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

## 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
addElapsedTime(long) - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
addInputConnection(Connection) - Method in class org.neuroph.core.Neuron
  Adds the specified input connection
addInputConnection(Neuron) - Method in class org.neuroph.core.Neuron
  Adds input connection from specified neuron
addInputConnection(Neuron, double) - Method in class org.neuroph.core.Neuron
  Adds input connection with the given weight, from given neuron
addInputConnection(Connection) - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
addInputConnection(Neuron, double) - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
addInputConnection(Neuron) - Method in class org.neuroph.nnet.comp.neuron.BiasNeuron
addInputConnection(Connection) - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
  Adds input connection for this competitive neuron
addLayer(Layer) - Method in class org.neuroph.core.NeuralNetwork
  Adds layer to neural network
addLayer(int, Layer) - Method in class org.neuroph.core.NeuralNetwork
  Adds layer to specified index position in network
addListener(LearningEventListener) - Method in class org.neuroph.core.learning.LearningRule
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
addRow(DataSetRow) - Method in class org.neuroph.core.learning.DataSet
  Adds new row element to this data set
addRow(double[]) - Method in class org.neuroph.core.learning.DataSet
addRow(double[], double[]) - Method in class org.neuroph.core.learning.DataSet
addTask(BenchmarkTask) - Method in class org.neuroph.util.benchmark.Benchmark
  Adds specified benchmark task
addToSquaredErrorSum(double[]) - Method in class org.neuroph.core.learning.SupervisedLearning
  Calculates and updates sum of squared errors for single pattern, and updates total sum of squared pattern errors
addToSquaredErrorSum(double[]) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
adjustLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
adjustMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
afterEpoch() - Method in class org.neuroph.core.learning.IterativeLearning
afterEpoch() - Method in class org.neuroph.core.learning.SupervisedLearning
And - Class in org.neuroph.core.input
  Performs logic AND operation on input vector.
And() - Constructor for class org.neuroph.core.input.And
AntiHebbianLearning - Class in org.neuroph.nnet.learning
  A variant of Hebbian learning called Anti-Hebbian learning.
AntiHebbianLearning() - Constructor for class org.neuroph.nnet.learning.AntiHebbianLearning
array2network(double[], NeuralNetwork) - Static method in class org.neuroph.util.NeuralNetworkCODEC
  Decode a network from an array.

## B

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

## C

calculate() - Method in class org.neuroph.core.Layer
  Performs calculaton for all neurons in this layer
calculate() - Method in class org.neuroph.core.NeuralNetwork
  Performs calculation on whole network
calculate() - Method in class org.neuroph.core.Neuron
  Calculates neuron's output
calculate() - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
  Performs calculaton for all neurons in this layer
calculate() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
calculate() - Method in class org.neuroph.nnet.comp.neuron.DelayedNeuron
calculate() - Method in class org.neuroph.nnet.comp.neuron.InputNeuron
  Calculate method of this type of neuron just transfers its externaly set input (with setNetInput) to its output
calculate() - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
  Calculates neuron output
calculate() - Method in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
  Calculates neuron's output
calculateErrorAndUpdateHiddenNeurons() - Method in class org.neuroph.nnet.learning.BackPropagation
  This method implements weights adjustment for the hidden layers
calculateErrorAndUpdateOutputNeurons(double[]) - Method in class org.neuroph.nnet.learning.SigmoidDeltaRule
  This method implements weights update procedure for the output neurons Calculates delta/error and calls updateNeuronWeights to update neuron's weights for each output neuron
calculateHiddenNeuronError(Neuron) - Method in class org.neuroph.nnet.learning.BackPropagation
  Calculates and returns the neuron's error (neuron's delta) for the given neuron param
calculateOutputError(double[], double[]) - Method in class org.neuroph.core.learning.SupervisedLearning
  Calculates the network error for the current input pattern - diference between desired and actual output
calculateStatistics() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
clear() - Method in class org.neuroph.core.learning.DataSet
  Removes all alements from training set
close() - Method in interface org.neuroph.util.io.InputAdapter
  Close data source after reading is finnished.
close() - Method in class org.neuroph.util.io.InputStreamAdapter
close() - Method in class org.neuroph.util.io.JDBCInputAdapter
  Closes result set used as data source.
close() - Method in class org.neuroph.util.io.JDBCOutputAdapter
close() - Method in interface org.neuroph.util.io.OutputAdapter
  Close destination after writing is finnished.
close() - Method in class org.neuroph.util.io.OutputStreamAdapter
  Closes output stream.
CompetitiveLayer - Class in org.neuroph.nnet.comp.layer
  Represents layer of competitive neurons, and provides methods for competition.
CompetitiveLayer(int, NeuronProperties) - Constructor for class org.neuroph.nnet.comp.layer.CompetitiveLayer
  Create an instance of CompetitiveLayer with the specified number of neurons with neuron properties
CompetitiveLearning - Class in org.neuroph.nnet.learning
  Competitive learning rule.
CompetitiveLearning() - Constructor for class org.neuroph.nnet.learning.CompetitiveLearning
  Creates new instance of CompetitiveLearning
CompetitiveNetwork - Class in org.neuroph.nnet
  Two layer neural network with competitive learning rule.
CompetitiveNetwork(int, int) - Constructor for class org.neuroph.nnet.CompetitiveNetwork
  Creates new competitive network with specified neuron number
CompetitiveNeuron - Class in org.neuroph.nnet.comp.neuron
  Provides neuron behaviour specific for competitive neurons which are used in competitive layers, and networks with competitive learning.
CompetitiveNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
  Creates an instance of CompetitiveNeuron with specified input and transfer functions
connectInputsToOutputs() - Method in class org.neuroph.nnet.MultiLayerPerceptron
Connection - Class in org.neuroph.core
  Weighted connection to another neuron.
Connection(Neuron, Neuron) - Constructor for class org.neuroph.core.Connection
  Creates a new connection between specified neurons with random weight
Connection(Neuron, Neuron, Weight) - Constructor for class org.neuroph.core.Connection
  Creates a new connection to specified neuron with specified weight object
Connection(Neuron, Neuron, double) - Constructor for class org.neuroph.core.Connection
  Creates a new connection to specified neuron with specified weight value
ConnectionFactory - Class in org.neuroph.util
  Provides methods to connect neurons by creating Connection objects.
ConnectionFactory() - Constructor for class org.neuroph.util.ConnectionFactory
convertToArray(List<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
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
createFromFile(String, int, int, String) - Static method in class org.neuroph.core.learning.DataSet
createHopfield(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Hopfield network
createInstar(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Instar network
createKeys(String...) - Method in class org.neuroph.util.Properties
createKohonen(int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Kohonen network
createLayer(int, NeuronProperties) - Static method in class org.neuroph.util.LayerFactory
createLayer(int, TransferFunctionType) - Static method in class org.neuroph.util.LayerFactory
createLayer(int, Class<? extends TransferFunction>) - Static method in class org.neuroph.util.LayerFactory
createLayer(List<NeuronProperties>) - Static method in class org.neuroph.util.LayerFactory
createMaxNet(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Max Net network
createMLPerceptron(String, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Multi Layer Perceptron
createMLPerceptron(String, TransferFunctionType, Class, boolean, boolean) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Multi Layer Perceptron
createNeuron(NeuronProperties) - Static method in class org.neuroph.util.NeuronFactory
  Creates and returns neuron instance according to the given specification in neuronProperties.
createOutstar(int) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Outstar network
createPerceptron(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Perceptron network
createPerceptron(int, int, TransferFunctionType, Class) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Perceptron network
createRbfNetwork(int, int, int) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of RBF network
createSupervisedHebbian(int, int, TransferFunctionType) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Creates and returns a new instance of Hebbian network
createTrainingAndTestSubsets(int, int) - Method in class org.neuroph.core.learning.DataSet
  Returns output vector size of training elements in this training set This method is implementation of EngineIndexableSet interface, and it is added to provide compatibility with Encog data sets and FlatNetwork
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

