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Package Bio :: Module MaxEntropy |
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Maximum Entropy code.
Uses Improved Iterative Scaling: XXX ref
# XXX need to define terminologyClasses | |
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MaxEntropy |
Holds information for a Maximum Entropy classifier. |
Function Summary | |
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calculate(me, observation) -> list of log probs | |
classify(me, observation) -> class | |
train(training_set, results, feature_fns[, update_fn]) -> MaxEntropy object | |
_calc_empirical_expects(xs, ys, classes, features) -> list of expectations | |
_calc_f_sharp(N, nclasses, features) -> matrix of f sharp values. | |
_calc_model_expects(xs, classes, features, alphas) -> list of expectations. | |
_calc_p_class_given_x(xs, classes, features, alphas) -> matrix | |
_eval_feature_fn(fn, xs, classes) -> dict of values | |
_iis_solve_delta(N,
feature,
f_sharp,
empirical,
prob_yx)
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_train_iis(xs,
classes,
features,
f_sharp,
alphas,
e_empirical)
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Function Details |
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calculate(me, observation)calculate(me, observation) -> list of log probs 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. |
classify(me, observation)classify(me, observation) -> class Classify an observation into a class. |
train(training_set, results, feature_fns, update_fn=None)train(training_set, results, feature_fns[, update_fn]) -> 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's called at each training iteration. It is passed a MaxEntropy object that encapsulates the current state of the training. |
_calc_empirical_expects(xs, ys, classes, features)_calc_empirical_expects(xs, ys, classes, features) -> list of expectations 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. |
_calc_f_sharp(N, nclasses, features)_calc_f_sharp(N, nclasses, features) -> matrix of f sharp values. |
_calc_model_expects(xs, classes, features, alphas)_calc_model_expects(xs, classes, features, alphas) -> list of expectations. Calculate the expectation of each feature from the model. This is not used in maximum entropy training, but provides a good function for debugging. |
_calc_p_class_given_x(xs, classes, features, alphas)_calc_p_class_given_x(xs, classes, features, alphas) -> matrix Calculate P(y|x), where y is the class and x is an instance from the training set. Return a XSxCLASSES matrix of probabilities. |
_eval_feature_fn(fn, xs, classes)_eval_feature_fn(fn, xs, classes) -> dict of values 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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