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A

accuracy - Variable in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
Adaline - Class in org.neuroph.nnet
Adaline neural network architecture with LMS learning rule.
Adaline(int) - Constructor for class org.neuroph.nnet.Adaline
Creates new Adaline network with specified number of neurons in input layer
add(DataSetRow) - Method in class org.neuroph.core.data.DataSet
Adds new row row to this data set
add(double[], double[]) - Method in class org.neuroph.core.data.DataSet
Adds a new dataset row with specified input and output
add(int, DataSetRow) - Method in class org.neuroph.core.data.DataSet
 
addElapsedTime(long) - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
addEvaluator(Evaluator) - Method in class org.neuroph.eval.CrossValidationBak
 
addEvaluator(Evaluator) - Method in class org.neuroph.eval.Evaluation
 
addFeatureMap(FeatureMapLayer) - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Adds a feature map (2d layer) to this feature map layer
addInputConnection(Connection) - Method in class org.neuroph.core.Neuron
Adds the specified input connection
addInputConnection(Neuron) - Method in class org.neuroph.core.Neuron
Adds input connection from specified neuron.
addInputConnection(Neuron, double) - Method in class org.neuroph.core.Neuron
Adds input connection with the given weight, from given neuron
addInputConnection(Connection) - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
 
addInputConnection(Neuron, double) - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
 
addInputConnection(Neuron) - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
 
addInputConnection(Connection) - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
Adds input connection for this competitive neuron
addLayer(Layer) - Method in class org.neuroph.core.NeuralNetwork
Adds layer to neural network
addLayer(int, Layer) - Method in class org.neuroph.core.NeuralNetwork
Adds layer to specified index position in network
addListener(LearningEventListener) - Method in class org.neuroph.core.learning.LearningRule
 
addListener(NeuralNetworkEventListener) - Method in class org.neuroph.core.NeuralNetwork
 
addNeuron(Neuron) - Method in class org.neuroph.core.Layer
Adds specified neuron to this layer
addNeuron(int, Neuron) - Method in class org.neuroph.core.Layer
Adds specified neuron to this layer,at specified index position Throws IllegalArgumentException if neuron is null, or index is illegal value (index<0 or index>neuronsCount)
addOutputConnection(Connection) - Method in class org.neuroph.core.Neuron
Adds the specified output connection
addPatternError(double[], double[]) - Method in interface org.neuroph.core.learning.error.ErrorFunction
Calculates and adds pattern error for the given predicted and target output vector and adds it to total error.
addPatternError(double[], double[]) - Method in class org.neuroph.core.learning.error.MeanAbsoluteError
 
addPatternError(double[], double[]) - Method in class org.neuroph.core.learning.error.MeanSquaredError
 
addPlugin(PluginBase) - Method in class org.neuroph.core.NeuralNetwork
Adds plugin to neural network
addRow(double[]) - Method in class org.neuroph.core.data.DataSet
Adds a new dataset row with specified input
addTask(BenchmarkTask) - Method in class org.neuroph.util.benchmark.Benchmark
Adds specified benchmark task
adjustLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
adjustMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
afterEpoch() - Method in class org.neuroph.core.learning.IterativeLearning
 
afterEpoch() - Method in class org.neuroph.core.learning.SupervisedLearning
 
And - Class in org.neuroph.core.input
Performs logic AND operation on input vector.
And() - Constructor for class org.neuroph.core.input.And
 
AntiHebbianLearning - Class in org.neuroph.nnet.learning
A variant of Hebbian learning called Anti-Hebbian learning.
AntiHebbianLearning() - Constructor for class org.neuroph.nnet.learning.AntiHebbianLearning
 
array2network(double[], NeuralNetwork) - Static method in class org.neuroph.util.NeuralNetworkCODEC
Decode a network from an array.
assignVector(KVector) - Method in class org.neuroph.nnet.learning.kmeans.Cluster
Assignes vector to this cluster.
AutoencoderNetwork - Class in org.neuroph.nnet
Auto Encoder Neural Network
AutoencoderNetwork(int, int) - Constructor for class org.neuroph.nnet.AutoencoderNetwork
 
average(ClassificationMetrics[]) - Static method in class org.neuroph.eval.classification.ClassificationMetrics
 
average(double[]) - Static method in class org.neuroph.eval.classification.Utils
 
averageClassificationMetrics(List<ClassificationMetrics.Stats>) - Static method in class org.neuroph.eval.KFoldCrossValidation
 

B

BackPropagation - Class in org.neuroph.nnet.learning
Back Propagation learning rule for Multi Layer Perceptron neural networks.
BackPropagation() - Constructor for class org.neuroph.nnet.learning.BackPropagation
Creates new instance of BackPropagation learning
BAM - Class in org.neuroph.nnet
Bidirectional Associative Memory
BAM(int, int) - Constructor for class org.neuroph.nnet.BAM
Creates an instance of BAM network with specified number of neurons in input and output layers.
beforeEpoch() - Method in class org.neuroph.core.learning.IterativeLearning
 
beforeEpoch() - Method in class org.neuroph.core.learning.SupervisedLearning
 
Benchmark - Class in org.neuroph.util.benchmark
This class is main benchmark driver.
Benchmark() - Constructor for class org.neuroph.util.benchmark.Benchmark
Creates a new Benchmark instance
BenchmarkSample - Class in org.neuroph.util.benchmark
This is an example how to use Neuroph microbenchmarking framework
BenchmarkSample() - Constructor for class org.neuroph.util.benchmark.BenchmarkSample
 
BenchmarkTask - Class in org.neuroph.util.benchmark
This class is an abstract base class for specific microbenchmarking tasks
BenchmarkTask(String) - Constructor for class org.neuroph.util.benchmark.BenchmarkTask
Creates a new instance of BenchmarkTask with specified name
BenchmarkTaskResults - Class in org.neuroph.util.benchmark
This class holds benchmarking results, elapsed times for all iterations and various statistics min, max, avg times and standard deviation
BenchmarkTaskResults(int) - Constructor for class org.neuroph.util.benchmark.BenchmarkTaskResults
 
BiasNeuron - Class in org.neuroph.nnet.comp.neuron
Neuron with constant high output (1), used as bias
BiasNeuron() - Constructor for class org.neuroph.nnet.comp.neuron.BiasNeuron
Creates an instance of BiasedNeuron.
Binary(double) - Constructor for class org.neuroph.eval.ClassifierEvaluator.Binary
 
BinaryDeltaRule - Class in org.neuroph.nnet.learning
Delta rule learning algorithm for perceptrons with step functions.
BinaryDeltaRule() - Constructor for class org.neuroph.nnet.learning.BinaryDeltaRule
Creates new BinaryDeltaRule learning
BinaryHebbianLearning - Class in org.neuroph.nnet.learning
Hebbian-like learning algorithm used for Hopfield network.
BinaryHebbianLearning() - Constructor for class org.neuroph.nnet.learning.BinaryHebbianLearning
Creates new instance of BinaryHebbianLearning
BufferedDataSet - Class in org.neuroph.core.data
This class can be used for large training sets, which are partially read from file during the training.
BufferedDataSet(File, int, String) - Constructor for class org.neuroph.core.data.BufferedDataSet
 
BufferedDataSet(File, int, int, String) - Constructor for class org.neuroph.core.data.BufferedDataSet
Creates new buffered data set with specified file, input and output size.
bufferedReader - Variable in class org.neuroph.util.io.InputStreamAdapter
 
bufferedWriter - Variable in class org.neuroph.util.io.OutputStreamAdapter
 
build() - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
Builder() - Constructor for class org.neuroph.nnet.ConvolutionalNetwork.Builder
 

C

calculate() - Method in class org.neuroph.core.Layer
Performs calculaton for all neurons in this layer
calculate() - Method in class org.neuroph.core.NeuralNetwork
Performs calculation on whole network
calculate() - Method in class org.neuroph.core.Neuron
Calculates neuron's output
calculate() - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
Performs calculaton for all neurons in this layer
calculate() - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
 
calculate() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
 
calculate() - Method in class org.neuroph.nnet.comp.neuron.DelayedNeuron
 
calculate() - Method in class org.neuroph.nnet.comp.neuron.InputNeuron
Calculate method of this type of neuron does nothing
calculate() - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
Calculates neuron output
calculate() - Method in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
Calculates neuron's output
calculateErrorAndUpdateHiddenNeurons() - Method in class org.neuroph.nnet.learning.BackPropagation
This method implements weights adjustment for the hidden layers
calculateErrorAndUpdateHiddenNeurons() - Method in class org.neuroph.nnet.learning.ConvolutionalBackpropagation
 
calculateErrorAndUpdateHiddenNeurons() - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
This method implements weights adjustment for the hidden layers Uses parallel processing on each layer with 100 or more neurons and a regular loop if less.
calculateErrorAndUpdateOutputNeurons(double[]) - Method in class org.neuroph.nnet.learning.BackPropagation
This method implements weights update procedure for the output neurons Calculates delta/error and calls updateNeuronWeights to update neuron's weights for each output neuron
calculateHiddenNeuronError(Neuron) - Method in class org.neuroph.nnet.learning.BackPropagation
Calculates and returns the neuron's error (neuron's delta) for the given neuron param
calculateHiddenNeuronError(Neuron) - Method in class org.neuroph.nnet.learning.ConvolutionalBackpropagation
 
calculateStatistics() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
calculateStatistics() - Method in class org.neuroph.util.DataSetStatistics
Calculates basic statistics by columns of the dataset.
calculateWeightChanges(double[]) - Method in class org.neuroph.core.learning.SupervisedLearning
This method should implement the weights update procedure for the whole network for the given output error vector.
calculateWeightChanges(double[]) - Method in class org.neuroph.nnet.learning.BackPropagation
This method implements weight update procedure for the whole network for the specified output error vector.
calculateWeightChanges(double[]) - Method in class org.neuroph.nnet.learning.BinaryDeltaRule
This method implements weight update procedure for the whole network for this learning rule
calculateWeightChanges(double[]) - Method in class org.neuroph.nnet.learning.LMS
This method calculates weight change for the network's output neurons for the given output error vector.
calculateWeightChanges(Neuron) - Method in class org.neuroph.nnet.learning.LMS
This method calculates weights changes for the single neuron.
calculateWeightChanges(Neuron) - Method in class org.neuroph.nnet.learning.ManhattanPropagation
 
calculateWeightChanges(Neuron) - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
This method implements weights update procedure for the single neuron for the back propagation with momentum factor
calculateWeightChanges(Neuron) - Method in class org.neuroph.nnet.learning.PerceptronLearning
This method implements weights update procedure for the single neuron In addition to weights change in LMS it applies change to neuron's threshold
calculateWeightChanges(Neuron) - Method in class org.neuroph.nnet.learning.QuickPropagation
 
calculateWeightChanges(Neuron) - Method in class org.neuroph.nnet.learning.ResilientPropagation
Calculate and sum gradients for each neuron's weight, the actual weight update is done in batch mode.
calculateWeightChanges(double[]) - Method in class org.neuroph.nnet.learning.SigmoidDeltaRule
This method implements weight update procedure for the whole network for this learning rule
calculateWeightChanges(double[]) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
Not used.
calculateWeightChanges(double[]) - Method in class org.neuroph.nnet.learning.SupervisedHebbianLearning
This method implements weight update procedure for the whole network for this learning rule
CLASS_LABELS - Static variable in class org.neuroph.eval.ClassifierEvaluator.Binary
 
ClassificationMetrics - Class in org.neuroph.eval.classification
Container class for all metrics which use confusion matrix for their computation Based on: http://java-ml.sourceforge.net/api/0.1.7/net/sf/javaml/classification/evaluation/PerformanceMeasure.html http://sourceforge.net/p/java-ml/java-ml-code/ci/a25ddde7c3677da44e47a643f88e32e2c8bbc32f/tree/net/sf/javaml/classification/evaluation/PerformanceMeasure.java http://en.wikipedia.org/wiki/Matthews_correlation_coefficient
ClassificationMetrics(int, int, int, int) - Constructor for class org.neuroph.eval.classification.ClassificationMetrics
Constructs a new measure using arguments TODO: add class to which measure corresponds?
ClassificationMetrics.Stats - Class in org.neuroph.eval.classification
 
ClassificationResult - Class in org.neuroph.eval.classification
Ovu klasu definitivno izabciti KOristi se samo getMaxOutput koja uz to i potpuno nebulozna jer vraca ClassificationResult vidi samo zasta nam treba u McNemar a pri tom uvek se poziva getActual Wrapper class used for ordering classification results
ClassificationResult(int, double) - Constructor for class org.neuroph.eval.classification.ClassificationResult
 
Classifier - Class in org.neuroph.eval.classification
Classifier plugin for neurla networks
Classifier() - Constructor for class org.neuroph.eval.classification.Classifier
 
ClassifierEvaluator - Class in org.neuroph.eval
 
ClassifierEvaluator.Binary - Class in org.neuroph.eval
Binary evaluator used for computation of metrics in case when data has only one output result (one output neuron)
ClassifierEvaluator.MultiClass - Class in org.neuroph.eval
Evaluator used for computation of metrics in case when data has multiple classes - one vs many classification
classify(double[]) - Method in class org.neuroph.eval.classification.Classifier
 
clear() - Method in class org.neuroph.core.data.DataSet
Removes all alements from training set
clone() - Method in class org.neuroph.core.Connection
 
clone() - Method in class org.neuroph.core.Neuron
 
clone() - Method in class org.neuroph.core.Weight
Returns cloned instance of this weight Important: trainingData will be lost in cloned instance
close() - Method in interface org.neuroph.util.io.InputAdapter
Close data source after reading is finnished.
close() - Method in class org.neuroph.util.io.InputStreamAdapter
 
close() - Method in class org.neuroph.util.io.JDBCInputAdapter
Closes result set used as data source.
close() - Method in class org.neuroph.util.io.JDBCOutputAdapter
 
close() - Method in interface org.neuroph.util.io.OutputAdapter
Close destination after writing is finnished.
close() - Method in class org.neuroph.util.io.OutputStreamAdapter
Closes output stream.
Cluster - Class in org.neuroph.nnet.learning.kmeans
This class represents a single cluster, with corresponding centroid and assigned vectors
Cluster() - Constructor for class org.neuroph.nnet.learning.kmeans.Cluster
 
CompetitiveLayer - Class in org.neuroph.nnet.comp.layer
Represents layer of competitive neurons, and provides methods for competition.
CompetitiveLayer(int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.CompetitiveLayer
Create an instance of CompetitiveLayer with the specified number of neurons with neuron properties
CompetitiveLearning - Class in org.neuroph.nnet.learning
Competitive learning rule.
CompetitiveLearning() - Constructor for class org.neuroph.nnet.learning.CompetitiveLearning
Creates new instance of CompetitiveLearning
CompetitiveNetwork - Class in org.neuroph.nnet
Two layer neural network with competitive learning rule.
CompetitiveNetwork(int, int) - Constructor for class org.neuroph.nnet.CompetitiveNetwork
Creates new competitive network with specified neuron number
CompetitiveNeuron - Class in org.neuroph.nnet.comp.neuron
Provides neuron behaviour specific for competitive neurons which are used in competitive layers, and networks with competitive learning.
CompetitiveNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
Creates an instance of CompetitiveNeuron with specified input and transfer functions
ConfusionMatrix - Class in org.neuroph.eval.classification
Confusion matrix container, holds class labels and matrix values .
ConfusionMatrix(String[]) - Constructor for class org.neuroph.eval.classification.ConfusionMatrix
Creates new confusion matrix with specified class labels and number of classes
connectFeatureMaps(FeatureMapsLayer, FeatureMapsLayer, int, int) - Static method in class org.neuroph.nnet.comp.ConvolutionalUtils
Creates connections between two feature maps - not used???
connectInputsToOutputs() - Method in class org.neuroph.nnet.MultiLayerPerceptron
 