DataSet - Class in org.neuroph.core.learning
  A set of training elements for training neural network.
DataSet(int) - Constructor for class org.neuroph.core.learning.DataSet
  Creates an instance of new empty training set
DataSet(int, int) - Constructor for class org.neuroph.core.learning.DataSet
  Creates an instance of new empty training set
DataSetRow - Class in org.neuroph.core.learning
  Represents training element for supervised learning algorithms.
DataSetRow(String, String) - Constructor for class org.neuroph.core.learning.DataSetRow
  Creates new training element with specified input and desired output vectors specifed as strings
DataSetRow(double[], double[]) - Constructor for class org.neuroph.core.learning.DataSetRow
  Creates new training element with specified input and desired output vectors
DataSetRow(double...) - Constructor for class org.neuroph.core.learning.DataSetRow
  Creates new training element with input array
DataSetRow(ArrayList<Double>, ArrayList<Double>) - Constructor for class org.neuroph.core.learning.DataSetRow
  Creates new training element with specified input and desired output vectors
DataSetRow(ArrayList<Double>) - Constructor for class org.neuroph.core.learning.DataSetRow
dec(double) - Method in class org.neuroph.core.Weight
  Decreases the weight for specified amount
DecimalScaleNormalizer - Class in org.neuroph.util.norm
  Decimal scaling normalization method, which normalize data by moving decimal point in regard to max element in training set (by columns) Normalization is done according to formula: normalizedVector[i] = vector[i] / scaleFactor[i]
DecimalScaleNormalizer() - Constructor for class org.neuroph.util.norm.DecimalScaleNormalizer
DelayedConnection - Class in org.neuroph.nnet.comp
  Represents the connection between neurons which can delay signal.
DelayedConnection(Neuron, Neuron, double, int) - Constructor for class org.neuroph.nnet.comp.DelayedConnection
  Creates an instance of delayed connection to cpecified neuron and with specified weight
DelayedNeuron - Class in org.neuroph.nnet.comp.neuron
  Provides behaviour for neurons with delayed output.
DelayedNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.DelayedNeuron
  Creates an instance of neuron which can delay output
determineArraySize(NeuralNetwork) - Static method in class org.neuroph.util.NeuralNetworkCODEC
  Determine the array size for the given neural network.
Difference - Class in org.neuroph.core.input
  Performs the vector difference operation on input and weight vector.
Difference() - Constructor for class org.neuroph.core.input.Difference
DistortRandomizer - Class in org.neuroph.util.random
  This class provides distort randomization technique, which distorts existing weight values using specified distortion factor.
DistortRandomizer(double) - Constructor for class org.neuroph.util.random.DistortRandomizer
  Create a new instance of DistortRandomizer with specified distortion factor
doBatchWeightsUpdate() - Method in class org.neuroph.core.learning.SupervisedLearning
  This method updates network weights in batch mode - use accumulated weights change stored in Weight.deltaWeight It is executed after each learning epoch, only if learning is done in batch mode.
doBatchWeightsUpdate() - Method in class org.neuroph.nnet.learning.ResilientPropagation
doLearningEpoch(DataSet) - Method in class org.neuroph.core.learning.IterativeLearning
  Override this method to implement specific learning epoch - one learning iteration, one pass through whole training set
doLearningEpoch(DataSet) - Method in class org.neuroph.core.learning.SupervisedLearning
  This method implements basic logic for one learning epoch for the supervised learning algorithms.
doLearningEpoch(DataSet) - Method in class org.neuroph.core.learning.UnsupervisedLearning
  This method does one learning epoch for the unsupervised learning rules.
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.CompetitiveLearning
  This method does one learning epoch for the unsupervised learning rules.
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  Perform one simulated annealing epoch.
doLearningEpoch(DataSet) - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
  This method does one learning epoch for the unsupervised learning rules.
doOneLearningIteration(DataSet) - Method in class org.neuroph.core.learning.IterativeLearning
  Runs one learning iteration for the specified training set and notfies observers.
DynamicBackPropagation - Class in org.neuroph.nnet.learning
  Backpropagation learning rule with dynamic learning rate and momentum
DynamicBackPropagation() - Constructor for class org.neuroph.nnet.learning.DynamicBackPropagation

## E

error - Variable in class org.neuroph.core.Neuron
  Local error for this neuron
errorChangeStalled() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns true if absolute error change is sufficently small (<=minErrorChange) for minErrorChangeStopIterations number of iterations

## F

FileInputAdapter - Class in org.neuroph.util.io
  Implementation of InputAdapter interface for reading neural network inputs from files.
FileInputAdapter(File) - Constructor for class org.neuroph.util.io.FileInputAdapter
  Creates a new FileInputAdapter by opening a connection to an actual file, specified by the file param
FileInputAdapter(String) - Constructor for class org.neuroph.util.io.FileInputAdapter
  Creates a new FileInputAdapter by opening a connection to an actual file, specified by the fileName param
FileOutputAdapter - Class in org.neuroph.util.io
  Implementation of OutputAdapter interface for writing neural network outputs to files.
FileOutputAdapter(File) - Constructor for class org.neuroph.util.io.FileOutputAdapter
  Creates a new FileOutputAdapter by opening a connection to an actual file, specified by the file param
FileOutputAdapter(String) - Constructor for class org.neuroph.util.io.FileOutputAdapter
  Creates a new FileOutputAdapter by opening a connection to an actual file, specified by the fileName param
FileUtils - Class in org.neuroph.util
  Utility methods for working with files.
FileUtils() - Constructor for class org.neuroph.util.FileUtils
findScaleVector() - Method in class org.neuroph.util.norm.DecimalScaleNormalizer
fireLearningEvent(LearningEvent) - Method in class org.neuroph.core.learning.LearningRule
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
fromNeuron - Variable in class org.neuroph.core.Connection
  From neuron for this connection (source neuron).
fullConnect(Layer, Layer) - Static method in class org.neuroph.util.ConnectionFactory
  Creates full connectivity between the two specified layers
fullConnect(Layer, Layer, boolean) - Static method in class org.neuroph.util.ConnectionFactory
  Creates full connectivity between the two specified layers
fullConnect(Layer, Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
  Creates full connectivity between two specified layers with specified weight for all connections
fullConnect(Layer) - Static method in class org.neuroph.util.ConnectionFactory
  Creates full connectivity within layer - each neuron with all other within the same layer
fullConnect(Layer, double) - Static method in class org.neuroph.util.ConnectionFactory
  Creates full connectivity within layer - each neuron with all other within the same layer with the specified weight values for all conections.
fullConnect(Layer, double, int) - Static method in class org.neuroph.util.ConnectionFactory
  Creates full connectivity within layer - each neuron with all other within the same layer with the specified weight and delay values for all conections.

