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

A

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
addElement(TrainingElement) - Method in class org.neuroph.core.learning.TrainingSet
Adds new training element to this training set
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.BiasNeuron
 
addInputConnection(Neuron, double) - Method in class org.neuroph.nnet.comp.BiasNeuron
 
addInputConnection(Neuron) - Method in class org.neuroph.nnet.comp.BiasNeuron
 
addInputConnection(Connection) - Method in class org.neuroph.nnet.comp.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
addNetwork(NeuralNetwork) - Method in class org.neuroph.nnet.ModularNetwork
Adds neural network
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
addOutputConnection(Connection) - Method in class org.neuroph.core.Neuron
Adds the specified output connection
addPlugin(PluginBase) - Method in class org.neuroph.core.NeuralNetwork
Adds plugin to neural network
adjustLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
adjustMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
adjustOutputNeurons(Vector<Double>) - Method in class org.neuroph.nnet.learning.SigmoidDeltaRule
This method implements weights update procedure for the output neurons
adjustWeights() - Method in class org.neuroph.core.learning.UnsupervisedLearning
This method implements the weight adjustment
adjustWeights() - Method in class org.neuroph.nnet.learning.CompetitiveLearning
Adjusts weights for the winning neuron
adjustWeights() - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
Adjusts weights for the output neurons
And - Class in org.neuroph.core.input
Performs logic AND operation on input vector.
And() - Constructor for class org.neuroph.core.input.And
 

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
BackPropagation(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.BackPropagation
Creates new instance of BackPropagation learning for the specified neural network
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.
BiasNeuron - Class in org.neuroph.nnet.comp
Neuron with Bias Settings
BiasNeuron() - Constructor for class org.neuroph.nnet.comp.BiasNeuron
Creates an instance of BiasedNeuron.
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
BinaryHebbianLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.BinaryHebbianLearning
Creates new instance of BinaryHebbianLearning for the specified neural network
blueValues - Variable in class org.neuroph.contrib.imgrec.FractionRgbData
Array which contains blue componenet of the color for each image pixel

C

calculate() - Method in class org.neuroph.contrib.IACNeuron
Calculates neuron output
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.CompetitiveLayer
Performs calculaton for all neurons in this layer
calculate() - Method in class org.neuroph.nnet.comp.CompetitiveNeuron
 
calculate() - Method in class org.neuroph.nnet.comp.DelayedNeuron
 
calculate() - Method in class org.neuroph.nnet.comp.InputOutputNeuron
Calculates neuron output
calculate() - Method in class org.neuroph.nnet.comp.ThresholdNeuron
Calculates neuron's output
clear() - Method in class org.neuroph.core.learning.TrainingSet
Removes all alements from training set
ColorMode - Enum in org.neuroph.contrib.imgrec
Represents the color modes for image recognition.
CompetitiveLayer - Class in org.neuroph.nnet.comp
Represents layer of competitive neurons, and provides methods for competition.
CompetitiveLayer(int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.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
CompetitiveLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.CompetitiveLearning
Creates new instance of CompetitiveLearning for the specified neural network
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
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.CompetitiveNeuron
Creates an instance of CompetitiveNeuron with specified input and transfer functions
connectedNeuron - Variable in class org.neuroph.core.Connection
Connected neuron
Connection - Class in org.neuroph.core
Weighted connection to another neuron.
Connection(Neuron) - Constructor for class org.neuroph.core.Connection
Creates a new connection to specified neuron with random weight
Connection(Neuron, Weight) - Constructor for class org.neuroph.core.Connection
Creates a new connection to specified neuron with specified weight object
Connection(Neuron, double) - Constructor for class org.neuroph.core.Connection
Creates a new connection to specified neuron with specified weight value
Connection(Neuron, Neuron) - Constructor for class org.neuroph.core.Connection
Creates a new connection between specified neurons with random 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
 
convertRgbInputToBinaryBlackAndWhite(double[]) - Static method in class org.neuroph.contrib.imgrec.FractionRgbData
Converts image rgb data to binary black and white data
convertToArray(Vector<Double>) - Static method in class org.neuroph.util.VectorParser
 
convertToVector(double[]) - Static method in class org.neuroph.util.VectorParser
 
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
createBlackAndWhiteTrainingSet(List<String>, Map<String, FractionRgbData>) - Static method in class org.neuroph.contrib.imgrec.ImageRecognitionHelper
Creates binary black and white training set for the specified image labels and rgb data
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
createDemoNetwork() - Method in class org.neuroph.contrib.RecommenderNetwork
 
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
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
 
createLayer(int, TransferFunctionType) - Static method in class org.neuroph.util.LayerFactory
 
createLayer(Vector<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, 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 according to the given specification.
createNewNeuralNetwork(String, Dimension, ColorMode, List<String>, Vector<Integer>, TransferFunctionType) - Static method in class org.neuroph.contrib.imgrec.ImageRecognitionHelper
Creates and returns new neural network for image recognition.
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
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
createTrainingSet(List<String>, Map<String, FractionRgbData>) - Static method in class org.neuroph.contrib.imgrec.ImageRecognitionHelper
Creates training set for the specified image labels and rgb data
createUnsupervisedHebbian(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns a new instance of Unsupervised Hebbian Network
currentIteration - Variable in class org.neuroph.core.learning.IterativeLearning
Current iteration counter