Connection - Class in org.neuroph.core
Weighted connection to another neuron.
Connection(Neuron, Neuron) - Constructor for class org.neuroph.core.Connection
Creates a new connection between specified neurons with random weight
Connection(Neuron, Neuron, Weight) - Constructor for class org.neuroph.core.Connection
Creates a new connection to specified neuron with specified weight object
Connection(Neuron, Neuron, double) - Constructor for class org.neuroph.core.Connection
Creates a new connection to specified neuron with specified weight value
ConnectionFactory - Class in org.neuroph.util
Provides methods to connect neurons by creating Connection objects.
ConnectionFactory() - Constructor for class org.neuroph.util.ConnectionFactory
 
connectMaps(FeatureMapLayer, FeatureMapLayer) - Method in class org.neuroph.nnet.comp.layer.ConvolutionalLayer
Creates connections with shared weights between two feature maps Assumes that toMap is from Convolutional layer.
connectMaps(FeatureMapLayer, FeatureMapLayer) - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Creates connections between two feature maps.
connectMaps(FeatureMapLayer, FeatureMapLayer) - Method in class org.neuroph.nnet.comp.layer.InputMapsLayer
 
connectMaps(FeatureMapLayer, FeatureMapLayer) - Method in class org.neuroph.nnet.comp.layer.PoolingLayer
Creates connections with shared weights between two feature maps Assumes that toMap is from Pooling layer.
convertToVector(double[]) - Static method in class org.neuroph.util.VectorParser
 
ConvolutionalBackpropagation - Class in org.neuroph.nnet.learning
 
ConvolutionalBackpropagation() - Constructor for class org.neuroph.nnet.learning.ConvolutionalBackpropagation
 
ConvolutionalLayer - Class in org.neuroph.nnet.comp.layer
Convolutional layer is a special type of layer, used in convolutional neural networks.
ConvolutionalLayer(FeatureMapsLayer, Dimension2D, int) - Constructor for class org.neuroph.nnet.comp.layer.ConvolutionalLayer
Creates convolutional layer with specified kernel, appropriate map dimensions in regard to previous layer (fromLayer param) and specified number of feature maps with default neuron settings for convolutional layer.
ConvolutionalLayer(FeatureMapsLayer, Dimension2D, int, Class<? extends TransferFunction>) - Constructor for class org.neuroph.nnet.comp.layer.ConvolutionalLayer
Creates convolutional layer with specified kernel, appropriate map dimensions in regard to previous layer (fromLayer param) and specified number of feature maps with default neuron settings for convolutional layer.
ConvolutionalLayer(FeatureMapsLayer, Dimension2D, int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.ConvolutionalLayer
Creates convolutional layer with specified kernel, appropriate map dimensions in regard to previous layer (fromLayer param) and specified number of feature maps with given neuron properties.
ConvolutionalNetwork - Class in org.neuroph.nnet
Convolutional neural network with backpropagation algorithm modified for convolutional networks.
ConvolutionalNetwork() - Constructor for class org.neuroph.nnet.ConvolutionalNetwork
 
ConvolutionalNetwork.Builder - Class in org.neuroph.nnet
 
ConvolutionalUtils - Class in org.neuroph.nnet.comp
Utility functions for convolutional networks
ConvolutionalUtils() - Constructor for class org.neuroph.nnet.comp.ConvolutionalUtils
 
correlationCoefficient - Variable in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
createAdaline(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Adaline network
createBam(int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of BAM network
createCompetitiveNetwork(int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of competitive network
createConnection(Neuron, Neuron, double) - Method in class org.neuroph.core.NeuralNetwork
Creates connection with specified weight value between specified neurons
createConnection(Neuron, Neuron) - Static method in class org.neuroph.util.ConnectionFactory
Creates connection between two specified neurons
createConnection(Neuron, Neuron, double) - Static method in class org.neuroph.util.ConnectionFactory
Creates connection between two specified neurons
createConnection(Neuron, Neuron, double, int) - Static method in class org.neuroph.util.ConnectionFactory
 
createConnection(Neuron, Neuron, Weight) - Static method in class org.neuroph.util.ConnectionFactory
Creates connection between two specified neurons
createConnection(Neuron, Layer) - Static method in class org.neuroph.util.ConnectionFactory
Creates connectivity between specified neuron and all neurons in specified layer
createFeatureMaps(int, Dimension2D, Dimension2D, NeuronProperties) - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Creates and adds specified number of feature maps to this layer
createFromFile(String, int, int, String, boolean) - Static method in class org.neuroph.core.data.DataSet
Creates and returns data set from specified csv file
createFromFile(String, int, int, String) - Static method in class org.neuroph.core.data.DataSet
Creates and returns data set from specified csv file
createFromFile(File) - Static method in class org.neuroph.core.NeuralNetwork
Loads and return s neural network instance from specified file
createFromFile(String) - Static method in class org.neuroph.core.NeuralNetwork
 
createFromMatrix(ConfusionMatrix) - Static method in class org.neuroph.eval.classification.ClassificationMetrics
 
createHopfield(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Hopfield network
createInstar(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Instar network
createKeys(String...) - Method in class org.neuroph.util.Properties
 
createKohonen(int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Kohonen network
createLayer(int, NeuronProperties) - Static method in class org.neuroph.util.LayerFactory
Creates and returns instance of Layer with specified number of neurons with specified properties
createLayer(int, TransferFunctionType) - Static method in class org.neuroph.util.LayerFactory
 
createLayer(int, Class<? extends TransferFunction>) - Static method in class org.neuroph.util.LayerFactory
 
createLayer(List<NeuronProperties>) - Static method in class org.neuroph.util.LayerFactory
 
createMaxNet(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Max Net network
createMLPerceptron(String, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Multi Layer Perceptron
createMLPerceptron(String, TransferFunctionType, Class, boolean, boolean) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Multi Layer Perceptron
createNeuron(NeuronProperties) - Static method in class org.neuroph.util.NeuronFactory
Creates and returns neuron instance according to the given specification in neuronProperties.
createOutstar(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Outstar network
createPerceptron(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Perceptron network
createPerceptron(int, int, TransferFunctionType, Class) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Perceptron network
createRbfNetwork(int, int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of RBF network
createSupervisedHebbian(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Hebbian network
createTrainingAndTestSubsets(double, double) - Method in class org.neuroph.core.data.DataSet
Returns training and test subsets in the specified percent ratio
createUnsupervisedHebbian(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Unsupervised Hebbian Network
CrossValidationBak - Class in org.neuroph.eval
This class implements multithreaded cross validation procedure.
CrossValidationBak(NeuralNetwork, DataSet, int) - Constructor for class org.neuroph.eval.CrossValidationBak
Creates a new instance of crrossvalidation for specified neural network, data set and number of folds.
currentIteration - Variable in class org.neuroph.core.learning.IterativeLearning
Current iteration counter

D

DataSet - Class in org.neuroph.core.data
This class represents a collection of data rows (DataSetRow instances) used for training and testing neural network.
DataSet(int) - Constructor for class org.neuroph.core.data.DataSet
Creates an instance of new empty training set
DataSet(int, int) - Constructor for class org.neuroph.core.data.DataSet
Creates an instance of new empty training set
DataSetColumnType - Enum in org.neuroph.util
 
DataSetRow - Class in org.neuroph.core.data
This class represents single data row in a data set.
DataSetRow(String, String) - Constructor for class org.neuroph.core.data.DataSetRow
Creates new training element with specified input and desired output vectors specifed as strings
DataSetRow(double[], double[]) - Constructor for class org.neuroph.core.data.DataSetRow
Creates new training element with specified input and desired output vectors
DataSetRow(double...) - Constructor for class org.neuroph.core.data.DataSetRow
Creates new training element with input array
DataSetRow(ArrayList<Double>, ArrayList<Double>) - Constructor for class org.neuroph.core.data.DataSetRow
Creates new training element with specified input and desired output vectors
DataSetRow(ArrayList<Double>) - Constructor for class org.neuroph.core.data.DataSetRow
 
DataSets - Class in org.neuroph.core.data
 
DataSets() - Constructor for class org.neuroph.core.data.DataSets
 
DataSetStatistics - Class in org.neuroph.util
This class calculates statistics for data set.
DataSetStatistics(DataSet) - Constructor for class org.neuroph.util.DataSetStatistics
 
DataSetStats - Class in org.neuroph.util
Utility class with methods for calculating dataset statistics Calculate everything in one pass and expose as attributes - like summary() in R Not only for inputs but also for outputs
DataSetStats() - Constructor for class org.neuroph.util.DataSetStats
 
dec(double) - Method in class org.neuroph.core.Weight
Decreases the weight for specified amount
DecimalScaleNormalizer - Class in org.neuroph.util.data.norm
Decimal scaling normalization method, which normalize data by moving decimal point in regard to max element in training set (by columns) Normalization is done according to formula: normalizedVector[i] = vector[i] / scaleFactor[i]
DecimalScaleNormalizer(DataSet) - Constructor for class org.neuroph.util.data.norm.DecimalScaleNormalizer
Initializes normalizer, finds right scale factor for each input and output column in vectors.
DEFAULT_FULL_CONNECTED_NEURON_PROPERTIES - Static variable in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
DEFAULT_NEURON_PROP - Static variable in class org.neuroph.nnet.comp.layer.ConvolutionalLayer
Default neuron properties for convolutional layer
DEFAULT_NEURON_PROP - Static variable in class org.neuroph.nnet.comp.layer.InputMapsLayer
Default neuron properties for InputMapsLayer is InputNeuron with Linear transfer function
DEFAULT_NEURON_PROP - Static variable in class org.neuroph.nnet.comp.layer.PoolingLayer
Default neuron properties for pooling layer
DelayedConnection - Class in org.neuroph.nnet.comp
Represents the connection between neurons which can delay signal.
DelayedConnection(Neuron, Neuron, double, int) - Constructor for class org.neuroph.nnet.comp.DelayedConnection
Creates an instance of delayed connection to cpecified neuron and with specified weight
DelayedNeuron - Class in org.neuroph.nnet.comp.neuron
Provides behaviour for neurons with delayed output.
DelayedNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.DelayedNeuron
Creates an instance of neuron which can delay output
delta - Variable in class org.neuroph.core.Neuron
Local error (delta) for this neuron *
determineArraySize(NeuralNetwork) - Static method in class org.neuroph.util.NeuralNetworkCODEC
Determine the array size for the given neural network.
Difference - Class in org.neuroph.core.input
Performs the vector difference operation on input and weight vector.
Difference() - Constructor for class org.neuroph.core.input.Difference
 
Dimension2D - Class in org.neuroph.nnet.comp
Dimensions (width and height) of the Layer2D
Dimension2D(int, int) - Constructor for class org.neuroph.nnet.comp.Dimension2D
Creates new dimensions with specified width and height
distanceFrom(KVector) - Method in class org.neuroph.nnet.learning.kmeans.KVector
Calculates and returns euclidean distance of this vector from the given cluster
DistortRandomizer - Class in org.neuroph.util.random
This class provides distort randomization technique, which distorts existing weight values using specified distortion factor.
DistortRandomizer(double) - Constructor for class org.neuroph.util.random.DistortRandomizer
Create a new instance of DistortRandomizer with specified distortion factor
doBatchWeightsUpdate() - Method in class org.neuroph.core.learning.SupervisedLearning
This method updates network weights in batch mode - use accumulated weights change stored in Weight.deltaWeight It is executed after each learning epoch, only if learning is done in batch mode.
doBatchWeightsUpdate() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
doClustering() - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
doLearningEpoch(DataSet) - Method in class org.neuroph.core.learning.IterativeLearning
Override this method to implement specific learning epoch - one learning iteration, one pass through whole training set
doLearningEpoch(DataSet) - Method in class org.neuroph.core.learning.SupervisedLearning
This method implements basic logic for one learning epoch for the supervised learning algorithms.
doLearningEpoch(DataSet) - Method in class org.neuroph.core.learning.UnsupervisedLearning
This method does one learning epoch for the unsupervised learning rules.
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.CompetitiveLearning
This method does one learning epoch for the unsupervised learning rules.
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
Perform one simulated annealing epoch.
doLearningEpoch(DataSet, double) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
 
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
This method does one learning epoch for the unsupervised learning rules.
doOneLearningIteration(DataSet) - Method in class org.neuroph.core.learning.IterativeLearning
Runs one learning iteration with the specified training set and fires event to notify observers.
DynamicBackPropagation - Class in org.neuroph.nnet.learning
Backpropagation learning rule with dynamic learning rate and momentum
DynamicBackPropagation() - Constructor for class org.neuroph.nnet.learning.DynamicBackPropagation
 

E

ElmanNetwork - Class in org.neuroph.nnet
Under development: Learning rule BackProp Through Time required
ElmanNetwork(int, int, int, int) - Constructor for class org.neuroph.nnet.ElmanNetwork
 
EPOCH_ENDED - Static variable in class org.neuroph.core.events.LearningEvent
 
equals(Object) - Method in class org.neuroph.core.Connection
 
equals(Object) - Method in class org.neuroph.core.data.DataSetRow
 
equals(Object) - Method in class org.neuroph.core.Weight
 
equals(Object) - Method in class org.neuroph.eval.classification.ClassificationResult
 
equals(Object) - Method in class org.neuroph.nnet.learning.kmeans.Cluster
Returns true if two clusters have same centroid
ErrorEvaluator - Class in org.neuroph.eval
Calculates scalar evaluation result using ErrorFunction
ErrorEvaluator(ErrorFunction) - Constructor for class org.neuroph.eval.ErrorEvaluator
 
ErrorFunction - Interface in org.neuroph.core.learning.error
Interface for calculating total network error during the learning.
EuclideanRBF - Class in org.neuroph.core.input
 
EuclideanRBF() - Constructor for class org.neuroph.core.input.EuclideanRBF
 
evaluate(NeuralNetwork, DataSet) - Method in class org.neuroph.eval.Evaluation
Runs evaluation procedure for given neural network and data set through all evaluatoors Evaluation results are stored in evaluators
evaluateNetworks(NeuralNetwork, NeuralNetwork, DataSet) - Method in class org.neuroph.eval.classification.McNemarTest
 
Evaluation - Class in org.neuroph.eval
Evaluation service used to run different evaluators on trained neural network
Evaluation() - Constructor for class org.neuroph.eval.Evaluation
 
EvaluationResult - Class in org.neuroph.eval
Create class that will hold statistics for all evaluated datasets - avgs, mx, min, std, variation Result of the evaluation procedure
EvaluationResult() - Constructor for class org.neuroph.eval.EvaluationResult
 
Evaluator<T> - Interface in org.neuroph.eval
Generic interface for all types of evaluators

F

FeatureMapLayer - Class in org.neuroph.nnet.comp.layer
FeatureMapLayer Layer provides 2D layout of the neurons in layer.
FeatureMapLayer(Dimension2D, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapLayer
Creates an empty 2D layer with specified dimensions
FeatureMapLayer(Dimension2D, Dimension2D) - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapLayer
Creates an empty 2D layer with specified dimensions and kernel
FeatureMapLayer(Dimension2D, NeuronProperties, Dimension2D) - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapLayer
Creates 2D layer with specified dimensions, filled with neurons with specified properties
FeatureMapsLayer - Class in org.neuroph.nnet.comp.layer
This class represents an array of feature maps which are 2 dimensional layers (Layer2D instances) and it is base class for Convolution and Pooling layers, which are used in ConvolutionalNetwork
FeatureMapsLayer() - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Creates a new empty feature maps layer with specified kernel
FeatureMapsLayer(Dimension2D) - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Creates a new empty feature maps layer with specified kernel and feature map dimensions.
FeatureMapsLayer(Dimension2D, Dimension2D, int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Creates new feature maps layer with specified kernel and feature maps.
FeatureMapsLayer(Dimension2D, int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.FeatureMapsLayer
 
