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

A

Adaline - Class in org.neuroph.nnet
The Adaline class represents 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, 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.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
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
adjustCellWeights(Neuron, int) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
adjustHiddenLayers() - Method in class org.neuroph.nnet.learning.BackPropagation
This method implements weights adjustment for the hidden layers
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
The And class performs logic AND operation on input Vector.
And() - Constructor for class org.neuroph.core.input.And
 

B

BackPropagation - Class in org.neuroph.nnet.learning
The BackPropagation class is the Back Propagation learning rule for Multi Layer Perceptron neural networks.
BackPropagation(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.BackPropagation
Creates new instance of BackPropagation learning for the specified neural network
bias - Variable in class org.neuroph.nnet.comp.HopfieldNeuron
Bias value for this neuron

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.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.HopfieldNeuron
Calculates neuron output
calculate() - Method in class org.neuroph.nnet.comp.ThresholdNeuron
Calculates neuron's output
calculateDelta(Neuron) - Method in class org.neuroph.nnet.learning.BackPropagation
Calculates and returns delta parameter (neuron error) for the specified neuron
clear() - Method in class org.neuroph.core.learning.TrainingSet
Removes all alements from training set
CompetitiveLayer - Class in org.neuroph.nnet.comp
The CompetitiveLayer class represents layer of competitive neurons, and it 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
The CompetitiveLearning class implements competitive learning rule.
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
The CompetitiveNetwork class represents 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
The CompetitiveNeuron class 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
The Connection class represents weighted connection to another neuron.
Connection(Neuron) - Constructor for class org.neuroph.core.Connection
Creates new connection to specified neuron with random weight
Connection(Neuron, Weight) - Constructor for class org.neuroph.core.Connection
Creates new connection to specified neuron with specified weight object
Connection(Neuron, double) - Constructor for class org.neuroph.core.Connection
Creates new connection to specified neuron with specified weight value
Connection(Neuron, Neuron) - Constructor for class org.neuroph.core.Connection
Creates new connection between specified neurons with random weight value
ConnectionFactory - Class in org.neuroph.util
This class provides methods to connect neurons by creating Connection objects.
ConnectionFactory() - Constructor for class org.neuroph.util.ConnectionFactory
 
connectionsFromOtherLayers - Variable in class org.neuroph.nnet.comp.CompetitiveNeuron
Collection of conections from neurons in other layers
connectionsFromThisLayer - Variable in class org.neuroph.nnet.comp.CompetitiveNeuron
Collection of connections from neurons in the same layer as this neuron (lateral connections used for competition)
createAdaline(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of Adaline network
createCompetitiveNetwork(int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an 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
createHopfield(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of Hopfield network
createKohonen(int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an 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 an instance of Max Net network
createMLPerceptron(String, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of Multi Layer Perceptron
createNetwork(int) - Method in class org.neuroph.nnet.Adaline
Creates adaline network architecture with specified number of input neurons
createNetwork(int, int) - Method in class org.neuroph.nnet.CompetitiveNetwork
Creates Competitive network architecture
createNetwork(int, NeuronProperties) - Method in class org.neuroph.nnet.Hopfield
Creates Hopfield network architecture
createNetwork(int, int) - Method in class org.neuroph.nnet.Kohonen
Creates Kohonen network architecture with specified number of neurons in input and map layer
createNetwork(int) - Method in class org.neuroph.nnet.MaxNet
Creates MaxNet network architecture
createNetwork(Vector<Integer>, NeuronProperties) - Method in class org.neuroph.nnet.MultiLayerPerceptron
Creates MultiLayerPerceptron Network architecture - fully connected feedforward with specified number of neurons in each layer
createNetwork(int, Vector<Integer>, int) - Method in class org.neuroph.nnet.NeuroFuzzyReasoner
Creates custom NFR architecture
createNetwork(int, int, TransferFunctionType) - Method in class org.neuroph.nnet.Perceptron
Creates perceptron architecture with specified number of neurons in input and output layer, specified transfer function
createNetwork(int, int, int) - Method in class org.neuroph.nnet.RbfNetwork
Creates RbfNetwork architecture with specified number of neurons in input layer, output layer and transfer function
createNetwork(int, int, TransferFunctionType) - Method in class org.neuroph.nnet.SupervisedHebbianNetwork
Creates an instance of Supervised Hebbian Network with specified number of neurons in input layer, output layer and transfer function
createNetwork(int, int, TransferFunctionType) - Method in 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
createNeuron(NeuronProperties) - Static method in class org.neuroph.util.NeuronFactory
Create neuron according to the given specification.
createPerceptron(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of Perceptron network
createRbfNetwork(int, int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of RBF network
createStudentNFR(int, Vector<Integer>, int, double[][], double[][]) - Method in class org.neuroph.nnet.NeuroFuzzyReasoner
 
