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annotate learner.py @ 49:718befdc8671

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Miscellaneous general-purpose functions
author bengioy@grenat.iro.umontreal.ca
date Tue, 29 Apr 2008 14:34:40 -0400
parents 266c68cb6136
children 90e4c0784d6e
rev   line source
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2cd82666b9a7 Added statscollector and started writing dataset and learner.
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2cd82666b9a7 Added statscollector and started writing dataset and learner.
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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
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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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
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9 A Learner can be seen as a learning algorithm, a function that when
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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10 applied to training data returns a learned function, an object that
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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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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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
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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20 training data. The operation may be non-deterministic if the
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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21 learner has a random number generator that is set to use a
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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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):
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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
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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32 semantics of the Learner.use method.
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33 The user may optionally provide a training StatsCollector that is used to record
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34 some statistics of the outputs computed during training.
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35 """
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36 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything
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38
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39 def __call__(self,training_set,train_stats_collector=None):
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40 """
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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41 Train a learner from scratch using the provided training set,
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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42 and return the learned function.
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43 """
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44 self.forget()
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45 return self.update(learning_task,train_stats_collector)
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46
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47 def use(self,input_dataset,output_fields=None,copy_inputs=True):
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48 """Once a Learner has been trained by one or more call to 'update', it can
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49 be used with one or more calls to 'use'. The argument is a DataSet (possibly
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50 containing a single example) and the result is a DataSet of the same length.
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51 If output_fields is specified, it may be use to indicate which fields should
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52 be constructed in the output DataSet (for example ['output','classification_error']).
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53 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made
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54 visible in the output DataSet returned by this function.
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55 """
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56 raise NotImplementedError