Uses of Class
pt.tumba.ngram.bayes.DiscreteFunction

Packages that use DiscreteFunction

pt.tumba.ngram.bayes

Implementation of Bayesian Network Classifiers that can be used to categorize text files using N-Grams as features.

Uses of DiscreteFunction in pt.tumba.ngram.bayes

Subclasses of DiscreteFunction in pt.tumba.ngram.bayes

class	ConstantDensityBoundedSet
class	ConstantDensityRatioSet
class	EpsilonContaminatedSet
(package private) class	FinitelyGeneratedSet
class	ProbabilityFunction ***************************************************************
class	QBProbabilityFunction
class	TotalVariationSet
class	TwoMonotoneCapacity
class	VertexSet

Fields in pt.tumba.ngram.bayes declared as DiscreteFunction

(package private) DiscreteFunction	Bucket.backward_pointers
(package private) DiscreteFunction	Bucket.cluster
protected DiscreteFunction	Expectation.current_function
private DiscreteFunction[]	Bucket.ordered_dfs
(package private) DiscreteFunction	Bucket.separator
private DiscreteFunction	ConstantDensityRatioSet.temporary_discrete_function
private DiscreteFunction	TwoMonotoneCapacity.temporary_discrete_function
(package private) DiscreteFunction	BucketTree.unnormalized_result
protected DiscreteFunction	BayesNet.utility_function

Methods in pt.tumba.ngram.bayes that return DiscreteFunction

private DiscreteFunction	Bucket.build_new_function(boolean is_bucket_variable_included)
(package private) DiscreteFunction	Bucket.combine()
private DiscreteFunction	Expectation.construct_values(ProbabilityVariable pv, int moment_order)
DiscreteFunction	DiscreteVariable.get_numeric_values() Produce an array of numeric values for the values of a variable.
DiscreteFunction	BucketTree.get_unnormalized_result() Get the unnormalized_result for the BucketTree.
DiscreteFunction	BayesNet.get_utility_function() Get the utility function.
DiscreteFunction	DiscreteFunction.multiply(DiscreteVariable[] dvs, DiscreteFunction mult) Multiply two DiscreteFunction objects.
DiscreteFunction	DiscreteFunction.sum_out(DiscreteVariable[] dvs, boolean[] markers) Sum out some variables in the function.

Methods in pt.tumba.ngram.bayes with parameters of type DiscreteFunction

private void	GeneralizedChoquetIntegral.bound_negative(TwoMonotoneCapacity tmc, DiscreteFunction df, java.util.Vector sorted_values, double[] lps, double[] ups) Obtain the lower and upper probability for the event { df(x) < sorted_value[i] }
private void	GeneralizedChoquetIntegral.bound_positive(TwoMonotoneCapacity tmc, DiscreteFunction df, java.util.Vector sorted_values, double[] lps, double[] ups) Obtain the lower and upper probability for the event { df(x) > sorted_value[i] }
private void	Bucket.build_new_variables(DiscreteFunction new_df, int[] joined_indexes, boolean is_bucket_variable_included, int n)
private void	Bucket.create_backward_pointers(DiscreteFunction new_df)
protected void	Expectation.do_expectation_from_inference(DiscreteFunction df)
protected void	QuasiBayesExpectation.do_expectation_from_inference(DiscreteFunction df)
private void	QuasiBayesExpectation.expectation_with_local_neighborhoods(DiscreteFunction df)
private void	QuasiBayesExpectation.expectation_without_local_neighborhoods(DiscreteFunction df)
void	Expectation.expectation(DiscreteFunction df) Do the Expectation, assuming the input DiscreteFunction is a function only of the queried variable.
void	Expectation.expectation(DiscreteFunction df, java.lang.String queried_variable_name) Do the Expectation, assuming the input DiscreteFunction is a function only of the queried variable.
void	Expectation.expectation(DiscreteFunction df, java.lang.String[] order) Do the Expectation given order, assuming the input DiscreteFunction is a function only of the queried variable.
double	ProbabilityFunction.expected_value(DiscreteFunction df) Obtain expected value of a DiscreteFunction The current implementation is very limited; it assumes that both the ProbabilityFunction object and the DiscreteFunctions object has a single variable, and the variable must be the same for both functions.
double[]	ConstantDensityRatioSet.expected_values(DiscreteFunction df) Perform calculation of expected value for density ratio.
double[]	EpsilonContaminatedSet.expected_values(DiscreteFunction df) Perform calculation of expected value.
double[]	TwoMonotoneCapacity.expected_values(DiscreteFunction df)
private void	BucketTree.insert(DiscreteFunction df)
private void	BucketTree.insert(DiscreteFunction df, boolean was_first_variable_cancelled_by_evidence)
private void	Bucket.max_out(DiscreteFunction new_df)
DiscreteFunction	DiscreteFunction.multiply(DiscreteVariable[] dvs, DiscreteFunction mult) Multiply two DiscreteFunction objects.
double	ProbabilityFunction.posterior_expected_value(DiscreteFunction df) Obtain posterior expected value of a DiscreteFunction This assumes that the probability values are unnormalized, equal to p(x, e) where e is the evidence.
double[]	ConstantDensityRatioSet.posterior_expected_values(DiscreteFunction df) Perform calculation of posterior expected value.
double[]	EpsilonContaminatedSet.posterior_expected_values(DiscreteFunction df) Perform calculation of posterior expected value.
double[]	TwoMonotoneCapacity.posterior_expected_values(DiscreteFunction df)
(package private) boolean	DiscreteFunction.same_variables(DiscreteFunction df)
private java.util.Vector	GeneralizedChoquetIntegral.sort_negative(DiscreteFunction df) Collect the negative values in df and sort them in decreasing order (first value is assumed zero).
private java.util.Vector	GeneralizedChoquetIntegral.sort_positive(DiscreteFunction df) Collect the positive values in df and sort them in increasing order (first value is assumed zero).
private void	Bucket.sum_out(DiscreteFunction new_df)
double	ProbabilityFunction.variance(DiscreteFunction df) Calculate the variance of a DiscreteFunction.

Constructors in pt.tumba.ngram.bayes with parameters of type DiscreteFunction

FinitelyGeneratedSet(DiscreteFunction df, double[] new_values)
Constructor for FinitelyGeneratedSet.

FinitelyGeneratedSet(DiscreteFunction df, double[] new_values, double[] new_lp, double[] new_up)
Constructor for FinitelyGeneratedSet.

GeneralizedChoquetIntegral(TwoMonotoneCapacity tmc, DiscreteFunction df)
Calculate the lower and upper Choquet integrals using Walley's generalization, for a total variation neighborhood.

ProbabilityFunction(DiscreteFunction df, BayesNet b_n)
Constructor for ProbabilityFunction.

ProbabilityFunction(DiscreteFunction df, double[] new_values)
Constructor for ProbabilityFunction.

QBProbabilityFunction(DiscreteFunction df, double[] new_values, double[] new_lp, double[] new_up)
Constructor for QBProbabilityFunction.