FileInputAdapter - Class in org.neuroph.util.io
Implementation of InputAdapter interface for reading neural network inputs from files.
FileInputAdapter(File) - Constructor for class org.neuroph.util.io.FileInputAdapter
Creates a new FileInputAdapter by opening a connection to an actual file, specified by the file param
FileInputAdapter(String) - Constructor for class org.neuroph.util.io.FileInputAdapter
Creates a new FileInputAdapter by opening a connection to an actual file, specified by the fileName param
FileOutputAdapter - Class in org.neuroph.util.io
Implementation of OutputAdapter interface for writing neural network outputs to files.
FileOutputAdapter(File) - Constructor for class org.neuroph.util.io.FileOutputAdapter
Creates a new FileOutputAdapter by opening a connection to an actual file, specified by the file param
FileOutputAdapter(String) - Constructor for class org.neuroph.util.io.FileOutputAdapter
Creates a new FileOutputAdapter by opening a connection to an actual file, specified by the fileName param
fireLearningEvent(LearningEvent) - Method in class org.neuroph.core.learning.LearningRule
 
fireNetworkEvent(NeuralNetworkEvent) - Method in class org.neuroph.core.NeuralNetwork
 
FoldResult - Class in org.neuroph.eval
Result from single cross-validation fold, includes neural network, training and validation set, and fold evaluation results (at the moment only confsionMatrix) TODO: add different eveluation metrics, for regression too.
FoldResult(NeuralNetwork, DataSet, DataSet) - Constructor for class org.neuroph.eval.FoldResult
 
forwardConnect(Layer, Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
Creates forward connectivity pattern between the specified layers
forwardConnect(Layer, Layer) - Static method in class org.neuroph.util.ConnectionFactory
Creates forward connection pattern between specified layers
FREQ - Static variable in class org.neuroph.util.DataSetStatistics
 
fromNeuron - Variable in class org.neuroph.core.Connection
From neuron for this connection (source neuron).
fScore - Variable in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
fullConnect(Layer, Layer) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity between the two specified layers
fullConnect(Layer, Layer, boolean) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity between the two specified layers
fullConnect(Layer, Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity between two specified layers with specified weight for all connections
fullConnect(Layer) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity within layer - each neuron with all other within the same layer
fullConnect(Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity within layer - each neuron with all other within the same layer with the specified weight values for all conections.
fullConnect(Layer, double, int) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity within layer - each neuron with all other within the same layer with the specified weight and delay values for all conections.
fullConnectMapLayers(FeatureMapsLayer, FeatureMapsLayer) - Static method in class org.neuroph.nnet.comp.ConvolutionalUtils
Creates full connectivity between feature maps in two layers

G

Gaussian - Class in org.neuroph.core.transfer
Gaussian neuron transfer function.
Gaussian() - Constructor for class org.neuroph.core.transfer.Gaussian
Creates an instance of Gaussian neuron transfer
Gaussian(Properties) - Constructor for class org.neuroph.core.transfer.Gaussian
Creates an instance of Gaussian neuron transfer function with the specified properties.
GaussianRandomizer - Class in org.neuroph.util.random
This class provides Gaussian randomization technique using Box Muller method.
GaussianRandomizer(double, double) - Constructor for class org.neuroph.util.random.GaussianRandomizer
 
GeneralizedHebbianLearning - Class in org.neuroph.nnet.learning
A variant of Hebbian learning called Generalized Hebbian learning.
GeneralizedHebbianLearning() - Constructor for class org.neuroph.nnet.learning.GeneralizedHebbianLearning
 
get(int) - Method in class org.neuroph.core.data.DataSet
 
get(int, int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getAccuracy() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Calculate and return classification accuracy measure.
getAmplitude() - Method in class org.neuroph.core.transfer.Tanh
Returns the amplitude parameter of this function
getArea() - Method in class org.neuroph.nnet.comp.Kernel
Returns area of this kernel (width*height)
getAverageTestTime() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
getAvgSum() - Method in class org.neuroph.nnet.learning.kmeans.Cluster
Calculate and return avg sum vector for all vectors
getBalancedClassificationRate() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getBias() - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
Returns bias value for this neuron
getCentroid() - Method in class org.neuroph.nnet.learning.kmeans.Cluster
 
getClassIdx() - Method in class org.neuroph.eval.classification.ClassificationResult
 
getClassificationMetricses() - Method in class org.neuroph.eval.EvaluationResult
 
getClassLabel() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Returns class label for
getClassLabels() - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getCluster() - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
getClusters() - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
getColumnName(int) - Method in class org.neuroph.core.data.DataSet
 
getColumnNames() - Method in class org.neuroph.core.data.DataSet
 
getColumnType(int) - Method in class org.neuroph.core.data.DataSet
 
getColumnTypes() - Method in class org.neuroph.core.data.DataSet
 
getConfusionMatrix() - Method in class org.neuroph.eval.EvaluationResult
 
getConfusionMatrix() - Method in class org.neuroph.eval.FoldResult
 
getConnectionFrom(Neuron) - Method in class org.neuroph.core.Neuron
Gets input connection from the specified neuron * @param fromNeuron neuron connected to this neuron as input
getConnectionsFromOtherLayers() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
Returns collection of connections from other layers
getCurrentIteration() - Method in class org.neuroph.core.learning.IterativeLearning
Returns current iteration of this learning algorithm
getDataSet() - Method in class org.neuroph.eval.EvaluationResult
 
getDataSet() - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
getDataSet() - Method in class org.neuroph.nnet.learning.knn.KNearestNeighbour
 
getDataSet() - Method in class org.neuroph.util.DataSetStatistics
Get original data set.
getDecreaseFactor() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
getDelay() - Method in class org.neuroph.nnet.comp.DelayedConnection
Returns delay value for this connection
getDelta() - Method in class org.neuroph.core.Neuron
Returns delta (error) for this neuron.
getDerivative(double) - Method in class org.neuroph.core.transfer.Gaussian
 
getDerivative(double) - Method in class org.neuroph.core.transfer.Linear
 
getDerivative(double) - Method in class org.neuroph.core.transfer.Log
 
getDerivative(double) - Method in class org.neuroph.core.transfer.RectifiedLinear
 
getDerivative(double) - Method in class org.neuroph.core.transfer.Sigmoid
 
getDerivative(double) - Method in class org.neuroph.core.transfer.Sin
 
getDerivative(double) - Method in class org.neuroph.core.transfer.Tanh
Returns the derivative of this function evaluated at x=input
getDerivative(double) - Method in class org.neuroph.core.transfer.TransferFunction
Returns the first derivative of this function.
getDesiredOutput() - Method in class org.neuroph.core.data.DataSetRow
 
getDimensions() - Method in class org.neuroph.nnet.comp.layer.FeatureMapLayer
Returns dimensions of this layer
getDistance() - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
getElapsedTime() - Method in class org.neuroph.util.benchmark.Stopwatch
Returns elapsed time in milliseconds between calls to start and stop methods If the watch has never been started, returns zero
getElapsedTimes() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
getErrorCorrection() - Method in class org.neuroph.nnet.learning.BinaryDeltaRule
Gets the errorCorrection parametar
getErrorFunction() - Method in class org.neuroph.core.learning.SupervisedLearning
 
getErrorRate() - Method in class org.neuroph.eval.classification.ClassificationMetrics
A number of wrong predictions made divided by the total number of predictions made.
getEvaluation() - Method in class org.neuroph.eval.CrossValidationBak
 
getEvaluator(Class<T>) - Method in class org.neuroph.eval.CrossValidationBak
 
getEvaluator(Class<T>) - Method in class org.neuroph.eval.Evaluation
 
getEvaluators() - Method in class org.neuroph.eval.Evaluation
Return all evaluators used for evaluation
getEventType() - Method in class org.neuroph.core.events.LearningEvent
 
getEventType() - Method in class org.neuroph.core.events.NeuralNetworkEvent
 
getFalseDiscoveryRate() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getFalseNegative(int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getFalseNegativeRate() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getFalsePositive(int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getFalsePositiveRate() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getFeatureMap(int) - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Returns feature map (Layer2D) at specified index
getFeatureMaps() - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
 
getFilePath() - Method in class org.neuroph.core.data.DataSet
Returns full file path for this training set
getFMeasure() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Calculates F-score for beta equal to 1.
getFMeasure(int) - Method in class org.neuroph.eval.classification.ClassificationMetrics
Returns the F-score.
getFrequency() - Method in class org.neuroph.util.DataSetStatistics
Get data set frequency for nominal columns.
getFromNeuron() - Method in class org.neuroph.core.Connection
Gets from neuron for this connection
getHeight() - Method in class org.neuroph.nnet.comp.Dimension2D
 
getHeight() - Method in class org.neuroph.nnet.comp.Kernel
Returns height of this kernel
getHeight() - Method in class org.neuroph.nnet.comp.layer.FeatureMapLayer
Returns height of this layer
getHighLimit() - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
getIncreaseFactor() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
getInitialDelta() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
getInput() - Method in class org.neuroph.core.Connection
Returns input received through this connection - the activation that comes from the output of the cell on the other end of connection Todo: make this final and solve delayed neuron connection in a different way
getInput() - Method in class org.neuroph.core.data.DataSetRow
Returns input vector
getInput() - Method in class org.neuroph.nnet.comp.DelayedConnection
Gets delayed input through this connection
getInputConnections() - Method in class org.neuroph.core.Neuron
Returns input connections for this neuron
getInputFunction() - Method in class org.neuroph.core.Neuron
Returns input function
getInputFunction() - Method in class org.neuroph.util.NeuronProperties
 
getInputFunctions() - Method in class org.neuroph.util.Neuroph
 
getInputNeurons() - Method in class org.neuroph.core.NeuralNetwork
Returns input neurons
getInputsCount() - Method in class org.neuroph.core.NeuralNetwork
Gets number of input neurons
getInputSize() - Method in class org.neuroph.core.data.DataSet
Returns input vector size of training elements in this training set This method is implementation of EngineIndexableSet interface, and it is added to provide compatibility with Encog data sets and FlatNetwork
getInstance() - Static method in class org.neuroph.util.Neuroph
 
getIntensity() - Method in class org.neuroph.nnet.learning.kmeans.KVector
Calculates and returns intensity of this vector
getItems() - Method in class org.neuroph.core.data.DataSet
 
getIteration() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getKernel() - Method in class org.neuroph.nnet.comp.layer.FeatureMapLayer
 
getKNearestNeighbours(KVector, int) - Method in class org.neuroph.nnet.learning.knn.KNearestNeighbour
http://en.wikipedia.org/wiki/Selection_algorithm
getLabel() - Method in class org.neuroph.core.data.DataSet
Returns label for this training set
getLabel() - Method in class org.neuroph.core.data.DataSetRow
Get training element label
getLabel() - Method in class org.neuroph.core.Layer
Get layer label
getLabel() - Method in class org.neuroph.core.NeuralNetwork
Get network label
getLabel() - Method in class org.neuroph.core.Neuron
Returns label for this neuron
getLabel() - Method in class org.neuroph.eval.classification.ClassificationResult
 
getLayerAt(int) - Method in class org.neuroph.core.NeuralNetwork
Returns layer at specified index
getLayers() - Method in class org.neuroph.core.NeuralNetwork
Returns layers array
getLayers() - Method in class org.neuroph.util.Neuroph
 
getLayersCount() - Method in class org.neuroph.core.NeuralNetwork
Returns number of layers in network
getLearningRate() - Method in class org.neuroph.core.learning.IterativeLearning
Returns learning rate for this algorithm
getLearningRate() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getLearningRateChange() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getLearningRule() - Method in class org.neuroph.core.NeuralNetwork
Returns the learning algorithm of this network
getLearningRules() - Method in class org.neuroph.util.Neuroph
 
getLeftHigh() - Method in class org.neuroph.core.transfer.Trapezoid
Returns left high point of trapezoid function
getLeftLow() - Method in class org.neuroph.core.transfer.Trapezoid
Returns left low point of trapezoid function
getLog() - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
getLowLimit() - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
getMapDimensions() - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Returns dimensions of feature maps in this layer
getMapSize() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getMatthewsCorrelationCoefficient() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getMax() - Method in class org.neuroph.util.DataSetStatistics
Get maximum for each data set column.
getMaxDelta() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
getMaxError() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns learning error tolerance - the value of total network error to stop learning.
getMaxIn() - Method in class org.neuroph.util.data.norm.DecimalScaleNormalizer
 
getMaxIn() - Method in class org.neuroph.util.data.norm.MaxMinNormalizer
 
getMaxIn() - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
getMaxIterations() - Method in class org.neuroph.core.learning.IterativeLearning
Returns max iterations limit of this learning algorithm
getMaxIterations() - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
Returns the maxIterations setting for this layer
getMaxLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getMaxMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getMaxOut() - Method in class org.neuroph.util.data.norm.DecimalScaleNormalizer
 
getMaxOut() - Method in class org.neuroph.util.data.norm.MaxMinNormalizer
 
getMaxOut() - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
getMaxTestTime() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
getMean() - Method in class org.neuroph.util.DataSetStatistics
Get mean for each data set column.
getMeanSquareError() - Method in class org.neuroph.eval.Evaluation
 
getMeanSquareError() - Method in class org.neuroph.eval.EvaluationResult
 
getMin() - Method in class org.neuroph.util.DataSetStatistics
Get minimum for each data set column.
getMinDelta() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
getMinErrorChange() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns min error change stopping criteria
getMinErrorChangeIterationsCount() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns number of iterations count for for min error change stopping criteria
getMinErrorChangeIterationsLimit() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns number of iterations for min error change stopping criteria
getMinIn() - Method in class org.neuroph.util.data.norm.MaxMinNormalizer
 
getMinIn() - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
getMinLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getMinMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getMinOut() - Method in class org.neuroph.util.data.norm.MaxMinNormalizer
 
getMinOut() - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
getMinTestTime() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
getMomentum() - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
Returns the momentum factor
getMomentumChange() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getName() - Method in class org.neuroph.util.benchmark.BenchmarkTask
Gets task name
getName() - Method in class org.neuroph.util.plugins.PluginBase
Returns the name of this plugin
getNetInput() - Method in class org.neuroph.core.Neuron
Returns total net input
getNetwork() - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
Get the best network from the training.
getNetworkType() - Method in class org.neuroph.core.NeuralNetwork
Returns type of this network
getNeuralNet() - Method in class org.neuroph.eval.FoldResult
Returns neural network trained in this cross-validation fold.
getNeuralNetwork() - Method in class org.neuroph.core.learning.LearningRule
Gets neural network
getNeuralNetwork() - Method in class org.neuroph.eval.EvaluationResult
 
getNeuronAt(int) - Method in class org.neuroph.core.Layer
Returns neuron at specified index position in this layer
getNeuronAt(int, int) - Method in class org.neuroph.nnet.comp.layer.FeatureMapLayer
Returns neuron at specified position in this layer
getNeuronAt(int, int, int) - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Returns neuron instance at specified (x, y) position at specified feature map layer
getNeuronOutput() - Method in class org.neuroph.eval.classification.ClassificationResult
 
getNeurons() - Method in class org.neuroph.core.Layer
Returns array neurons in this layer as array
getNeurons() - Method in class org.neuroph.util.Neuroph
 
getNeuronsCount() - Method in class org.neuroph.core.Layer
Returns number of neurons in this layer
getNeuronsCount() - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Returns total number of neurons in all feature maps
getNeuronType() - Method in class org.neuroph.util.NeuronProperties
 
getNumberOfMaps() - Method in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Returns number of feature maps in this layer
getOutConnections() - Method in class org.neuroph.core.Neuron
Returns output connections from this neuron
getOutput(List<Connection>) - Method in class org.neuroph.core.input.And
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.Difference
 
getOutput(double[], double[]) - Method in class org.neuroph.core.input.Difference
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.EuclideanRBF
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.InputFunction
Returns ouput value of this input function for the given neuron inputs
getOutput(List<Connection>) - Method in class org.neuroph.core.input.Max
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.Min
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.Or
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.Product
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.Sum
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.SumSqr
 
