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annotate doc/v2_planning/plugin_greenlet.py @ 1419:cff305ad9f60

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TensorFnDataset - added x_ attribute that caches the dataset function return value, but does not get pickled.
author James Bergstra <bergstrj@iro.umontreal.ca>
date Fri, 04 Feb 2011 16:05:22 -0500
parents acfd5e747a75
children
rev   line source
1200
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1 """plugin_greenlet - draft of library architecture using greenlets
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2
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3 HISTORICAL - NOT ACTUALLY A PROPOSAL
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4 ====================================
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5
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6 This was the original approach for what I renamed to plugin_JB, until I realized that I could
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7 get the end result without using greenlets at all.
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8
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9 Still, greenlets seem like they could be neat and making this program stretched my mind so I
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10 keep it. There's something wrong when you run with the kfold validation, but until that point
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11 I think it works.
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12
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13 """
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14
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15 __license__ = None
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16 __copyright__ = None
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17
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18 import copy, sys
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19
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20 import numpy
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21 from greenlet import greenlet
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22
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23 def vm_unpack(incoming):
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24 # can't reliably distinguish between a kwargs-only switch and a switch with one dict
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25 # argument
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26 if incoming is None:
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27 rval = (), {}
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28 if isinstance(incoming, dict):
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29 rval = (), incoming
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30 elif isinstance(incoming, tuple):
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31 if (len(incoming)==2
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32 and isinstance(incoming[0], tuple)
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33 and isinstance(incoming[1], dict)):
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34 rval = incoming
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35 else:
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36 rval = incoming, {}
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37 else:
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38 rval = (incoming,), {}
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39 #print 'unpack', incoming, rval
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40 return rval[0][0], rval[0][1], rval[0][2:], rval[1]
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41
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42 def unpack_from_vm(incoming):
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43 assert isinstance(incoming, tuple)
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44 assert len(incoming)==4
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45 return incoming
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46
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47 def vm_run(prog, *args, **kwargs):
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48 #TODO: make this into a class with different ways to start the loop
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49 # for example, if the (gr, dest, a, kw) tuple is returned,
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50 # then a program could be run for N steps and then paused,
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51 # saved, and restarted.
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52 n_steps = kwargs.pop('n_steps', float('inf'))
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53 def vm_loop(gr, dest, a, kw):
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54 loop_iter = 0
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55 while loop_iter < n_steps:
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56 if gr == 'return':
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57 break
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58 #print 'vm_loop gr=',gr,'args=',a, 'kwargs=', kw
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59 gr, dest, a, kw = gr.switch(vm, gr, dest, a, kw)
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60 #print 'gmain incoming', incoming
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61 loop_iter += 1
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62 # permit restarting
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63 return gr, dest, a, kw
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64 vm = greenlet(vm_loop)
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65 return vm.switch(prog, 'return', args, kwargs)
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66
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67 ####################################################
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68 # CONTROL-FLOW CONSTRUCTS
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69
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70 def SEQ(glets):
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71 return REPEAT(1, glets)
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72
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73 def REPEAT(N, glets):
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74 def repeat_task(vm, gself, dest, args, kwargs):
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75 while True:
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76 for i in xrange(N):
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77 for glet in glets:
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78 #print 'repeat_task_i dest=%(dest)s args=%(args)s, kw=%(kwargs)s'%locals()
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79 # jump to task `glet`
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80 # with instructions to report results back to this loop `g`
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81 _vm, _gself, _dest, args, kwargs = vm.switch(glet, gself, args, kwargs)
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82 assert _gself is gself
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83 assert _dest is None # instructions can't tell us where to jump
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84 vm, gself, dest, args, kwargs = vm.switch(dest, None, args, kwargs)
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85 return greenlet(repeat_task)
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86
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87 def LOOP(seq):
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88 #TODO: implement a true infinite loop
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89 try:
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90 iter(seq)
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91 return REPEAT(sys.maxint, seq)
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92 except TypeError:
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93 return REPEAT(sys.maxint, [seq])
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94
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95 def CHOOSE(which, options):
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96 raise NotImplementedError()
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97
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98 def WEAVE(threads):
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99 def weave_task(vm, gself, dest, args, kwargs):
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100 # weave works by telling its threads that *it* is the vm
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101 # and reporting back to the real vm indirectly
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102 while True: # execution of weave is an iteration through this loop
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103 # initially broadcast the args and kwargs to all threads
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104 all_threads_live = True
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105 thread_info = [(t, 'return', args, kwargs) for t in threads]
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106 #print 'weave start -------------'
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107 while all_threads_live:
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108 #print 'weave iter'
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109 for i in xrange(len(threads)):
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110 t_next, t_dest, t_args, t_kwargs = thread_info[i]
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111 #tell the vm we're up to something, but ask it to come right back
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112 #print 'weave 1'
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113 _ignore = vm.switch(gself, None, (), {})
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114
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115 # pretend we're the vm_loop and tell the
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116 # thread to advance by one and report back to us
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117 #print 'weave 2', thread_info[i]
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118 thread_info[i] = t_next.switch(gself, t_next, t_dest, t_args, t_kwargs)
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119 #print 'weave 3', thread_info[i]
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120 if thread_info[i][0] is 'return':
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121 #print 'thread has finished', i
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122 all_threads_live = False
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123
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124 # some thread has died so we return control to parent
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125 #####print 'weave returning', dest, args, kwargs
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126 vm, gself, dest, args, kwargs = vm.switch(dest, None, args, kwargs)
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127 #####print 'weave continuing', dest, args, kwargs
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128 return greenlet(weave_task)
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129
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130 def BUFFER(N, glet):
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131 def BUFFER_loop(vm, gself, dest, args, kwargs):
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132 while True: #body runs once per execution
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133 buf = []
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134 for i in xrange(N):
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135 # jump to task `glet`
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136 # with instructions to report results back to this loop `g`
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137 _vm, _gself, _dest, _args, _kwargs = vm.switch(glet, gself, args, kwargs)
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138 buf.append(_args[0])
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139 assert len(_args)==1
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140 assert _kwargs=={}
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141 assert _gself is gself
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142 assert _dest is None # instructions can't tell us where to jump
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143 buf = numpy.asarray(buf)
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144 vm, gself, dest, args, kwargs = vm.switch(dest, None, (buf,), {})
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145 return greenlet(BUFFER_loop)
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146
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147 def CALL(fn):
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148 """
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149 Create a greenlet whose first argument is the return-jump location.