## G

Gaussian - Class in org.neuroph.core.transfer
  Gaussian neuron transfer function.
Gaussian() - Constructor for class org.neuroph.core.transfer.Gaussian
  Creates an instance of Gaussian neuron transfer
Gaussian(Properties) - Constructor for class org.neuroph.core.transfer.Gaussian
  Creates an instance of Gaussian neuron transfer function with the specified properties.
GaussianRandomizer - Class in org.neuroph.util.random
  This class provides Gaussian randomization technique using Box Muller method.
GaussianRandomizer(double, double) - Constructor for class org.neuroph.util.random.GaussianRandomizer
GeneralizedHebbianLearning - Class in org.neuroph.nnet.learning
  A variant of Hebbian learning called Generalized Hebbian learning.
GeneralizedHebbianLearning() - Constructor for class org.neuroph.nnet.learning.GeneralizedHebbianLearning
getAverageTestTime() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
getBias() - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
  Returns bias value for this neuron
getColumnNames() - Method in class org.neuroph.core.learning.DataSet
getConnectionFrom(Neuron) - Method in class org.neuroph.core.Neuron
  Gets input connection from the specified neuron * @param fromNeuron neuron connected to this neuron as input
getConnectionsFromOtherLayers() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
  Returns collection of connections from other layers
getCurrentIteration() - Method in class org.neuroph.core.learning.IterativeLearning
  Returns current iteration of this learning algorithm
getDecreaseFactor() - Method in class org.neuroph.nnet.learning.ResilientPropagation
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.Log
getDerivative(double) - Method in class org.neuroph.core.transfer.Sigmoid
getDerivative(double) - Method in class org.neuroph.core.transfer.Sin
getDerivative(double) - Method in class org.neuroph.core.transfer.Tanh
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.DataSetRow
getElapsedTime() - Method in class org.neuroph.util.benchmark.Stopwatch
  Returns elapsed time in milliseconds between calls to start and stop methods If the watch has never been started, returns zero
getElapsedTimes() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
getError() - Method in class org.neuroph.core.Neuron
  Returns error for this neuron.
getErrorCorrection() - Method in class org.neuroph.nnet.learning.BinaryDeltaRule
  Gets the errorCorrection parametar
getFilePath() - Method in class org.neuroph.core.learning.DataSet
  Returns full file path for this training set
getFromNeuron() - Method in class org.neuroph.core.Connection
  Gets from neuron for this connection
getIncreaseFactor() - Method in class org.neuroph.nnet.learning.ResilientPropagation
getInitialDelta() - Method in class org.neuroph.nnet.learning.ResilientPropagation
getInput() - Method in class org.neuroph.core.Connection
  Returns input received through this connection - the activation that comes from the output of the cell on the other end of connection
getInput() - Method in class org.neuroph.core.learning.DataSetRow
  Returns input vector
getInput() - Method in class org.neuroph.nnet.comp.DelayedConnection
  Gets delayed input through this connection
getInputConnections() - Method in class org.neuroph.core.Neuron
  Returns input connections for this neuron
getInputFunction() - Method in class org.neuroph.core.Neuron
  Returns input function
getInputFunction() - Method in class org.neuroph.util.NeuronProperties
getInputFunctions() - Method in class org.neuroph.util.Neuroph
getInputNeurons() - Method in class org.neuroph.core.NeuralNetwork
  Returns input neurons
getInputsCount() - Method in class org.neuroph.core.NeuralNetwork
  Gets number of input neurons
getInputSize() - Method in class org.neuroph.core.learning.DataSet
  Returns input vector size of training elements in this training set This method is implementation of EngineIndexableSet interface, and it is added to provide compatibility with Encog data sets and FlatNetwork
getInstance() - Static method in class org.neuroph.util.Neuroph
getIteration() - Method in class org.neuroph.nnet.learning.KohonenLearning
getLabel() - Method in class org.neuroph.core.Layer
  Get layer label
getLabel() - Method in class org.neuroph.core.learning.DataSet
  Returns label for this training set
getLabel() - Method in class org.neuroph.core.learning.DataSetRow
  Get training element label
getLabel() - Method in class org.neuroph.core.NeuralNetwork
  Get network label
getLabel() - Method in class org.neuroph.core.Neuron
  Returns label for this neuron
getLayerAt(int) - Method in class org.neuroph.core.NeuralNetwork
  Returns layer at specified index
getLayers() - Method in class org.neuroph.core.NeuralNetwork
  Returns layers array
getLayers() - Method in class org.neuroph.util.Neuroph
getLayersCount() - Method in class org.neuroph.core.NeuralNetwork
  Returns number of layers in network
getLearningRate() - Method in class org.neuroph.core.learning.IterativeLearning
  Returns learning rate for this algorithm
getLearningRate() - Method in class org.neuroph.nnet.learning.KohonenLearning
getLearningRateChange() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getLearningRule() - Method in class org.neuroph.core.NeuralNetwork
  Returns the learning algorithm of this network
getLearningRules() - Method in class org.neuroph.util.Neuroph
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
getMaxDelta() - Method in class org.neuroph.nnet.learning.ResilientPropagation
getMaxError() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns learning error tolerance - the value of total network error to stop learning.
getMaxIterations() - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
  Returns the maxIterations setting for this layer
getMaxLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getMaxMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getMaxTestTime() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
getMinDelta() - Method in class org.neuroph.nnet.learning.ResilientPropagation
getMinErrorChange() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns min error change stopping criteria
getMinErrorChangeIterationsCount() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns number of iterations count for for min error change stopping criteria
getMinErrorChangeIterationsLimit() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns number of iterations for min error change stopping criteria
getMinLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getMinMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getMinTestTime() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
getMomentum() - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
  Returns the momentum factor
getMomentumChange() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getName() - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Gets task name
getName() - Method in class org.neuroph.util.plugins.PluginBase
  Returns the name of this plugin
getNetInput() - Method in class org.neuroph.core.Neuron
  Returns total net input
getNetwork() - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  Get the best network from the training.
getNetworkType() - Method in class org.neuroph.core.NeuralNetwork
  Returns type of this network
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 array of neurons in this layer
getNeurons() - Method in class org.neuroph.util.Neuroph
getNeuronsCount() - Method in class org.neuroph.core.Layer
  Returns number of neurons in this layer
getNeuronType() - Method in class org.neuroph.util.NeuronProperties
getOutConnections() - Method in class org.neuroph.core.Neuron
  Returns output connections from this neuron
getOutput(Connection[]) - Method in class org.neuroph.core.input.And
getOutput(Connection[]) - Method in class org.neuroph.core.input.Difference
getOutput(double[], double[]) - Method in class org.neuroph.core.input.Difference
getOutput(Connection[]) - Method in class org.neuroph.core.input.InputFunction
  Returns ouput value of this input function for the given neuron inputs
getOutput(Connection[]) - Method in class org.neuroph.core.input.Max
getOutput(Connection[]) - Method in class org.neuroph.core.input.Min
getOutput(Connection[]) - Method in class org.neuroph.core.input.Or
getOutput(Connection[]) - Method in class org.neuroph.core.input.Product
getOutput(Connection[]) - Method in class org.neuroph.core.input.Sum
getOutput(Connection[]) - Method in class org.neuroph.core.input.SumSqr
getOutput(Connection[]) - Method in class org.neuroph.core.input.WeightedSum
getOutput(double[], double[]) - Static method in class org.neuroph.core.input.WeightedSum
getOutput() - Method in class org.neuroph.core.NeuralNetwork
  Returns network output Vector.
getOutput() - Method in class org.neuroph.core.Neuron
  Returns neuron's output
getOutput(double) - Method in class org.neuroph.core.transfer.Gaussian
getOutput(double) - Method in class org.neuroph.core.transfer.Linear
getOutput(double) - Method in class org.neuroph.core.transfer.Log
getOutput(double) - Method in class org.neuroph.core.transfer.Ramp
getOutput(double) - Method in class org.neuroph.core.transfer.Sgn
  y = 1, x > 0 y = -1, x <= 0
getOutput(double) - Method in class org.neuroph.core.transfer.Sigmoid
getOutput(double) - Method in class org.neuroph.core.transfer.Sin
getOutput(double) - Method in class org.neuroph.core.transfer.Step
getOutput(double) - Method in class org.neuroph.core.transfer.Tanh
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.neuron.BiasNeuron
getOutput(int) - Method in class org.neuroph.nnet.comp.neuron.DelayedNeuron
  Returns neuron output with the specified delay
getOutputNeurons() - Method in class org.neuroph.core.NeuralNetwork
  Returns output neurons
getOutputsCount() - Method in class org.neuroph.core.NeuralNetwork
getOutputSize() - Method in class org.neuroph.core.learning.DataSet
  Returns output vector size of training elements in this training set.
getParentLayer() - Method in class org.neuroph.core.Neuron
  Returns reference to parent layer for this neuron
getParentNetwork() - Method in class org.neuroph.core.Layer
  Returns reference to parent network
getParentNetwork() - Method in class org.neuroph.util.plugins.PluginBase
  Returns the parent network for this plugin
getPlugin(Class) - Method in class org.neuroph.core.NeuralNetwork
  Returns the requested plugin
getPreviousEpochError() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns total network error in previous learning epoch
getProperties() - Method in class org.neuroph.core.transfer.Sgn
  Returns the properties of this function
getProperties() - Method in class org.neuroph.core.transfer.Step
  Returns the properties of this function
getProperty(String) - Method in class org.neuroph.util.Properties
getRandomGenerator() - Method in class org.neuroph.util.random.WeightsRandomizer
  Gets random generator used to generate random values
getRightHigh() - Method in class org.neuroph.core.transfer.Trapezoid
  Returns right high point of trapezoid function
getRightLow() - Method in class org.neuroph.core.transfer.Trapezoid
  Returns right low point of trapezoid function
getRowAt(int) - Method in class org.neuroph.core.learning.DataSet
  Returns training element at specified index position
getRows() - Method in class org.neuroph.core.learning.DataSet
  Returns elements of this training set
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
getStandardDeviation() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
getTestIterations() - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Gets number of test (benchmarking) iterations
getTestIterations() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
getThresh() - Method in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
  Returns threshold value for this neuron
getToNeuron() - Method in class org.neuroph.core.Connection
  Gets to neuron for this connection
getTotalNetworkError() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns total network error in current learning epoch
getTrainingData() - Method in class org.neuroph.core.Weight
  Returns training data buffer for this weight
getTrainingSet() - Method in class org.neuroph.core.learning.LearningRule
  Gets training set
getTransferFunction() - Method in class org.neuroph.core.Neuron
  Returns transfer function
getTransferFunction() - Method in class org.neuroph.util.NeuronProperties
getTransferFunctionProperties() - Method in class org.neuroph.util.NeuronProperties
getTransferFunctions() - Method in class org.neuroph.util.Neuroph
getTypeClass() - Method in enum org.neuroph.util.TransferFunctionType
getTypeLabel() - Method in enum org.neuroph.util.NeuralNetworkType
getTypeLabel() - Method in enum org.neuroph.util.TransferFunctionType
getUseDynamicLearningRate() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getUseDynamicMomentum() - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
getValue() - Method in class org.neuroph.core.Weight
  Returns weight value
getWarmupIterations() - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Gets number of warmup iterations.
getWeight() - Method in class org.neuroph.core.Connection
  Returns weight for this connection
getWeightedInput() - Method in class org.neuroph.core.Connection
  Returns the weighted input received through this connection
getWeights() - Method in class org.neuroph.core.Neuron
  Returns weights vector of input connections
getWinner() - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
  Returns the winning neuron for this layer
getXHigh() - Method in class org.neuroph.core.transfer.Ramp
  Returns threshold value for the high output level
getXLow() - Method in class org.neuroph.core.transfer.Ramp
  Returns threshold value for the low output level
getYHigh() - Method in class org.neuroph.core.transfer.Ramp
  Returns output value for high output level
getYHigh() - Method in class org.neuroph.core.transfer.Step
  Returns output value for high output level
getYLow() - Method in class org.neuroph.core.transfer.Ramp
  Returns output value for low output level
getYLow() - Method in class org.neuroph.core.transfer.Step
  Returns output value for low output level
gradient - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData

## H

handleLearningEvent(LearningEvent) - Method in interface org.neuroph.core.events.LearningEventListener
  This method gets executed when LearningRule fires LearningEvent which some class is listening to.
hasInputConnectionFrom(Neuron) - Method in class org.neuroph.core.Neuron
hasInputConnections() - Method in class org.neuroph.core.Neuron
  Returns true if there are input connections for this neuron, false otherwise
hasOutputConnectionTo(Neuron) - Method in class org.neuroph.core.Neuron
hasProperty(String) - Method in class org.neuroph.util.Properties
hasReachedStopCondition() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns true if stop condition has been reached, 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