getOutput(List<Connection>) - Method in class org.neuroph.core.input.WeightedSum
 
getOutput(double[], double[]) - Static method in class org.neuroph.core.input.WeightedSum
 
getOutput() - Method in class org.neuroph.core.NeuralNetwork
Returns network output vector.
getOutput() - Method in class org.neuroph.core.Neuron
Returns neuron's output
getOutput(double) - Method in class org.neuroph.core.transfer.Gaussian
 
getOutput(double) - Method in class org.neuroph.core.transfer.Linear
 
getOutput(double) - Method in class org.neuroph.core.transfer.Log
 
getOutput(double) - Method in class org.neuroph.core.transfer.Ramp
 
getOutput(double) - Method in class org.neuroph.core.transfer.RectifiedLinear
 
getOutput(double) - Method in class org.neuroph.core.transfer.Sgn
y = 1, x > 0 y = -1, x <= 0
getOutput(double) - Method in class org.neuroph.core.transfer.Sigmoid
 
getOutput(double) - Method in class org.neuroph.core.transfer.Sin
 
getOutput(double) - Method in class org.neuroph.core.transfer.Step
 
getOutput(double) - Method in class org.neuroph.core.transfer.Tanh
Returns the value of this function at x=input
getOutput(double) - Method in class org.neuroph.core.transfer.TransferFunction
Returns the ouput of this function.
getOutput(double) - Method in class org.neuroph.core.transfer.Trapezoid
 
getOutput(int) - Method in class org.neuroph.nnet.comp.neuron.DelayedNeuron
Returns neuron output with the specified delay
getOutput() - Method in class org.neuroph.nnet.comp.neuron.InputNeuron
 
getOutputLabels() - Method in class org.neuroph.core.NeuralNetwork
 
getOutputNeurons() - Method in class org.neuroph.core.NeuralNetwork
Returns output neurons
getOutputsCount() - Method in class org.neuroph.core.NeuralNetwork
 
getOutputSize() - Method in class org.neuroph.core.data.DataSet
Returns output vector size of training elements in this training set.
getParentLayer() - Method in class org.neuroph.core.Neuron
Returns reference to parent layer for this neuron
getParentNetwork() - Method in class org.neuroph.core.Layer
Returns reference to parent network
getParentNetwork() - Method in class org.neuroph.util.plugins.PluginBase
Returns the parent network for this plugin
getPlugin(Class<T>) - Method in class org.neuroph.core.NeuralNetwork
Returns the requested plugin
getPoints() - Method in class org.neuroph.nnet.learning.kmeans.Cluster
 
getPrecision() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Calculate and return classification precision measure.
getPreviousEpochError() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns total network error in previous learning epoch
getProperties() - Method in class org.neuroph.core.transfer.Sgn
Returns the properties of this function
getProperties() - Method in class org.neuroph.core.transfer.Step
Returns the properties of this function
getProperty(String) - Method in class org.neuroph.util.Properties
 
getQ9() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getRandomGen() - Method in class org.neuroph.util.random.WeightsRandomizer
 
getRecall() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
getResult() - Method in class org.neuroph.eval.ClassifierEvaluator
 
getResult() - Method in class org.neuroph.eval.ErrorEvaluator
 
getResult() - Method in interface org.neuroph.eval.Evaluator
This method should return final evaluation result
getResultsByFolds() - Method in class org.neuroph.eval.KFoldCrossValidation
 
getRightHigh() - Method in class org.neuroph.core.transfer.Trapezoid
Returns right high point of trapezoid function
getRightLow() - Method in class org.neuroph.core.transfer.Trapezoid
Returns right low point of trapezoid function
getRowAt(int) - Method in class org.neuroph.core.data.DataSet
Returns training row at specified index position
getRows() - Method in class org.neuroph.core.data.DataSet
Returns elements of this training set
getSampling() - Method in class org.neuroph.eval.CrossValidationBak
 
getScaleFactorIn() - Method in class org.neuroph.util.data.norm.DecimalScaleNormalizer
 
getScaleFactorOut() - Method in class org.neuroph.util.data.norm.DecimalScaleNormalizer
 
getSensitivity() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Calculate and return classification sensitivity (recall, true positive rate) number of correctly classified positive examples divided by the total number of actual positive examples
getSigma() - Method in class org.neuroph.core.transfer.Gaussian
Returns the sigma parametar of this function
getSlope() - Method in class org.neuroph.core.transfer.Linear
Returns the slope parametar of this function
getSlope() - Method in class org.neuroph.core.transfer.Sigmoid
Returns the slope parametar of this function
getSlope() - Method in class org.neuroph.core.transfer.Tanh
Returns the slope parameter of this function
getSpecificity() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Specifity , true negative rate
getStandardDeviation() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
getStdDev() - Method in class org.neuroph.util.DataSetStatistics
Get standard deviation for each data set column.
getTestIterations() - Method in class org.neuroph.util.benchmark.BenchmarkTask
Gets number of test (benchmarking) iterations
getTestIterations() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
getThresh() - Method in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
Returns threshold value for this neuron
getThreshold() - Method in class org.neuroph.eval.ClassifierEvaluator
 
getToNeuron() - Method in class org.neuroph.core.Connection
Gets to neuron for this connection
getTotal() - Method in class org.neuroph.eval.classification.ClassificationMetrics
Returns total number of classifications.
getTotal() - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getTotalError() - Method in interface org.neuroph.core.learning.error.ErrorFunction
Return total network error
getTotalError() - Method in class org.neuroph.core.learning.error.MeanAbsoluteError
 
getTotalError() - Method in class org.neuroph.core.learning.error.MeanSquaredError
 
getTotalNetworkError() - Method in class org.neuroph.core.learning.SupervisedLearning
 
getTrainingData() - Method in class org.neuroph.core.Weight
Returns training data buffer for this weight
getTrainingSet() - Method in class org.neuroph.core.learning.LearningRule
Gets training set
getTrainingSet() - Method in class org.neuroph.eval.FoldResult
 
getTransferFunction() - Method in class org.neuroph.core.Neuron
Returns transfer function
getTransferFunction() - Method in class org.neuroph.util.NeuronProperties
 
getTransferFunctionProperties() - Method in class org.neuroph.util.NeuronProperties
 
getTransferFunctions() - Method in class org.neuroph.util.Neuroph
 
getTrueNegative(int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getTruePositive(int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
getTypeClass() - Method in enum org.neuroph.util.TransferFunctionType
 
getTypeLabel() - Method in enum org.neuroph.util.NeuralNetworkType
 
getTypeLabel() - Method in enum org.neuroph.util.TransferFunctionType
 
getUseDynamicLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getUseDynamicMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
getValidationSet() - Method in class org.neuroph.eval.FoldResult
 
getValue() - Method in class org.neuroph.core.Weight
Returns weight value
getValueAt(int, int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
Returns value of confusion matrix at specified position
getValueAt(int) - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
getValues() - Method in class org.neuroph.eval.classification.ConfusionMatrix
Returns confusion matrix values as double array
getValues() - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
getVar() - Method in class org.neuroph.util.DataSetStatistics
Get variant for each data set column.
getVersion() - Static method in class org.neuroph.util.Neuroph
 
getWarmupIterations() - Method in class org.neuroph.util.benchmark.BenchmarkTask
Gets number of warmup iterations.
getWeight() - Method in class org.neuroph.core.Connection
Returns weight for this connection
getWeightedInput() - Method in class org.neuroph.core.Connection
Returns the weighted input received through this connection
getWeights() - Method in class org.neuroph.core.NeuralNetwork
Returns all network weights as an double array
getWeights() - Method in class org.neuroph.core.Neuron
Returns weights vector of input connections
getWeights() - Method in class org.neuroph.nnet.comp.Kernel
 
getWidth() - Method in class org.neuroph.nnet.comp.Dimension2D
 
getWidth() - Method in class org.neuroph.nnet.comp.Kernel
Returns width of this kernel
getWidth() - Method in class org.neuroph.nnet.comp.layer.FeatureMapLayer
Returns width of this layer
getWinner() - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
Returns the winning neuron for this layer
getXHigh() - Method in class org.neuroph.core.transfer.Ramp
Returns threshold value for the high output level
getXLow() - Method in class org.neuroph.core.transfer.Ramp
Returns threshold value for the low output level
getYHigh() - Method in class org.neuroph.core.transfer.Ramp
Returns output value for high output level
getYHigh() - Method in class org.neuroph.core.transfer.Step
Returns output value for high output level
getYLow() - Method in class org.neuroph.core.transfer.Ramp
Returns output value for low output level
getYLow() - Method in class org.neuroph.core.transfer.Step
Returns output value for low output level
gradient - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
 

H

handleLearningEvent(LearningEvent) - Method in interface org.neuroph.core.events.LearningEventListener
This method gets executed when LearningRule fires LearningEvent which some class is listening to.
handleNeuralNetworkEvent(NeuralNetworkEvent) - Method in interface org.neuroph.core.events.NeuralNetworkEventListener
 
hashCode() - Method in class org.neuroph.core.Connection
 
hashCode() - Method in class org.neuroph.core.data.DataSetRow
 
hashCode() - Method in class org.neuroph.core.Weight
 
hashCode() - Method in class org.neuroph.nnet.learning.kmeans.Cluster
 
hasInputConnectionFrom(Neuron) - Method in class org.neuroph.core.Neuron
Check the connection from neuron
hasInputConnections() - Method in class org.neuroph.core.Neuron
Returns true if there are input connections for this neuron, false otherwise
hasNext() - Method in class org.neuroph.core.data.BufferedDataSet
Returns true if there are more rows, false otherwise
hasOutputConnectionTo(Neuron) - Method in class org.neuroph.core.Neuron
Check the connection to neuron
hasProperty(String) - Method in class org.neuroph.util.Properties
 
hasReachedStopCondition() - Method in class org.neuroph.core.learning.IterativeLearning
Returns true if any of the stop conditions has been reached.
HeZhangRenSunUniformWeightsRandomizer - Class in org.neuroph.util.random
Sources: https://arxiv.org/abs/1502.01852 Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification https://stats.stackexchange.com/questions/47590/what-are-good-initial-weights-in-a-neural-network https://github.com/keras-team/keras/blob/master/keras/initializers.py
HeZhangRenSunUniformWeightsRandomizer() - Constructor for class org.neuroph.util.random.HeZhangRenSunUniformWeightsRandomizer
 
Hopfield - Class in org.neuroph.nnet
Hopfield neural network.
Hopfield(int) - Constructor for class org.neuroph.nnet.Hopfield
Creates new Hopfield network with specified neuron number
Hopfield(int, NeuronProperties) - Constructor for class org.neuroph.nnet.Hopfield
Creates new Hopfield network with specified neuron number and neuron properties
HopfieldLearning - Class in org.neuroph.nnet.learning
Learning algorithm for the Hopfield neural network.
HopfieldLearning() - Constructor for class org.neuroph.nnet.learning.HopfieldLearning
Creates new HopfieldLearning

I

importFromFile(String, int, int, String) - Static method in class org.neuroph.util.TrainingSetImport
 
inc(double) - Method in class org.neuroph.core.Weight
Increases the weight for the specified amount
incrementElement(int, int) - Method in class org.neuroph.eval.classification.ConfusionMatrix
Increments matrix value at specified position
indexOf(Object) - Method in class org.neuroph.core.data.DataSet
 
indexOf(Neuron) - Method in class org.neuroph.core.Layer
Returns the index position in layer for the specified neuron
indexOf(Layer) - Method in class org.neuroph.core.NeuralNetwork
Returns index position of the specified layer
initClusters() - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
initializeWeights(double) - Method in class org.neuroph.core.Layer
Initialize connection weights for the whole layer to to specified value
initializeWeights(double) - Method in class org.neuroph.core.Neuron
Initialize weights for all input connections to specified value
initWeights(double, double) - Method in class org.neuroph.nnet.comp.Kernel
 
input - Variable in class org.neuroph.core.data.DataSetRow
Input vector for this training element
InputAdapter - Interface in org.neuroph.util.io
Interface for reading neural network inputs from various data sources.
inputConnections - Variable in class org.neuroph.core.Neuron
Collection of neuron's input connections (connections to this neuron)
InputFunction - Class in org.neuroph.core.input
Neuron's input function.
InputFunction() - Constructor for class org.neuroph.core.input.InputFunction
 
inputFunction - Variable in class org.neuroph.core.Neuron
Input function for this neuron
InputLayer - Class in org.neuroph.nnet.comp.layer
Represents a layer of input neurons - a typical neural network input layer
InputLayer(int) - Constructor for class org.neuroph.nnet.comp.layer.InputLayer
Creates a new instance of InputLayer with specified number of input neurons
InputMapsLayer - Class in org.neuroph.nnet.comp.layer
Input layer for convolutional networks
InputMapsLayer(Dimension2D, int) - Constructor for class org.neuroph.nnet.comp.layer.InputMapsLayer
Create InputMapsLayer with specified number of maps with specified dimensions
InputNeuron - Class in org.neuroph.nnet.comp.neuron
Provides input neuron behaviour - neuron with input extranaly set, which just transfer that input to output without change.
InputNeuron() - Constructor for class org.neuroph.nnet.comp.neuron.InputNeuron
Creates a new instance of InputNeuron with linear transfer function
InputOutputNeuron - Class in org.neuroph.nnet.comp.neuron
Provides behaviour specific for neurons which act as input and the output neurons within the same layer.
InputOutputNeuron() - Constructor for class org.neuroph.nnet.comp.neuron.InputOutputNeuron
Creates an instance of neuron for Hopfield network
InputOutputNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.InputOutputNeuron
Creates an instance of neuron for Hopfield network with specified input and transfer functions
inputsMax(DataSet) - Static method in class org.neuroph.util.DataSetStats
 
inputsMean(DataSet) - Static method in class org.neuroph.util.DataSetStats
 
inputsMin(DataSet) - Static method in class org.neuroph.util.DataSetStats
 
inputsStandardDeviation(DataSet, double[]) - Static method in class org.neuroph.util.DataSetStats
 
InputStreamAdapter - Class in org.neuroph.util.io
Implementation of InputAdapter interface for reading neural network inputs from input stream.
InputStreamAdapter(InputStream) - Constructor for class org.neuroph.util.io.InputStreamAdapter
 
InputStreamAdapter(BufferedReader) - Constructor for class org.neuroph.util.io.InputStreamAdapter
 
Instar - Class in org.neuroph.nnet
Instar neural network with Instar learning rule.
Instar(int) - Constructor for class org.neuroph.nnet.Instar
Creates new Instar with specified number of input neurons.
InstarLearning - Class in org.neuroph.nnet.learning
Hebbian-like learning rule for Instar network.
InstarLearning() - Constructor for class org.neuroph.nnet.learning.InstarLearning
Creates new instance of InstarLearning algorithm
IOHelper - Class in org.neuroph.util.io
This class is helper for feeding neural network with data using some InputAdapter and writing network output using OutputAdapter
IOHelper() - Constructor for class org.neuroph.util.io.IOHelper
 
isBatchMode() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns true if learning is performed in batch mode, false otherwise
isCompeting() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
Retruns true if this neuron is in competing mode, false otherwise
isEmpty() - Method in class org.neuroph.core.data.DataSet
Returns true if training set is empty, false otherwise
isEmpty() - Method in class org.neuroph.core.Layer
 
isEmpty() - Method in class org.neuroph.core.NeuralNetwork
 
isIterationsLimited() - Method in class org.neuroph.core.learning.IterativeLearning
 
isPausedLearning() - Method in class org.neuroph.core.learning.IterativeLearning
Returns true if learning thread is paused, false otherwise
isReached() - Method in class org.neuroph.core.learning.stop.MaxErrorStop
 
isReached() - Method in class org.neuroph.core.learning.stop.MaxIterationsStop
 
isReached() - Method in class org.neuroph.core.learning.stop.SmallErrorChangeStop
 
isReached() - Method in interface org.neuroph.core.learning.stop.StopCondition
Returns true if learning rule should stop, false otherwise
isStopped() - Method in class org.neuroph.core.learning.LearningRule
Returns true if learning has stopped, false otherwise
isSupervised() - Method in class org.neuroph.core.data.DataSet
Returns true if data set is supervised, false otherwise
isSupervised() - Method in class org.neuroph.core.data.DataSetRow
 
