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Maximum Entropy code.
Uses Improved Iterative Scaling: XXX ref
# XXX need to define terminology
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MaxEntropy Holds information for a Maximum Entropy classifier. |
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list of log probs |
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class |
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dict of values |
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list of expectations |
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list of expectations |
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matrix |
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matrix of f sharp values. |
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__package__ =
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Calculate the log of the probability for each class. me is a MaxEntropy object that has been trained. observation is a vector representing the observed data. The return value is a list of unnormalized log probabilities for each class.
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Evaluate a feature function on every instance of the training set and class. fn is a callback function that takes two parameters: a training instance and a class. Return a dictionary of (training set index, class index) -> non-zero value. Values of 0 are not stored in the dictionary.
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Calculate the expectation of each function from the data. This is the constraint for the maximum entropy distribution. Return a list of expectations, parallel to the list of features.
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Calculate the expectation of each feature from the model. This is not used in maximum entropy training, but provides a good function for debugging.
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Calculate P(y|x), where y is the class and x is an instance from the training set. Return a XSxCLASSES matrix of probabilities.
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Train a maximum entropy classifier, returns MaxEntropy object. Train a maximum entropy classifier on a training set. training_set is a list of observations. results is a list of the class assignments for each observation. feature_fns is a list of the features. These are callback functions that take an observation and class and return a 1 or 0. update_fn is a callback function that is called at each training iteration. It is passed a MaxEntropy object that encapsulates the current state of the training. The maximum number of iterations and the convergence criterion for IIS are given by max_iis_iterations and iis_converge, respectively, while max_newton_iterations and newton_converge are the maximum number of iterations and the convergence criterion for Newton's method. |
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