A B C D E F G H I J K L M N O P Q R S T U V W X Y _

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SECOND_MOMENT - Static variable in class pt.tumba.ngram.bayes.Expectation

SENSITIVITY_ANALYSIS - Static variable in class pt.tumba.ngram.bayes.InferenceGraph

SIGMOID - Static variable in class pt.tumba.ngram.svm.SVMParameter

Sigmoid kernel type

SIMILARITYJIANG - Static variable in class pt.tumba.ngram.NGramConstants

Use the similarity metric proposed by Jiand & Conranth in "Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy".

SIMILARITYLIN - Static variable in class pt.tumba.ngram.NGramConstants

Use the similarity metric proposed by Lin in "An information-theoretic definition of similarity".

SIMILARITYOUTOFPLACE - Static variable in class pt.tumba.ngram.NGramConstants

Use the similarity metric proposed by Cavnar & Trenkle.

SINGLE_LINE_COMMENT - Variable in class pt.tumba.ngram.bayes.BayesInterchangeFormat

SKIPABLE - Static variable in class pt.tumba.ngram.NGramConstants

Bytes skipable while building the proviles.

SMOOTHING - Static variable in class pt.tumba.ngram.NGramConstants

Use Good-Turing smoothing on the NGram occurence frequency.

SOT - Variable in class pt.tumba.ngram.bayes.XMLInterchangeFormat

STATIC_LEXER_ERROR - Static variable in error pt.tumba.ngram.bayes.TokenMgrError

An attempt wass made to create a second instance of a static token manager.

STRING - Variable in class pt.tumba.ngram.bayes.BayesInterchangeFormat

SUM_OUT - Static variable in class pt.tumba.ngram.bayes.BucketTree

SVCQ - Class in pt.tumba.ngram.svm

Inner class representing a Kernel matrix for Support Vector classification.

SVCQ(SVMProblem, SVMParameter, byte[]) - Constructor for class pt.tumba.ngram.svm.SVCQ

SVM - Class in pt.tumba.ngram.svm

Construct and solve various formulations of the support vector machine (SVM) problem.

SVM() - Constructor for class pt.tumba.ngram.svm.SVM

SVM.decisionFunction - Class in pt.tumba.ngram.svm

Inner class modeling the data for the SVM decision function, used for classifying points with respect to the hyperplane.

SVM.decisionFunction() - Constructor for class pt.tumba.ngram.svm.SVM.decisionFunction

SVMCategorizer - Class in pt.tumba.ngram.svm

Simple, easy-to-use, and efficient software for SVM classification and regression, based on the LIBSVM implementation of Chin-Chung Chang and Chin-Jen Lin.

SVMCategorizer() - Constructor for class pt.tumba.ngram.svm.SVMCategorizer

Construct an uninitialized Cathegorizer.

SVMCategorizer(String) - Constructor for class pt.tumba.ngram.svm.SVMCategorizer

Construct an Cathegorizer from a whole Directory of resources.

SVMCategorizer(String[]) - Constructor for class pt.tumba.ngram.svm.SVMCategorizer

Construct an Cathegorizer from a List of resource file names.

SVMModel - Class in pt.tumba.ngram.svm

SVMModel encondes a classification model, describing both the model parameters and the Support Vectors.

SVMModel() - Constructor for class pt.tumba.ngram.svm.SVMModel

SVMNode - Class in pt.tumba.ngram.svm

SVMNode is used to model dimentions in vectors.

SVMNode() - Constructor for class pt.tumba.ngram.svm.SVMNode

SVMParameter - Class in pt.tumba.ngram.svm

Constants and Parameters used used in the SVM package.

SVMParameter() - Constructor for class pt.tumba.ngram.svm.SVMParameter

SVMProblem - Class in pt.tumba.ngram.svm

Class to model an SVM Problem, containing both the training vectors and the class (value) associated with each vector.

SVMProblem() - Constructor for class pt.tumba.ngram.svm.SVMProblem

SVRQ - Class in pt.tumba.ngram.svm

Inner class representing a Kernel matrix for Support Vector regression.

SVRQ(SVMProblem, SVMParameter) - Constructor for class pt.tumba.ngram.svm.SVRQ

SaveBugs - Class in pt.tumba.ngram.bayes

SaveBugs(BayesNet) - Constructor for class pt.tumba.ngram.bayes.SaveBugs

Default constructor for a SaveBUGS object.

