__init__(encoding, weights[, logarithmic]) |
Construct a new maxent classifier model. |
classify(featureset) |
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classify_many(featuresets) |
Apply self.classify() to each element of featuresets. |
explain(featureset[, columns]) |
Print a table showing the effect of each of the features in the given feature set, and how they combine to determine the probabilities of each label for that featureset. |
labels() |
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prob_classify(featureset) |
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prob_classify_many(featuresets) |
Apply self.prob_classify() to each element of featuresets. |
set_weights(new_weights) |
Set the feature weight vector for this classifier. |
show_most_informative_features([n, show]) |
| param show: | all, neg, or pos (for negative-only or positive-only) |
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train(train_toks[, algorithm, trace, ...]) |
Train a new maxent classifier based on the given corpus of training samples. |
unicode_repr() |
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weights() |
| return: | The feature weight vector for this classifier. |
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