IterativeLearning - Class in org.neuroph.core.learning
Base class for all iterative learning algorithms.
IterativeLearning() - Constructor for class org.neuroph.core.learning.IterativeLearning
Creates new instance of IterativeLearning learning algorithm
iterator() - Method in class org.neuroph.core.data.BufferedDataSet
Returns iterator for buffered data set
iterator() - Method in class org.neuroph.core.data.DataSet
Returns Iterator for iterating training elements collection
iterator() - Method in class org.neuroph.core.Layer
 

J

JDBCInputAdapter - Class in org.neuroph.util.io
Implementation of InputAdapter interface for reading neural network inputs from database.
JDBCInputAdapter(Connection, String) - Constructor for class org.neuroph.util.io.JDBCInputAdapter
 
JDBCOutputAdapter - Class in org.neuroph.util.io
Implementation of OutputAdapter interface for writing neural network outputs to database.
JDBCOutputAdapter(Connection, String) - Constructor for class org.neuroph.util.io.JDBCOutputAdapter
Creates new JDBCOutputAdapter with specifed database connection and table
JordanNetwork - Class in org.neuroph.nnet
Under development: Learning rule BackProp Through Time required
JordanNetwork(int, int, int, int) - Constructor for class org.neuroph.nnet.JordanNetwork
 

K

Kernel - Class in org.neuroph.nnet.comp
Kernel used in convolution networks.
Kernel(Dimension2D) - Constructor for class org.neuroph.nnet.comp.Kernel
Creates new kernel with specified width and height
Kernel(int, int) - Constructor for class org.neuroph.nnet.comp.Kernel
Creates new kernel with specified width and height
KFoldCrossValidation - Class in org.neuroph.eval
This class implements multi-threaded cross validation procedure.
KFoldCrossValidation(NeuralNetwork, DataSet, int) - Constructor for class org.neuroph.eval.KFoldCrossValidation
 
KMeansClustering - Class in org.neuroph.nnet.learning.kmeans
1.
KMeansClustering(DataSet) - Constructor for class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
KMeansClustering(DataSet, int) - Constructor for class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
KNearestNeighbour - Class in org.neuroph.nnet.learning.knn
for given vector calculate distances to all vectors from list and finds minimum vector or sort vectors by distance and seelct
KNearestNeighbour() - Constructor for class org.neuroph.nnet.learning.knn.KNearestNeighbour
 
Kohonen - Class in org.neuroph.nnet
Kohonen neural network.
Kohonen(int, int) - Constructor for class org.neuroph.nnet.Kohonen
Creates new Kohonen network with specified number of neurons in input and map layer
KohonenLearning - Class in org.neuroph.nnet.learning
Learning algorithm for Kohonen network.
KohonenLearning() - Constructor for class org.neuroph.nnet.learning.KohonenLearning
 
KVector - Class in org.neuroph.nnet.learning.kmeans
Represents feature vector used in k-means clustering algorithm
KVector(int) - Constructor for class org.neuroph.nnet.learning.kmeans.KVector
 
KVector(double[]) - Constructor for class org.neuroph.nnet.learning.kmeans.KVector
 

L

label - Variable in class org.neuroph.core.data.DataSetRow
Label for this training element
Layer - Class in org.neuroph.core
Layer of neurons in a neural network.
Layer() - Constructor for class org.neuroph.core.Layer
Creates an instance of empty Layer
Layer(int) - Constructor for class org.neuroph.core.Layer
Creates an instance of empty Layer for specified number of neurons
Layer(int, NeuronProperties) - Constructor for class org.neuroph.core.Layer
Creates an instance of Layer with the specified number of neurons with specified neuron properties
LayerFactory - Class in org.neuroph.util
Provides methods to create instance of a Layer with various setting (number of neurons and neuron's properties, etc.
learn(DataSet) - Method in class org.neuroph.core.learning.IterativeLearning
 
learn(DataSet, int) - Method in class org.neuroph.core.learning.IterativeLearning
Trains network for the specified training set and number of iterations
learn(DataSet) - Method in class org.neuroph.core.learning.LearningRule
Override this method to implement specific learning procedures
learn(DataSet, double) - Method in class org.neuroph.core.learning.SupervisedLearning
Trains network for the specified training set and maxError
learn(DataSet, double, int) - Method in class org.neuroph.core.learning.SupervisedLearning
Trains network for the specified training set, maxError and number of iterations
learn(DataSet) - Method in class org.neuroph.core.NeuralNetwork
Learn the specified training set
learn(DataSet, L) - Method in class org.neuroph.core.NeuralNetwork
Learn the specified training set, using specified learning rule
learn(DataSet) - Method in class org.neuroph.nnet.learning.HopfieldLearning
Calculates weights for the hopfield net to learn the specified training set
learn(DataSet) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
LEARNING_STOPPED - Static variable in class org.neuroph.core.events.LearningEvent
 
LearningEvent - Class in org.neuroph.core.events
This class holds information about the source and type of learning event.
LearningEvent(LearningRule, LearningEvent.Type) - Constructor for class org.neuroph.core.events.LearningEvent
 
LearningEvent.Type - Enum in org.neuroph.core.events
 
LearningEventListener - Interface in org.neuroph.core.events
This interface is implemented by classes who are listening to learning events (iterations, error etc.) LearningEvent class holds the information about event.
learningRate - Variable in class org.neuroph.core.learning.IterativeLearning
Learning rate parametar
LearningRule - Class in org.neuroph.core.learning
Base class for all neural network learning algorithms.
LearningRule() - Constructor for class org.neuroph.core.learning.LearningRule
Creates new instance of learning rule
learnPattern(DataSetRow) - Method in class org.neuroph.core.learning.SupervisedLearning
Trains network with the input and desired output pattern from the specified training element
learnPattern(DataSetRow) - Method in class org.neuroph.core.learning.UnsupervisedLearning
Trains network with the pattern from the specified training element
Linear - Class in org.neuroph.core.transfer
Linear neuron transfer function.
Linear() - Constructor for class org.neuroph.core.transfer.Linear
Creates an instance of Linear transfer function
Linear(double) - Constructor for class org.neuroph.core.transfer.Linear
Creates an instance of Linear transfer function with specified value for getSlope parametar.
Linear(Properties) - Constructor for class org.neuroph.core.transfer.Linear
Creates an instance of Linear transfer function with specified properties
listeners - Variable in class org.neuroph.core.learning.LearningRule
List of learning rule listeners
LMS - Class in org.neuroph.nnet.learning
LMS learning rule for neural networks.
LMS() - Constructor for class org.neuroph.nnet.learning.LMS
Creates a new LMS learning rule
load(String) - Static method in class org.neuroph.core.data.DataSet
Loads training set from the specified file TODO: throw checked exceptionse here
load(String) - Static method in class org.neuroph.core.NeuralNetwork
Deprecated.
Use createFromFile method instead
load(InputStream) - Static method in class org.neuroph.core.NeuralNetwork
Loads neural network from the specified InputStream.
Log - Class in org.neuroph.core.transfer
Log neuron transfer function.
Log() - Constructor for class org.neuroph.core.transfer.Log
 

M

main(String[]) - Static method in class org.neuroph.util.benchmark.BenchmarkSample
 
ManhattanPropagation - Class in org.neuroph.nnet.learning
 
ManhattanPropagation() - Constructor for class org.neuroph.nnet.learning.ManhattanPropagation
 
mapDimensions - Variable in class org.neuroph.nnet.comp.layer.FeatureMapsLayer
Dimensions for all 2D layers (feature maps)
Max - Class in org.neuroph.core.input
Performs max function on input vector
Max() - Constructor for class org.neuroph.core.input.Max
 
MAX - Static variable in class org.neuroph.util.DataSetStatistics
 
max - Variable in class org.neuroph.util.random.RangeRandomizer
Upper range limit
maxError - Variable in class org.neuroph.core.learning.SupervisedLearning
Max allowed network error (condition to stop learning)
MaxErrorStop - Class in org.neuroph.core.learning.stop
Stops learning rule if total network error is below some specified value
MaxErrorStop(SupervisedLearning) - Constructor for class org.neuroph.core.learning.stop.MaxErrorStop
 
maxIdx(double[]) - Static method in class org.neuroph.eval.classification.Utils
 
MaxIterationsStop - Class in org.neuroph.core.learning.stop
Stops learning rule if specified number of iterations has been reached
MaxIterationsStop(IterativeLearning) - Constructor for class org.neuroph.core.learning.stop.MaxIterationsStop
 
MaxMinNormalizer - Class in org.neuroph.util.data.norm
MaxMin normalization method, which normalize data in regard to min and max elements in training set (by columns) Normalization is done according to formula: normalizedVector[i] = (vector[i] - min[i]) / (max[i] - min[i]) This class works fine if max and min are both positive and we want to normalize to [0,1]
MaxMinNormalizer(DataSet) - Constructor for class org.neuroph.util.data.norm.MaxMinNormalizer
 
MaxNet - Class in org.neuroph.nnet
Max Net neural network with competitive learning rule.
MaxNet(int) - Constructor for class org.neuroph.nnet.MaxNet
Creates new Maxnet network with specified neuron number
MaxNormalizer - Class in org.neuroph.util.data.norm
Max normalization method, which normalize data in regard to max element in training set (by columns) Normalization is done according to formula: normalizedVector[i] = vector[i] / abs(max[i])
MaxNormalizer(DataSet) - Constructor for class org.neuroph.util.data.norm.MaxNormalizer
Creates new instance of MaxNormalizer initialized with max values from specified data set.
McNemarTest - Class in org.neuroph.eval.classification
Class used to test if there is statistical significant difference between two classifiers
McNemarTest() - Constructor for class org.neuroph.eval.classification.McNemarTest
 
MEAN - Static variable in class org.neuroph.util.DataSetStatistics
 
MeanAbsoluteError - Class in org.neuroph.core.learning.error
 
MeanAbsoluteError() - Constructor for class org.neuroph.core.learning.error.MeanAbsoluteError
 
MeanSquaredError - Class in org.neuroph.core.learning.error
Commonly used mean squared error
MeanSquaredError() - Constructor for class org.neuroph.core.learning.error.MeanSquaredError
 
Min - Class in org.neuroph.core.input
Performs min function on input vector
Min() - Constructor for class org.neuroph.core.input.Min
 
MIN - Static variable in class org.neuroph.util.DataSetStatistics
 
min - Variable in class org.neuroph.util.random.RangeRandomizer
Lower range limit
momentum - Variable in class org.neuroph.nnet.learning.MomentumBackpropagation
Momentum factor
MomentumBackpropagation - Class in org.neuroph.nnet.learning
Backpropagation learning rule with momentum.
MomentumBackpropagation() - Constructor for class org.neuroph.nnet.learning.MomentumBackpropagation
Creates new instance of MomentumBackpropagation learning
MomentumBackpropagation.MomentumTrainingData - Class in org.neuroph.nnet.learning
 
MomentumTrainingData() - Constructor for class org.neuroph.nnet.learning.MomentumBackpropagation.MomentumTrainingData
 
mserror - Variable in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
MultiClass(String[]) - Constructor for class org.neuroph.eval.ClassifierEvaluator.MultiClass
 
MultiLayerPerceptron - Class in org.neuroph.nnet
Multi Layer Perceptron neural network with Back propagation learning algorithm.
MultiLayerPerceptron(List<Integer>) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
Creates new MultiLayerPerceptron with specified number of neurons in layers
MultiLayerPerceptron(int...) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
 
MultiLayerPerceptron(TransferFunctionType, int...) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
 
MultiLayerPerceptron(List<Integer>, TransferFunctionType) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
 
MultiLayerPerceptron(List<Integer>, NeuronProperties) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
Creates new MultiLayerPerceptron net with specified number neurons in getLayersIterator
MyBenchmarkTask - Class in org.neuroph.util.benchmark
This class is example of custom benchmarking task for Multi Layer Perceptorn network Note that this benchmark only measures the speed at implementation level - the speed of data flow forward and backward through network
MyBenchmarkTask(String) - Constructor for class org.neuroph.util.benchmark.MyBenchmarkTask
 

N

network2array(NeuralNetwork, double[]) - Static method in class org.neuroph.util.NeuralNetworkCODEC
Encode a network to an array.
neuralNetwork - Variable in class org.neuroph.core.learning.LearningRule
Neural network to train
NeuralNetwork<L extends LearningRule> - Class in org.neuroph.core
Base class for artificial neural networks.
NeuralNetwork() - Constructor for class org.neuroph.core.NeuralNetwork
Creates an instance of empty neural network.
NeuralNetworkCODEC - Class in org.neuroph.util
A CODEC encodes and decodes neural networks, much like the more standard definition of a CODEC encodes and decodes audio/video.
NeuralNetworkEvent - Class in org.neuroph.core.events
This class holds information about the source and type of some neural network event.
NeuralNetworkEvent(NeuralNetwork, NeuralNetworkEvent.Type) - Constructor for class org.neuroph.core.events.NeuralNetworkEvent
 
NeuralNetworkEvent(Layer, NeuralNetworkEvent.Type) - Constructor for class org.neuroph.core.events.NeuralNetworkEvent
 
NeuralNetworkEvent(Neuron, NeuralNetworkEvent.Type) - Constructor for class org.neuroph.core.events.NeuralNetworkEvent
 
NeuralNetworkEvent.Type - Enum in org.neuroph.core.events
Types of neural network events
NeuralNetworkEventListener - Interface in org.neuroph.core.events
This interface is implemented by classes who are listening to neural network events events (to be defined) NeuralNetworkEvent class holds the information about event.
NeuralNetworkFactory - Class in org.neuroph.util
Provides methods to create various neural networks.
NeuralNetworkFactory() - Constructor for class org.neuroph.util.NeuralNetworkFactory
 
NeuralNetworkType - Enum in org.neuroph.util
Contains neural network types and labels.
NeuroFuzzyPerceptron - Class in org.neuroph.nnet
The NeuroFuzzyReasoner class represents Neuro Fuzzy Reasoner architecture.
NeuroFuzzyPerceptron(double[][], double[][]) - Constructor for class org.neuroph.nnet.NeuroFuzzyPerceptron
 
NeuroFuzzyPerceptron(int, Vector<Integer>, int) - Constructor for class org.neuroph.nnet.NeuroFuzzyPerceptron
 
Neuron - Class in org.neuroph.core
Basic general neuron model according to McCulloch-Pitts neuron model.
Neuron() - Constructor for class org.neuroph.core.Neuron
Creates an instance of Neuron with default settings: weighted sum input function and Step transfer function.
Neuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.core.Neuron
Creates an instance of Neuron with the specified input and transfer functions.
NeuronFactory - Class in org.neuroph.util
Provides methods to create customized instances of Neuron.
NeuronProperties - Class in org.neuroph.util
Represents properties of a neuron.
NeuronProperties() - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(Class<? extends Neuron>) - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(Class<? extends Neuron>, Class<? extends TransferFunction>) - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(Class<? extends Neuron>, Class<? extends InputFunction>, Class<? extends TransferFunction>) - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(Class<? extends Neuron>, TransferFunctionType) - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(TransferFunctionType, boolean) - Constructor for class org.neuroph.util.NeuronProperties
 
neurons - Variable in class org.neuroph.core.Layer
Collection of neurons in this layer
Neuroph - Class in org.neuroph.util
This singleton holds global settings for the whole framework
Neuroph() - Constructor for class org.neuroph.util.Neuroph
 