Solve(int, Kernel, double[], byte[], double[], double, double, double, Solver.SolutionInfo, boolean) - Method in class pt.tumba.ngram.svm.Solver

Solve(int, Kernel, double[], byte[], double[], double, double, double, Solver.SolutionInfo, boolean) - Method in class pt.tumba.ngram.svm.SolverNU

Solver - Class in pt.tumba.ngram.svm

Generalized SMO+SVMlight algorithm Solves: min 0.5(\alpha^T Q \alpha) + b^T \alpha y^T \alpha = \delta y_i = +1 or -1 0 <= alpha_i <= Cp for y_i = 1 0 <= alpha_i <= Cn for y_i = -1 Given: Q, b, y, Cp, Cn, and an initial feasible point \alpha l is the size of vectors and matrices eps is the stopping criterion solution will be put in \alpha, objective value will be put in obj

Solver() - Constructor for class pt.tumba.ngram.svm.Solver

Solver.SolutionInfo - Class in pt.tumba.ngram.svm

Solver.SolutionInfo() - Constructor for class pt.tumba.ngram.svm.Solver.SolutionInfo

SolverNU - Class in pt.tumba.ngram.svm

Solver for nu-svm classification and regression additional constraint: e^T \alpha = constant

SolverNU() - Constructor for class pt.tumba.ngram.svm.SolverNU

SwitchTo(int) - Method in class pt.tumba.ngram.bayes.BayesInterchangeFormat

SwitchTo(int) - Method in class pt.tumba.ngram.bayes.XMLInterchangeFormat

s_variables - Variable in class pt.tumba.ngram.bayes.IFProbabilityFunction

same_variables(DiscreteFunction) - Method in class pt.tumba.ngram.bayes.DiscreteFunction

save(PrintStream) - Method in class pt.tumba.ngram.bayes.SaveBugs

Save a BayesNet in a stream in the BUGS format.

save_bif(PrintStream) - Method in class pt.tumba.ngram.bayes.BayesNet

Save a BayesNet object in a stream, in the BIF InterchangeFormat.

save_bif(PrintStream) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Save the Bayesian network into a PrintStream in the BIF InterchangeFormat.

save_bugs(PrintStream) - Method in class pt.tumba.ngram.bayes.BayesNet

Save a BayesNet object into a stream, in the BUGS format.

save_bugs(PrintStream) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Save the Bayesian networks in BUGS format into a PrintStream.

save_data(PrintStream) - Method in class pt.tumba.ngram.bayes.SaveBugs

The numeric values for the distributions, saved in the Splus format understood by BUGS.

save_embayes(PrintStream) - Method in class pt.tumba.ngram.bayes.BayesNet

Save a BayesNet object in a stream for the EBayes engine.

save_model(PrintStream) - Method in class pt.tumba.ngram.bayes.SaveBugs

Save the name of the network in BUGS format.

save_structure(PrintStream) - Method in class pt.tumba.ngram.bayes.SaveBugs

The parenthood relationships in the network.

save_variables(PrintStream) - Method in class pt.tumba.ngram.bayes.SaveBugs

Declare all the variables used in the network.

save_xml(PrintStream) - Method in class pt.tumba.ngram.bayes.BayesNet

Save a BayesNet object in a stream, in the XMLBIF format version 0.3 (most recent version).

save_xml(PrintStream) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Save the Bayesian network into a PrintStream in the XML InterchangeFormat.

save_xml(PrintStream) - Method in class pt.tumba.ngram.bayes.ProbabilityFunction

Save the contents of a ProbabilityFunction object into a PrintStream.

save_xml(PrintStream) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Save the contents of a ProbabilityVariable object into a PrintStream.

save_xml_0_2(PrintStream) - Method in class pt.tumba.ngram.bayes.BayesNet

Save a BayesNet object in a stream, in the XMLBIF format version 0.2.

save_xml_0_3(PrintStream) - Method in class pt.tumba.ngram.bayes.ProbabilityFunction

Save the contents of a ProbabilityFunction object into a PrintStream in the XMLBIF v0.3 format.

save_xml_0_3(PrintStream) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Save the contents of a ProbabilityVariable object into a PrintStream using the XMLBIF v0.3 format.