D

dec(double) - Method in class org.neuroph.core.Weight
Decreases the weight for specified amount
DelayedConnection - Class in org.neuroph.nnet.comp
Represents the connection between neurons which can delay signal.
DelayedConnection(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
Provides behaviour for neurons with delayed output.
DelayedNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.DelayedNeuron
Creates an instance of neuron which can delay output
Diference - Class in org.neuroph.core.input
Performs the vector difference operation on input and weight vector.
Diference() - Constructor for class org.neuroph.core.input.Diference
 
DLRSample - Class in org.neuroph.samples
 
DLRSample() - Constructor for class org.neuroph.samples.DLRSample
 
doLearningEpoch(TrainingSet) - 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(TrainingSet) - Method in class org.neuroph.core.learning.SupervisedLearning
This method implements basic logic for one learning epoch for the supervised learning algorithms.
doLearningEpoch(TrainingSet) - Method in class org.neuroph.core.learning.UnsupervisedLearning
This method does one learning epoch for the unsupervised learning rules.
doLearningEpoch(TrainingSet) - Method in class org.neuroph.nnet.learning.CompetitiveLearning
This method does one learning epoch for the unsupervised learning rules.
doLearningEpoch(TrainingSet) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
 
doLearningEpoch(TrainingSet) - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
This method does one learning epoch for the unsupervised learning rules.
doOneLearningIteration(TrainingSet) - Method in class org.neuroph.core.learning.IterativeLearning
Runs one learning iteration for the specified training set and notfies observers.
downSampleImage(Dimension, BufferedImage) - Static method in class org.neuroph.contrib.imgrec.ImageSampler
Scales image to the specified dimension
downSampleImage(Dimension, BufferedImage, int) - Static method in class org.neuroph.contrib.imgrec.ImageSampler
Scales image to the specified dimension
DynamicBackPropagation - Class in org.neuroph.nnet.learning
Backpropagation learning rule with momentum and dynamic learning rate
DynamicBackPropagation() - Constructor for class org.neuroph.nnet.learning.DynamicBackPropagation
 
DynamicBackPropagation(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.DynamicBackPropagation
 

E

elementAt(int) - Method in class org.neuroph.core.learning.TrainingSet
Returns training element at specified index position
elements() - Method in class org.neuroph.core.learning.TrainingSet
Returns Enumeration for iterating training elements collection
equals(Object) - Method in class org.neuroph.contrib.imgrec.FractionRgbData
 
error - Variable in class org.neuroph.core.Neuron
Local error for this neuron

F

FileUtils - Class in org.neuroph.util
Utility methods for working with files.
FileUtils() - Constructor for class org.neuroph.util.FileUtils
 
flattenedRgbValues - Variable in class org.neuroph.contrib.imgrec.FractionRgbData
Single array with the red, green and blue componenets of the color for each image pixel
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
FractionRgbData - Class in org.neuroph.contrib.imgrec
The intention of this class is to allow you to pay up front (at construction) the compute cost of converting the RGB values in a BufferedImage into a derived form.
FractionRgbData(BufferedImage) - Constructor for class org.neuroph.contrib.imgrec.FractionRgbData
Creates rgb data for the specified image.
fullConnect(Layer, Layer) - 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.

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.
getBias() - Method in class org.neuroph.nnet.comp.InputOutputNeuron
Returns bias value for this neuron
getBlueValues() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Returns blue color component for the entire image
getColorMode() - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Returns color mode used for image recognition
getConnectedNeuron() - Method in class org.neuroph.core.Connection
Returns the connected neuron of this connection
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.CompetitiveNeuron
Returns collection of connections from other layers
getCurrentIteration() - Method in class org.neuroph.core.learning.IterativeLearning
Returns current iteration of this learning algorithm
getDelay() - Method in class org.neuroph.nnet.comp.DelayedConnection
Returns delay value for this connection
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.Sigmoid
 
getDerivative(double) - Method in class org.neuroph.core.transfer.Tanh
 
getDerivative(double) - Method in class org.neuroph.core.transfer.TransferFunction
Returns the first derivative of this function.
getDesiredOutput() - Method in class org.neuroph.core.learning.SupervisedTrainingElement
Returns desired output for this training element
getE() - Method in class org.neuroph.nnet.learning.StepDeltaRule
Gets the e parametar
getError() - Method in class org.neuroph.core.Neuron
Returns error for this neuron.
getFilePath() - Method in class org.neuroph.core.learning.TrainingSet
Returns full file path for this training set
getFlattenedRgbValues() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Returns rgb data in a form: all red rows, all green rows, all blue rows
getGreenValues() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Returns green color component for the entire image
getHeight() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Get image height
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
getInput() - Method in class org.neuroph.core.learning.TrainingElement
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 as Vector collection
getInputFunction() - Method in class org.neuroph.core.Neuron
Returns input function
getInputNeurons() - Method in class org.neuroph.core.NeuralNetwork
Gets reference to input neurons Vector.
getInputsIterator() - Method in class org.neuroph.core.Neuron
Returns Iterator interface for accessing input connections
getIteration() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getLabel() - Method in class org.neuroph.core.learning.TrainingElement
Get training element label
getLabel() - Method in class org.neuroph.core.learning.TrainingSet
Returns label for this training set
getLabel(Object) - Method in class org.neuroph.util.plugins.LabelsPlugin
Returns label for the specified object
getLayerAt(int) - Method in class org.neuroph.core.NeuralNetwork
Returns layer at specified index
getLayers() - Method in class org.neuroph.core.NeuralNetwork
Returns getLayersIterator Vector collection
getLayersCount() - Method in class org.neuroph.core.NeuralNetwork
Returns number of layers in network
getLayersIterator() - Method in class org.neuroph.core.NeuralNetwork
Returns interface for iterating layers
getLearningRate() - Method in class org.neuroph.core.learning.IterativeLearning
Returns learning rate for this algorithm
getLearningRate() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getLearningRule() - Method in class org.neuroph.core.NeuralNetwork
Returns the learning algorithm of this network
getLearningThread() - Method in class org.neuroph.core.NeuralNetwork
Returns the current learning thread (if it is learning in the new thread Check what happens if it learns in the same thread)
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
getMapSize() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getMaxIterations() - Method in class org.neuroph.nnet.comp.CompetitiveLayer
Returns the maxIterations setting for this layer
getMaxOutput() - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Returns one or more image labels with the maximum output - recognized images
getMomentum() - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
Returns the momentum factor
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(String) - Method in class org.neuroph.nnet.ModularNetwork
Gets neural network
getNetworkType() - Method in class org.neuroph.core.NeuralNetwork
Returns type of this network
getNeuralNetwork() - Method in class org.neuroph.core.learning.LearningRule
Gets neural network
getNeuronAt(int) - Method in class org.neuroph.core.Layer
Returns neuron at specified index position in this layer
getNeurons() - Method in class org.neuroph.core.Layer
Returns collection of neurons in this layer
getNeuronsCount() - Method in class org.neuroph.core.Layer
Returns number of neurons in this layer
getNeuronsIterator() - Method in class org.neuroph.core.Layer
Returns interface for iterating neurons in this layer
getOutConnections() - Method in class org.neuroph.core.Neuron
Returns output connections from this neuron
getOutput(double, double) - Method in class org.neuroph.contrib.IACFunction
 