createSummingFunction(SummingFunctionType) - Static method in class org.neuroph.util.NeuronFactory
Kreira ulaznu vektorsku funkciju.
createSupervisedHebbian(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of Hebbian network
createTransferFunction(Properties) - Static method in class org.neuroph.util.NeuronFactory
Creates and returns transfer function
createUnsupervisedHebbian(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
Creates and returns an instance of Unsupervised Hebbian Network
createWeightsFunction(WeightsFunctionType) - Static method in class org.neuroph.util.NeuronFactory
Kreira funkciju mreznog ulaza.
currentIteration - Variable in class org.neuroph.core.learning.IterativeLearning
Current iteration counter
currentIteration - Variable in class org.neuroph.nnet.learning.KohonenLearning
 

D

dec(double) - Method in class org.neuroph.core.Weight
Decreases the weight for specified amount
decStep - Variable in class org.neuroph.nnet.learning.KohonenLearning
 
delay - Variable in class org.neuroph.nnet.comp.DelayedConnection
Delay factor for this conection
DelayedConnection - Class in org.neuroph.nnet.comp
The DelayedConnection class represents 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
The DelayedNeuron class 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
desiredOutput - Variable in class org.neuroph.core.learning.SupervisedTrainingElement
Desired output for this training element
Diference - Class in org.neuroph.core.input
The Diference class performs the vector difference operation on input and weight vector.
Diference() - Constructor for class org.neuroph.core.input.Diference
 
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.UnsupervisedHebbianLearning
This method does one learning epoch for the unsupervised learning rules.

E

e - Variable in class org.neuroph.nnet.learning.StepDeltaRule
The e parametar of this learning algorithm
elementAt(int) - Method in class org.neuroph.core.learning.TrainingSet
Returns training element at specified index position
elements - Variable in class org.neuroph.core.learning.TrainingSet
Collection of training elements
elements() - Method in class org.neuroph.core.learning.TrainingSet
Returns Enumeration for iterating training elements collection
error - Variable in class org.neuroph.core.Neuron
Local error for this neuron
extInputSet - Variable in class org.neuroph.nnet.comp.HopfieldNeuron
Flag which is set true if neuron input is externaly set

F

filePath - Variable in class org.neuroph.core.learning.TrainingSet
Full file path incuding file name
forwardConnect(Layer, Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
Creates forward connectivity pattern between specified layers
forwardConnect(Layer, Layer) - Static method in class org.neuroph.util.ConnectionFactory
Creates forward connection pattern between specified layers
fullConnect(Layer, Layer) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity between two layers
fullConnect(Layer, Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
Creates full connectivity between two getLayersIterator with specified weight
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
The Gaussian class provides 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.HopfieldNeuron
Returns bias value for this neuron
getClosest() - Method in class org.neuroph.nnet.learning.KohonenLearning
 
getConnectedNeuron() - Method in class org.neuroph.core.Connection
Returns the connectedNeuron neuron
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
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
Returns 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.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 learning algorithm
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
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
getNetworkType() - Method in class org.neuroph.core.NeuralNetwork
Returns network type
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(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(int) - Method in class org.neuroph.nnet.comp.DelayedNeuron
Returns neuron output with the specified delay
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
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
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
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 conection weight object
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
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

H

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
The Hopfield class represents 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
The HopfieldLearning class implements learning algorithm for the Hopfield neural network.
HopfieldLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.HopfieldLearning
Creates new HopfieldLearning for the specified neural network
HopfieldNeuron - Class in org.neuroph.nnet.comp
The HopfieldNeuron class provides neuron behaviour specific for Hopfield network.
HopfieldNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.HopfieldNeuron
Creates an instance of neuron for Hopfield network with specified input and transfer functions

