Uses of Class
marf.Classification.ClassificationException

Packages that use ClassificationException

marf.Classification
marf.Classification.Distance
marf.Classification.Markov
marf.Classification.NeuralNetwork
marf.Classification.RandomClassification
marf.Classification.Similarity
marf.Classification.Stochastic
marf.util

Uses of ClassificationException in marf.Classification

Methods in marf.Classification that throw ClassificationException

boolean	Classification.classify() Generic classification routine that assumes a presence of a valid non-null feature extraction module for pipeline operation.
boolean	IClassification.classify() Generic classification routine.
boolean	IClassification.classify(double[] padFeatureVector) Generic classification routine.
static IClassification	ClassificationFactory.create(java.lang.Integer poClassificationMethod, IFeatureExtraction poFeatureExtraction) Instantiates a Classification module indicated by the first parameter with the 2nd parameter as an argument.
static IClassification	ClassificationFactory.create(int piClassificationMethod, IFeatureExtraction poFeatureExtraction) Instantiates a Classification module indicated by the first parameter with the 2nd parameter as an argument.
boolean	Classification.train() Generic training routine for building/updating mean vectors in the training set.
boolean	IClassification.train() Generic training routine for building/updating mean vectors in the training set.
boolean	Classification.train(double[] padFeatureVector) Generic training routine for building/updating mean vectors in the training set.
boolean	IClassification.train(double[] padFeatureVector) Generic training routine for building/updating mean vectors in the training set.

Uses of ClassificationException in marf.Classification.Distance

Methods in marf.Classification.Distance that throw ClassificationException

boolean

Distance.classify(double[] padFeatureVector) Classify the feature vector based on whatever distance() derivatives implement.

Uses of ClassificationException in marf.Classification.Markov

Methods in marf.Classification.Markov that throw ClassificationException

boolean	Markov.classify() Not Implemented.
boolean	Markov.train() Not Implemented.

Uses of ClassificationException in marf.Classification.NeuralNetwork

Methods in marf.Classification.NeuralNetwork that throw ClassificationException

boolean	NeuralNetwork.classify(double[] padFeatureVector) Neural Network implementation of classification routine.
void	NeuralNetwork.generate() Generates the initial network at random with the default parameters.
void	NeuralNetwork.generate(int piNumOfInputs, int[] paiHiddenLayers, int piNumOfOutputs) Generates a virgin net at random.
void	NeuralNetwork.setInputs(double[] padInputs) Sets inputs.
boolean	NeuralNetwork.train() Implements training of Neural Net.
boolean	NeuralNetwork.train(double[] padFeatureVector) Implements training of Neural Net given the feature vector.
void	NeuralNetwork.train(double[] padInput, int piExpectedLength, double pdTrainConst) Performs Actual training of the net.

Uses of ClassificationException in marf.Classification.RandomClassification

Methods in marf.Classification.RandomClassification that throw ClassificationException

boolean	RandomClassification.classify(double[] padFeatureVector) Picks an ID at random.
boolean	RandomClassification.train(double[] padFeatureVector) Simply stores incoming ID's to later pick one at random.

Uses of ClassificationException in marf.Classification.Similarity

Methods in marf.Classification.Similarity that throw ClassificationException

boolean	CosineSimilarityMeasure.classify(double[] padFeatureVector) Classify the feature vector based on whatever similarity() derivatives implement.
double	CosineSimilarityMeasure.similarity(double[] padVector1, double[] padVector2) Generic distance routine.

Uses of ClassificationException in marf.Classification.Stochastic

Methods in marf.Classification.Stochastic that throw ClassificationException

boolean	MaxProbabilityClassifier.classify() Performs language classification.
boolean	Stochastic.classify(double[] padFeatureVector) Not Implemented.
boolean	ZipfLaw.classify(double[] padFeatureVector)
void	ZipfLaw.collectStatistics(double[] padFeatures) Collects result statistics.
void	ZipfLaw.collectStatistics(java.io.StreamTokenizer poStreamTokenizer) Collects result statistics.
boolean	MaxProbabilityClassifier.train() Performs training of underlying statistical estimator and goes through restore/dump cycle to save the available languages.
boolean	Stochastic.train(double[] padFeatureVector) Not Implemented.
boolean	ZipfLaw.train(double[] padFeatureVector)

Uses of ClassificationException in marf.util

Methods in marf.util that return ClassificationException

static ClassificationException	ExceptionFactory.createClassificationException()
static ClassificationException	ExceptionFactory.createClassificationException(java.lang.Exception poException)
static ClassificationException	ExceptionFactory.createClassificationException(java.lang.String pstrMessage)
static ClassificationException	ExceptionFactory.createClassificationException(java.lang.String pstrMessage, java.lang.Exception poException)