getOutput(double) - Method in class org.neuroph.contrib.IACFunction
 
getOutput() - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Returns image recognition result as map with image labels as keys and recogition result as value
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.And
 
getOutput(Vector<Connection>) - Method in class org.neuroph.core.input.Diference
 
getOutput(Vector<Connection>) - Method in class org.neuroph.core.input.InputFunction
Returns ouput value of this input function for the given neuron inputs
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.Intensity
 
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.Max
 
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.Min
 
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.Or
 
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.Product
 
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.Sum
 
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.SummingFunction
Returns summing function output
getOutput(Vector<Double>) - Method in class org.neuroph.core.input.SumSqr
 
getOutput(Vector<Connection>) - Method in class org.neuroph.core.input.WeightedInput
Returns weighted input vector.
getOutput(Vector<Connection>) - Method in class org.neuroph.core.input.WeightsFunction
Returns function's output
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.Ramp
 
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.Step
 
getOutput(double) - Method in class org.neuroph.core.transfer.Tanh
 
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() - Method in class org.neuroph.nnet.comp.BiasNeuron
 
getOutput(int) - Method in class org.neuroph.nnet.comp.DelayedNeuron
Returns neuron output with the specified delay
getOutputAsArray() - Method in class org.neuroph.core.NeuralNetwork
Returns network output vector as double array
getOutputNeurons() - Method in class org.neuroph.core.NeuralNetwork
Returns reference to output neurons Vector.
getParentLayer() - Method in class org.neuroph.core.Neuron
Returns reference on 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
getPatternError(Vector<Double>, Vector<Double>) - Method in class org.neuroph.core.learning.SupervisedLearning
Calculates the network error for the current pattern - diference between desired and actual output
getPlugin(String) - Method in class org.neuroph.core.NeuralNetwork
Returns the requested plugin
getPreviousValue() - Method in class org.neuroph.core.Weight
Returns previous weight value
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
getRedValues() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Returns red color component for the entire image
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
getSamplingResolution() - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Returns image sampling resolution (dimensions)
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 parametar of this function
getSummingFunction() - Method in class org.neuroph.core.input.InputFunction
Returns summing function component of this InputFunction
getSummingFunction() - Method in class org.neuroph.util.NeuronProperties
 
getThresh() - Method in class org.neuroph.nnet.comp.ThresholdNeuron
Returns threshold value for this neuron
getTotalNetworkError() - Method in class org.neuroph.core.learning.SupervisedLearning
Returns total network error
getTrainingSet() - Method in class org.neuroph.core.learning.LearningRule
Gets training set
getTransferFunction() - Method in class org.neuroph.core.Neuron
Returns transfer function
getTransferFunctionProperties() - Method in class org.neuroph.util.NeuronProperties
 
getTypeLabel() - Method in enum org.neuroph.util.NeuralNetworkType
 
getTypeLabel() - Method in enum org.neuroph.util.SummingFunctionType
 
getTypeLabel() - Method in enum org.neuroph.util.TransferFunctionType
 
getTypeLabel() - Method in enum org.neuroph.util.WeightsFunctionType
 
getValue() - Method in class org.neuroph.core.Weight
Returns weight value
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
getWeightsFunction() - Method in class org.neuroph.core.input.InputFunction
Returns weights functioncomponent of this InputFunction
getWeightsFunction() - Method in class org.neuroph.util.NeuronProperties
 
getWeightsVector() - Method in class org.neuroph.core.Neuron
Returns weights vector of input connections
getWidth() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Get image width
getWinner() - Method in class org.neuroph.nnet.comp.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
greenValues - Variable in class org.neuroph.contrib.imgrec.FractionRgbData
Array which contains green componenet of the color for each image pixel

H

hashCode() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
 
hasInputConnections() - Method in class org.neuroph.core.Neuron
Returns true if there are input connections for this neuron, false otherwise
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
HopfieldLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.HopfieldLearning
Creates new HopfieldLearning for the specified neural network
HopfieldSample - Class in org.neuroph.samples
This sample shows how to create and train Hopfield neural network
HopfieldSample() - Constructor for class org.neuroph.samples.HopfieldSample
 

