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annotate doc/v2_planning/plugin_greenlet.py @ 1199:98954d8cb92d

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v2planning - modifs to plugin_JB
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 20 Sep 2010 02:56:11 -0400
parents a60b3472c4ba
children acfd5e747a75
rev   line source
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1 """plugin_greenlet - draft of library architecture using greenlets"""
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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
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40
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41 import numpy
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42 from greenlet import greenlet
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43
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44 def vm_unpack(incoming):
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45 # can't reliably distinguish between a kwargs-only switch and a switch with one dict
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46 # argument
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47 if incoming is None:
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48 rval = (), {}
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49 if isinstance(incoming, dict):
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50 rval = (), incoming
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51 elif isinstance(incoming, tuple):
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52 if (len(incoming)==2
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53 and isinstance(incoming[0], tuple)
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54 and isinstance(incoming[1], dict)):
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55 rval = incoming
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56 else:
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57 rval = incoming, {}
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58 else:
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59 rval = (incoming,), {}
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60 #print 'unpack', incoming, rval
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61 return rval[0][0], rval[0][1], rval[0][2:], rval[1]
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62
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63 def unpack_from_vm(incoming):
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64 assert isinstance(incoming, tuple)
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65 assert len(incoming)==4
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66 return incoming
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67
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68 def vm_run(prog, *args, **kwargs):
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69 #TODO: make this into a class with different ways to start the loop
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70 # for example, if the (gr, dest, a, kw) tuple is returned,
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71 # then a program could be run for N steps and then paused,
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72 # saved, and restarted.
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73 n_steps = kwargs.pop('n_steps', float('inf'))
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74 def vm_loop(gr, dest, a, kw):
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75 loop_iter = 0
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76 while loop_iter < n_steps:
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77 if gr == 'return':
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78 break
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79 #print 'vm_loop gr=',gr,'args=',a, 'kwargs=', kw
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80 gr, dest, a, kw = gr.switch(vm, gr, dest, a, kw)
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81 #print 'gmain incoming', incoming
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82 loop_iter += 1
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83 # permit restarting
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84 return gr, dest, a, kw
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85 vm = greenlet(vm_loop)
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86 return vm.switch(prog, 'return', args, kwargs)
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87
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88 ####################################################
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89 # CONTROL-FLOW CONSTRUCTS
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90
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91 def SEQ(glets):
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92 return REPEAT(1, glets)
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93
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94 def REPEAT(N, glets):
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95 def repeat_task(vm, gself, dest, args, kwargs):
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96 while True:
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97 for i in xrange(N):
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98 for glet in glets:
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99 #print 'repeat_task_i dest=%(dest)s args=%(args)s, kw=%(kwargs)s'%locals()
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100 # jump to task `glet`
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101 # with instructions to report results back to this loop `g`
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102 _vm, _gself, _dest, args, kwargs = vm.switch(glet, gself, args, kwargs)
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103 assert _gself is gself
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104 assert _dest is None # instructions can't tell us where to jump
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105 vm, gself, dest, args, kwargs = vm.switch(dest, None, args, kwargs)
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106 return greenlet(repeat_task)
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107
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108 def LOOP(seq):
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109 #TODO: implement a true infinite loop
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110 try:
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111 iter(seq)
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112 return REPEAT(sys.maxint, seq)
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113 except TypeError:
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114 return REPEAT(sys.maxint, [seq])
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115
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116 def CHOOSE(which, options):
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117 raise NotImplementedError()
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118
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119 def WEAVE(threads):
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120 def weave_task(vm, gself, dest, args, kwargs):
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121 # weave works by telling its threads that *it* is the vm
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122 # and reporting back to the real vm indirectly
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123 while True: # execution of weave is an iteration through this loop
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124 # initially broadcast the args and kwargs to all threads
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125 all_threads_live = True
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126 thread_info = [(t, 'return', args, kwargs) for t in threads]
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127 #print 'weave start -------------'
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128 while all_threads_live:
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129 #print 'weave iter'
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130 for i in xrange(len(threads)):
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131 t_next, t_dest, t_args, t_kwargs = thread_info[i]
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132 #tell the vm we're up to something, but ask it to come right back
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133 #print 'weave 1'
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134 _ignore = vm.switch(gself, None, (), {})
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135
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136 # pretend we're the vm_loop and tell the
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137 # thread to advance by one and report back to us
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138 #print 'weave 2', thread_info[i]
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139 thread_info[i] = t_next.switch(gself, t_next, t_dest, t_args, t_kwargs)
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140 #print 'weave 3', thread_info[i]
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141 if thread_info[i][0] is 'return':
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142 #print 'thread has finished', i
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143 all_threads_live = False
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144
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145 # some thread has died so we return control to parent
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146 #####print 'weave returning', dest, args, kwargs
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147 vm, gself, dest, args, kwargs = vm.switch(dest, None, args, kwargs)
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148 #####print 'weave continuing', dest, args, kwargs
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149 return greenlet(weave_task)
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150
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151 def BUFFER(N, glet):
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152 def BUFFER_loop(vm, gself, dest, args, kwargs):
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153 while True: #body runs once per execution
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154 buf = []
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155 for i in xrange(N):
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156 # jump to task `glet`
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157 # with instructions to report results back to this loop `g`
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158 _vm, _gself, _dest, _args, _kwargs = vm.switch(glet, gself, args, kwargs)
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159 buf.append(_args[0])
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160 assert len(_args)==1
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161 assert _kwargs=={}
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162 assert _gself is gself
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163 assert _dest is None # instructions can't tell us where to jump
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164 buf = numpy.asarray(buf)
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165 vm, gself, dest, args, kwargs = vm.switch(dest, None, (buf,), {})
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166 return greenlet(BUFFER_loop)
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167
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168 def CALL(fn):
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169 """
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170 Create a greenlet whose first argument is the return-jump location.