NeurophException - Exception in org.neuroph.core.exceptions
Base exception type for Neuroph.
NeurophException() - Constructor for exception org.neuroph.core.exceptions.NeurophException
Default constructor.
NeurophException(String) - Constructor for exception org.neuroph.core.exceptions.NeurophException
Construct a message exception.
NeurophException(Throwable) - Constructor for exception org.neuroph.core.exceptions.NeurophException
Construct an exception that holds another exception.
NeurophException(String, Throwable) - Constructor for exception org.neuroph.core.exceptions.NeurophException
Construct an exception that holds another exception.
NeurophInputException - Exception in org.neuroph.util.io
This exception is thrown when error occurs when reading input using some InputAdapter
NeurophInputException() - Constructor for exception org.neuroph.util.io.NeurophInputException
Constructs an NeurophInputException with no detail message.
NeurophInputException(String) - Constructor for exception org.neuroph.util.io.NeurophInputException
Constructs an NeurophInputException with the specified detail message.
NeurophInputException(String, Throwable) - Constructor for exception org.neuroph.util.io.NeurophInputException
Constructs a NeurophInputException with the specified detail message and specified cause.
NeurophInputException(Throwable) - Constructor for exception org.neuroph.util.io.NeurophInputException
Constructs a new runtime exception with the specified cause
NeurophOutputException - Exception in org.neuroph.util.io
This exception is thrown when some error occurs when writing neural network output using some output adapter.
NeurophOutputException() - Constructor for exception org.neuroph.util.io.NeurophOutputException
Constructs an NeurophOutputException with no detail message.
NeurophOutputException(String) - Constructor for exception org.neuroph.util.io.NeurophOutputException
Constructs an NeurophOutputException with the specified detail message.
NeurophOutputException(String, Throwable) - Constructor for exception org.neuroph.util.io.NeurophOutputException
Constructs a NeurophOutputException with the specified detail message and specified cause.
NeurophOutputException(Throwable) - Constructor for exception org.neuroph.util.io.NeurophOutputException
Constructs a new runtime exception with the specified cause
next() - Method in class org.neuroph.core.data.BufferedDataSet
Returns next data set row.
nextRandomWeight() - Method in class org.neuroph.util.random.GaussianRandomizer
 
nextRandomWeight() - Method in class org.neuroph.util.random.RangeRandomizer
Generates next random value within [min, max] range determined by the settings in this randomizer
nextRandomWeight() - Method in class org.neuroph.util.random.WeightsRandomizer
Returns next random value from random generator, that will be used to initialize weight Override this method to implement custom random number generators
NguyenWidrowRandomizer - Class in org.neuroph.util.random
This class provides NguyenWidrow randmization technique, which gives very good results for Multi Layer Perceptrons trained with back propagation family of learning rules.
NguyenWidrowRandomizer(double, double) - Constructor for class org.neuroph.util.random.NguyenWidrowRandomizer
 
normalize(DataSet) - Method in class org.neuroph.util.data.norm.DecimalScaleNormalizer
 
normalize(DataSet) - Method in class org.neuroph.util.data.norm.MaxMinNormalizer
 
normalize(DataSet) - Method in class org.neuroph.util.data.norm.MaxNormalizer
 
normalize(DataSet) - Method in interface org.neuroph.util.data.norm.Normalizer
Normalize specified data set
normalize(DataSet) - Method in class org.neuroph.util.data.norm.RangeNormalizer
 
normalize(DataSet) - Method in class org.neuroph.util.data.norm.ZeroMeanNormalizer
 
normalizeMax(DataSet) - Static method in class org.neuroph.core.data.DataSets
 
Normalizer - Interface in org.neuroph.util.data.norm
Interface for data set normalization methods.

O

OjaLearning - Class in org.neuroph.nnet.learning
Oja learning rule wich is a modification of unsupervised hebbian learning.
OjaLearning() - Constructor for class org.neuroph.nnet.learning.OjaLearning
Creates an instance of OjaLearning algorithm
onStart() - Method in class org.neuroph.core.learning.IterativeLearning
This method is executed when learning starts, before the first epoch.
onStart() - Method in class org.neuroph.core.learning.LearningRule
Prepares the learning rule to run by setting stop flag to false If you override this method make sure you call parent method first
onStart() - Method in class org.neuroph.core.learning.SupervisedLearning
 
onStart() - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
 
onStart() - Method in class org.neuroph.nnet.learning.QuickPropagation
 
onStart() - Method in class org.neuroph.nnet.learning.RBFLearning
 
onStart() - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
onStop() - Method in class org.neuroph.core.learning.LearningRule
Invoked after the learning has stopped
Or - Class in org.neuroph.core.input
Performs logic OR operation on input vector.
Or() - Constructor for class org.neuroph.core.input.Or
 
org.neuroph.core - package org.neuroph.core
Provides base classes and basic building components for neural networks.
org.neuroph.core.data - package org.neuroph.core.data
Provides data set related classes and manipulation methods.
org.neuroph.core.events - package org.neuroph.core.events
Provides neural network learning events system
org.neuroph.core.exceptions - package org.neuroph.core.exceptions
Provides specific exceptions when working with neural networks
org.neuroph.core.input - package org.neuroph.core.input
Provides common neuron input functions
org.neuroph.core.learning - package org.neuroph.core.learning
Provides base classes for neural network learning algorithms.
org.neuroph.core.learning.error - package org.neuroph.core.learning.error
Provides error functions for learning rules
org.neuroph.core.learning.stop - package org.neuroph.core.learning.stop
Provides stop functions for learning rules
org.neuroph.core.transfer - package org.neuroph.core.transfer
Provides common neuron transfer functions
org.neuroph.eval - package org.neuroph.eval
 
org.neuroph.eval.classification - package org.neuroph.eval.classification
 
org.neuroph.nnet - package org.neuroph.nnet
Provides out-of-the-box neural networks
org.neuroph.nnet.comp - package org.neuroph.nnet.comp
Provides components for the specific neural network models.
org.neuroph.nnet.comp.layer - package org.neuroph.nnet.comp.layer
Provides various specific layer types
org.neuroph.nnet.comp.neuron - package org.neuroph.nnet.comp.neuron
Provides various specific neuron types
org.neuroph.nnet.learning - package org.neuroph.nnet.learning
Provides implementations of specific neural network learning algorithms.
org.neuroph.nnet.learning.kmeans - package org.neuroph.nnet.learning.kmeans
 
org.neuroph.nnet.learning.knn - package org.neuroph.nnet.learning.knn
 
org.neuroph.util - package org.neuroph.util
Provides various utility classes for creating neural networks, type codes, parsing vectors, etc.
org.neuroph.util.benchmark - package org.neuroph.util.benchmark
Provides microbenchmaarking framework for measuring and comparing Neuroph performance.
org.neuroph.util.data.norm - package org.neuroph.util.data.norm
Provides data normalization techniques.
org.neuroph.util.data.sample - package org.neuroph.util.data.sample
Provides data sampling techniques
org.neuroph.util.io - package org.neuroph.util.io
Provides input/output adapters for file, JDBC, URL, stream
org.neuroph.util.plugins - package org.neuroph.util.plugins
Provides various plugins for neural networks.
org.neuroph.util.random - package org.neuroph.util.random
Provides weights randomization techniques
outConnections - Variable in class org.neuroph.core.Neuron
Collection of neuron's output connections (connections from this to other neurons)
output - Variable in class org.neuroph.core.Neuron
Neuron output
output - Variable in class org.neuroph.core.transfer.TransferFunction
Output result
OutputAdapter - Interface in org.neuroph.util.io
Interface for writing neural network outputs to some destination.
outputBuffer - Variable in class org.neuroph.core.NeuralNetwork
Neural network output buffer
outputHistory - Variable in class org.neuroph.nnet.comp.neuron.DelayedNeuron
Output history for this neuron
OutputStreamAdapter - Class in org.neuroph.util.io
Implementation of OutputAdapter interface for writing neural network outputs to output stream.
OutputStreamAdapter(OutputStream) - Constructor for class org.neuroph.util.io.OutputStreamAdapter
Creates a new OutputStreamAdapter for specified output stream.
OutputStreamAdapter(BufferedWriter) - Constructor for class org.neuroph.util.io.OutputStreamAdapter
Creates a new OutputStreamAdapter for specified BufferedWriter.
Outstar - Class in org.neuroph.nnet
Outstar neural network with Outstar learning rule.
Outstar(int) - Constructor for class org.neuroph.nnet.Outstar
Creates an instance of Outstar network with specified number of neurons in output layer.
OutstarLearning - Class in org.neuroph.nnet.learning
Hebbian-like learning rule for Outstar network.
OutstarLearning() - Constructor for class org.neuroph.nnet.learning.OutstarLearning
Creates new instance of OutstarLearning algorithm

P

parentLayer - Variable in class org.neuroph.core.Neuron
Parent layer for this neuron
parseDoubleArray(String) - Static method in class org.neuroph.util.VectorParser
This method parses input String and returns double array
parseInteger(String) - Static method in class org.neuroph.util.VectorParser
This method parses input String and returns Integer vector
pause() - Method in class org.neuroph.core.learning.IterativeLearning
Pause the learning
pauseLearning() - Method in class org.neuroph.core.NeuralNetwork
Pause the learning - puts learning thread in ca state.
Perceptron - Class in org.neuroph.nnet
Perceptron neural network with some LMS based learning algorithm.
Perceptron(int, int) - Constructor for class org.neuroph.nnet.Perceptron
Creates new Perceptron with specified number of neurons in input and output layer, with Step trqansfer function
Perceptron(int, int, TransferFunctionType) - Constructor for class org.neuroph.nnet.Perceptron
Creates new Perceptron with specified number of neurons in input and output layer, and specified transfer function
PerceptronLearning - Class in org.neuroph.nnet.learning
Perceptron learning rule for perceptron neural networks.
PerceptronLearning() - Constructor for class org.neuroph.nnet.learning.PerceptronLearning
Creates new PerceptronLearning instance
PluginBase - Class in org.neuroph.util.plugins
Base class for all neural network plugins.
PluginBase() - Constructor for class org.neuroph.util.plugins.PluginBase
 
PluginBase(String) - Constructor for class org.neuroph.util.plugins.PluginBase
Creates an instance of plugin for neural network
PoolingLayer - Class in org.neuroph.nnet.comp.layer
Pooling layer is a special type of feature maps layer (FeatureMapsLayer) which is used in convolutional networks.
PoolingLayer(FeatureMapsLayer, Dimension2D) - Constructor for class org.neuroph.nnet.comp.layer.PoolingLayer
Creates pooling layer with specified kernel, appropriate map dimensions in regard to previous layer (fromLayer param) and specified number of feature maps with default neuron settings for pooling layer.
PoolingLayer(FeatureMapsLayer, Dimension2D, int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.PoolingLayer
Creates pooling layer with specified kernel, appropriate map dimensions in regard to previous layer (fromLayer param) and specified number of feature maps with given neuron properties.
precision - Variable in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
prepareTest() - Method in class org.neuroph.util.benchmark.BenchmarkTask
Any initialization before running performance test (benchmark) goes here
prepareTest() - Method in class org.neuroph.util.benchmark.MyBenchmarkTask
Benchmrk preparation consists of training set and neural networ creatiion.
previousDelta - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
 
previousEpochError - Variable in class org.neuroph.core.learning.SupervisedLearning
Total network error in previous epoch
previousGradient - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
 
previousWeightChange - Variable in class org.neuroph.nnet.learning.MomentumBackpropagation.MomentumTrainingData
 
previousWeightChange - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
 
printFoldResults(ConfusionMatrix, int) - Method in class org.neuroph.eval.KFoldCrossValidation
 
printResults(DataSet, ClassificationMetrics.Stats, int) - Method in class org.neuroph.eval.KFoldCrossValidation
 
printStats(ConfusionMatrix) - Method in class org.neuroph.eval.KFoldCrossValidation
 
process(NeuralNetwork, InputAdapter, OutputAdapter) - Static method in class org.neuroph.util.io.IOHelper
Feeds specified neural network with data from InputAdapter and writes output using OutputAdapter
processNetworkResult(double[], double[]) - Method in class org.neuroph.eval.ClassifierEvaluator.Binary
 
processNetworkResult(double[], double[]) - Method in class org.neuroph.eval.ClassifierEvaluator.MultiClass
 
processNetworkResult(double[], double[]) - Method in class org.neuroph.eval.ErrorEvaluator
 
processNetworkResult(double[], double[]) - Method in interface org.neuroph.eval.Evaluator
This method should handle processing of a single network output within an evaluation procedure
Product - Class in org.neuroph.core.input
Performs multiplication of all input vector elements.
Product() - Constructor for class org.neuroph.core.input.Product
 
Properties - Class in org.neuroph.util
Represents a general set of properties for neuroph objects
Properties() - Constructor for class org.neuroph.util.Properties
 

Q

QuickPropagation - Class in org.neuroph.nnet.learning
 
QuickPropagation() - Constructor for class org.neuroph.nnet.learning.QuickPropagation
 
QuickPropagation.QuickPropData - Class in org.neuroph.nnet.learning
 
QuickPropData() - Constructor for class org.neuroph.nnet.learning.QuickPropagation.QuickPropData
 

R

Ramp - Class in org.neuroph.core.transfer
Ramp neuron transfer function.
Ramp() - Constructor for class org.neuroph.core.transfer.Ramp
Creates an instance of Ramp transfer function with default settings
Ramp(double, double, double, double, double) - Constructor for class org.neuroph.core.transfer.Ramp
Creates an instance of Ramp transfer function with specified settings
Ramp(Properties) - Constructor for class org.neuroph.core.transfer.Ramp
Creates an instance of Ramp transfer function with specified properties.
randomGen - Variable in class org.neuroph.util.random.WeightsRandomizer
Random number genarator used to generate random values for weights
randomize(double, double) - Method in class org.neuroph.core.Weight
Deprecated.
randomize(double) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
Randomize the weights and thresholds.
randomize(Neuron) - Method in class org.neuroph.util.random.DistortRandomizer
Iterate all layers, neurons and connection weight and apply distort randomization
randomize(Neuron) - Method in class org.neuroph.util.random.HeZhangRenSunUniformWeightsRandomizer
"He" uniform distribution [-limit, limit] where limit is 3 * sqrt(2 / fan in)
randomize(NeuralNetwork) - Method in class org.neuroph.util.random.NguyenWidrowRandomizer
 
randomize(NeuralNetwork<?>) - Method in class org.neuroph.util.random.WeightsRandomizer
Iterates and randomizes all layers in specified network
randomize(Layer) - Method in class org.neuroph.util.random.WeightsRandomizer
Iterate and randomizes all neurons in specified layer
randomize(Neuron) - Method in class org.neuroph.util.random.WeightsRandomizer
Iterates and randomizes all connection weights in specified neuron
randomizeWeights() - Method in class org.neuroph.core.NeuralNetwork
Randomizes connection weights for the whole network
randomizeWeights(double, double) - Method in class org.neuroph.core.NeuralNetwork
Randomizes connection weights for the whole network within specified value range
randomizeWeights(Random) - Method in class org.neuroph.core.NeuralNetwork
Randomizes connection weights for the whole network using specified random generator
randomizeWeights(WeightsRandomizer) - Method in class org.neuroph.core.NeuralNetwork
Randomizes connection weights for the whole network using specified randomizer
RangeNormalizer - Class in org.neuroph.util.data.norm
Performs normalization of a data set inputs and outputs to specified range.
RangeNormalizer(double, double) - Constructor for class org.neuroph.util.data.norm.RangeNormalizer
 
RangeRandomizer - Class in org.neuroph.util.random
This class provides ranged weights randomizer, which randomize weights in specified [min, max] range.
RangeRandomizer(double, double) - Constructor for class org.neuroph.util.random.RangeRandomizer
Creates a new instance of RangeRandomizer within specified .
RangeRandomizer(double, double, Random) - Constructor for class org.neuroph.util.random.RangeRandomizer
 