I

IACFunction - Class in org.neuroph.contrib
Transfer function for Neuron in Interactive Activation Neural Network.
IACFunction() - Constructor for class org.neuroph.contrib.IACFunction
 
IACNetwork - Class in org.neuroph.contrib
Interactive Activation Controller neural network.
IACNetwork() - Constructor for class org.neuroph.contrib.IACNetwork
Constructs a new Interactive Activation Neural Network
IACNeuron - Class in org.neuroph.contrib
Neuron for Interactive Activation Neural Network.
IACNeuron() - Constructor for class org.neuroph.contrib.IACNeuron
 
ImageRecognitionHelper - Class in org.neuroph.contrib.imgrec
Provides methods to create neural network and training set for image recognition.
ImageRecognitionHelper() - Constructor for class org.neuroph.contrib.imgrec.ImageRecognitionHelper
 
ImageRecognitionPlugin - Class in org.neuroph.contrib.imgrec
Provides image recognition specific properties like sampling resolution, and easy to use image recognition interface for neural network.
ImageRecognitionPlugin(Dimension) - Constructor for class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Constructor
ImageRecognitionPlugin(Dimension, ColorMode) - Constructor for class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Constructor
ImageRecognitionSample - Class in org.neuroph.contrib.imgrec
This sample shows how to use the image recognition neural network in your applications.
ImageRecognitionSample() - Constructor for class org.neuroph.contrib.imgrec.ImageRecognitionSample
 
ImageSampler - Class in org.neuroph.contrib.imgrec
This class uses a given Robot object to sample images from the screen at an arbitrary sampling resolution.
ImageSampler() - Constructor for class org.neuroph.contrib.imgrec.ImageSampler
 
IMG_REC_PLUGIN_NAME - Static variable in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
 
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
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
input - Variable in class org.neuroph.core.learning.TrainingElement
Input vector for this training element
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
Creates an instance of WeightedSum input function by default.
InputFunction(WeightsFunction, SummingFunction) - Constructor for class org.neuroph.core.input.InputFunction
Creates an instance of input function with specified weights and summing function
inputFunction - Variable in class org.neuroph.core.Neuron
Input function for this neuron
InputOutputNeuron - Class in org.neuroph.nnet.comp
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.InputOutputNeuron
Creates an instance of neuron for Hopfield network
InputOutputNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.InputOutputNeuron
Creates an instance of neuron for Hopfield network with specified input and transfer functions
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
InstarLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.InstarLearning
Creates new instance of InstarLearning algorithm for the specified neural network.
Intensity - Class in org.neuroph.core.input
Calculates the input vector intensity.
Intensity() - Constructor for class org.neuroph.core.input.Intensity
 
isCompeting() - Method in class org.neuroph.nnet.comp.CompetitiveNeuron
Retruns true if this neuron is in competing mode, false otherwise
isEmpty() - Method in class org.neuroph.core.learning.TrainingSet
Returns true if training set is empty, false otherwise
isPausedLearning() - Method in class org.neuroph.core.learning.IterativeLearning
Returns true if learning thread is paused, false otherwise
isStopped() - Method in class org.neuroph.core.learning.LearningRule
Returns true if learning has stopped, false otherwise
iterationsLimited - Variable in class org.neuroph.core.learning.IterativeLearning
Flag for indicating if the training iteration number is limited
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 instannce of IterativeLearning learning algorithm
IterativeLearning(NeuralNetwork) - Constructor for class org.neuroph.core.learning.IterativeLearning
Creates new instannce of IterativeLearning learning algorithm for the specified neural network.
iterator() - Method in class org.neuroph.core.learning.TrainingSet
Returns Iterator for iterating training elements collection

K

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(Kohonen) - Constructor for class org.neuroph.nnet.learning.KohonenLearning
 

L

label - Variable in class org.neuroph.core.learning.TrainingElement
Label for this training element
LABELS_PLUGIN_NAME - Static variable in class org.neuroph.util.plugins.LabelsPlugin
 
LabelsPlugin - Class in org.neuroph.util.plugins
Provides labeling of all neural network components
LabelsPlugin() - Constructor for class org.neuroph.util.plugins.LabelsPlugin
 
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, 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 specifed number of neurons and neuron's properties.
LayerFactory() - Constructor for class org.neuroph.util.LayerFactory
 
