Uses of Class
org.neuroph.core.data.DataSet

Packages that use DataSet

Package	Description
org.neuroph.core	Provides base classes and basic building components for neural networks.
org.neuroph.core.data	Provides data set related classes and manipulation methods.
org.neuroph.core.learning	Provides base classes for neural network learning algorithms.
org.neuroph.eval
org.neuroph.eval.classification
org.neuroph.nnet.learning	Provides implementations of specific neural network learning algorithms.
org.neuroph.nnet.learning.kmeans
org.neuroph.util	Provides various utility classes for creating neural networks, type codes, parsing vectors, etc.
org.neuroph.util.data.norm	Provides data normalization techniques.
org.neuroph.util.data.sample	Provides data sampling techniques

Uses of DataSet in org.neuroph.core

Methods in org.neuroph.core with parameters of type DataSet

Modifier and Type	Method and Description
void	NeuralNetwork.learn(DataSet trainingSet) Learn the specified training set
void	NeuralNetwork.learn(DataSet trainingSet, L learningRule) Learn the specified training set, using specified learning rule

Uses of DataSet in org.neuroph.core.data

Subclasses of DataSet in org.neuroph.core.data

Modifier and Type	Class and Description
class	BufferedDataSet This class can be used for large training sets, which are partially read from file during the training.

Methods in org.neuroph.core.data that return DataSet

Modifier and Type	Method and Description
DataSet	DataSet.add(DataSetRow row) Adds new row row to this data set
static DataSet	DataSet.createFromFile(String filePath, int inputsCount, int outputsCount, String delimiter) Creates and returns data set from specified csv file
static DataSet	DataSet.createFromFile(String filePath, int inputsCount, int outputsCount, String delimiter, boolean loadColumnNames) Creates and returns data set from specified csv file
DataSet[]	DataSet.createTrainingAndTestSubsets(double trainSetPercent, double testSetPercent) Returns training and test subsets in the specified percent ratio
static DataSet	DataSet.load(String filePath) Loads training set from the specified file TODO: throw checked exceptionse here
static DataSet	DataSets.readFromCsv(String filePath, int inputsCount, int outputsCount)
static DataSet	DataSets.readFromCsv(String filePath, int inputsCount, int outputsCount, String delimiter)
DataSet[]	DataSet.sample(Sampling sampling)
DataSet[]	DataSet.split(double... parts) Splits data sets into parts of specified sizes.
DataSet[]	DataSet.split(int numParts) Splits data set into specified number of parts and returns them as a list.
static DataSet[]	DataSets.trainTestSplit(DataSet dataSet, double split)

Methods in org.neuroph.core.data with parameters of type DataSet

Modifier and Type	Method and Description
static MaxNormalizer	DataSets.normalizeMax(DataSet dataSet)
static DataSet[]	DataSets.trainTestSplit(DataSet dataSet, double split)

Uses of DataSet in org.neuroph.core.learning

Fields in org.neuroph.core.learning declared as DataSet

Modifier and Type	Field and Description
protected DataSet	LearningRule.trainingSet Training data set

Methods in org.neuroph.core.learning that return DataSet

Modifier and Type	Method and Description
DataSet	LearningRule.getTrainingSet() Gets training set

Methods in org.neuroph.core.learning with parameters of type DataSet

Modifier and Type	Method and Description
void	UnsupervisedLearning.doLearningEpoch(DataSet trainingSet) This method does one learning epoch for the unsupervised learning rules.
void	SupervisedLearning.doLearningEpoch(DataSet trainingSet) This method implements basic logic for one learning epoch for the supervised learning algorithms.
abstract void	IterativeLearning.doLearningEpoch(DataSet trainingSet) Override this method to implement specific learning epoch - one learning iteration, one pass through whole training set
void	IterativeLearning.doOneLearningIteration(DataSet trainingSet) Runs one learning iteration with the specified training set and fires event to notify observers.
abstract void	LearningRule.learn(DataSet trainingSet) Override this method to implement specific learning procedures
void	IterativeLearning.learn(DataSet trainingSet)
void	SupervisedLearning.learn(DataSet trainingSet, double maxError) Trains network for the specified training set and maxError
void	SupervisedLearning.learn(DataSet trainingSet, double maxError, int maxIterations) Trains network for the specified training set, maxError and number of iterations
void	IterativeLearning.learn(DataSet trainingSet, int maxIterations) Trains network for the specified training set and number of iterations
void	LearningRule.setTrainingSet(DataSet trainingSet) Sets training set for this learning rule

Uses of DataSet in org.neuroph.eval

Methods in org.neuroph.eval that return DataSet

Modifier and Type	Method and Description
DataSet	EvaluationResult.getDataSet()
DataSet	FoldResult.getTrainingSet()
DataSet	FoldResult.getValidationSet()