## I

importFromFile(String, int, int, String) - Static method in class org.neuroph.util.TrainingSetImport
inc(double) - Method in class org.neuroph.core.Weight
  Increases the weight for the specified amount
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
initializeWeights(double) - Method in class org.neuroph.core.Layer
  Initialize connection weights for the whole layer to to specified value
initializeWeights(double) - Method in class org.neuroph.core.Neuron
  Initialize weights for all input connections to specified value
input - Variable in class org.neuroph.core.learning.DataSetRow
  Input vector for this training element
InputAdapter - Interface in org.neuroph.util.io
  Interface for reading neural network inputs from various data sources.
inputConnections - Variable in class org.neuroph.core.Neuron
  Array of neuron's input connections (connections to this neuron)
InputFunction - Class in org.neuroph.core.input
  Neuron's input function.
InputFunction() - Constructor for class org.neuroph.core.input.InputFunction
inputFunction - Variable in class org.neuroph.core.Neuron
  Input function for this neuron
InputLayer - Class in org.neuroph.nnet.comp.layer
  Represents a layer of input neurons - a typical neural network input layer
InputLayer(int) - Constructor for class org.neuroph.nnet.comp.layer.InputLayer
  Creates a new instance of InputLayer with specified number of input neurons
InputNeuron - Class in org.neuroph.nnet.comp.neuron
  Provides input neuron behaviour - neuron with input extranaly set, which just transfer that input to output without change.
InputNeuron() - Constructor for class org.neuroph.nnet.comp.neuron.InputNeuron
  Creates a new instance of InputNeuron with linear transfer function
InputOutputNeuron - Class in org.neuroph.nnet.comp.neuron
  Provides behaviour specific for neurons which act as input and the output neurons within the same layer.
InputOutputNeuron() - Constructor for class org.neuroph.nnet.comp.neuron.InputOutputNeuron
  Creates an instance of neuron for Hopfield network
InputOutputNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.InputOutputNeuron
  Creates an instance of neuron for Hopfield network with specified input and transfer functions
InputStreamAdapter - Class in org.neuroph.util.io
  Implementation of InputAdapter interface for reading neural network inputs from input stream.
InputStreamAdapter(InputStream) - Constructor for class org.neuroph.util.io.InputStreamAdapter
InputStreamAdapter(BufferedReader) - Constructor for class org.neuroph.util.io.InputStreamAdapter
Instar - Class in org.neuroph.nnet
  Instar neural network with Instar learning rule.
Instar(int) - Constructor for class org.neuroph.nnet.Instar
  Creates new Instar with specified number of input neurons.
InstarLearning - Class in org.neuroph.nnet.learning
  Hebbian-like learning rule for Instar network.
InstarLearning() - Constructor for class org.neuroph.nnet.learning.InstarLearning
  Creates new instance of InstarLearning algorithm
IOHelper - Class in org.neuroph.util.io
  This class is helper for feeding neural network with data using some InputAdapter and writing network output using OutputAdapter
IOHelper() - Constructor for class org.neuroph.util.io.IOHelper
isCompeting() - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
  Retruns true if this neuron is in competing mode, false otherwise
isEmpty() - Method in class org.neuroph.core.learning.DataSet
  Returns true if training set is empty, false otherwise
isInBatchMode() - Method in class org.neuroph.core.learning.SupervisedLearning
  Returns true if learning is performed in batch mode, 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
isSupervised() - Method in class org.neuroph.core.learning.DataSetRow
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 instance of IterativeLearning learning algorithm
iterator() - Method in class org.neuroph.core.learning.DataSet
  Returns Iterator for iterating training elements collection

## J

JDBCInputAdapter - Class in org.neuroph.util.io
  Implementation of InputAdapter interface for reading neural network inputs from database.
JDBCInputAdapter(Connection, String) - Constructor for class org.neuroph.util.io.JDBCInputAdapter
JDBCOutputAdapter - Class in org.neuroph.util.io
  Implementation of OutputAdapter interface for writing neural network outputs to database.
JDBCOutputAdapter(Connection, String) - Constructor for class org.neuroph.util.io.JDBCOutputAdapter
  Creates new JDBCOutputAdapter with specifed database connection and table

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

## L

label - Variable in class org.neuroph.core.learning.DataSetRow
  Label for this training element
Layer - Class in org.neuroph.core
  Layer of neurons in a neural network.
Layer() - Constructor for class org.neuroph.core.Layer
  Creates an instance of empty Layer
Layer(int, 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(DataSet) - Method in class org.neuroph.core.learning.IterativeLearning
learn(DataSet, int) - Method in class org.neuroph.core.learning.IterativeLearning
  Trains network for the specified training set and number of iterations
learn(DataSet) - Method in class org.neuroph.core.learning.LearningRule
  Override this method to implement specific learning procedures
learn(DataSet, double) - Method in class org.neuroph.core.learning.SupervisedLearning
  Trains network for the specified training set and number of iterations
learn(DataSet, double, int) - Method in class org.neuroph.core.learning.SupervisedLearning
  Trains network for the specified training set and number of iterations
learn(DataSet) - Method in class org.neuroph.core.NeuralNetwork
  Learn the specified training set
learn(DataSet, LearningRule) - Method in class org.neuroph.core.NeuralNetwork
  Learn the specified training set, using specified learning rule
learn(DataSet) - Method in class org.neuroph.nnet.learning.HopfieldLearning
  Calculates weights for the hopfield net to learn the specified training set
learn(DataSet) - Method in class org.neuroph.nnet.learning.KohonenLearning
LearningEvent - Class in org.neuroph.core.events
  This class holds information about the source of some learning event.
LearningEvent(LearningRule) - Constructor for class org.neuroph.core.events.LearningEvent
LearningEventListener - Interface in org.neuroph.core.events
  This interface is implemented by classes who are listening to learning events (iterations, error etc.) LearningEvent class holds the information about event.
learningRate - Variable in class org.neuroph.core.learning.IterativeLearning
  Learning rate parametar
LearningRule - Class in org.neuroph.core.learning
  Base class for all neural network learning algorithms.
LearningRule() - Constructor for class org.neuroph.core.learning.LearningRule
  Creates new instance of learning rule
learnInNewThread(DataSet) - 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(DataSet, 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
learnPattern(DataSetRow) - Method in class org.neuroph.core.learning.SupervisedLearning
  Trains network with the input and desired output pattern from the specified training element
learnPattern(DataSetRow) - Method in class org.neuroph.core.learning.UnsupervisedLearning
  Trains network with the pattern from the specified training element
learnPattern(DataSetRow) - 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
listeners - Variable in class org.neuroph.core.learning.LearningRule
  List of learning rule listeners
LMS - Class in org.neuroph.nnet.learning
  LMS learning rule for neural networks.
LMS() - Constructor for class org.neuroph.nnet.learning.LMS
  Creates a new LMS learning rule This learning rule is used to train Adaline neural network, and this class is base for all LMS based learning rules like PerceptronLearning, DeltaRule, SigmoidDeltaRule, Backpropagation etc.
load(String) - Static method in class org.neuroph.core.learning.DataSet
  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.
Log - Class in org.neuroph.core.transfer
  Log neuron transfer function.
Log() - Constructor for class org.neuroph.core.transfer.Log

## M

main(String[]) - Static method in class org.neuroph.util.benchmark.BenchmarkSample
main(String[]) - Static method in class test.Test
Max - Class in org.neuroph.core.input
  Performs max function on input vector
Max() - Constructor for class org.neuroph.core.input.Max
max - Variable in class org.neuroph.util.random.RangeRandomizer
  Upper range limit
maxError - Variable in class org.neuroph.core.learning.SupervisedLearning
  Max allowed network error (condition to stop learning)
maxIterations - Variable in class org.neuroph.core.learning.IterativeLearning
  Max training iterations (when to stopLearning training) TODO: this field should be private, to force use of setMaxIterations from derived classes, so iterationsLimited flag is also set at the sam etime.Wil that break backward compatibility with serialized networks?
MaxMinNormalizer - Class in org.neuroph.util.norm
  MaxMin normalization method, which normalize data in regard to min and max elements in training set (by columns) Normalization is done according to formula: normalizedVector[i] = (vector[i] - min[i]) / (max[i] - min[i])
MaxMinNormalizer() - Constructor for class org.neuroph.util.norm.MaxMinNormalizer
MaxNet - Class in org.neuroph.nnet
  Max Net neural network with competitive learning rule.
MaxNet(int) - Constructor for class org.neuroph.nnet.MaxNet
  Creates new Maxnet network with specified neuron number
MaxNormalizer - Class in org.neuroph.util.norm
  Max normalization method, which normalize data in regard to max element in training set (by columns) Normalization is done according to formula: normalizedVector[i] = vector[i] / max[i]
MaxNormalizer() - Constructor for class org.neuroph.util.norm.MaxNormalizer
Min - Class in org.neuroph.core.input
  Performs min function on input vector
Min() - Constructor for class org.neuroph.core.input.Min
min - Variable in class org.neuroph.util.random.RangeRandomizer
  Lower range limit
momentum - Variable in class org.neuroph.nnet.learning.MomentumBackpropagation
  Momentum factor
MomentumBackpropagation - Class in org.neuroph.nnet.learning
  Backpropagation learning rule with momentum.
MomentumBackpropagation() - Constructor for class org.neuroph.nnet.learning.MomentumBackpropagation
  Creates new instance of MomentumBackpropagation learning
MomentumBackpropagation.MomentumWeightTrainingData - Class in org.neuroph.nnet.learning
MomentumBackpropagation.MomentumWeightTrainingData() - Constructor for class org.neuroph.nnet.learning.MomentumBackpropagation.MomentumWeightTrainingData
MultiLayerPerceptron - Class in org.neuroph.nnet
  Multi Layer Perceptron neural network with Back propagation learning algorithm.
MultiLayerPerceptron(List<Integer>) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
  Creates new MultiLayerPerceptron with specified number of neurons in layers
MultiLayerPerceptron(int...) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
MultiLayerPerceptron(TransferFunctionType, int...) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
MultiLayerPerceptron(List<Integer>, TransferFunctionType) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
MultiLayerPerceptron(List<Integer>, NeuronProperties) - Constructor for class org.neuroph.nnet.MultiLayerPerceptron
  Creates new MultiLayerPerceptron net with specified number neurons in getLayersIterator
MyBenchmarkTask - Class in org.neuroph.util.benchmark
  This class is example of custom benchmarking task for Multi Layer Perceptorn network Note that this benchmark only measures the speed at implementation level - the speed of data flow forward and backward through network
MyBenchmarkTask(String) - Constructor for class org.neuroph.util.benchmark.MyBenchmarkTask

