Mercurial > pylearn

annotate learner.py @ 112:5bfcb7e5df4a

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author Frederic Bastien <bastienf@iro.umontreal.ca>
date Wed, 07 May 2008 12:12:43 -0400
parents c4726e19b8ec
children c4916445e025
rev   line source
1
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1
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2 from dataset import *
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3
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4 class Learner(object):
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5 """Base class for learning algorithms, provides an interface
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6 that allows various algorithms to be applicable to generic learning
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7 algorithms.
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8
10
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9 A Learner can be seen as a learning algorithm, a function that when
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10 applied to training data returns a learned function, an object that
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11 can be applied to other data and return some output data.
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12 """
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13
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14 def __init__(self):
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15 pass
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16
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17 def forget(self):
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18 """
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19 Reset the state of the learner to a blank slate, before seeing
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20 training data. The operation may be non-deterministic if the
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21 learner has a random number generator that is set to use a
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22 different seed each time it forget() is called.
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23 """
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24 raise NotImplementedError
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25
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26 def update(self,training_set,train_stats_collector=None):
10
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27 """
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28 Continue training a learner, with the evidence provided by the given training set.
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29 Hence update can be called multiple times. This is particularly useful in the
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30 on-line setting or the sequential (Bayesian or not) settings.
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31 The result is a function that can be applied on data, with the same
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32 semantics of the Learner.use method.
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33
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34 The user may optionally provide a training StatsCollector that is used to record
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35 some statistics of the outputs computed during training. It is update(d) during
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36 training.
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37 """
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38 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything
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39
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40
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41 def __call__(self,training_set,train_stats_collector=None):
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42 """
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43 Train a learner from scratch using the provided training set,
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44 and return the learned function.
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45 """
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46 self.forget()
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47 return self.update(learning_task,train_stats_collector)
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48
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49 def use(self,input_dataset,output_fields=None,copy_inputs=True):
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50 """Once a Learner has been trained by one or more call to 'update', it can
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51 be used with one or more calls to 'use'. The argument is a DataSet (possibly
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52 containing a single example) and the result is a DataSet of the same length.
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53 If output_fields is specified, it may be use to indicate which fields should
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54 be constructed in the output DataSet (for example ['output','classification_error']).
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55 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made
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56 visible in the output DataSet returned by this method.
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57 """
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58 raise NotImplementedError
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59
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60 def attributeNames(self):
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61 """
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62 A Learner may have attributes that it wishes to export to other objects. To automate
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63 such export, sub-classes should define here the names (list of strings) of these attributes.
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64 """
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65 return []
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66
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67 class TLearner(Learner):
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68 """
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69 TLearner is a virtual class of Learners that attempts to factor out of the definition
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70 of a learner the steps that are common to many implementations of learning algorithms,
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71 so as to leave only "the equations" to define in particular sub-classes, using Theano.
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72
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73 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods
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74 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done,
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75 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this
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76 basic model is not appropriate can simply redefine update or use.
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77
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78 Sub-classes must provide the following functions and functionalities:
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79 - attributeNames(): defines all the names of attributes which can be used as fields or
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80 attributes in input/output datasets or in stats collectors.
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81 All these attributes are expected to be theano.Result objects
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82 (with a .data property and recognized by theano.Function for compilation).
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83 The sub-class constructor defines the relations between
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84 the Theano variables that may be used by 'use' and 'update'
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85 or by a stats collector.
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86 - defaultOutputFields(input_fields): return a list of default dataset output fields when
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87 None are provided by the caller of use.
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88 - update_start(), update_end(), update_minibatch(minibatch): functions
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89 executed at the beginning, the end, and in the middle
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90 (for each minibatch) of the update method. This model only
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91 works for 'online' or one-short learning that requires
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92 going only once through the training data. For more complicated
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93 models, more specialized subclasses of TLearner should be used
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94 or a learning-algorithm specific update method should be defined.
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95
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96 The following naming convention is assumed and important.