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150
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151 fn must accept as the first positional argument this greenlet itself, which can be used as
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152 the return-jump location for internal greenlet switches (ideally using gswitch).
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153 """
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154 def CALL_loop(vm, gself, dest, args, kwargs):
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155 while True:
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156 #print 'CALL calling', fn.__name__, args, kwargs
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157 t = fn(*args, **kwargs)
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158 #TODO consider a protocol for returning args, kwargs
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159 if t is None:
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160 _vm,_gself,dest, args, kwargs = vm.switch(dest, None, (), {})
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161 else:
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162 _vm,_gself,dest, args, kwargs = vm.switch(dest, None, (t,), {})
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163
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164 assert gself is _gself
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165 return greenlet(CALL_loop)
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166
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167 ####################################################
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168 # Components involved in the learning process
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169
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170 class Dataset(object):
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171 def __init__(self, data):
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172 self.pos = 0
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173 self.data = data
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174 def next(self):
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175 rval = self.data[self.pos]
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176 self.pos += 1
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177 if self.pos == len(self.data):
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178 self.pos = 0
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179 return rval
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180 def seek(self, pos):
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181 self.pos = pos
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182
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183 class KFold(object):
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184 def __init__(self, data, K):
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185 self.data = data
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186 self.k = -1
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187 self.scores = [None]*K
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188 self.K = K
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189 def next_fold(self):
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190 self.k += 1
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191 self.data.seek(0) # restart the stream
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192 def next(self):
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193 #TODO: skip the examples that are ommitted in this split
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194 return self.data.next()
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195 def init_test(self):
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196 pass
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197 def next_test(self):
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198 return self.data.next()
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199 def test_size(self):
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200 return 5
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201 def store_scores(self, scores):
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202 self.scores[self.k] = scores
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203
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204 class PCA_Analysis(object):
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205 def __init__(self):
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206 self.clear()
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207
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208 def clear(self):
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209 self.mean = 0
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210 self.eigvecs=0
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211 self.eigvals=0
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212 def analyze(self, X):
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213 self.mean = X.mean(axis=0)
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214 self.eigvecs=1
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215 self.eigvals=1
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216 def filt(self, X):
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217 return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
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218 def pseudo_inverse(self, Y):
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219 return Y
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220
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221 class Layer(object):
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222 def __init__(self, w):
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223 self.w = w
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224 def filt(self, x):
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225 return self.w*x
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226 def clear(self):
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227 self.w =0
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228
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229 def print_obj(vm, gself, obj):
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230 print obj
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231 def no_op(*args, **kwargs):
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232 pass
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233
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234 class cd1_update(object):
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235 def __init__(self, layer, lr):
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236 self.layer = layer
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237 self.lr = lr
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238
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239 def __call__(self, X):
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240 # update self.layer from observation X
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241 print 'cd1', X
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242 print X.mean()
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243 self.layer.w += X.mean() * self.lr #TODO: not exactly correct math
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244
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245 def main():
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246 # create components
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247 dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
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248 pca = PCA_Analysis()
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249 layer1 = Layer(w=4)
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250 layer2 = Layer(w=3)
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251 kf = KFold(dataset, K=10)
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252
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253 # create algorithm
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254
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255 train_pca = SEQ([
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256 BUFFER(1000, CALL(kf.next)),
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257 CALL(pca.analyze)])
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258
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259 train_layer1 = REPEAT(10, [
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260 BUFFER(10, CALL(kf.next)),
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261 CALL(pca.filt),
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262 CALL(cd1_update(layer1, lr=.01))])
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263
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264 train_layer2 = REPEAT(10, [
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265 BUFFER(10, CALL(kf.next)),
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266 CALL(pca.filt),
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267 CALL(layer1.filt),
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268 CALL(cd1_update(layer2, lr=.01))])
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269
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270 def print_layer_w(*a,**kw):
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271 print layer1.w
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272
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273 train_prog = SEQ([
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274 train_pca,
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275 WEAVE([
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276 train_layer1,
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277 LOOP(CALL(print_layer_w))]),
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278 train_layer2,
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279 ])
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280
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281 kfold_prog = REPEAT(10, [
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282 CALL(kf.next_fold),
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283 CALL(pca.clear),
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284 CALL(layer1.clear),
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285 CALL(layer2.clear),
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286 train_prog,
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287 CALL(kf.init_test),
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288 BUFFER(kf.test_size(),
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289 SEQ([
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290 CALL(kf.next_test),
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291 CALL(pca.filt), # may want to allow this SEQ to be
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292 CALL(layer1.filt), # optimized into a shorter one that
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293 CALL(layer2.filt),
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294 CALL(numpy.mean)])), # chains together theano graphs
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295 CALL(kf.store_scores),
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296 ])
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297
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298 vm_run(kfold_prog, n_steps=500)
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299 print kf.scores
1195
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300
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301
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302 if __name__ == '__main__':
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303 sys.exit(main())
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304