RBFLearning - Class in org.neuroph.nnet.learning
Learning rule for Radial Basis Function networks.
RBFLearning() - Constructor for class org.neuroph.nnet.learning.RBFLearning
 
RBFNetwork - Class in org.neuroph.nnet
Radial basis function neural network.
RBFNetwork(int, int, int) - Constructor for class org.neuroph.nnet.RBFNetwork
Creates new RBFNetwork with specified number of neurons in input, rbf and output layer
readFromCsv(String, int, int, String) - Static method in class org.neuroph.core.data.DataSets
 
readFromCsv(String, int, int) - Static method in class org.neuroph.core.data.DataSets
 
readInput() - Method in interface org.neuroph.util.io.InputAdapter
Reads input from data source and returns input for neural network as array of doubles.
readInput() - Method in class org.neuroph.util.io.InputStreamAdapter
 
readInput() - Method in class org.neuroph.util.io.JDBCInputAdapter
Reads next row from result set and returns input for neural network as array of doubles.
recall - Variable in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
RectifiedLinear - Class in org.neuroph.core.transfer
 
RectifiedLinear() - Constructor for class org.neuroph.core.transfer.RectifiedLinear
 
RectifierNeuralNetwork - Class in org.neuroph.nnet
 
RectifierNeuralNetwork(List<Integer>) - Constructor for class org.neuroph.nnet.RectifierNeuralNetwork
 
remove() - Method in class org.neuroph.core.data.BufferedDataSet
 
remove(Object) - Method in class org.neuroph.core.data.DataSet
 
remove(int) - Method in class org.neuroph.core.data.DataSet
 
removeAllConnections() - Method in class org.neuroph.core.Neuron
 
removeAllInputConnections() - Method in class org.neuroph.core.Neuron
 
removeAllNeurons() - Method in class org.neuroph.core.Layer
 
removeAllOutputConnections() - Method in class org.neuroph.core.Neuron
 
removeInputConnection(Connection) - Method in class org.neuroph.core.Neuron
 
removeInputConnectionFrom(Neuron) - Method in class org.neuroph.core.Neuron
Removes input connection which is connected to specified neuron
removeLayer(Layer) - Method in class org.neuroph.core.NeuralNetwork
Removes specified layer from network
removeLayerAt(int) - Method in class org.neuroph.core.NeuralNetwork
Removes layer at specified index position from net
removeListener(LearningEventListener) - Method in class org.neuroph.core.learning.LearningRule
 
removeListener(NeuralNetworkEventListener) - Method in class org.neuroph.core.NeuralNetwork
 
removeNeuron(Neuron) - Method in class org.neuroph.core.Layer
Removes neuron from layer
removeNeuronAt(int) - Method in class org.neuroph.core.Layer
Removes neuron at specified index position in this layer
removeOutputConnection(Connection) - Method in class org.neuroph.core.Neuron
 
removeOutputConnectionTo(Neuron) - Method in class org.neuroph.core.Neuron
 
removePlugin(Class) - Method in class org.neuroph.core.NeuralNetwork
Removes the plugin with specified name
removePoint(KVector) - Method in class org.neuroph.nnet.learning.kmeans.Cluster
 
removeRowAt(int) - Method in class org.neuroph.core.data.DataSet
Removes training row at specified index position
reset() - Method in class org.neuroph.core.Layer
Resets the activation and input levels for all neurons in this layer
reset() - Method in interface org.neuroph.core.learning.error.ErrorFunction
Sets total error and pattern count to zero.
reset() - Method in class org.neuroph.core.learning.error.MeanAbsoluteError
 
reset() - Method in class org.neuroph.core.learning.error.MeanSquaredError
 
reset() - Method in class org.neuroph.core.NeuralNetwork
Resets the activation levels for whole network
reset() - Method in class org.neuroph.core.Neuron
Sets input and output activation levels to zero
reset() - Method in class org.neuroph.eval.ClassifierEvaluator
 
reset() - Method in class org.neuroph.eval.ErrorEvaluator
 
reset() - Method in interface org.neuroph.eval.Evaluator
 
reset() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
Resets the input, output and mode for this neuron
reset() - Method in class org.neuroph.util.benchmark.Stopwatch
Resets the stopwatch (clears start and stop time)
ResilientPropagation - Class in org.neuroph.nnet.learning
Resilient Propagation learning rule used for Multi Layer Perceptron neural networks.
ResilientPropagation() - Constructor for class org.neuroph.nnet.learning.ResilientPropagation
 
ResilientPropagation.ResilientWeightTrainingtData - Class in org.neuroph.nnet.learning
 
ResilientWeightTrainingtData() - Constructor for class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
 
resillientWeightUpdate(Weight) - Method in class org.neuroph.nnet.learning.ResilientPropagation
Weight update by done by ResilientPropagation learning rule Executed at the end of epoch (in batch mode)
resume() - Method in class org.neuroph.core.learning.IterativeLearning
Resumes the paused learning
resumeLearning() - Method in class org.neuroph.core.NeuralNetwork
Resumes paused learning - notifies the learning rule to continue
run() - Method in class org.neuroph.eval.CrossValidationBak
 
run() - Method in class org.neuroph.eval.KFoldCrossValidation
 
run() - Method in class org.neuroph.util.benchmark.Benchmark
Runs all benchmark tasks
runFullEvaluation(NeuralNetwork<?>, DataSet) - Static method in class org.neuroph.eval.Evaluation
Out of the box method (util) which computes all metrics for given neural network and test data set
runTask(BenchmarkTask) - Static method in class org.neuroph.util.benchmark.Benchmark
Runs specified benchmark tasks, the basic benchmarking workflow.
runTest() - Method in class org.neuroph.util.benchmark.BenchmarkTask
This method should hold the code to benchmark
runTest() - Method in class org.neuroph.util.benchmark.MyBenchmarkTask
 

S

sample(Sampling) - Method in class org.neuroph.core.data.DataSet
 
sample(DataSet) - Method in interface org.neuroph.util.data.sample.Sampling
 
sample(DataSet) - Method in class org.neuroph.util.data.sample.SubSampling
 
Sampling - Interface in org.neuroph.util.data.sample
Interface for data set sampling methods.
save(String) - Method in class org.neuroph.core.data.DataSet
Saves this training set to the specified file
save() - Method in class org.neuroph.core.data.DataSet
Saves this training set to file specified in its filePath field
save(String) - Method in class org.neuroph.core.NeuralNetwork
Saves neural network into the specified file.
saveAsTxt(String, String) - Method in class org.neuroph.core.data.DataSet
 
setAmplitude(double) - Method in class org.neuroph.core.transfer.Tanh
Sets the slope parameter for this function
setBatchMode(boolean) - Method in class org.neuroph.core.learning.SupervisedLearning
Sets batch mode on/off (true/false)
setBatchMode(boolean) - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
setBias(double) - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
Sets bias value for this neuron
setCentroid(KVector) - Method in class org.neuroph.nnet.learning.kmeans.Cluster
 
setClassLabel(String) - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
setCluster(Cluster) - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
setColumnName(int, String) - Method in class org.neuroph.core.data.DataSet
 
setColumnNames(String[]) - Method in class org.neuroph.core.data.DataSet
 
setColumnType(int, DataSetColumnType) - Method in class org.neuroph.core.data.DataSet
Sets column type for the given index.
setConfusionMatrix(ConfusionMatrix) - Method in class org.neuroph.eval.EvaluationResult
 
setConfusionMatrix(ConfusionMatrix) - Method in class org.neuroph.eval.FoldResult
 
setDataSet(DataSet) - Method in class org.neuroph.eval.EvaluationResult
 
setDataSet(DataSet) - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
setDataSet(List<KVector>) - Method in class org.neuroph.nnet.learning.knn.KNearestNeighbour
 
setDecreaseFactor(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
setDefaultIO(NeuralNetwork) - Static method in class org.neuroph.util.NeuralNetworkFactory
Sets default input and output neurons for network (first layer as input, last as output)
setDelay(int) - Method in class org.neuroph.nnet.comp.DelayedConnection
Sets delay value for this connection
setDelta(double) - Method in class org.neuroph.core.Neuron
Sets delta for this neuron.
setDesiredOutput(double[]) - Method in class org.neuroph.core.data.DataSetRow
 
setDistance(double) - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
setErrorCorrection(double) - Method in class org.neuroph.nnet.learning.BinaryDeltaRule
Sets the errorCorrection parametar
setErrorFunction(ErrorFunction) - Method in class org.neuroph.core.learning.SupervisedLearning
 
setFilePath(String) - Method in class org.neuroph.core.data.DataSet
Sets full file path for this training set
setFlattenNetworks(boolean) - Method in class org.neuroph.util.Neuroph
Turn on/off flat networ support from Encog
setHeight(int) - Method in class org.neuroph.nnet.comp.Dimension2D
 
setHeight(int) - Method in class org.neuroph.nnet.comp.Kernel
Sets height of this kernel
setIncreaseFactor(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
setInitialDelta(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
setInput(double[]) - Method in class org.neuroph.core.data.DataSetRow
Sets input vector
setInput(double...) - Method in class org.neuroph.core.NeuralNetwork
Sets network input.
setInput(double) - Method in class org.neuroph.core.Neuron
Sets neuron's input
setInput(double) - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
Sets total net input for this cell
setInput(double...) - Method in class org.neuroph.nnet.ConvolutionalNetwork
Sets network input, to all feature maps in input layer
setInputFunction(InputFunction) - Method in class org.neuroph.core.Neuron
Sets input function
setInputNeurons(List<Neuron>) - Method in class org.neuroph.core.NeuralNetwork
Sets input neurons
setIsCompeting(boolean) - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
Sets the flag to indicate that this neuron is in competing mode
setIterations(int, int) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
setLabel(String) - Method in class org.neuroph.core.data.DataSet
Sets label for this training set
setLabel(String) - Method in class org.neuroph.core.data.DataSetRow
Set training element label
setLabel(String) - Method in class org.neuroph.core.Layer
Set layer label
setLabel(String) - Method in class org.neuroph.core.NeuralNetwork
Set network label
setLabel(String) - Method in class org.neuroph.core.Neuron
Sets the label for this neuron
setLearningRate(double) - Method in class org.neuroph.core.learning.IterativeLearning
Sets learning rate for this algorithm
setLearningRate(double) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
setLearningRateChange(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setLearningRule(L) - Method in class org.neuroph.core.NeuralNetwork
Sets learning algorithm for this network
setLeftHigh(double) - Method in class org.neuroph.core.transfer.Trapezoid
Sets left high point of trapezoid function
setLeftLow(double) - Method in class org.neuroph.core.transfer.Trapezoid
Sets left low point of trapezoid function
setMaxDelta(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
setMaxError(double) - Method in class org.neuroph.core.learning.SupervisedLearning
Sets allowed network error, which indicates when to stopLearning training
setMaxIterations(int) - Method in class org.neuroph.core.learning.IterativeLearning
Sets iteration limit for this learning algorithm
setMaxIterations(int) - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
Sets max iterations for neurons to compete in this layer
setMaxLearningRate(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setMaxMomentum(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setMeanSquareError(double) - Method in class org.neuroph.eval.EvaluationResult
 
setMinDelta(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
 
setMinErrorChange(double) - Method in class org.neuroph.core.learning.SupervisedLearning
Sets min error change stopping criteria
setMinErrorChangeIterationsLimit(int) - Method in class org.neuroph.core.learning.SupervisedLearning
Sets number of iterations for min error change stopping criteria
setMinLearningRate(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setMinMomentum(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setMomentum(double) - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
Sets the momentum factor
setMomentumChange(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setName(String) - Method in class org.neuroph.util.benchmark.BenchmarkTask
Sets task name
setNetworkType(NeuralNetworkType) - Method in class org.neuroph.core.NeuralNetwork
Sets type for this network
setNeuralNetwork(NeuralNetwork) - Method in class org.neuroph.core.learning.LearningRule
Sets neural network for this learning rule
setNeuralNetwork(NeuralNetwork) - Method in class org.neuroph.eval.EvaluationResult
 
setNeuralNetwork(NeuralNetwork) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
setNeuron(int, Neuron) - Method in class org.neuroph.core.Layer
Sets (replace) the neuron at specified position in layer
setNumberOfClusters(int) - Method in class org.neuroph.nnet.learning.kmeans.KMeansClustering
 
setOutput(double) - Method in class org.neuroph.core.Neuron
Sets this neuron output
setOutputLabels(String[]) - Method in class org.neuroph.core.NeuralNetwork
Sets labels for output neurons
setOutputNeurons(List<Neuron>) - Method in class org.neuroph.core.NeuralNetwork
Sets output neurons
setParentLayer(Layer) - Method in class org.neuroph.core.Neuron
Sets reference to parent layer for this neuron (layer in which the neuron is located)
setParentNetwork(NeuralNetwork) - Method in class org.neuroph.core.Layer
Sets reference on parent network
setParentNetwork(NeuralNetwork) - Method in class org.neuroph.util.plugins.PluginBase
Sets the parent network for this plugin
setProperty(String, Object) - Method in class org.neuroph.util.NeuronProperties
 
setProperty(String, Object) - Method in class org.neuroph.util.Properties
 
setRightHigh(double) - Method in class org.neuroph.core.transfer.Trapezoid
Sets right high point of trapezoid function
setRightLow(double) - Method in class org.neuroph.core.transfer.Trapezoid
Sets right low point of trapezoid function
setSampling(Sampling) - Method in class org.neuroph.eval.CrossValidationBak
 
setSigma(double) - Method in class org.neuroph.core.transfer.Gaussian
Sets the sigma parametar for this function
setSlope(double) - Method in class org.neuroph.core.transfer.Linear
Sets the slope parametar for this function
setSlope(double) - Method in class org.neuroph.core.transfer.Sigmoid
Sets the slope parametar for this function
setSlope(double) - Method in class org.neuroph.core.transfer.Tanh
Sets the slope parameter for this function
setTestIterations(int) - Method in class org.neuroph.util.benchmark.BenchmarkTask
Sets number of test (benchmarking) iterations
setThresh(double) - Method in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
Sets threshold value for this neuron
setThreshold(double) - Method in class org.neuroph.eval.ClassifierEvaluator
 
setTrainingData(T) - Method in class org.neuroph.core.Weight
 
setTrainingSet(DataSet) - Method in class org.neuroph.core.learning.LearningRule
Sets training set for this learning rule
setTransferFunction(TransferFunction) - Method in class org.neuroph.core.Neuron
Sets transfer function
setUseDynamicLearningRate(boolean) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setUseDynamicMomentum(boolean) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
setValue(double) - Method in class org.neuroph.core.Weight
Sets the weight value
setValueAt(int, double) - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
setValues(int[][]) - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
setValues(double[]) - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
setWarmupIterations(int) - Method in class org.neuroph.util.benchmark.BenchmarkTask
Sets the number of warmup iterations
setWeight(Weight) - Method in class org.neuroph.core.Connection
Set the weight of the connection.
setWeights(double[]) - Method in class org.neuroph.core.NeuralNetwork
Sets network weights from the specified double array
setWeights(Weight[][]) - Method in class org.neuroph.nnet.comp.Kernel
 
setWidth(int) - Method in class org.neuroph.nnet.comp.Dimension2D
 
setWidth(int) - Method in class org.neuroph.nnet.comp.Kernel
Sets width of this kernel
setXHigh(double) - Method in class org.neuroph.core.transfer.Ramp
Sets threshold for the high output level
setXLow(double) - Method in class org.neuroph.core.transfer.Ramp
Sets threshold for the low output level
setYHigh(double) - Method in class org.neuroph.core.transfer.Ramp
Sets output value for the high output level
setYHigh(double) - Method in class org.neuroph.core.transfer.Step
Set output value for the high output level
setYLow(double) - Method in class org.neuroph.core.transfer.Ramp
Sets output value for the low output level
setYLow(double) - Method in class org.neuroph.core.transfer.Step
Set output value for the low output level
Sgn - Class in org.neuroph.core.transfer
Sgn neuron transfer function.
Sgn() - Constructor for class org.neuroph.core.transfer.Sgn
 
shouldFlattenNetworks() - Method in class org.neuroph.util.Neuroph
Get setting for flatten network (from Encog engine)
shuffle() - Method in class org.neuroph.core.data.DataSet
 
shutdown() - Method in class org.neuroph.util.Neuroph
Shuts down the Encog engine
Sigmoid - Class in org.neuroph.core.transfer
Sigmoid neuron transfer function.
Sigmoid() - Constructor for class org.neuroph.core.transfer.Sigmoid
Creates an instance of Sigmoid neuron transfer function with default slope=1.
Sigmoid(double) - Constructor for class org.neuroph.core.transfer.Sigmoid
Creates an instance of Sigmoid neuron transfer function with specified value for slope parametar.
Sigmoid(Properties) - Constructor for class org.neuroph.core.transfer.Sigmoid
Creates an instance of Sigmoid neuron transfer function with the specified properties.
SigmoidDeltaRule - Class in org.neuroph.nnet.learning
Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.
SigmoidDeltaRule() - Constructor for class org.neuroph.nnet.learning.SigmoidDeltaRule
Creates new SigmoidDeltaRule
SimulatedAnnealingLearning - Class in org.neuroph.nnet.learning
This class implements a simulated annealing learning rule for supervised neural networks.
SimulatedAnnealingLearning(NeuralNetwork, double, double, int) - Constructor for class org.neuroph.nnet.learning.SimulatedAnnealingLearning
Construct a simulated annleaing trainer for a feedforward neural network.
SimulatedAnnealingLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.SimulatedAnnealingLearning
 