## N

netInput - Variable in class org.neuroph.core.Neuron
  Total net input for this neuron.
network - Variable in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  The neural network that is to be trained.
network2array(NeuralNetwork, double[]) - Static method in class org.neuroph.util.NeuralNetworkCODEC
  Encode a network to an array.
neuralNetwork - Variable in class org.neuroph.core.learning.LearningRule
  Neural network to train
NeuralNetwork - Class in org.neuroph.core
  Base class for artificial neural networks.
NeuralNetwork() - Constructor for class org.neuroph.core.NeuralNetwork
  Creates an instance of empty neural network.
NeuralNetworkCODEC - Class in org.neuroph.util
  A CODEC encodes and decodes neural networks, much like the more standard definition of a CODEC encodes and decodes audio/video.
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 customized 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(Class<? extends Neuron>) - Constructor for class org.neuroph.util.NeuronProperties
NeuronProperties(Class<? extends Neuron>, Class<? extends TransferFunction>) - Constructor for class org.neuroph.util.NeuronProperties
NeuronProperties(Class<? extends Neuron>, Class<? extends InputFunction>, Class<? extends TransferFunction>) - Constructor for class org.neuroph.util.NeuronProperties
NeuronProperties(Class<? extends Neuron>, TransferFunctionType) - Constructor for class org.neuroph.util.NeuronProperties
NeuronProperties(TransferFunctionType, boolean) - Constructor for class org.neuroph.util.NeuronProperties
neurons - Variable in class org.neuroph.core.Layer
  Array of neurons (Neuron instances)
Neuroph - Class in org.neuroph.util
  This singleton holds global settings for the whole framework
Neuroph() - Constructor for class org.neuroph.util.Neuroph
NeurophException - Exception in org.neuroph.core.exceptions
  Base exception type for Neuroph.
NeurophException() - Constructor for exception org.neuroph.core.exceptions.NeurophException
  Default constructor.
NeurophException(String) - Constructor for exception org.neuroph.core.exceptions.NeurophException
  Construct a message exception.
NeurophException(Throwable) - Constructor for exception org.neuroph.core.exceptions.NeurophException
  Construct an exception that holds another exception.
NeurophException(String, Throwable) - Constructor for exception org.neuroph.core.exceptions.NeurophException
  Construct an exception that holds another exception.
NeurophInputException - Exception in org.neuroph.util.io
  This exception is thrown when error occurs when reading input using some InputAdapter
NeurophInputException() - Constructor for exception org.neuroph.util.io.NeurophInputException
  Constructs an NeurophInputException with no detail message.
NeurophInputException(String) - Constructor for exception org.neuroph.util.io.NeurophInputException
  Constructs an NeurophInputException with the specified detail message.
NeurophInputException(String, Throwable) - Constructor for exception org.neuroph.util.io.NeurophInputException
  Constructs a NeurophInputException with the specified detail message and specified cause.
NeurophInputException(Throwable) - Constructor for exception org.neuroph.util.io.NeurophInputException
  Constructs a new runtime exception with the specified cause
NeurophOutputException - Exception in org.neuroph.util.io
  This exception is thrown when some error occurs when writing neural network output using some output adapter.
NeurophOutputException() - Constructor for exception org.neuroph.util.io.NeurophOutputException
  Constructs an NeurophOutputException with no detail message.
NeurophOutputException(String) - Constructor for exception org.neuroph.util.io.NeurophOutputException
  Constructs an NeurophOutputException with the specified detail message.
NeurophOutputException(String, Throwable) - Constructor for exception org.neuroph.util.io.NeurophOutputException
  Constructs a NeurophOutputException with the specified detail message and specified cause.
NeurophOutputException(Throwable) - Constructor for exception org.neuroph.util.io.NeurophOutputException
  Constructs a new runtime exception with the specified cause
nextRandomWeight() - Method in class org.neuroph.util.random.GaussianRandomizer
nextRandomWeight() - Method in class org.neuroph.util.random.RangeRandomizer
  Generates next random value within [min, max] range determined by the settings in this randomizer
nextRandomWeight() - Method in class org.neuroph.util.random.WeightsRandomizer
  Returns next random value from random generator, that will be used to initialize weight
NguyenWidrowRandomizer - Class in org.neuroph.util.random
  This class provides NguyenWidrow randmization technique, which gives very good results for Multi Layer Perceptrons trained with back propagation family of learning rules.
NguyenWidrowRandomizer(double, double) - Constructor for class org.neuroph.util.random.NguyenWidrowRandomizer
normalize() - Method in class org.neuroph.core.learning.DataSet
normalize(Normalizer) - Method in class org.neuroph.core.learning.DataSet
normalize(DataSet) - Method in class org.neuroph.util.norm.DecimalScaleNormalizer
normalize(DataSet) - Method in class org.neuroph.util.norm.MaxMinNormalizer
normalize(DataSet) - Method in class org.neuroph.util.norm.MaxNormalizer
normalize(DataSet) - Method in interface org.neuroph.util.norm.Normalizer
  Normalize specified training set
normalizeMax(double[]) - Method in class org.neuroph.util.norm.MaxNormalizer
Normalizer - Interface in org.neuroph.util.norm
  Interface for training set normalization methods.
normalizeScale(double[]) - Method in class org.neuroph.util.norm.DecimalScaleNormalizer
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
onStart() - Method in class org.neuroph.core.learning.IterativeLearning
  This method is executed when learning starts, before the first epoch.
onStart() - Method in class org.neuroph.core.learning.LearningRule
  Prepares the learning rule to run by setting stop flag to false
onStart() - Method in class org.neuroph.core.learning.SupervisedLearning
onStart() - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
onStart() - Method in class org.neuroph.nnet.learning.ResilientPropagation
Or - Class in org.neuroph.core.input
  Performs logic OR operation on input vector.
Or() - Constructor for class org.neuroph.core.input.Or
org.neuroph.core - package org.neuroph.core
  Provides base classes and basic building components for neural networks.
org.neuroph.core.events - package org.neuroph.core.events
  Provides neural network learning events system
org.neuroph.core.exceptions - package org.neuroph.core.exceptions
  Provides specific exceptions when working with neural networks
org.neuroph.core.input - package org.neuroph.core.input
  Provides common neuron input functions
org.neuroph.core.learning - package org.neuroph.core.learning
  Provides base classes for neural network learning algorithms.
org.neuroph.core.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.comp.layer - package org.neuroph.nnet.comp.layer
  Provides various specific layer types
org.neuroph.nnet.comp.neuron - package org.neuroph.nnet.comp.neuron
  Provides various specific neuron types
org.neuroph.nnet.learning - package org.neuroph.nnet.learning
  Provides implementations of specific neural network learning algorithms.
org.neuroph.util - package org.neuroph.util
  Provides various utility classes for creating neural networks, type codes, parsing vectors, etc.
org.neuroph.util.benchmark - package org.neuroph.util.benchmark
  Provides microbenchmaarking framework for measuring and comparing Neuroph performance.
org.neuroph.util.io - package org.neuroph.util.io
  Provides input/output adapters for file, JDBC, URL, stream
org.neuroph.util.norm - package org.neuroph.util.norm
  Provides data normalization techniques
org.neuroph.util.plugins - package org.neuroph.util.plugins
  Provides various plugins for neural networks.
org.neuroph.util.random - package org.neuroph.util.random
  Provides weights randomization techniques
outConnections - Variable in class org.neuroph.core.Neuron
  Array of neuron's output connections (connections from this to other neurons)
output - Variable in class org.neuroph.core.NeuralNetwork
  Neural network output buffer
output - Variable in class org.neuroph.core.Neuron
  Neuron output
output - Variable in class org.neuroph.core.transfer.TransferFunction
  Output result
OutputAdapter - Interface in org.neuroph.util.io
  Interface for writing neural network outputs to some destination.
outputError - Variable in class org.neuroph.core.learning.SupervisedLearning
  Stores network output error vector
outputHistory - Variable in class org.neuroph.nnet.comp.neuron.DelayedNeuron
  Output history for this neuron
OutputStreamAdapter - Class in org.neuroph.util.io
  Implementation of OutputAdapter interface for writing neural network outputs to output stream.
OutputStreamAdapter(OutputStream) - Constructor for class org.neuroph.util.io.OutputStreamAdapter
  Creates a new OutputStreamAdapter for specified output stream.
OutputStreamAdapter(BufferedWriter) - Constructor for class org.neuroph.util.io.OutputStreamAdapter
  Creates a new OutputStreamAdapter for specified BufferedWriter.
Outstar - Class in org.neuroph.nnet
  Outstar neural network with Outstar learning rule.
Outstar(int) - Constructor for class org.neuroph.nnet.Outstar
  Creates an instance of Outstar network with specified number of neurons in output layer.
OutstarLearning - Class in org.neuroph.nnet.learning
  Hebbian-like learning rule for Outstar network.
OutstarLearning() - Constructor for class org.neuroph.nnet.learning.OutstarLearning
  Creates new instance of OutstarLearning algorithm