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97 Attributes whose names are listed in attributeNames() can be of any type,
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98 but those that can be referenced as input/output dataset fields or as
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99 output attributes in 'use' or as input attributes in the stats collector
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100 should be associated with a Theano Result variable. If the exported attribute
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101 name is <name>, the corresponding Result name (an internal attribute of
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102 the TLearner, created in the sub-class constructor) should be _<name>.
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103 Typically <name> will be numpy ndarray and _<name> will be the corresponding
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104 Theano Tensor (for symbolic manipulation).
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105 """
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106
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107 def __init__(self):
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108 Learner.__init__(self)
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109
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110 def _minibatchwise_use_functions(self, input_fields, output_fields, stats_collector):
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111 """
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112 Private helper function called by the generic TLearner.use. It returns a function
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113 that can map the given input fields to the given output fields (along with the
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114 attributes that the stats collector needs for its computation.
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115 """
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116 if not output_fields:
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117 output_fields = self.defaultOutputFields(input_fields)
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118 if stats_collector:
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119 stats_collector_inputs = stats_collector.inputUpdateAttributes()
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120 for attribute in stats_collector_inputs:
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121 if attribute not in input_fields:
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122 output_fields.append(attribute)
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123 key = (input_fields,output_fields)
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124 if key not in self.use_functions_dictionary:
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125 self.use_functions_dictionary[key]=Function(self._names2attributes(input_fields),
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126 self._names2attributes(output_fields))
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127 return self.use_functions_dictionary[key]
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128
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129 def attributes(self,return_copy=False):
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130 """
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131 Return a list with the values of the learner's attributes (or optionally, a deep copy).
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132 """
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133 return self.names2attributes(self.attributeNames())
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134
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135 def _names2attributes(self,names,return_Result=False, return_copy=False):
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136 """
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137 Private helper function that maps a list of attribute names to a list
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138 of (optionally copies) values or of the Result objects that own these values.
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139 """
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140 if return_Result:
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141 if return_copy:
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142 return [copy.deepcopy(self.__getattr__(name)) for name in names]
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143 else:
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144 return [self.__getattr__(name) for name in names]
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145 else:
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146 if return_copy:
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147 return [copy.deepcopy(self.__getattr__(name).data) for name in names]
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148 else:
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149 return [self.__getattr__(name).data for name in names]
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150
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151 def use(self,input_dataset,output_fieldnames=None,output_attributes=None,
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152 test_stats_collector=None,copy_inputs=True):
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153 """
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154 The learner tries to compute in the output dataset the output fields specified
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155 """
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156 minibatchwise_use_function = _minibatchwise_use_functions(input_dataset.fieldNames(),
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157 output_fieldnames,
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158 test_stats_collector)
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159 virtual_output_dataset = ApplyFunctionDataSet(input_dataset,
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160 minibatchwise_use_function,
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161 True,DataSet.numpy_vstack,
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162 DataSet.numpy_hstack)
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163 # actually force the computation
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164 output_dataset = CachedDataSet(virtual_output_dataset,True)
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165 if copy_inputs:
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166 output_dataset = input_dataset | output_dataset
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167 # copy the wanted attributes in the dataset
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168 if output_attributes:
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169 assert set(output_attributes) <= set(self.attributeNames())
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170 output_dataset.setAttributes(output_attributes,
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171 self._names2attributes(output_attributes,return_copy=True))
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172 if test_stats_collector:
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173 test_stats_collector.update(output_dataset)
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174 output_dataset.setAttributes(test_stats_collector.attributeNames(),
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175 test_stats_collector.attributes())
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176 return output_dataset
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177
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178 def update_start(self): pass
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179 def update_end(self): pass
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180 def update_minibatch(self,minibatch):
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181 raise AbstractFunction()
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182
92
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183 def update(self,training_set,train_stats_collector=None):
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184
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185 self.update_start()
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186 for minibatch in training_set.minibatches(self.training_set_input_fields,
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187 minibatch_size=self.minibatch_size):
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188 self.update_minibatch(minibatch)
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189 if train_stats_collector:
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190 minibatch_set = minibatch.examples()
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191 minibatch_set.setAttributes(self.attributeNames(),self.attributes())
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192 train_stats_collector.update(minibatch_set)
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193 self.update_end()
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194 return self.use
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195