learn(TrainingSet) - Method in class org.neuroph.core.learning.IterativeLearning
 
learn(TrainingSet, int) - Method in class org.neuroph.core.learning.IterativeLearning
Trains network for the specified training set and number of iterations
learn(TrainingSet) - Method in class org.neuroph.core.learning.LearningRule
Override this method to implement specific learning procedures
learn(TrainingSet, double) - Method in class org.neuroph.core.learning.SupervisedLearning
Trains network for the specified training set and number of iterations
learn(TrainingSet, double, int) - Method in class org.neuroph.core.learning.SupervisedLearning
Trains network for the specified training set and number of iterations
learn(TrainingSet) - Method in class org.neuroph.core.NeuralNetwork
Deprecated.  
learn(TrainingSet) - Method in class org.neuroph.nnet.learning.HopfieldLearning
Calculates weights for the hopfield net to learn the specified training set
learn(TrainingSet) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
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 instance of learning rule
LearningRule(NeuralNetwork) - Constructor for class org.neuroph.core.learning.LearningRule
Sets neural network for this learning rule
learnInNewThread(TrainingSet) - Method in class org.neuroph.core.NeuralNetwork
Starts learning in a new thread to learn the specified training set, and immediately returns from method to the current thread execution
learnInNewThread(TrainingSet, LearningRule) - Method in class org.neuroph.core.NeuralNetwork
Starts learning with specified learning rule in new thread to learn the specified training set, and immediately returns from method to the current thread execution
learnInSameThread(TrainingSet) - Method in class org.neuroph.core.NeuralNetwork
Starts the learning in the current running thread to learn the specified training set, and returns from method when network is done learning
learnInSameThread(TrainingSet, LearningRule) - Method in class org.neuroph.core.NeuralNetwork
Starts the learning with specified learning rule in the current running thread to learn the specified training set, and returns from method when network is done learning
learnPattern(SupervisedTrainingElement) - Method in class org.neuroph.core.learning.SupervisedLearning
Trains network with the pattern from the specified training element
learnPattern(TrainingElement) - Method in class org.neuroph.core.learning.UnsupervisedLearning
Trains network with the pattern from the specified training element
learnPattern(SupervisedTrainingElement) - Method in class org.neuroph.nnet.learning.SupervisedHebbianLearning
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
LMS - Class in org.neuroph.nnet.learning
LMS learning rule for neural networks.
LMS() - Constructor for class org.neuroph.nnet.learning.LMS
Creates new LMS learning rule
LMS(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.LMS
Creates new LMS learning rule for specified neural network
load(String) - Static method in class org.neuroph.core.learning.TrainingSet
Loads training set from the specified file
load(String) - Static method in class org.neuroph.core.NeuralNetwork
Loads neural network from the specified file.
load(InputStream) - Static method in class org.neuroph.core.NeuralNetwork
Loads neural network from the specified InputStream.

M

main(String[]) - Static method in class org.neuroph.contrib.imgrec.ImageRecognitionSample
 
main(String[]) - Static method in class org.neuroph.samples.DLRSample
Runs this sample
main(String[]) - Static method in class org.neuroph.samples.HopfieldSample
Runs this sample
main(String[]) - Static method in class org.neuroph.samples.PauseLearningSample
 
main(String[]) - Static method in class org.neuroph.samples.PerceptronSample
Runs this sample
main(String[]) - Static method in class org.neuroph.samples.XorMultiLayerPerceptronSample
Runs this sample
Max - Class in org.neuroph.core.input
Performs max function on input vector
Max() - Constructor for class org.neuroph.core.input.Max
 
maxError - Variable in class org.neuroph.core.learning.SupervisedLearning
Max allowed network error (condition to stop learning)
maxIterations - Variable in class org.neuroph.core.learning.IterativeLearning
Max training iterations (when to stopLearning training)
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
Min - Class in org.neuroph.core.input
Performs min function on input vector
Min() - Constructor for class org.neuroph.core.input.Min
 
ModularNetwork - Class in org.neuroph.nnet
Neural networks container for creating modular neural networks
ModularNetwork() - Constructor for class org.neuroph.nnet.ModularNetwork
 
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(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.MomentumBackpropagation
Creates new instance of MomentumBackpropagation learning for the specified neural network
MultiLayerPerceptron - Class in org.neuroph.nnet
Multi Layer Perceptron neural network with Back propagation learning algorithm.
MultiLayerPerceptron(Vector<Integer>) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
Creates new MultiLayerPerceptron with specified number neurons in getLayersIterator
MultiLayerPerceptron(int...) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
 
MultiLayerPerceptron(TransferFunctionType, int...) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
 
MultiLayerPerceptron(Vector<Integer>, TransferFunctionType) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
 
MultiLayerPerceptron(Vector<Integer>, NeuronProperties) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
Creates new MultiLayerPerceptron net with specified number neurons in getLayersIterator

N

netInput - Variable in class org.neuroph.core.Neuron
Total net input for this neuron.
neuralNetwork - Variable in class org.neuroph.core.learning.LearningRule
Neural network to train
NeuralNetwork - 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.
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 the 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 instances of Neurons.
NeuronFactory() - Constructor for class org.neuroph.util.NeuronFactory
 
NeuronProperties - Class in org.neuroph.util
Represents properties of a neuron.
NeuronProperties() - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(TransferFunctionType) - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(TransferFunctionType, boolean) - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(WeightsFunctionType, SummingFunctionType, TransferFunctionType) - Constructor for class org.neuroph.util.NeuronProperties
 
neurons - Variable in class org.neuroph.core.Layer
Neurons collection
notifyChange() - Method in class org.neuroph.core.learning.LearningRule
Notify observers about change
notifyChange() - Method in class org.neuroph.core.NeuralNetwork
Notifies observers about some change

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
OjaLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.OjaLearning
Creates an instance of OjaLearning algorithm for the specified neural network
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.contrib - package org.neuroph.contrib
Provides various contributions from the Neuroph community.
org.neuroph.contrib.imgrec - package org.neuroph.contrib.imgrec
Provides classes for image recognition with neural networks.
org.neuroph.core - package org.neuroph.core
Provides base classes and basic building components for 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.transfer - package org.neuroph.core.transfer
Provides common neuron transfer functions
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.learning - package org.neuroph.nnet.learning
Provides implementations of specific neural network learning algorithms.
org.neuroph.samples - package org.neuroph.samples
Provides some basic examples of how to use Neuroph.
org.neuroph.util - package org.neuroph.util
Provides various utility classes for creating neural networks, type codes, parsing vectors, etc.
org.neuroph.util.plugins - package org.neuroph.util.plugins
Provides various plugins for neural networks.
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
outputHistory - Variable in class org.neuroph.nnet.comp.DelayedNeuron
Output history for this neuron
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
OutstarLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.OutstarLearning
Creates new instance of OutstarLearning algorithm for the specified neural network.