I

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
The InputFunction class represents the 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
inputNeurons - Variable in class org.neuroph.core.NeuralNetwork
Reference to network input neurons
Intensity - Class in org.neuroph.core.input
The Intensity class provides method for calculating the Vector intensity.
Intensity() - Constructor for class org.neuroph.core.input.Intensity
 
isCompeting - Variable in class org.neuroph.nnet.comp.CompetitiveNeuron
Flag indicates if this neuron is in competing mode
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
isNeighbor(int, int, int) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
isStopped() - Method in class org.neuroph.core.learning.LearningRule
Returns true if learning has stopped, false otherwise
iterations - Variable in class org.neuroph.nnet.learning.KohonenLearning
 
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
The IterativeLearning class is the base class for all iterative learning algorithms.
IterativeLearning(NeuralNetwork) - Constructor for class org.neuroph.core.learning.IterativeLearning
Sets neural network for this learning algorithm
iterator() - Method in class org.neuroph.core.learning.TrainingSet
Returns Iterator for iterating training elements collection

K

Kohonen - Class in org.neuroph.nnet
The Kohonen class represents 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
The KohonenLearning implements the learning algorithm for Kohonen network.
KohonenLearning(Kohonen) - Constructor for class org.neuroph.nnet.learning.KohonenLearning
 

L

label - Variable in class org.neuroph.core.learning.TrainingSet
Label for this training set
labels - Variable in class org.neuroph.util.plugins.LabelsPlugin
Collection of labels for the neural network components
LabelsPlugin - Class in org.neuroph.util.plugins
 
LabelsPlugin() - Constructor for class org.neuroph.util.plugins.LabelsPlugin
 
Layer - Class in org.neuroph.core
The Layer class represents layer (collection) of neurons in 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
This class provides methods to create instance of Layer.
LayerFactory() - Constructor for class org.neuroph.util.LayerFactory
 
layers - Variable in class org.neuroph.core.NeuralNetwork
Neural network getLayersIterator
learn(TrainingSet) - Method in class org.neuroph.core.learning.IterativeLearning
 
learn(TrainingSet) - Method in class org.neuroph.core.learning.LearningRule
Override this method to implement specific learning procedures
learn(TrainingSet) - Method in class org.neuroph.core.NeuralNetwork
Trains the network to leran the specified training set.
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
learningRate - Variable in class org.neuroph.nnet.learning.KohonenLearning
 
LearningRule - Class in org.neuroph.core.learning
The LearningRule class is the base class for all neural network learning algorithms.
LearningRule(NeuralNetwork) - Constructor for class org.neuroph.core.learning.LearningRule
Sets neural network for this learning rule
learningRule - Variable in class org.neuroph.core.NeuralNetwork
Learning rule for this network
learningThread - Variable in class org.neuroph.core.NeuralNetwork
Separate thread for learning rule
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(TrainingElement, int) - Method in class org.neuroph.nnet.learning.KohonenLearning
 
learnPattern(SupervisedTrainingElement) - Method in class org.neuroph.nnet.learning.SupervisedHebbianLearning
Trains network with the pattern from the specified training element
leftHigh - Variable in class org.neuroph.core.transfer.Trapezoid
 
leftLow - Variable in class org.neuroph.core.transfer.Trapezoid
 
Linear - Class in org.neuroph.core.transfer
The Linear class provides 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
The LMS class implements LMS learning rule for neural networks.
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.

M

mapSize - Variable in class org.neuroph.nnet.learning.KohonenLearning
 
Max - Class in org.neuroph.core.input
The Max class 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)
maxIterations - Variable in class org.neuroph.nnet.comp.CompetitiveLayer
Max iterations for neurons to compete This is neccesery to limit, otherwise if there is no winner there will be endless loop.
MaxNet - Class in org.neuroph.nnet
The MaxNet class represents 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
The Min class performs min() function on input Vector
Min() - Constructor for class org.neuroph.core.input.Min
 