## P

parentLayer - Variable in class org.neuroph.core.Neuron
  Parent layer for this neuron
parseDoubleArray(String) - Static method in class org.neuroph.util.VectorParser
  This method parses input String and returns double array
parseInteger(String) - Static method in class org.neuroph.util.VectorParser
  This method parses input String and returns Integer vector
pause() - Method in class org.neuroph.core.learning.IterativeLearning
  Pause the learning
pauseLearning() - Method in class org.neuroph.core.NeuralNetwork
  Pause the learning - puts learning thread in wait state.
Perceptron - Class in org.neuroph.nnet
  Perceptron neural network with some LMS based learning algorithm.
Perceptron(int, int) - Constructor for class org.neuroph.nnet.Perceptron
  Creates new Perceptron with specified number of neurons in input and output layer, with Step trqansfer function
Perceptron(int, int, TransferFunctionType) - Constructor for class org.neuroph.nnet.Perceptron
  Creates new Perceptron with specified number of neurons in input and output layer, and specified transfer function
PerceptronLearning - Class in org.neuroph.nnet.learning
  Perceptron learning rule for perceptron neural networks.
PerceptronLearning() - Constructor for class org.neuroph.nnet.learning.PerceptronLearning
  Creates new PerceptronLearning instance
PluginBase - Class in org.neuroph.util.plugins
  Base class for all neural network plugins.
PluginBase() - Constructor for class org.neuroph.util.plugins.PluginBase
PluginBase(String) - Constructor for class org.neuroph.util.plugins.PluginBase
  Creates an instance of plugin for neural network
prepareTest() - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Any initialization before running performance test (benchmark) goes here
prepareTest() - Method in class org.neuroph.util.benchmark.MyBenchmarkTask
  Benchmrk preparation consists of training set and neural networ creatiion.
previousDelta - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
previousEpochError - Variable in class org.neuroph.core.learning.SupervisedLearning
  Total network error in previous epoch
previousGradient - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
previousValue - Variable in class org.neuroph.nnet.learning.MomentumBackpropagation.MomentumWeightTrainingData
previousWeightChange - Variable in class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
process(NeuralNetwork, InputAdapter, OutputAdapter) - Static method in class org.neuroph.util.io.IOHelper
  Feeds specified neural network with data from InputAdapter and writes output using OutputAdapter
Product - Class in org.neuroph.core.input
  Performs multiplication of all input vector elements.
Product() - Constructor for class org.neuroph.core.input.Product
Properties - Class in org.neuroph.util
  Represents a general set of properties for neuroph objects
Properties() - Constructor for class org.neuroph.util.Properties

## 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.
randomGenerator - Variable in class org.neuroph.util.random.WeightsRandomizer
  Random number genarator used by randomizers
randomize() - Method in class org.neuroph.core.Weight
  Sets random weight value
randomize(double, double) - Method in class org.neuroph.core.Weight
  Sets random weight value within specified interval
randomize(Random) - Method in class org.neuroph.core.Weight
randomize() - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  Randomize the weights and thresholds.
randomize(NeuralNetwork) - Method in class org.neuroph.util.random.DistortRandomizer
  Iterate all layers, neurons and connection weight and apply distort randomization
randomize(NeuralNetwork) - Method in class org.neuroph.util.random.NguyenWidrowRandomizer
randomize(NeuralNetwork) - Method in class org.neuroph.util.random.WeightsRandomizer
  Iterate all layers, neurons and connections in network, and randomize connection weights
randomizeWeights() - Method in class org.neuroph.core.Layer
  Randomize input connection weights for all neurons in this layer
randomizeWeights(double, double) - Method in class org.neuroph.core.Layer
  Randomize input connection weights for all neurons in this layer within specified value range
randomizeWeights(Random) - Method in class org.neuroph.core.Layer
  Initialize connection weights for all neurons in this layer using a random number generator
randomizeWeights() - Method in class org.neuroph.core.NeuralNetwork
  Randomizes connection weights for the whole network
randomizeWeights(double, double) - Method in class org.neuroph.core.NeuralNetwork
  Randomizes connection weights for the whole network within specified value range
randomizeWeights(Random) - Method in class org.neuroph.core.NeuralNetwork
  Randomizes connection weights for the whole network using specified random generator
randomizeWeights(WeightsRandomizer) - Method in class org.neuroph.core.NeuralNetwork
  Randomizes connection weights for the whole network using specified randomizer
randomizeWeights() - Method in class org.neuroph.core.Neuron
  Randomize all input weights
randomizeWeights(double, double) - Method in class org.neuroph.core.Neuron
  Randomize all input weights within specified value range
randomizeWeights(Random) - Method in class org.neuroph.core.Neuron
  Randomize weights for all input connections to using random number generator
RangeRandomizer - Class in org.neuroph.util.random
  This class provides ranged weights randomizer, which randomize weights in specified [min, max] range.
RangeRandomizer(double, double) - Constructor for class org.neuroph.util.random.RangeRandomizer
  Creates a new instance of RangeRandomizer within specified .
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
readInput() - Method in interface org.neuroph.util.io.InputAdapter
  Reads input from data source and returns input for neural network as array of doubles.
readInput() - Method in class org.neuroph.util.io.InputStreamAdapter
readInput() - Method in class org.neuroph.util.io.JDBCInputAdapter
  Reads next row from result set and returns input for neural network as array of doubles.
readStringFromFile(File) - Static method in class org.neuroph.util.FileUtils
removeAllConnections() - Method in class org.neuroph.core.Neuron
removeAllInputConnections() - Method in class org.neuroph.core.Neuron
removeAllNeurons() - Method in class org.neuroph.core.Layer
removeAllOutputConnections() - Method in class org.neuroph.core.Neuron
removeInputConnection(Connection) - Method in class org.neuroph.core.Neuron
removeInputConnectionFrom(Neuron) - Method in class org.neuroph.core.Neuron
  Removes input connection which is connected to specified neuron
removeLayer(Layer) - Method in class org.neuroph.core.NeuralNetwork
  Removes specified layer from network
removeLayerAt(int) - Method in class org.neuroph.core.NeuralNetwork
  Removes layer at specified index position from net
removeListener(LearningEventListener) - Method in class org.neuroph.core.learning.LearningRule
removeNeuron(Neuron) - Method in class org.neuroph.core.Layer
  Removes neuron from layer
removeNeuronAt(int) - Method in class org.neuroph.core.Layer
  Removes neuron at specified index position in this layer
removeOutputConnection(Connection) - Method in class org.neuroph.core.Neuron
removeOutputConnectionTo(Neuron) - Method in class org.neuroph.core.Neuron
removePlugin(Class) - Method in class org.neuroph.core.NeuralNetwork
  Removes the plugin with specified name
removeRowAt(int) - Method in class org.neuroph.core.learning.DataSet
  Removes training element at specified index position
reset() - Method in class org.neuroph.core.Layer
  Resets the activation and input levels for all neurons in this layer
reset() - Method in 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.neuron.CompetitiveNeuron
  Resets the input, output and mode for this neuron
reset() - Method in class org.neuroph.util.benchmark.Stopwatch
  Resets the stopwatch (clears start and stop time)
ResilientPropagation - Class in org.neuroph.nnet.learning
  Resilient Propagation learning rule used for Multi Layer Perceptron neural networks.
ResilientPropagation() - Constructor for class org.neuroph.nnet.learning.ResilientPropagation
ResilientPropagation.ResilientWeightTrainingtData - Class in org.neuroph.nnet.learning
ResilientPropagation.ResilientWeightTrainingtData() - Constructor for class org.neuroph.nnet.learning.ResilientPropagation.ResilientWeightTrainingtData
resillientWeightUpdate(Weight) - Method in class org.neuroph.nnet.learning.ResilientPropagation
  Weight update by done by ResilientPropagation learning rule Executed at the end of epoch (in batch mode)
resume() - Method in class org.neuroph.core.learning.IterativeLearning
  Resumes the paused learning
resumeLearning() - Method in class org.neuroph.core.NeuralNetwork
  Resumes paused learning - notifies the learning rule to continue
run() - Method in class org.neuroph.util.benchmark.Benchmark
  Runs all benchmark tasks
runTask(BenchmarkTask) - Static method in class org.neuroph.util.benchmark.Benchmark
  Runs specified benchmark tasks, the basic benchmarking workflow.
runTest() - Method in class org.neuroph.util.benchmark.BenchmarkTask
  This method should hold the code to benchmark
runTest() - Method in class org.neuroph.util.benchmark.MyBenchmarkTask