selectWorkingSet(int[]) - Method in class pt.tumba.ngram.svm.Solver

selectWorkingSet(int[]) - Method in class pt.tumba.ngram.svm.SolverNU

separation(int, int) - Method in class pt.tumba.ngram.bayes.DSeparation

separation_relations(int, int) - Method in class pt.tumba.ngram.bayes.DSeparation

separator - Variable in class pt.tumba.ngram.bayes.Bucket

set_conditional_index(int) - Method in class pt.tumba.ngram.bayes.IFProbabilityFunction

set_defaults(Vector) - Method in class pt.tumba.ngram.bayes.IFProbabilityFunction

set_entries(Vector) - Method in class pt.tumba.ngram.bayes.IFProbabilityFunction

set_explanation(boolean) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set the explanatory status of the node.

set_explanation_value(int) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set the variable as explanatory with a given value.

set_extreme_point(int, double[]) - Method in class pt.tumba.ngram.bayes.VertexSet

Set an extreme point of the credal set.

set_function_properties(Vector) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set the function properties.

set_function_value(String[][], double, int) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set a single value of the probability function in the node given a list of pairs (Variable Value).

set_function_values(double[]) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set an array containing probability values; if credal set, insert the array in the first extreme point.

set_function_values(int, double[]) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set an array containing an extreme point of the credal set.

set_global_neighborhood(int) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Set the global neighborhood type.

set_global_neighborhood_parameter(double) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Set the parameter for the global neighborhood modeled by the network.

set_global_neighborhood_parameter(double) - Method in class pt.tumba.ngram.bayes.QuasiBayesNet

Set the parameter for the global neighborhood modeled by the network.

set_global_neighborhood_type(int) - Method in class pt.tumba.ngram.bayes.QuasiBayesNet

Set the type of global neighborhood.

set_index(int) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set the index of the variable.

set_invalid_index() - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set the index of the current ProbabilityVariable as invalid (variable is not observed).

set_invalid_observed_index() - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set the ProbabilityVariable as not observed..

set_local_credal_set(int) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Make sure the node represents a VertexSet a given number of extreme distributions.

set_local_credal_set() - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Make sure the node represents a VertexSet.

set_local_credal_set(int) - Method in class pt.tumba.ngram.bayes.VertexSet

Set the number of extreme distributions in the credal set.

set_name(String) - Method in class pt.tumba.ngram.bayes.BayesNet

Set the name of the network.

set_name(String) - Method in class pt.tumba.ngram.bayes.DiscreteVariable

Set the name of the current DiscreteVariable.

set_name(String) - Method in class pt.tumba.ngram.bayes.IFProbabilityVariable

set_name(String) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Set the name of the network.

set_name(String) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set the name of the variable.

set_network_properties(Vector) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Set the properties of the network.

set_no_local_credal_set() - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Make sure the node represents a single distribution.

set_observation_value(String) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set the observation for the node.

set_observed_value(String) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set a value of the current ProbabilityVariable as observed.

set_pos(InferenceGraphNode, Point) - Method in class pt.tumba.ngram.bayes.InferenceGraph

Set a value for the position of the node.

set_probability_function(int, ProbabilityVariable[], double[], Vector) - Method in class pt.tumba.ngram.bayes.BayesNet

Set a probability function given its constituents.

set_probability_function(int, ProbabilityFunction) - Method in class pt.tumba.ngram.bayes.BayesNet

Set a probability variable given its index.

set_probability_functions(ProbabilityFunction[]) - Method in class pt.tumba.ngram.bayes.BayesNet

Set the vector of probability functions.

set_probability_variable(int, String, String[], Vector) - Method in class pt.tumba.ngram.bayes.BayesNet

Set a probability variable given its constituents.

set_probability_variable(int, ProbabilityVariable) - Method in class pt.tumba.ngram.bayes.BayesNet

Set a probability variable given its index.

set_probability_variables(ProbabilityVariable[]) - Method in class pt.tumba.ngram.bayes.BayesNet

Set the vector of probability variables.

set_properties(Vector) - Method in class pt.tumba.ngram.bayes.BayesNet

Set the properties.

set_properties(Vector) - Method in class pt.tumba.ngram.bayes.IFProbabilityFunction

set_properties(Vector) - Method in class pt.tumba.ngram.bayes.IFProbabilityVariable

set_properties(Vector) - Method in class pt.tumba.ngram.bayes.ProbabilityFunction

Set the properties.

set_properties(Vector) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set the properties.