Sin - Class in org.neuroph.core.transfer
Sin neuron transfer function.
Sin() - Constructor for class org.neuroph.core.transfer.Sin
 
size() - Method in class org.neuroph.core.data.DataSet
Returns number of training elements in this training set set
size() - Method in class org.neuroph.nnet.learning.kmeans.Cluster
Returns number of vectors assigned to this cluster.
size() - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
SmallErrorChangeStop - Class in org.neuroph.core.learning.stop
Stops learning rule if error change has been too small for specified number of iterations
SmallErrorChangeStop(SupervisedLearning) - Constructor for class org.neuroph.core.learning.stop.SmallErrorChangeStop
 
split(int) - Method in class org.neuroph.core.data.DataSet
Splits data set into specified number of parts and returns them as a list.
split(double...) - Method in class org.neuroph.core.data.DataSet
Splits data sets into parts of specified sizes.
split(int, Random) - Method in class org.neuroph.core.data.DataSet
 
split(Random, double...) - Method in class org.neuroph.core.data.DataSet
 
start() - Method in class org.neuroph.util.benchmark.Stopwatch
Starts measuring time
Stats() - Constructor for class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
STD_DEV - Static variable in class org.neuroph.util.DataSetStatistics
 
Step - Class in org.neuroph.core.transfer
Step neuron transfer function.
Step() - Constructor for class org.neuroph.core.transfer.Step
Creates an instance of Step transfer function
Step(Properties) - Constructor for class org.neuroph.core.transfer.Step
Creates an instance of Step transfer function with specified properties
stop() - Method in class org.neuroph.util.benchmark.Stopwatch
Stops measuring time
StopCondition - Interface in org.neuroph.core.learning.stop
Interface for learning rule stop condition.
stopConditions - Variable in class org.neuroph.core.learning.IterativeLearning
 
stopLearning() - Method in class org.neuroph.core.learning.LearningRule
Stops learning
stopLearning() - Method in class org.neuroph.core.NeuralNetwork
Stops learning
Stopwatch - Class in org.neuroph.util.benchmark
A class to help benchmark code, it simulates a real stop watch.
Stopwatch() - Constructor for class org.neuroph.util.benchmark.Stopwatch
 
SubSampling - Class in org.neuroph.util.data.sample
This class provides sub-sampling of a data set, and creates a specified number of subsets form given data set.
SubSampling(int) - Constructor for class org.neuroph.util.data.sample.SubSampling
Sampling will produce a specified number of subsets of equal sizes.
SubSampling(double...) - Constructor for class org.neuroph.util.data.sample.SubSampling
Sampling will create subsets of specified sizes.
Sum - Class in org.neuroph.core.input
Performs summing of all input vector elements.
Sum() - Constructor for class org.neuroph.core.input.Sum
 
SUM - Static variable in class org.neuroph.util.DataSetStatistics
 
sumConfusionMatrix(List<ConfusionMatrix>, DataSet) - Method in class org.neuroph.eval.KFoldCrossValidation
 
SumSqr - Class in org.neuroph.core.input
Calculates squared sum of all input vector elements.
SumSqr() - Constructor for class org.neuroph.core.input.SumSqr
 
SupervisedHebbianLearning - Class in org.neuroph.nnet.learning
Supervised hebbian learning rule.
SupervisedHebbianLearning() - Constructor for class org.neuroph.nnet.learning.SupervisedHebbianLearning
Creates new instance of SupervisedHebbianLearning algorithm
SupervisedHebbianNetwork - Class in org.neuroph.nnet
Hebbian neural network with supervised Hebbian learning algorithm.
SupervisedHebbianNetwork(int, int) - Constructor for class org.neuroph.nnet.SupervisedHebbianNetwork
Creates an instance of Supervised Hebbian Network net with specified number neurons in input and output layer
SupervisedHebbianNetwork(int, int, TransferFunctionType) - Constructor for class org.neuroph.nnet.SupervisedHebbianNetwork
Creates an instance of Supervised Hebbian Network with specified number of neurons in input layer and output layer, and transfer function
SupervisedLearning - Class in org.neuroph.core.learning
Base class for all supervised learning algorithms.
SupervisedLearning() - Constructor for class org.neuroph.core.learning.SupervisedLearning
Creates new supervised learning rule

T

Tanh - Class in org.neuroph.core.transfer
Tanh neuron transfer function.
Tanh() - Constructor for class org.neuroph.core.transfer.Tanh
Creates an instance of Tanh neuron transfer function with default slope=amplitude=1
Tanh(double) - Constructor for class org.neuroph.core.transfer.Tanh
Creates an instance of Tanh neuron transfer function with specified value for slope parameter.
Tanh(Properties) - Constructor for class org.neuroph.core.transfer.Tanh
Creates an instance of Tanh neuron transfer function with the specified properties.
temperature - Variable in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
The current temperature.
thresh - Variable in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
Threshold value for this neuron
ThresholdNeuron - Class in org.neuroph.nnet.comp.neuron
Provides behaviour for neurons with threshold.
ThresholdNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.ThresholdNeuron
Creates a neuron with threshold behaviour, and with the specified input and transfer functions.
toArray() - Method in class org.neuroph.core.data.DataSetRow
 
toCSV() - Method in class org.neuroph.core.data.DataSet
Returns enire dataset in csv format
toCSV() - Method in class org.neuroph.core.data.DataSetRow
 
toDoubleArray(List<Double>) - Static method in class org.neuroph.util.VectorParser
 
toNeuron - Variable in class org.neuroph.core.Connection
To neuron for this connection (target, destination neuron) This connection is input connection for to neuron.
toString() - Method in class org.neuroph.core.Connection
 
toString() - Method in class org.neuroph.core.data.DataSet
Returns string representation of this data set
toString() - Method in class org.neuroph.core.data.DataSetRow
 
toString() - Method in class org.neuroph.core.NeuralNetwork
 
toString() - Method in class org.neuroph.core.Weight
Returns weight value as String
toString() - Method in class org.neuroph.eval.classification.ClassificationMetrics.Stats
 
toString() - Method in class org.neuroph.eval.classification.ClassificationMetrics
 
toString() - Method in class org.neuroph.eval.classification.ClassificationResult
 
toString() - Method in class org.neuroph.eval.classification.ConfusionMatrix
 
toString() - Method in class org.neuroph.eval.EvaluationResult
 
toString() - Method in class org.neuroph.nnet.comp.Dimension2D
 
toString() - Method in class org.neuroph.nnet.learning.kmeans.KVector
 
toString() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
 
totalInput - Variable in class org.neuroph.core.Neuron
Total net input for this neuron.
trainingSet - Variable in class org.neuroph.core.learning.LearningRule
Training data set
TrainingSetImport - Class in org.neuroph.util
Handles training set imports
TrainingSetImport() - Constructor for class org.neuroph.util.TrainingSetImport
 
trainTestSplit(DataSet, double) - Static method in class org.neuroph.core.data.DataSets
 
transferFunction - Variable in class org.neuroph.core.Neuron
Transfer function for this neuron
TransferFunction - Class in org.neuroph.core.transfer
Abstract base class for all neuron tranfer functions.
TransferFunction() - Constructor for class org.neuroph.core.transfer.TransferFunction
 
TransferFunctionType - Enum in org.neuroph.util
Contains transfer functions types and labels.
Trapezoid - Class in org.neuroph.core.transfer
Fuzzy trapezoid neuron tranfer function.
Trapezoid() - Constructor for class org.neuroph.core.transfer.Trapezoid
Creates an instance of Trapezoid transfer function
Trapezoid(double, double, double, double) - Constructor for class org.neuroph.core.transfer.Trapezoid
Creates an instance of Trapezoid transfer function with the specified setting.
Trapezoid(Properties) - Constructor for class org.neuroph.core.transfer.Trapezoid
Creates an instance of Trapezoid transfer function with the specified properties.

U

UnsupervisedHebbianLearning - Class in org.neuroph.nnet.learning
Unsupervised hebbian learning rule.
UnsupervisedHebbianLearning() - Constructor for class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
Creates new instance of UnsupervisedHebbianLearning algorithm
UnsupervisedHebbianNetwork - Class in org.neuroph.nnet
Hebbian neural network with unsupervised Hebbian learning algorithm.
UnsupervisedHebbianNetwork(int, int) - Constructor for class org.neuroph.nnet.UnsupervisedHebbianNetwork
Creates an instance of Unsuervised Hebian net with specified number of neurons in input and output layer
UnsupervisedHebbianNetwork(int, int, TransferFunctionType) - Constructor for class org.neuroph.nnet.UnsupervisedHebbianNetwork
Creates an instance of Unsuervised Hebian net with specified number of neurons in input layer and output layer, and transfer function
UnsupervisedLearning - Class in org.neuroph.core.learning
Base class for all unsupervised learning algorithms.
UnsupervisedLearning() - Constructor for class org.neuroph.core.learning.UnsupervisedLearning
Creates new unsupervised learning rule
updateNetworkWeights() - Method in class org.neuroph.core.learning.UnsupervisedLearning
This method implements the weight adjustment
updateNetworkWeights() - Method in class org.neuroph.nnet.learning.CompetitiveLearning
Adjusts weights for the winning neuron
updateNetworkWeights() - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
Adjusts weights for the output neurons
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.AntiHebbianLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.BinaryHebbianLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.GeneralizedHebbianLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.InstarLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.OjaLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.OutstarLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron, double) - Method in class org.neuroph.nnet.learning.SupervisedHebbianLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
This method implements weights update procedure for the single neuron
URLInputAdapter - Class in org.neuroph.util.io
Implementation of InputAdapter interface for reading neural network inputs from URL.
URLInputAdapter(URL) - Constructor for class org.neuroph.util.io.URLInputAdapter
Creates a new URLInputAdapter by opening a connection to URL specified by the input param
URLInputAdapter(String) - Constructor for class org.neuroph.util.io.URLInputAdapter
Creates a new URLInputAdapter by opening a connection to URL specified by the input param
URLOutputAdapter - Class in org.neuroph.util.io
Implementation of OutputAdapter interface for writing neural network outputs to URL.
URLOutputAdapter(URL) - Constructor for class org.neuroph.util.io.URLOutputAdapter
Creates a new URLOutputAdapter by opening a connection to URL specified by the url input param
URLOutputAdapter(String) - Constructor for class org.neuroph.util.io.URLOutputAdapter
Creates a new URLOutputAdapter by opening a connection to URL specified by the string url input param
Utils - Class in org.neuroph.eval.classification
 

V

value - Variable in class org.neuroph.core.Weight
Weight value
valueOf(String) - Static method in enum org.neuroph.core.events.LearningEvent.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.neuroph.core.events.NeuralNetworkEvent.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.neuroph.util.DataSetColumnType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.neuroph.util.NeuralNetworkType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.neuroph.util.TransferFunctionType
Returns the enum constant of this type with the specified name.
values() - Static method in enum org.neuroph.core.events.LearningEvent.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.neuroph.core.events.NeuralNetworkEvent.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.neuroph.util.DataSetColumnType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.neuroph.util.NeuralNetworkType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.neuroph.util.TransferFunctionType
Returns an array containing the constants of this enum type, in the order they are declared.
VAR - Static variable in class org.neuroph.util.DataSetStatistics
 
VectorParser - Class in org.neuroph.util
Provides methods to parse strings as Integer or Double vectors.
VectorParser() - Constructor for class org.neuroph.util.VectorParser
 
VectorSizeMismatchException - Exception in org.neuroph.core.exceptions
Thrown to indicate that vector size does not match the network input or training element size.
VectorSizeMismatchException() - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
Constructs an VectorSizeMismatchException with no detail message.
VectorSizeMismatchException(String) - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
Constructs an VectorSizeMismatchException with the specified detail message.
VectorSizeMismatchException(String, Throwable) - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
Constructs a VectorSizeMismatchException with the specified detail message and specified cause.
VectorSizeMismatchException(Throwable) - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
Constructs a new runtime exception with the specified cause

W

weight - Variable in class org.neuroph.core.Connection
Weight for this connection
Weight<T> - Class in org.neuroph.core
Neuron connection weight.
Weight() - Constructor for class org.neuroph.core.Weight
Creates an instance of connection weight with random weight value in range [-0.5 ..
Weight(double) - Constructor for class org.neuroph.core.Weight
Creates an instance of connection weight with the specified weight value
weightChange - Variable in class org.neuroph.core.Weight
Weight change
WeightedSum - Class in org.neuroph.core.input
Optimized version of weighted input function
WeightedSum() - Constructor for class org.neuroph.core.input.WeightedSum
 
WeightsRandomizer - Class in org.neuroph.util.random
Basic weights randomizer, iterates and randomizes all connection weights in network.
WeightsRandomizer() - Constructor for class org.neuroph.util.random.WeightsRandomizer
Create a new instance of WeightsRandomizer
WeightsRandomizer(Random) - Constructor for class org.neuroph.util.random.WeightsRandomizer
Create a new instance of WeightsRandomizer with specified random generator If you use the same random generators, you'll get the same random sequences
withConvolutionLayer(int, int, int) - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
withConvolutionLayer(Dimension2D, int, Class<? extends TransferFunction>) - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
withFullConnectedLayer(int) - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
withFullConnectedLayer(Layer) - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
withInputLayer(int, int, int) - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
withPoolingLayer(int, int) - Method in class org.neuroph.nnet.ConvolutionalNetwork.Builder
 
writeOutput(double[]) - Method in class org.neuroph.util.io.JDBCOutputAdapter
Writes specified output to table in database
writeOutput(double[]) - Method in interface org.neuroph.util.io.OutputAdapter
Write neural network output to some destination.
writeOutput(double[]) - Method in class org.neuroph.util.io.OutputStreamAdapter
Writes specified output to output stream

Z

ZeroMeanNormalizer - Class in org.neuroph.util.data.norm
Normalizes data sets by shifting all values in such way that data set has mean of 0 and standard deviation 1.
ZeroMeanNormalizer(DataSet) - Constructor for class org.neuroph.util.data.norm.ZeroMeanNormalizer
 
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