## S

save(String) - Method in class org.neuroph.core.learning.DataSet
  Saves this training set to the specified file
save() - Method in class org.neuroph.core.learning.DataSet
  Saves this training set to file specified in its filePath field
save(String) - Method in class org.neuroph.core.NeuralNetwork
  Saves neural network into the specified file.
saveAsTxt(String, String) - Method in class org.neuroph.core.learning.DataSet
setBatchMode(boolean) - Method in class org.neuroph.core.learning.SupervisedLearning
  Sets batch mode on/off (true/false)
setBias(double) - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
  Sets bias value for this neuron
setColumnNames(String[]) - Method in class org.neuroph.core.learning.DataSet
setDecreaseFactor(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
setDefaultIO(NeuralNetwork) - Static method in class org.neuroph.util.NeuralNetworkFactory
  Sets default input and output neurons for network (first layer as input, last as output)
setDelay(int) - Method in class org.neuroph.nnet.comp.DelayedConnection
  Sets delay value for this connection
setDesiredOutput(double[]) - Method in class org.neuroph.core.learning.DataSetRow
setError(double) - Method in class org.neuroph.core.Neuron
  Sets error for this neuron.
setErrorCorrection(double) - Method in class org.neuroph.nnet.learning.BinaryDeltaRule
  Sets the errorCorrection parametar
setFilePath(String) - Method in class org.neuroph.core.learning.DataSet
  Sets full file path for this training set
setFlattenNetworks(boolean) - Method in class org.neuroph.util.Neuroph
  Turn on/off flat networ support from Encog
setFromNeuron(Neuron) - Method in class org.neuroph.core.Connection
  Sets from neuron for this connection
setIncreaseFactor(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
setInitialDelta(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
setInput(double[]) - Method in class org.neuroph.core.learning.DataSetRow
  Sets input vector
setInput(double...) - Method in class org.neuroph.core.NeuralNetwork
  Sets network input.
setInput(double) - Method in class org.neuroph.core.Neuron
  Sets neuron's input
setInput(double) - Method in class org.neuroph.nnet.comp.neuron.InputOutputNeuron
  Sets total net input for this cell
setInputFunction(InputFunction) - Method in class org.neuroph.core.Neuron
  Sets input function
setInputNeurons(Neuron[]) - Method in class org.neuroph.core.NeuralNetwork
  Sets input neurons
setIsCompeting(boolean) - Method in class org.neuroph.nnet.comp.neuron.CompetitiveNeuron
  Sets the flag to indicate that this neuron is in competing mode
setIterations(int, int) - Method in class org.neuroph.nnet.learning.KohonenLearning
setLabel(String) - Method in class org.neuroph.core.Layer
  Set layer label
setLabel(String) - Method in class org.neuroph.core.learning.DataSet
  Sets label for this training set
setLabel(String) - Method in class org.neuroph.core.learning.DataSetRow
  Set training element label
setLabel(String) - Method in class org.neuroph.core.NeuralNetwork
  Set network label
setLabel(String) - Method in class org.neuroph.core.Neuron
  Sets the label for this neuron
setLearningRate(double) - Method in class org.neuroph.core.learning.IterativeLearning
  Sets learning rate for this algorithm
setLearningRate(double) - Method in class org.neuroph.nnet.learning.KohonenLearning
setLearningRateChange(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setLearningRule(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
setMaxDelta(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
setMaxError(double) - Method in class org.neuroph.core.learning.SupervisedLearning
  Sets allowed network error, which indicates when to stopLearning training
setMaxIterations(int) - Method in class org.neuroph.core.learning.IterativeLearning
  Sets iteration limit for this learning algorithm
setMaxIterations(int) - Method in class org.neuroph.nnet.comp.layer.CompetitiveLayer
  Sets max iterations for neurons to compete in this layer
setMaxLearningRate(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setMaxMomentum(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setMinDelta(double) - Method in class org.neuroph.nnet.learning.ResilientPropagation
setMinErrorChange(double) - Method in class org.neuroph.core.learning.SupervisedLearning
  Sets min error change stopping criteria
setMinErrorChangeIterationsLimit(int) - Method in class org.neuroph.core.learning.SupervisedLearning
  Sets number of iterations for min error change stopping criteria
setMinLearningRate(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setMinMomentum(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setMomentum(double) - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
  Sets the momentum factor
setMomentumChange(double) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setName(String) - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Sets task name
setNetworkType(NeuralNetworkType) - Method in class org.neuroph.core.NeuralNetwork
  Sets type for this network
setNeuralNetwork(NeuralNetwork) - Method in class org.neuroph.core.learning.LearningRule
  Sets neural network for this learning rule
setNeuralNetwork(NeuralNetwork) - Method in class org.neuroph.nnet.learning.KohonenLearning
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(Neuron[]) - Method in class org.neuroph.core.NeuralNetwork
  Sets output neurons
setParentLayer(Layer) - Method in class org.neuroph.core.Neuron
  Sets reference to parent layer for this neuron (layer in which the neuron is located)
setParentNetwork(NeuralNetwork) - Method in class org.neuroph.core.Layer
  Sets reference on parent network
setParentNetwork(NeuralNetwork) - Method in class org.neuroph.util.plugins.PluginBase
  Sets the parent network for this plugin
setProperty(String, Object) - Method in class org.neuroph.util.NeuronProperties
setProperty(String, Object) - Method in class org.neuroph.util.Properties
setRightHigh(double) - Method in class org.neuroph.core.transfer.Trapezoid
  Sets right high point of trapezoid function
setRightLow(double) - Method in class org.neuroph.core.transfer.Trapezoid
  Sets right low point of trapezoid function
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
setTestIterations(int) - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Sets number of test (benchmarking) iterations
setThresh(double) - Method in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
  Sets threshold value for this neuron
setToNeuron(Neuron) - Method in class org.neuroph.core.Connection
  Sets to neuron for this connection
setTrainingData(Object) - Method in class org.neuroph.core.Weight
setTrainingSet(DataSet) - Method in class org.neuroph.core.learning.LearningRule
  Sets training set for this learning rule
setTransferFunction(TransferFunction) - Method in class org.neuroph.core.Neuron
  Sets transfer function
setUseDynamicLearningRate(boolean) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setUseDynamicMomentum(boolean) - Method in class org.neuroph.nnet.learning.DynamicBackPropagation
setValue(double) - Method in class org.neuroph.core.Weight
  Sets the weight value
setWarmupIterations(int) - Method in class org.neuroph.util.benchmark.BenchmarkTask
  Sets the number of warmup iterations
setWeight(Weight) - Method in class org.neuroph.core.Connection
  Set the weight of the connection.
setXHigh(double) - Method in class org.neuroph.core.transfer.Ramp
  Sets threshold for the high output level
setXLow(double) - Method in class org.neuroph.core.transfer.Ramp
  Sets threshold for the low output level
setYHigh(double) - Method in class org.neuroph.core.transfer.Ramp
  Sets output value for the high output level
setYHigh(double) - Method in class org.neuroph.core.transfer.Step
  Set output value for the high output level
setYLow(double) - Method in class org.neuroph.core.transfer.Ramp
  Sets output value for the low output level
setYLow(double) - Method in class org.neuroph.core.transfer.Step
  Set output value for the low output level
Sgn - Class in org.neuroph.core.transfer
  Sgn neuron transfer function.
Sgn() - Constructor for class org.neuroph.core.transfer.Sgn
shouldFlattenNetworks() - Method in class org.neuroph.util.Neuroph
  Get setting for flatten network (from Encog engine)
shuffle() - Method in class org.neuroph.core.learning.DataSet
shutdown() - Method in class org.neuroph.util.Neuroph
  Shuts down the Encog engine
Sigmoid - Class in org.neuroph.core.transfer
  Sigmoid neuron transfer function.
Sigmoid() - Constructor for class org.neuroph.core.transfer.Sigmoid
  Creates an instance of Sigmoid neuron transfer function with default slope=1.
Sigmoid(double) - Constructor for class org.neuroph.core.transfer.Sigmoid
  Creates an instance of Sigmoid neuron transfer function with specified value for slope parametar.
Sigmoid(Properties) - Constructor for class org.neuroph.core.transfer.Sigmoid
  Creates an instance of Sigmoid neuron transfer function with the specified properties.
SigmoidDeltaRule - Class in org.neuroph.nnet.learning
  Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.
SigmoidDeltaRule() - Constructor for class org.neuroph.nnet.learning.SigmoidDeltaRule
  Creates new SigmoidDeltaRule
SimulatedAnnealingLearning - Class in org.neuroph.nnet.learning
  This class implements a simulated annealing learning rule for supervised neural networks.
SimulatedAnnealingLearning(NeuralNetwork, double, double, int) - Constructor for class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  Construct a simulated annleaing trainer for a feedforward neural network.
SimulatedAnnealingLearning(NeuralNetwork) - Constructor for class org.neuroph.nnet.learning.SimulatedAnnealingLearning
Sin - Class in org.neuroph.core.transfer
  Sin neuron transfer function.
Sin() - Constructor for class org.neuroph.core.transfer.Sin
size() - Method in class org.neuroph.core.learning.DataSet
  Returns number of training elements in this training set set
start() - Method in class org.neuroph.util.benchmark.Stopwatch
  Starts measuring time
Step - Class in org.neuroph.core.transfer
  Step neuron transfer function.
Step() - Constructor for class org.neuroph.core.transfer.Step
  Creates an instance of Step transfer function
Step(Properties) - Constructor for class org.neuroph.core.transfer.Step
  Creates an instance of Step transfer function with specified properties
stop() - Method in class org.neuroph.util.benchmark.Stopwatch
  Stops measuring time
stopLearning() - Method in class org.neuroph.core.learning.LearningRule
  Stops learning
stopLearning() - Method in class org.neuroph.core.NeuralNetwork
  Stops learning
Stopwatch - Class in org.neuroph.util.benchmark
  A class to help benchmark code, it simulates a real stop watch.
Stopwatch() - Constructor for class org.neuroph.util.benchmark.Stopwatch
Sum - Class in org.neuroph.core.input
  Performs summing of all input vector elements.
Sum() - Constructor for class org.neuroph.core.input.Sum
SumSqr - Class in org.neuroph.core.input
  Calculates squared sum of all input vector elements.
SumSqr() - Constructor for class org.neuroph.core.input.SumSqr
SupervisedHebbianLearning - Class in org.neuroph.nnet.learning
  Supervised hebbian learning rule.
SupervisedHebbianLearning() - Constructor for class org.neuroph.nnet.learning.SupervisedHebbianLearning
  Creates new instance of SupervisedHebbianLearning algorithm
SupervisedHebbianNetwork - Class in org.neuroph.nnet
  Hebbian neural network with supervised Hebbian learning algorithm.
SupervisedHebbianNetwork(int, int) - Constructor for class org.neuroph.nnet.SupervisedHebbianNetwork
  Creates an instance of Supervised Hebbian Network net with specified number neurons in input and output layer
SupervisedHebbianNetwork(int, int, TransferFunctionType) - Constructor for class org.neuroph.nnet.SupervisedHebbianNetwork
  Creates an instance of Supervised Hebbian Network with specified number of neurons in input layer and output layer, and transfer function
SupervisedLearning - Class in org.neuroph.core.learning
  Base class for all supervised learning algorithms.
SupervisedLearning() - Constructor for class org.neuroph.core.learning.SupervisedLearning
  Creates new supervised learning rule