set_stream(InputStream) - Method in class pt.tumba.ngram.bayes.InterchangeFormat

set_stream(Reader) - Method in class pt.tumba.ngram.bayes.InterchangeFormat

set_tables(Vector) - Method in class pt.tumba.ngram.bayes.IFProbabilityFunction

set_type(int) - Method in class pt.tumba.ngram.bayes.ProbabilityVariable

Set the type of the current ProbabilityVariable.

set_value(int, double) - Method in class pt.tumba.ngram.bayes.DiscreteFunction

Set a value in the current DiscreteFunction given its position in the array of values.

set_value(String[][], double) - Method in class pt.tumba.ngram.bayes.ProbabilityFunction

Set a single value of the probability function.

set_value(String[][], double, int) - Method in class pt.tumba.ngram.bayes.VertexSet

Set a single value of the probability function.

set_values(double[]) - Method in class pt.tumba.ngram.bayes.DiscreteFunction

Set the values in the DiscreteFunction.

set_values(String[]) - Method in class pt.tumba.ngram.bayes.DiscreteVariable

Set the values of the current DiscreteVariable.

set_values(String[]) - Method in class pt.tumba.ngram.bayes.IFProbabilityVariable

set_variable(int, DiscreteVariable) - Method in class pt.tumba.ngram.bayes.DiscreteFunction

Set a DiscreteVariable in the current DiscreteFunction given its position in the array of values.

set_variable_properties(Vector) - Method in class pt.tumba.ngram.bayes.InferenceGraphNode

Set the variable properties.

set_variables(String[]) - Method in class pt.tumba.ngram.bayes.IFProbabilityFunction

shrinking - Variable in class pt.tumba.ngram.svm.SVMParameter

Boolean flag indicating the use of the shrinking heuristics

si - Variable in class pt.tumba.ngram.svm.SolverNU

sigmoidPredict(double, double, double) - Static method in class pt.tumba.ngram.svm.SVM

sigmoidTrain(int, double[], double[], double[]) - Static method in class pt.tumba.ngram.svm.SVM

Platt's binary SVM Probablistic Output: an improvement from Lin et al.

sign - Variable in class pt.tumba.ngram.svm.SVRQ

similarityJiang(Profile, Profile) - Static method in class pt.tumba.ngram.NGramCathegorizer

Calculate "the distance" between two profiles, using Jiang's & Conranth similarity measure, as proposed in "Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy".

similarityLin(Profile, Profile) - Static method in class pt.tumba.ngram.NGramCathegorizer

Calculate "the distance" between two profiles, using Lin's similarity measure as proposed in "An information-theoretic definition of similarity".

similarityMetric - Variable in class pt.tumba.ngram.NGramCathegorizer

The metric used to measure the distance between the profiles.

size - Variable in class pt.tumba.ngram.NGram

Size of this N-gram.

size() - Method in class pt.tumba.ngram.compression.ByteSet

Returns number of elements in this set.

size - Variable in class pt.tumba.ngram.svm.Cache

The cache size limit in bytes.

skip(long) - Method in class pt.tumba.ngram.compression.ArithCodeInputStream

Skips the given number of bytes from the input.

solveCSVC(SVMProblem, SVMParameter, double[], Solver.SolutionInfo, double, double) - Static method in class pt.tumba.ngram.svm.SVM

Solve the C-Support Vector classification problem.

solveEpsilonSVR(SVMProblem, SVMParameter, double[], Solver.SolutionInfo) - Static method in class pt.tumba.ngram.svm.SVM

Solve the Epsilon-Support Vector Regression Problem

solveNUSVC(SVMProblem, SVMParameter, double[], Solver.SolutionInfo) - Static method in class pt.tumba.ngram.svm.SVM

Solve the Nu-Support Vector Classification problem.

solveNUSVR(SVMProblem, SVMParameter, double[], Solver.SolutionInfo) - Static method in class pt.tumba.ngram.svm.SVM

Solve the Nu-Support Vector Regression Problem

solveOneClass(SVMProblem, SVMParameter, double[], Solver.SolutionInfo) - Static method in class pt.tumba.ngram.svm.SVM

Solve the SVM Distribution Estimation Problem.

sort_negative(DiscreteFunction) - Method in class pt.tumba.ngram.bayes.GeneralizedChoquetIntegral

Collect the negative values in df and sort them in decreasing order (first value is assumed zero).