Methods in org.neuroph.eval with parameters of type DataSet

Modifier and Type	Method and Description
EvaluationResult	Evaluation.evaluate(NeuralNetwork neuralNetwork, DataSet testSet) Runs evaluation procedure for given neural network and data set through all evaluatoors Evaluation results are stored in evaluators
void	KFoldCrossValidation.printResults(DataSet dataset, ClassificationMetrics.Stats nst, int numfolds)
static void	Evaluation.runFullEvaluation(NeuralNetwork<?> neuralNet, DataSet dataSet) Out of the box method (util) which computes all metrics for given neural network and test data set
void	EvaluationResult.setDataSet(DataSet dataSet)
ConfusionMatrix	KFoldCrossValidation.sumConfusionMatrix(List<ConfusionMatrix> cmList, DataSet dataSet)

Constructors in org.neuroph.eval with parameters of type DataSet

Constructor and Description
CrossValidationBak(NeuralNetwork neuralNetwork, DataSet dataSet, int foldCount) Creates a new instance of crrossvalidation for specified neural network, data set and number of folds.
FoldResult(NeuralNetwork neuralNet, DataSet trainingSet, DataSet validationSet)
KFoldCrossValidation(NeuralNetwork neuralNetwork, DataSet dataSet, int numFolds)

Uses of DataSet in org.neuroph.eval.classification

Methods in org.neuroph.eval.classification with parameters of type DataSet

Modifier and Type	Method and Description
boolean	McNemarTest.evaluateNetworks(NeuralNetwork network1, NeuralNetwork network2, DataSet dataSet)

Uses of DataSet in org.neuroph.nnet.learning

Methods in org.neuroph.nnet.learning with parameters of type DataSet

Modifier and Type	Method and Description
void	UnsupervisedHebbianLearning.doLearningEpoch(DataSet trainingSet) This method does one learning epoch for the unsupervised learning rules.
void	SimulatedAnnealingLearning.doLearningEpoch(DataSet trainingSet) Perform one simulated annealing epoch.
void	DynamicBackPropagation.doLearningEpoch(DataSet trainingSet)
void	CompetitiveLearning.doLearningEpoch(DataSet trainingSet) This method does one learning epoch for the unsupervised learning rules.
void	SimulatedAnnealingLearning.doLearningEpoch(DataSet trainingSet, double randomChance)
void	KohonenLearning.learn(DataSet trainingSet)
void	HopfieldLearning.learn(DataSet trainingSet) Calculates weights for the hopfield net to learn the specified training set

Uses of DataSet in org.neuroph.nnet.learning.kmeans

Methods in org.neuroph.nnet.learning.kmeans that return DataSet

Modifier and Type	Method and Description
DataSet	KMeansClustering.getDataSet()

Methods in org.neuroph.nnet.learning.kmeans with parameters of type DataSet

Modifier and Type	Method and Description
void	KMeansClustering.setDataSet(DataSet vectors)

Constructors in org.neuroph.nnet.learning.kmeans with parameters of type DataSet

Constructor and Description
KMeansClustering(DataSet dataSet)
KMeansClustering(DataSet dataSet, int numberOfClusters)

Uses of DataSet in org.neuroph.util

Methods in org.neuroph.util that return DataSet

Modifier and Type	Method and Description
DataSet	DataSetStatistics.getDataSet() Get original data set.
static DataSet	TrainingSetImport.importFromFile(String filePath, int inputsCount, int outputsCount, String separator)

Methods in org.neuroph.util with parameters of type DataSet

Modifier and Type	Method and Description
static double[]	DataSetStats.inputsMax(DataSet dataSet)
static double[]	DataSetStats.inputsMean(DataSet dataSet)
static double[]	DataSetStats.inputsMin(DataSet dataSet)
static double[]	DataSetStats.inputsStandardDeviation(DataSet dataSet, double[] mean)

Constructors in org.neuroph.util with parameters of type DataSet

Constructor and Description
DataSetStatistics(DataSet dataSet)

Uses of DataSet in org.neuroph.util.data.norm

Methods in org.neuroph.util.data.norm with parameters of type DataSet

Modifier and Type	Method and Description
void	ZeroMeanNormalizer.normalize(DataSet dataSet)
void	RangeNormalizer.normalize(DataSet dataSet)
void	Normalizer.normalize(DataSet dataSet) Normalize specified data set
void	MaxNormalizer.normalize(DataSet dataSet)
void	MaxMinNormalizer.normalize(DataSet dataSet)
void	DecimalScaleNormalizer.normalize(DataSet dataSet)

Constructors in org.neuroph.util.data.norm with parameters of type DataSet

Constructor and Description
DecimalScaleNormalizer(DataSet dataSet) Initializes normalizer, finds right scale factor for each input and output column in vectors.
MaxMinNormalizer(DataSet dataSet)
MaxNormalizer(DataSet dataSet) Creates new instance of MaxNormalizer initialized with max values from specified data set.
ZeroMeanNormalizer(DataSet dataSet)

Uses of DataSet in org.neuroph.util.data.sample

Methods in org.neuroph.util.data.sample that return DataSet

Modifier and Type	Method and Description
DataSet[]	SubSampling.sample(DataSet dataSet)
DataSet[]	Sampling.sample(DataSet dataSet)

Methods in org.neuroph.util.data.sample with parameters of type DataSet

Modifier and Type	Method and Description
DataSet[]	SubSampling.sample(DataSet dataSet)
DataSet[]	Sampling.sample(DataSet dataSet)