MultiLayerPerceptron - Class in org.neuroph.nnet
The MultiLayerPerceptron class that represents 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(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

name - Variable in class org.neuroph.util.plugins.PluginBase
Name for this plugin
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
The NeuralNetwork class is the 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
This class provides methods to create various neural networks.
NeuralNetworkFactory() - Constructor for class org.neuroph.util.NeuralNetworkFactory
 
NeuralNetworkType - Enum in org.neuroph.util
This enum contains neural network types and labels.
NeuralNetworkType(String) - Constructor for enum org.neuroph.util.NeuralNetworkType
 
NeuroFuzzyReasoner - Class in org.neuroph.nnet
The NeuroFuzzyReasoner class represents Neuro Fuzzy Reasoner architecture.
NeuroFuzzyReasoner(double[][], double[][]) - Constructor for class org.neuroph.nnet.NeuroFuzzyReasoner
 
NeuroFuzzyReasoner(int, Vector<Integer>, int) - Constructor for class org.neuroph.nnet.NeuroFuzzyReasoner
 
Neuron - Class in org.neuroph.core
The Neuron class represents basic artificial neuron according to McCulloch-Pitts neuron model (basic general 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
This class provides methods to create instances of Neurons.
NeuronFactory() - Constructor for class org.neuroph.util.NeuronFactory
 
NeuronProperties - Class in org.neuroph.util
This class represents properties of the neuron.
NeuronProperties() - Constructor for class org.neuroph.util.NeuronProperties
 
NeuronProperties(TransferFunctionType) - 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
nR - Variable in class org.neuroph.nnet.learning.KohonenLearning
 

O

OjaLearning - Class in org.neuroph.nnet.learning
 
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
The Or class performs logic OR operation on input Vector.
Or() - Constructor for class org.neuroph.core.input.Or
 
org.neuroph.core - package org.neuroph.core
This package provides base classes and basic building components for neural networks.
org.neuroph.core.input - package org.neuroph.core.input
This package provides common neuron input functions
org.neuroph.core.learning - package org.neuroph.core.learning
This package provides base classes for neural network learning algorithms.
org.neuroph.core.transfer - package org.neuroph.core.transfer
This package provides common neuron transfer functions
org.neuroph.nnet - package org.neuroph.nnet
This package provides out-of-the-box neural networks
org.neuroph.nnet.comp - package org.neuroph.nnet.comp
This package provides components for the specific neural network models.
org.neuroph.nnet.learning - package org.neuroph.nnet.learning
This package provides implementations of concrete neural network learning algorithms.
org.neuroph.util - package org.neuroph.util
This package provides various utility classes for creating neural networks, type codes, parsing vectors, etc.
org.neuroph.util.plugins - package org.neuroph.util.plugins
This package 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
outputNeurons - Variable in class org.neuroph.core.NeuralNetwork
Reference to newtwork output neurons

P

parentLayer - Variable in class org.neuroph.core.Neuron
Parent layer for this neuron
parentNetwork - Variable in class org.neuroph.core.Layer
Reference to parent neural network
parentNetwork - Variable in class org.neuroph.util.plugins.PluginBase
Reference to parent neural network
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
Perceptron - Class in org.neuroph.nnet
The Perceptron class that represents 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
PluginBase - Class in org.neuroph.util.plugins
The PluginBase class is 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
plugins - Variable in class org.neuroph.core.NeuralNetwork
Plugins collection
Product - Class in org.neuroph.core.input
The Product class provide method for multiplying all input Vector elements.
Product() - Constructor for class org.neuroph.core.input.Product
 

R

Ramp - Class in org.neuroph.core.transfer
The Ramp class provides 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
Randomize all network weights
RbfNetwork - Class in org.neuroph.nnet
The RbfNetwork class represents 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
removeElementAt(int) - Method in class org.neuroph.core.learning.TrainingSet
Removes training element at specified index position
removeInput(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
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 specified plugin
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
rightHigh - Variable in class org.neuroph.core.transfer.Trapezoid
 