P

parentLayer - Variable in class org.neuroph.core.Neuron
Parent layer for this neuron
parseDouble(String) - Static method in class org.neuroph.util.VectorParser
This method parses input String and returns Double vector
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 wait state.
PauseLearningSample - Class in org.neuroph.samples
 
PauseLearningSample() - Constructor for class org.neuroph.samples.PauseLearningSample
Creates new form TestPauseLearning
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
PerceptronSample - Class in org.neuroph.samples
This sample shows how to create, train, save and load simple Perceptron neural network
PerceptronSample() - Constructor for class org.neuroph.samples.PerceptronSample
 
PluginBase - Class in org.neuroph.util.plugins
Base class for all neural network plugins.
PluginBase(String) - Constructor for class org.neuroph.util.plugins.PluginBase
Creates an instance of plugin for neural network
populateRGBArrays(BufferedImage) - Method in class org.neuroph.contrib.imgrec.FractionRgbData
Fills the rgb arrays from image
processInput() - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
 
Product - Class in org.neuroph.core.input
Performs multiplication of all input vector elements.
Product() - Constructor for class org.neuroph.core.input.Product
 

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.
randomize() - Method in class org.neuroph.core.Weight
Sets random weight value
randomizeInputWeights() - Method in class org.neuroph.core.Neuron
Randomize all input weights
randomizeWeights() - Method in class org.neuroph.core.Layer
Randomize input connection weights for all neurons in this layer
randomizeWeights() - Method in class org.neuroph.core.NeuralNetwork
Randomizes connection weights for the whole network
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
readStringFromFile(File) - Static method in class org.neuroph.util.FileUtils
 
recognizeImage(BufferedImage) - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
This method performs the image recognition for specified image.
recognizeImage(File) - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
This method performs the image recognition for specified image file.
recognizeImage(URL) - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
This method performs the image recognition for specified image URL.
RecommenderNetwork - Class in org.neuroph.contrib
E-commerce recommender neural network based on hebbian learning.
RecommenderNetwork() - Constructor for class org.neuroph.contrib.RecommenderNetwork
 
redValues - Variable in class org.neuroph.contrib.imgrec.FractionRgbData
Array which contains red componenet of the color for each image pixel
removeElementAt(int) - Method in class org.neuroph.core.learning.TrainingSet
Removes training element at specified index position
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
removeNetwork(String) - Method in class org.neuroph.nnet.ModularNetwork
Removes neural network
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
removePlugin(String) - Method in class org.neuroph.core.NeuralNetwork
Removes the plugin with specified name
reset() - Method in class org.neuroph.core.Layer
Resets the activation and input levels for all neurons in this layer
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.nnet.comp.CompetitiveNeuron
Resets the input, output and mode for this neuron
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 thread to continue
run() - Method in class org.neuroph.core.learning.LearningRule
Method from Runnable interface for running learning procedure in separate thread.
run() - Method in class org.neuroph.core.NeuralNetwork
Implementation of Runnable interface for calculating network in the separate thread.

S

save(String) - Method in class org.neuroph.core.learning.TrainingSet
Saves this training set to the specified file
save() - Method in class org.neuroph.core.learning.TrainingSet
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.
scanLocationUsingSampling(Robot, Rectangle2D.Double, Dimension) - Static method in class org.neuroph.contrib.imgrec.ImageSampler
Scans screen location using sampling
scanLocationUsingSampling(Robot, Rectangle2D.Double, Dimension, int) - Static method in class org.neuroph.contrib.imgrec.ImageSampler
Scans screen location using sampling
scanLocationUsingScreenshot(Robot, Rectangle2D.Double, Dimension) - Static method in class org.neuroph.contrib.imgrec.ImageSampler
Scans screen location using screenshot
scanLocationUsingScreenshot(Robot, Rectangle2D.Double, Dimension, int) - Static method in class org.neuroph.contrib.imgrec.ImageSampler
Scans screen location using screenshot
setBias(double) - Method in class org.neuroph.nnet.comp.InputOutputNeuron
Sets bias value for this neuron
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
setDesiredOutput(Vector<Double>) - Method in class org.neuroph.core.learning.SupervisedTrainingElement
Sets desired output vector for this training element
setE(Double) - Method in class org.neuroph.nnet.learning.StepDeltaRule
Sets the e parametar
setError(double) - Method in class org.neuroph.core.Neuron
Sets error for this neuron.
setFilePath(String) - Method in class org.neuroph.core.learning.TrainingSet
Sets full file path for this training set
setInput(double) - Method in class org.neuroph.contrib.IACNeuron
Sets total net input for this cell
setInput(BufferedImage) - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Sets network input (image to recognize) from the specified BufferedImage object
setInput(File) - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Sets network input (image to recognize) from the specified File object
setInput(URL) - Method in class org.neuroph.contrib.imgrec.ImageRecognitionPlugin
Sets network input (image to recognize) from the specified URL object
setInput(Vector<Double>) - Method in class org.neuroph.core.learning.TrainingElement
Sets input vector
setInput(Vector<Double>) - Method in class org.neuroph.core.NeuralNetwork
Sets network input.
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.InputOutputNeuron
Sets total net input for this cell
setInputFunction(InputFunction) - Method in class org.neuroph.core.Neuron
Sets input function
setInputNeurons(Vector<Neuron>) - Method in class org.neuroph.core.NeuralNetwork
Sets reference to input neurons Vector
setIsCompeting(boolean) - Method in class org.neuroph.nnet.comp.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.learning.TrainingElement
Set training element label
setLabel(String) - Method in class org.neuroph.core.learning.TrainingSet
Sets label for this training set
setLabel(Object, String) - Method in class org.neuroph.util.plugins.LabelsPlugin
Sets label for the specified object
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
 
