B C D G H I L M N P R S T U W

B

BayesianLogisticRegression - Class in weka.classifiers.bayes
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.

For more information, see

Alexander Genkin, David D.
BayesianLogisticRegression() - Constructor for class weka.classifiers.bayes.BayesianLogisticRegression
 
BetaVector - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Array for storing coefficients of Bayesian regression model.
bigF(double, double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
(1) Set the data to the class attribute m_Instances. (2)Call the method initialize() to initialize the values.

C

Change - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
This variable is used to keep track of change in the value of delta summation of r(i).
classifyInstance(Instance) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Classifies the given instance using the Bayesian Logistic Regression function.
ClassIndex - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
The class index from the training data
classSgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
This class is used to mask the internal class labels.
computeLoglikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
This method calls the log-likelihood implemented in the Prior abstract class.
computeLogLikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
Computes the log-likelihood values using the implementation in the Prior class.
computelogLikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.Prior
Function computes the log-likelihood value: -sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
This function computes the penalty term specific to Gaussian distribution.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
This function computes the penalty term specific to Laplacian distribution.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.Prior
Skeleton function to compute penalty terms.
CV_BASED - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
CVBasedHyperparameter() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood.

D

debugTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
Delta - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Trust Region Radius
DeltaBeta - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Array to store Regression Coefficient updates.
DeltaR - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
This vector is used to store the increments on the R(i).
DeltaUpdate - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Trust Region Radius Update

G

GAUSSIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Distributions available
GaussianPriorImpl - Class in weka.classifiers.bayes.blr
Implementation of the Gaussian Prior update function based on CLG Algorithm with a certain Trust Region Update.
GaussianPriorImpl() - Constructor for class weka.classifiers.bayes.blr.GaussianPriorImpl
 
getCapabilities() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
This method tests what kind of data this classifier can handle.
getHyperparameterRange() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the range of hyperparameter values to consider during CV-based selection.
getHyperparameterSelection() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the method used to select the hyperparameter
getHyperparameterValue() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the hyperparameter value.
getLoglikeliHood(double[], Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
getLoglikelihood() - Method in class weka.classifiers.bayes.blr.Prior
 
getLogPosterior() - Method in class weka.classifiers.bayes.blr.Prior
 
getMaxIterations() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the maximum number of iterations to perform
getNumFolds() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Return the number of folds for CV-based hyperparameter selection
getOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
getPenalty() - Method in class weka.classifiers.bayes.blr.Prior
 
getPriorClass() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the type of prior to use.
getRevision() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
Returns the revision string.
getSeed() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the seed for randomizing the instances for CV-based hyperparameter selection
getTechnicalInformation() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getThreshold() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Return the threshold being used.
getTolerance() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Get the tolerance value
globalInfo() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 

H

HyperparameterRange - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
CV Hyperparameter Range
hyperparameterRangeTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
Hyperparameters - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Array to store Hyperparameter values for each feature.
HyperparameterSelection - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Hyperparameter selection method
hyperparameterSelectionTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
HyperparameterValue - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Best hyperparameter for test phase
hyperparameterValueTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property

I

initialize() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
(1)Initialize m_Beta[j] to 0.
InputHyperparameterValues - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Set of values to be used as hyperparameter values during Cross-Validation.
isDebug() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns true if debug is turned on.
isNormalizeData() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns true if the data is to be normalized first
iterationCounter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Iteration counter

L

LaplacePriorImpl - Class in weka.classifiers.bayes.blr
Implementation of the Gaussian Prior update function based on modified CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based on Laplace Priors.
LaplacePriorImpl() - Constructor for class weka.classifiers.bayes.blr.LaplacePriorImpl
 
laplaceUpdate(int, double) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
This is the CLG-lasso update function described in the
LAPLACIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
listOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns an enumeration describing the available options.
logisticLinkFunction(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
This method computes the values for the logistic link function.
LogLikelihood - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Log-likelihood values to be used to choose the best hyperparameter.

M

m_Filter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
m_seed - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
seed for randomizing the instances before CV
main(String[]) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
Main method for testing this class.
maxIterations - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Maximum number of iterations
maxIterationsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property

N

NORM_BASED - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
Methods for selecting the hyperparameter value
NormalizeData - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Choose whether to normalize data or not
normalizeDataTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
normBasedHyperParameter() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.
NumFolds - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
NumFolds for CV based Hyperparameters selection
numFoldsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property

P

Prior - Class in weka.classifiers.bayes.blr
This is an interface to plug various priors into the Bayesian Logistic Regression Model.
Prior() - Constructor for class weka.classifiers.bayes.blr.Prior
 
PriorClass - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Distribution Prior class
priorClassTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property

R

R - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
R(i)= BetaVector X x(i) X y(i).

S

seedTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
setDebug(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
 
setHyperparameterRange(String) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the range of hyperparameter values to consider during CV-based selection
setHyperparameterSelection(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the method used to select the hyperparameter
setHyperparameterValue(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the hyperparameter value.
setMaxIterations(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the maximum number of iterations to perform
setNormalizeData(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set whether to normalize the data or not
setNumFolds(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the number of folds to use for CV-based hyperparameter selection
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Parses a given list of options.
setPriorClass(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the type of prior to use.
setSeed(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the seed for randomizing the instances for CV-based hyperparameter selection
setThreshold(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the threshold to use.
setTolerance(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Set the tolerance value
sgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression
Sign for a given value.
SPECIFIC_VALUE - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
stoppingCriterion() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
This method implements the stopping criterion function.

T

TAGS_HYPER_METHOD - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
TAGS_PRIOR - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
 
Threshold - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Threshold for binary classification of probabilisitic estimate
thresholdTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
Tolerance - Variable in class weka.classifiers.bayes.BayesianLogisticRegression
Tolerance criteria for the stopping criterion.
toleranceTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Returns the tip text for this property
toString() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
Outputs the linear regression model as a string.

U

update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl
Update function specific to Laplace Prior.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl
Update function specific to Laplace Prior.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.Prior
Interface for the update functions for different types of priors.

W

weka.classifiers.bayes - package weka.classifiers.bayes
 
weka.classifiers.bayes.blr - package weka.classifiers.bayes.blr
 

B C D G H I L M N P R S T U W