rightLow - Variable in class org.neuroph.core.transfer.Trapezoid
 
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.
serialVersionUID - Static variable in class org.neuroph.core.Connection
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.input.And
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Diference
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.InputFunction
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Intensity
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Max
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Min
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Or
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Product
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.Sum
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.SumSqr
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.input.WeightedInput
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.Layer
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.IterativeLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.LearningRule
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.SupervisedLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.SupervisedTrainingElement
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.TrainingElement
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.TrainingSet
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.learning.UnsupervisedLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.NeuralNetwork
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.Neuron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.core.transfer.Gaussian
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Linear
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Ramp
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Sgn
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Sigmoid
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Step
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Tanh
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.TransferFunction
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.transfer.Trapezoid
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.core.Weight
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class
serialVersionUID - Static variable in class org.neuroph.nnet.Adaline
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.comp.CompetitiveLayer
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.comp.CompetitiveNeuron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.comp.DelayedConnection
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.comp.DelayedNeuron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.comp.HopfieldNeuron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.comp.ThresholdNeuron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.CompetitiveNetwork
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.Hopfield
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.Kohonen
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.BackPropagation
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.CompetitiveLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.HopfieldLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.KohonenLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.LMS
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.OjaLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.SigmoidDeltaRule
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.StepDeltaRule
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.SupervisedHebbianLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.MaxNet
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.MultiLayerPerceptron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.NeuroFuzzyReasoner
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.Perceptron
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.RbfNetwork
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.SupervisedHebbianNetwork
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.nnet.UnsupervisedHebbianNetwork
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.util.NeuronProperties
 
serialVersionUID - Static variable in class org.neuroph.util.plugins.LabelsPlugin
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
serialVersionUID - Static variable in class org.neuroph.util.plugins.PluginBase
The class fingerprint that is set to indicate serialization compatibility with a previous version of the class.
setBias(double) - Method in class org.neuroph.nnet.comp.HopfieldNeuron
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(Vector<Double>) - Method in class org.neuroph.core.learning.TrainingElement
Sets input vector
setInput(Vector<Double>) - Method in class org.neuroph.core.NeuralNetwork
Set network input.
setInput(double) - Method in class org.neuroph.core.Neuron
Sets neuron's input
setInput(double) - Method in class org.neuroph.nnet.comp.HopfieldNeuron
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.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
setNetworkType(NeuralNetworkType) - Method in class org.neuroph.core.NeuralNetwork
Sets network type
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
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
The Sgn class provides sgn neuron transfer function.
Sgn() - Constructor for class org.neuroph.core.transfer.Sgn
 
sigma - Variable in class org.neuroph.core.transfer.Gaussian
The sigma parametetar of the gaussian function
Sigmoid - Class in org.neuroph.core.transfer
The Sigmoid class provides 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
The SigmoidDeltaRule class extends LMS learning rule an implements Delta rule learning algorithm for perceptrons with sigmoid functions.
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
slope - Variable in class org.neuroph.core.transfer.Linear
The slope parametetar of the linear function
slope - Variable in class org.neuroph.core.transfer.Ramp
The slope parametetar of the ramp function
slope - Variable in class org.neuroph.core.transfer.Sigmoid
The slope parametetar of the sigmoid function
slope - Variable in class org.neuroph.core.transfer.Tanh
The slope parametetar of the Tanh function
Step - Class in org.neuroph.core.transfer
The Step class provides 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
The StepDeltaRule class implements Delta rule learning algorithm for perceptrons with step functions.
StepDeltaRule(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.StepDeltaRule
Creates new StepDeltaRule learning for the specified neural network
stopLearning - Variable in class org.neuroph.core.learning.LearningRule
Flag to stop learning
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
The class Sum provides method to sum all input vector elements.
Sum() - Constructor for class org.neuroph.core.input.Sum
 
summingFunction - Variable in class org.neuroph.core.input.InputFunction
Summing function component of the input function.
SummingFunction - Class in org.neuroph.core.input
The SummingFunction is abstract base class for components of neuron's InputFunction which performs some summing operation on weighted input vector and returns scalar
SummingFunction() - Constructor for class org.neuroph.core.input.SummingFunction
 
SummingFunctionType - Enum in org.neuroph.util
This enum contains summing functions types and labels.
SummingFunctionType(String) - Constructor for enum org.neuroph.util.SummingFunctionType
 
SumSqr - Class in org.neuroph.core.input
The class SumSqr provides method which return squared sum of all input vector elements.
SumSqr() - Constructor for class org.neuroph.core.input.SumSqr
 