setLearningRule(LearningRule) - 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
setMaxError(Double) - Method in class org.neuroph.core.learning.SupervisedLearning
Sets allowed network error, which indicates when to stopLearning training
setMaxIterations(Integer) - Method in class org.neuroph.core.learning.IterativeLearning
Sets iteration limit for this learning algorithm
setMaxIterations(int) - Method in class org.neuroph.nnet.comp.CompetitiveLayer
Sets max iterations for neurons to compete in this layer
setMomentum(double) - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
Sets the momentum factor
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
setNeuron(int, Neuron) - Method in class org.neuroph.core.Layer
Sets (replace) the neuron at specified position in layer
setOutput(double) - Method in class org.neuroph.core.Neuron
Sets this neuron output
setOutputNeurons(Vector<Neuron>) - Method in class org.neuroph.core.NeuralNetwork
Sets reference to output neurons Vector.
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
setPreviousValue(double) - Method in class org.neuroph.core.Weight
Sets the previous weight value
setProperty(String, Object) - Method in class org.neuroph.util.NeuronProperties
 
setProperty(String, Double) - Method in class org.neuroph.util.NeuronProperties
 
setProperty(String, TransferFunctionType) - Method in class org.neuroph.util.NeuronProperties
 
setProperty(String, WeightsFunctionType) - Method in class org.neuroph.util.NeuronProperties
 
setProperty(String, SummingFunctionType) - Method in class org.neuroph.util.NeuronProperties
 
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
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 parametar for this function
setThresh(double) - Method in class org.neuroph.nnet.comp.ThresholdNeuron
Sets threshold value for this neuron
setTrainingSet(TrainingSet) - 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
setValue(double) - Method in class org.neuroph.core.Weight
Sets the weight value
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
 
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 functions.
SigmoidDeltaRule() - Constructor for class org.neuroph.nnet.learning.SigmoidDeltaRule
Creates new SigmoidDeltaRule
SigmoidDeltaRule(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.SigmoidDeltaRule
Creates new SigmoidDeltaRule for the specified neural network
size() - Method in class org.neuroph.core.learning.TrainingSet
Returns number of training elements in this training set set
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
StepDeltaRule - Class in org.neuroph.nnet.learning
Delta rule learning algorithm for perceptrons with step functions.
StepDeltaRule() - Constructor for class org.neuroph.nnet.learning.StepDeltaRule
Creates new StepDeltaRule learning
StepDeltaRule(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.StepDeltaRule
Creates new StepDeltaRule learning for the specified neural network
stopLearning() - Method in class org.neuroph.core.learning.LearningRule
Stops learning
stopLearning() - Method in class org.neuroph.core.NeuralNetwork
Stops learning
Sum - Class in org.neuroph.core.input
Performs summing of all input vector elements.
Sum() - Constructor for class org.neuroph.core.input.Sum
 
SummingFunction - Class in org.neuroph.core.input
Abstract base class for all summing functions, which perform some summing operation on weighted input vector and return scalar.
SummingFunction() - Constructor for class org.neuroph.core.input.SummingFunction
 
SummingFunctionType - Enum in org.neuroph.util
Contains summing functions types and labels.
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
SupervisedHebbianLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.SupervisedHebbianLearning
Creates new instance of SupervisedHebbianLearning algorithm for the specified neural network.
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
SupervisedLearning(NeuralNetwork) - Constructor for class org.neuroph.core.learning.SupervisedLearning
Creates new supervised learning rule and sets the neural network to train
SupervisedTrainingElement - Class in org.neuroph.core.learning
Represents training element for supervised learning algorithms.
SupervisedTrainingElement(Vector<Double>, Vector<Double>) - Constructor for class org.neuroph.core.learning.SupervisedTrainingElement
Creates new training element with specified input and desired output vectors
SupervisedTrainingElement(String, String) - Constructor for class org.neuroph.core.learning.SupervisedTrainingElement
Creates new training element with specified input and desired output vectors specifed as strings
SupervisedTrainingElement(double[], double[]) - Constructor for class org.neuroph.core.learning.SupervisedTrainingElement
Creates new training element with specified input and desired output vectors

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=1.
Tanh(double) - Constructor for class org.neuroph.core.transfer.Tanh
Creates an instance of Tanh neuron transfer function with specified value for slope parametar.
Tanh(Properties) - Constructor for class org.neuroph.core.transfer.Tanh
Creates an instance of Tanh neuron transfer function with the specified properties.
testNeuralNetwork(NeuralNetwork, TrainingSet) - Static method in class org.neuroph.samples.DLRSample
Prints network output for the each element from the specified training set.
testNeuralNetwork(NeuralNetwork, TrainingSet) - Static method in class org.neuroph.samples.PerceptronSample
Prints network output for the each element from the specified training set.
testNeuralNetwork(NeuralNetwork, TrainingSet) - Static method in class org.neuroph.samples.XorMultiLayerPerceptronSample
Prints network output for the each element from the specified training set.
thresh - Variable in class org.neuroph.nnet.comp.ThresholdNeuron
Threshold value for this neuron
ThresholdNeuron - Class in org.neuroph.nnet.comp
Provides behaviour for neurons with threshold.
ThresholdNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.ThresholdNeuron
Creates a neuron with threshold behaviour, and with the specified input and transfer functions.
toString() - Method in class org.neuroph.contrib.imgrec.FractionRgbData
 