sort_positive(DiscreteFunction) - Method in class pt.tumba.ngram.bayes.GeneralizedChoquetIntegral

Collect the positive values in df and sort them in increasing order (first value is assumed zero).

sortedGrams - Variable in class pt.tumba.ngram.blr.BayesianLogReg

sortedGrams - Variable in class pt.tumba.ngram.svm.SVMCategorizer

specialConstructor - Variable in exception pt.tumba.ngram.bayes.ParseException

This variable determines which constructor was used to create this object and thereby affects the semantics of the "getMessage" method (see below).

specialToken - Variable in class pt.tumba.ngram.bayes.Token

This field is used to access special tokens that occur prior to this token, but after the immediately preceding regular (non-special) token.

speedString(int, long) - Static method in class pt.tumba.ngram.compression.Test

Returns a string representation of the speed of compression indicated by the specified number of original bytes and time in milliseconds.

staticFlag - Static variable in class pt.tumba.ngram.bayes.ASCII_CharStream

staticFlag - Static variable in class pt.tumba.ngram.bayes.ASCII_UCodeESC_CharStream

status - Variable in class pt.tumba.ngram.bayes.Bracketing

sum_out(DiscreteFunction) - Method in class pt.tumba.ngram.bayes.Bucket

sum_out(DiscreteVariable[], boolean[]) - Method in class pt.tumba.ngram.bayes.DiscreteFunction

Sum out some variables in the function.

supportVectors - Variable in class pt.tumba.ngram.svm.SVMModel

The Support Vectors.

supportVectorsCoef - Variable in class pt.tumba.ngram.svm.SVMModel

The coefficients for Support Vectors in decision functions.

svmBinarySVCProbability(SVMProblem, SVMParameter, double, double, double[]) - Static method in class pt.tumba.ngram.svm.SVM

Cross-validation decision values for probability estimates.

svmCheckParameter(SVMProblem, SVMParameter) - Static method in class pt.tumba.ngram.svm.SVM

Check the parameters given to an SVM problem.

svmCheckProbabilityModel(SVMModel) - Static method in class pt.tumba.ngram.svm.SVM

svmCrossValidation(SVMProblem, SVMParameter, int, double[]) - Static method in class pt.tumba.ngram.svm.SVM

Perform cross validation

svmGetLabels(SVMModel, int[]) - Static method in class pt.tumba.ngram.svm.SVM

svmGetSVRProbability(SVMModel) - Static method in class pt.tumba.ngram.svm.SVM

svmLoadModel(String) - Static method in class pt.tumba.ngram.svm.SVM

Loads a trained SVM model from disk.

svmPredict(SVMModel, SVMNode[]) - Static method in class pt.tumba.ngram.svm.SVM

svmPredictProbability(SVMModel, SVMNode[], double[]) - Static method in class pt.tumba.ngram.svm.SVM

svmPredictValues(SVMModel, SVMNode[], double[]) - Static method in class pt.tumba.ngram.svm.SVM

svmSVRProbability(SVMProblem, SVMParameter) - Static method in class pt.tumba.ngram.svm.SVM

Return parameter of a Laplace distribution

svmSaveModel(String, SVMModel) - Static method in class pt.tumba.ngram.svm.SVM

Outputs a trained model to disk, for use in future classification tasks.

svmTrain(SVMProblem, SVMParameter) - Static method in class pt.tumba.ngram.svm.SVM

Train the SVM.

svmTrainOne(SVMProblem, SVMParameter, double, double) - Static method in class pt.tumba.ngram.svm.SVM

Computation for one step of the training procedure.

svmType - Variable in class pt.tumba.ngram.svm.SVMParameter

Parameter specifying the SVM type

svmTypeTable - Static variable in class pt.tumba.ngram.svm.SVM

Table with all the SVM problem formulation names.

swapIndex(int, int) - Method in class pt.tumba.ngram.svm.Cache

Swap two nodes in the cache.

swapIndex(int, int) - Method in class pt.tumba.ngram.svm.Kernel

Swap two nodes of the Kernel matrix.

swapIndex(int, int) - Method in class pt.tumba.ngram.svm.ONECLASSQ

swapIndex(int, int) - Method in class pt.tumba.ngram.svm.SVCQ

swapIndex(int, int) - Method in class pt.tumba.ngram.svm.SVRQ

swapIndex(int, int) - Method in class pt.tumba.ngram.svm.Solver