SupervisedHebbianLearning - Class in org.neuroph.nnet.learning
The SupervisedHebbianLearning class implements supervised hebbian learning rule.
SupervisedHebbianLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.SupervisedHebbianLearning
Creates an instance of SupervisedHebbianLearning algorithm for the specified neural network
SupervisedHebbianNetwork - Class in org.neuroph.nnet
The SupervisedHebbianNetwork class that represents 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
The SupervisedLearning class is the base class for all supervised learning algorithms.
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
The SupervisedTrainingElement class represents training element for supervised neural network learning.
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

T

Tanh - Class in org.neuroph.core.transfer
The Tanh class provides 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.
thresh - Variable in class org.neuroph.nnet.comp.ThresholdNeuron
Threshold value for this neuron
ThresholdNeuron - Class in org.neuroph.nnet.comp
The ThresholdNeuron class 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.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
The TrainingElement class 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
trainingElements() - Method in class org.neuroph.core.learning.TrainingSet
Returns training elements collection
trainingSet - Variable in class org.neuroph.core.learning.LearningRule
Collection of training elements
TrainingSet - Class in org.neuroph.core.learning
The TrainingSet class represents set of training elements for neural network learning.
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
transferFunction - Variable in class org.neuroph.core.Neuron
Transfer function for this neuron
TransferFunction - Class in org.neuroph.core.transfer
The TransferFunction is abstract base class for all neuron tranfer functions.
TransferFunction() - Constructor for class org.neuroph.core.transfer.TransferFunction
 
TransferFunctionType - Enum in org.neuroph.util
This enum contains transfer functions types and labels.
TransferFunctionType(String) - Constructor for enum org.neuroph.util.TransferFunctionType
 
Trapezoid - Class in org.neuroph.core.transfer
The Trapezoid class provides 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.
type - Variable in class org.neuroph.core.NeuralNetwork
Network type id (see neuroph.util.NeuralNetworkType)
typeLabel - Variable in enum org.neuroph.util.NeuralNetworkType
 
typeLabel - Variable in enum org.neuroph.util.SummingFunctionType
 
typeLabel - Variable in enum org.neuroph.util.TransferFunctionType
 
typeLabel - Variable in enum org.neuroph.util.WeightsFunctionType
 

U

UnsupervisedHebbianLearning - Class in org.neuroph.nnet.learning
The UnsupervisedHebbianLearning class implements unsupervised hebbian learning rule.
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
The UnsupervisedHebbianNetwork class that represents 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
The UnsupervisedLearning class is the base class for all unsupervised learning algorithms.
UnsupervisedLearning(NeuralNetwork) - Constructor for class org.neuroph.core.learning.UnsupervisedLearning
Creates new unsupervised learning rule and sets the neural network to train
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.LMS
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, 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

value - Variable in class org.neuroph.core.Weight
Weight value
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.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
This class 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
The Weight class represents 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
The WeightedInput class implements weighted input operation for neuron InputFunction.
WeightedInput() - Constructor for class org.neuroph.core.input.WeightedInput
 
weightsFunction - Variable in class org.neuroph.core.input.InputFunction
Weights function component of the input function.
WeightsFunction - Class in org.neuroph.core.input
The WeightsFunction is abstract base class for components of neuron's InputFunction which perform some operation on neuron input vector and weight vector.
WeightsFunction() - Constructor for class org.neuroph.core.input.WeightsFunction
 
WeightsFunctionType - Enum in org.neuroph.util
This enum contains weights functions types and labels.
WeightsFunctionType(String) - Constructor for enum org.neuroph.util.WeightsFunctionType
 
winner - Variable in class org.neuroph.nnet.comp.CompetitiveLayer
The competition winner for this layer

X

xHigh - Variable in class org.neuroph.core.transfer.Ramp
Threshold for the high output level
xLow - Variable in class org.neuroph.core.transfer.Ramp
Threshold for the low output level

Y

yHigh - Variable in class org.neuroph.core.transfer.Ramp
Output value for the high output level
yHigh - Variable in class org.neuroph.core.transfer.Step
Output value for high output level
yLow - Variable in class org.neuroph.core.transfer.Ramp
Output value for the low output level
yLow - Variable in class org.neuroph.core.transfer.Step
Output value for low output level

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