list of log probs

calculate(me,
observation)
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. 
source code


class

classify(me,
observation)
Classify an observation into a class. 
source code


dict of values

_eval_feature_fn(fn,
xs,
classes)
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) > nonzero value. Values of 0 are not
stored in the dictionary. 
source code


list of expectations

_calc_empirical_expects(xs,
ys,
classes,
features)
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. 
source code


list of expectations

_calc_model_expects(xs,
classes,
features,
alphas)
Calculate the expectation of each feature from the model. This is
not used in maximum entropy training, but provides a good function
for debugging. 
source code


matrix

_calc_p_class_given_x(xs,
classes,
features,
alphas)
Calculate P(yx), where y is the class and x is an instance from
the training set. Return a XSxCLASSES matrix of probabilities. 
source code


matrix of f sharp values.



_iis_solve_delta(N,
feature,
f_sharp,
empirical,
prob_yx,
max_newton_iterations,
newton_converge) 
source code



_train_iis(xs,
classes,
features,
f_sharp,
alphas,
e_empirical,
max_newton_iterations,
newton_converge)
Do one iteration of hill climbing to find better alphas (PRIVATE). 
source code



train(training_set,
results,
feature_fns,
update_fn=None,
max_iis_iterations=10000,
iis_converge=1e05,
max_newton_iterations=100,
newton_converge=1e10)
Train a maximum entropy classifier, returns MaxEntropy object. 
source code

