Classifiers implemented in TRIOSlib¶
All classifiers in TRIOSlib inherit from trios.Classifier, documented below. Classifiers should be interchangeable and typically do not depend on a specific FeatureExtractor to work. bla
These methods are not usually directly called in scripts. Instances of WOperator automatically call these methods during its training and application phase.
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class
trios.WOperator.
Classifier
¶ Classifies patterns extracted from the images. This is an abstract class.
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__init__
¶ Classifiers have two basic attributes: minimize and ordered.
- if minimize is True then WOperator modifies all labels in the training set such that train receives a training set with only consistent patterns (no \(x_i = x_j\) and \(y_i \neq y_j\)).
- if ordered is True train receives as input a tuple \((X, y)\), where \(X\) contains input patterns in its rows and \(y\) contains the labels. If ordered is False train receives a dictionary with patterns and keys and a dictionary with the frequency of each output as values.
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apply
()¶ Override this method with the application procedure for a single pattern.
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apply_batch
()¶ Classifies a batch of patterns. Each one is stored on the rows of fmatrix.
Override this method if the classifier can do batch classification faster than classifiyng each pattern individually in a loop.
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partial_train
()¶ Executes one training iteration using inputs X and labels y.
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train
()¶ Each classification method must override this method with its training procedure.
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The following classifiers are implemented in TRIOSlib:
trios.classifiers.isi.ISI ([win]) |
description here |