toString() - Method in class org.neuroph.core.input.SummingFunction
 
toString() - Method in class org.neuroph.core.input.WeightsFunction
 
toString() - Method in class org.neuroph.core.learning.TrainingSet
Returns label of this training set
toString() - Method in class org.neuroph.core.NeuralNetwork
 
toString() - Method in class org.neuroph.core.transfer.TransferFunction
Returns the class name
toString() - Method in class org.neuroph.core.Weight
Returns weight value as String
totalNetworkError - Variable in class org.neuroph.core.learning.SupervisedLearning
Total network error
TrainingElement - Class in org.neuroph.core.learning
Represents single training element for neural network learning.
TrainingElement() - Constructor for class org.neuroph.core.learning.TrainingElement
Creates new training element with specified input vector
TrainingElement(Vector<Double>) - Constructor for class org.neuroph.core.learning.TrainingElement
Creates new training element with specified input vector
TrainingElement(String) - Constructor for class org.neuroph.core.learning.TrainingElement
Creates new training element with specified input vector
TrainingElement(double...) - Constructor for class org.neuroph.core.learning.TrainingElement
Creates new training element with input array
trainingElements() - Method in class org.neuroph.core.learning.TrainingSet
Returns training elements collection
TrainingSet - Class in org.neuroph.core.learning
A set of training elements for training neural network.
TrainingSet() - Constructor for class org.neuroph.core.learning.TrainingSet
Creates an instance of new empty TrainingSet
TrainingSet(String) - Constructor for class org.neuroph.core.learning.TrainingSet
Creates an instance of new empty TrainingSet with given label
TrainingSetImport - Class in org.neuroph.util
Handles training set imports
TrainingSetImport() - Constructor for class org.neuroph.util.TrainingSetImport
 
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
UnsupervisedHebbianLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
Creates an instance of UnsupervisedHebbianLearning algorithm for the specified neural network
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
UnsupervisedLearning(NeuralNetwork) - Constructor for class org.neuroph.core.learning.UnsupervisedLearning
Creates new unsupervised learning rule and sets the neural network to train
update(Observable, Object) - Method in class org.neuroph.samples.PauseLearningSample
 
updateNetworkWeights(Vector<Double>) - Method in class org.neuroph.core.learning.SupervisedLearning
This method should implement the weights update procedure
updateNetworkWeights(Vector<Double>) - Method in class org.neuroph.nnet.learning.BackPropagation
This method implements weight update procedure for the whole network for the specified error vector
updateNetworkWeights(Vector<Double>) - Method in class org.neuroph.nnet.learning.LMS
This method implements weight update procedure for the whole network for this learning rule
updateNetworkWeights(Vector<Double>) - Method in class org.neuroph.nnet.learning.SigmoidDeltaRule
This method implements weight update procedure for the whole network for this learning rule
updateNetworkWeights(Vector<Double>) - Method in class org.neuroph.nnet.learning.StepDeltaRule
This method implements weight update procedure for the whole network for this learning rule
updateNetworkWeights(Vector<Double>) - Method in class org.neuroph.nnet.learning.SupervisedHebbianLearning
This method implements weight update procedure for the whole network for this learning rule
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.InstarLearning
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.LMS
This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
This method implements weights update procedure for the single neuron for the backpropagation with momentum factor
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
updateTotalNetworkError(Vector<Double>) - Method in class org.neuroph.core.learning.SupervisedLearning
Subclasses update total network error for each training pattern with this method.
updateTotalNetworkError(Vector<Double>) - Method in class org.neuroph.nnet.learning.LMS
Updates total network error with specified pattern error vector

V

valueOf(String) - Static method in enum org.neuroph.contrib.imgrec.ColorMode
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.SummingFunctionType
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.
valueOf(String) - Static method in enum org.neuroph.util.WeightsFunctionType
Returns the enum constant of this type with the specified name.
values() - Static method in enum org.neuroph.contrib.imgrec.ColorMode
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.SummingFunctionType
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.
values() - Static method in enum org.neuroph.util.WeightsFunctionType
Returns an array containing the constants of this enum type, in the order they are declared.
VectorParser - Class in org.neuroph.util
Provides methods to parse strings as Integer or Double vectors.
VectorParser() - Constructor for class org.neuroph.util.VectorParser
 

W

weight - Variable in class org.neuroph.core.Connection
Weight for this connection
Weight - 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..1]
Weight(double) - Constructor for class org.neuroph.core.Weight
Creates an instance of connection weight with the specified weight value
WeightedInput - Class in org.neuroph.core.input
Calculates weighted input for neuron's InputFunction.
WeightedInput() - Constructor for class org.neuroph.core.input.WeightedInput
 
WeightsFunction - Class in org.neuroph.core.input
Abstract base class for all weights functions, which perform some operation on neuron's input vector and weights vector and return vector.
WeightsFunction() - Constructor for class org.neuroph.core.input.WeightsFunction
 
WeightsFunctionType - Enum in org.neuroph.util
Contains weights functions types and labels.
writeStringToFile(File, String) - Static method in class org.neuroph.util.FileUtils
 

X

XorMultiLayerPerceptronSample - Class in org.neuroph.samples
This sample shows how to create, train, save and load simple Multi Layer Perceptron
XorMultiLayerPerceptronSample() - Constructor for class org.neuroph.samples.XorMultiLayerPerceptronSample
 

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