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212 self.idx = 0 |
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213 self.elements[self.idx].start(r) |
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214 return INCOMPLETE |
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215 else: |
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216 self.finished = True |
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217 return r |
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218 |
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219 def SEQ(elements): |
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220 return REPEAT(1, elements) |
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221 |
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222 class WEAVE(ELEMENT): |
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223 """ |
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224 Interleave execution of a number of elements. |
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225 |
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226 TODO: allow a schedule (at least relative frequency) of elements from each program |
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227 """ |
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228 def __init__(self, elements): |
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229 self.elements = elements |
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230 def start(self, arg): |
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231 for el in self.elements: |
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232 el.start(arg) |
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233 self.idx = 0 |
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234 self.any_is_finished = False |
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235 self.finished= False |
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236 def step(self): |
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237 assert not self.finished # if this is triggered, we have a broken driver |
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238 self.idx = self.idx % len(self.elements) |
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239 r = self.elements[self.idx].step() |
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240 if r is not INCOMPLETE: |
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241 self.any_is_finished = True |
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242 self.idx += 1 |
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243 if self.idx == len(self.elements) and self.any_is_finished: |
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244 self.finished = True |
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245 return None # dummy completion value |
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246 else: |
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247 return INCOMPLETE |
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248 |
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249 |
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250 #################################################### |
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251 # [Dummy] Components involved in learning algorithms |
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252 |
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253 class Dataset(object): |
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254 def __init__(self, data): |
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255 self.pos = 0 |
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256 self.data = data |
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257 def next(self): |
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258 rval = self.data[self.pos] |
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259 self.pos += 1 |
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260 if self.pos == len(self.data): |
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261 self.pos = 0 |
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262 return rval |
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263 def seek(self, pos): |
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264 self.pos = pos |
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265 |
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266 class KFold(object): |
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267 def __init__(self, data, K): |
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268 self.data = data |
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269 self.k = -1 |
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270 self.scores = [None]*K |
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271 self.K = K |
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272 def next_fold(self): |
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273 self.k += 1 |
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274 self.data.seek(0) # restart the stream |
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275 def next(self): |
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276 #TODO: skip the examples that are ommitted in this split |
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277 return self.data.next() |
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278 def init_test(self): |
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279 pass |
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280 def next_test(self): |
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281 return self.data.next() |
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282 def test_size(self): |
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283 return 5 |
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284 def store_scores(self, scores): |
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285 self.scores[self.k] = scores |
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286 |
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287 class PCA_Analysis(object): |
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288 def __init__(self): |
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289 self.clear() |
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290 |
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291 def clear(self): |
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292 self.mean = 0 |
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293 self.eigvecs=0 |
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294 self.eigvals=0 |
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295 def analyze(self, X): |
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296 self.mean = numpy.mean(X, axis=0) |
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297 self.eigvecs=1 |
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298 self.eigvals=1 |
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299 def filt(self, X): |
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300 return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals? |
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301 def pseudo_inverse(self, Y): |
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302 return Y |
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303 |
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304 class Layer(object): |
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305 def __init__(self, w): |
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306 self.w = w |
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307 def filt(self, x): |
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308 return self.w*x |
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309 def clear(self): |
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310 self.w =0 |
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311 |
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312 def print_obj(obj): |
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313 print obj |
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314 def print_obj_attr(obj, attr): |
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315 print getattr(obj, attr) |
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316 def no_op(*args, **kwargs): |
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317 pass |
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318 |
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319 class cd1_update(object): |
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320 def __init__(self, layer, lr): |
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321 self.layer = layer |
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322 self.lr = lr |
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323 |
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324 def __call__(self, X): |
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325 # update self.layer from observation X |
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326 self.layer.w += X.mean() * self.lr #TODO: not exactly correct math |
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327 |
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328 def simple_main(): |
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329 |
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330 l = [0] |
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331 def f(a): |
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332 print l |
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333 l[0] += a |
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334 return l[0] |
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335 |
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336 print VirtualMachine(WEAVE([ |
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337 BUFFER_REPEAT(3,CALL(f,1)), |
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338 BUFFER_REPEAT(5,CALL(f,1)), |
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339 ])).run() |
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340 |
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341 def main(): |
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342 # create components |
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343 dataset = Dataset(numpy.random.RandomState(123).randn(13,1)) |
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344 pca = PCA_Analysis() |
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345 layer1 = Layer(w=4) |
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346 layer2 = Layer(w=3) |
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347 kf = KFold(dataset, K=10) |
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348 |
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349 # create algorithm |
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350 |
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351 train_pca = SEQ([ |
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352 BUFFER_REPEAT(1000, CALL(kf.next)), |
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353 FILT(pca.analyze)]) |
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354 |
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355 train_layer1 = REPEAT(10, [ |
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356 BUFFER_REPEAT(10, CALL(kf.next)), |
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357 FILT(pca.filt), |
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358 FILT(cd1_update(layer1, lr=.01))]) |
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359 |
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360 train_layer2 = REPEAT(10, [ |
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361 BUFFER_REPEAT(10, CALL(kf.next)), |
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362 FILT(pca.filt), |
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363 FILT(layer1.filt), |
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364 FILT(cd1_update(layer2, lr=.01))]) |
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365 |
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366 train_prog = SEQ([ |
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367 train_pca, |
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368 WEAVE([ |
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369 train_layer1, |
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370 LOOP(CALL(print_obj_attr, layer1, 'w'))]), |
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371 train_layer2, |
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372 ]) |
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373 |
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374 kfold_prog = REPEAT(10, [ |
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375 CALL(kf.next_fold), |
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376 CALL(pca.clear), |
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377 CALL(layer1.clear), |
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378 CALL(layer2.clear), |
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379 train_prog, |
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380 CALL(kf.init_test), |
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381 BUFFER_REPEAT(kf.test_size(), |
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382 SEQ([ |
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383 CALL(kf.next_test), |
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384 FILT(pca.filt), # may want to allow this SEQ to be |
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385 FILT(layer1.filt), # optimized into a shorter one that |
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386 FILT(layer2.filt), |
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387 FILT(numpy.mean)])), # chains together theano graphs |
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388 FILT(kf.store_scores), |
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389 ]) |
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390 |
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391 vm = VirtualMachine(kfold_prog) |
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392 |
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393 #vm2 = copy.deepcopy(vm) |
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394 vm.run(n_steps=200000) |
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395 print kf.scores |
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396 |
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397 |
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398 if __name__ == '__main__': |
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399 sys.exit(main()) |
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400 |