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171
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172 fn must accept as the first positional argument this greenlet itself, which can be used as
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173 the return-jump location for internal greenlet switches (ideally using gswitch).
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174 """
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175 def CALL_loop(vm, gself, dest, args, kwargs):
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176 while True:
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177 #print 'CALL calling', fn.__name__, args, kwargs
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178 t = fn(*args, **kwargs)
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179 #TODO consider a protocol for returning args, kwargs
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180 if t is None:
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181 _vm,_gself,dest, args, kwargs = vm.switch(dest, None, (), {})
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182 else:
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183 _vm,_gself,dest, args, kwargs = vm.switch(dest, None, (t,), {})
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184
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185 assert gself is _gself
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186 return greenlet(CALL_loop)
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187
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188 ####################################################
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189 # Components involved in the learning process
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190
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191 class Dataset(object):
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192 def __init__(self, data):
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193 self.pos = 0
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194 self.data = data
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195 def next(self):
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196 rval = self.data[self.pos]
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197 self.pos += 1
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198 if self.pos == len(self.data):
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199 self.pos = 0
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200 return rval
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201 def seek(self, pos):
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202 self.pos = pos
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203
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204 class KFold(object):
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205 def __init__(self, data, K):
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206 self.data = data
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207 self.k = -1
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208 self.scores = [None]*K
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209 self.K = K
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210 def next_fold(self):
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211 self.k += 1
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212 self.data.seek(0) # restart the stream
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213 def next(self):
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214 #TODO: skip the examples that are ommitted in this split
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215 return self.data.next()
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216 def init_test(self):
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217 pass
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218 def next_test(self):
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219 return self.data.next()
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220 def test_size(self):
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221 return 5
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222 def store_scores(self, scores):
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223 self.scores[self.k] = scores
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224
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225 class PCA_Analysis(object):
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226 def __init__(self):
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227 self.clear()
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228
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229 def clear(self):
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230 self.mean = 0
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231 self.eigvecs=0
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232 self.eigvals=0
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233 def analyze(self, X):
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234 self.mean = X.mean(axis=0)
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235 self.eigvecs=1
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236 self.eigvals=1
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237 def filt(self, X):
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238 return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
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239 def pseudo_inverse(self, Y):
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240 return Y
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241
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242 class Layer(object):
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243 def __init__(self, w):
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244 self.w = w
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245 def filt(self, x):
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246 return self.w*x
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247 def clear(self):
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248 self.w =0
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249
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250 def print_obj(vm, gself, obj):
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251 print obj
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252 def no_op(*args, **kwargs):
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253 pass
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254
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255 class cd1_update(object):
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256 def __init__(self, layer, lr):
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257 self.layer = layer
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258 self.lr = lr
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259
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260 def __call__(self, X):
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261 # update self.layer from observation X
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262 print 'cd1', X
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263 print X.mean()
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264 self.layer.w += X.mean() * self.lr #TODO: not exactly correct math
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265
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266 def main():
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267 # create components
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268 dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
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269 pca = PCA_Analysis()
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270 layer1 = Layer(w=4)
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271 layer2 = Layer(w=3)
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272 kf = KFold(dataset, K=10)
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273
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274 # create algorithm
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275
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276 train_pca = SEQ([
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277 BUFFER(1000, CALL(kf.next)),
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278 CALL(pca.analyze)])
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279
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280 train_layer1 = REPEAT(10, [
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281 BUFFER(10, CALL(kf.next)),
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282 CALL(pca.filt),
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283 CALL(cd1_update(layer1, lr=.01))])
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284
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285 train_layer2 = REPEAT(10, [
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286 BUFFER(10, CALL(kf.next)),
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287 CALL(pca.filt),
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288 CALL(layer1.filt),
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289 CALL(cd1_update(layer2, lr=.01))])
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290
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291 def print_layer_w(*a,**kw):
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292 print layer1.w
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293
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294 train_prog = SEQ([
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295 train_pca,
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296 WEAVE([
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297 train_layer1,
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298 LOOP(CALL(print_layer_w))]),
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299 train_layer2,
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300 ])
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301
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302 kfold_prog = REPEAT(10, [
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303 CALL(kf.next_fold),
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304 CALL(pca.clear),
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305 CALL(layer1.clear),
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306 CALL(layer2.clear),
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307 train_prog,
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308 CALL(kf.init_test),
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309 BUFFER(kf.test_size(),
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310 SEQ([
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311 CALL(kf.next_test),
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312 CALL(pca.filt), # may want to allow this SEQ to be
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313 CALL(layer1.filt), # optimized into a shorter one that
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314 CALL(layer2.filt),
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315 CALL(numpy.mean)])), # chains together theano graphs
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316 CALL(kf.store_scores),
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317 ])
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318
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319 vm_run(kfold_prog, n_steps=500)
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320 print kf.scores
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321
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322
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323 if __name__ == '__main__':
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324 sys.exit(main())
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325