## T

Tanh - Class in org.neuroph.core.transfer
  Tanh neuron transfer function.
Tanh() - Constructor for class org.neuroph.core.transfer.Tanh
  Creates an instance of Tanh neuron transfer function with default slope=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.
temperature - Variable in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  The current temperature.
test - package test
Test - Class in test
Test() - Constructor for class test.Test
thresh - Variable in class org.neuroph.nnet.comp.neuron.ThresholdNeuron
  Threshold value for this neuron
ThresholdNeuron - Class in org.neuroph.nnet.comp.neuron
  Provides behaviour for neurons with threshold.
ThresholdNeuron(InputFunction, TransferFunction) - Constructor for class org.neuroph.nnet.comp.neuron.ThresholdNeuron
  Creates a neuron with threshold behaviour, and with the specified input and transfer functions.
toDoubleArray(List<Double>) - Static method in class org.neuroph.util.VectorParser
toNeuron - Variable in class org.neuroph.core.Connection
  To neuron for this connection (target, destination neuron) This connection is input connection for to neuron.
toString() - Method in class org.neuroph.core.learning.DataSet
  Returns label of this training set
toString() - Method in class org.neuroph.core.NeuralNetwork
toString() - Method in class org.neuroph.core.Weight
  Returns weight value as String
toString() - Method in class org.neuroph.util.benchmark.BenchmarkTaskResults
totalNetworkError - Variable in class org.neuroph.core.learning.SupervisedLearning
  Total network error
totalSquaredErrorSum - Variable in class org.neuroph.core.learning.SupervisedLearning
  Total squared sum of all pattern errors
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
UnsupervisedHebbianNetwork - Class in org.neuroph.nnet
  Hebbian neural network with unsupervised Hebbian learning algorithm.
UnsupervisedHebbianNetwork(int, int) - Constructor for class org.neuroph.nnet.UnsupervisedHebbianNetwork
  Creates an instance of Unsuervised Hebian net with specified number of neurons in input and output layer
UnsupervisedHebbianNetwork(int, int, TransferFunctionType) - Constructor for class org.neuroph.nnet.UnsupervisedHebbianNetwork
  Creates an instance of Unsuervised Hebian net with specified number of neurons in input layer and output layer, and transfer function
UnsupervisedLearning - Class in org.neuroph.core.learning
  Base class for all unsupervised learning algorithms.
UnsupervisedLearning() - Constructor for class org.neuroph.core.learning.UnsupervisedLearning
  Creates new unsupervised learning rule
updateNetworkWeights(double[]) - Method in class org.neuroph.core.learning.SupervisedLearning
  This method should implement the weights update procedure for the whole network for the given output error vector.
updateNetworkWeights() - Method in class org.neuroph.core.learning.UnsupervisedLearning
  This method implements the weight adjustment
updateNetworkWeights(double[]) - Method in class org.neuroph.nnet.learning.BackPropagation
  This method implements weight update procedure for the whole network for the specified output error vector
updateNetworkWeights(double[]) - Method in class org.neuroph.nnet.learning.BinaryDeltaRule
  This method implements weight update procedure for the whole network for this learning rule
updateNetworkWeights() - Method in class org.neuroph.nnet.learning.CompetitiveLearning
  Adjusts weights for the winning neuron
updateNetworkWeights(double[]) - Method in class org.neuroph.nnet.learning.LMS
  This method implements the weights update procedure for the whole network for the given output error vector.
updateNetworkWeights(double[]) - Method in class org.neuroph.nnet.learning.SigmoidDeltaRule
  This method implements weight update procedure for the whole network for this learning rule
updateNetworkWeights(double[]) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  Not used.
updateNetworkWeights(double[]) - Method in class org.neuroph.nnet.learning.SupervisedHebbianLearning
  This method implements weight update procedure for the whole network for this learning rule
updateNetworkWeights() - Method in class org.neuroph.nnet.learning.UnsupervisedHebbianLearning
  Adjusts weights for the output neurons
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.AntiHebbianLearning
  This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.BinaryHebbianLearning
  This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.GeneralizedHebbianLearning
  This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.InstarLearning
  This method implements weights update procedure for the single neuron
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.LMS
  This method implements weights update procedure for the single neuron It iterates through all neuron's input connections, and calculates/set weight change for each weight using formula deltaWeight = learningRate * neuronError * input where neuronError is difference between desired and actual output for specific neuron neuronError = desiredOutput[i] - actualOutput[i] (see method SuprevisedLearning.calculateOutputError)
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.MomentumBackpropagation
  This method implements weights update procedure for the single neuron for the back propagation with momentum factor
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) - Method in class org.neuroph.nnet.learning.PerceptronLearning
  This method implements weights update procedure for the single neuron In addition to weights change in LMS it applies change to neuron's threshold
updateNeuronWeights(Neuron) - Method in class org.neuroph.nnet.learning.ResilientPropagation
  Calculate and sum gradients for each neuron's weight, the actual weight update is done in batch mode
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(double[]) - Method in class org.neuroph.nnet.learning.SimulatedAnnealingLearning
  Update the total error.
URLInputAdapter - Class in org.neuroph.util.io
  Implementation of InputAdapter interface for reading neural network inputs from URL.
URLInputAdapter(URL) - Constructor for class org.neuroph.util.io.URLInputAdapter
  Creates a new URLInputAdapter by opening a connection to URL specified by the input param
URLInputAdapter(String) - Constructor for class org.neuroph.util.io.URLInputAdapter
  Creates a new URLInputAdapter by opening a connection to URL specified by the input param
URLOutputAdapter - Class in org.neuroph.util.io
  Implementation of OutputAdapter interface for writing neural network outputs to URL.
URLOutputAdapter(URL) - Constructor for class org.neuroph.util.io.URLOutputAdapter
  Creates a new URLOutputAdapter by opening a connection to URL specified by the url input param
URLOutputAdapter(String) - Constructor for class org.neuroph.util.io.URLOutputAdapter
  Creates a new URLOutputAdapter by opening a connection to URL specified by the string url input param

## 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.TransferFunctionType
  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.TransferFunctionType
  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
VectorSizeMismatchException - Exception in org.neuroph.core.exceptions
  Thrown to indicate that vector size does not match the network input or training element size.
VectorSizeMismatchException() - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
  Constructs an VectorSizeMismatchException with no detail message.
VectorSizeMismatchException(String) - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
  Constructs an VectorSizeMismatchException with the specified detail message.
VectorSizeMismatchException(String, Throwable) - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
  Constructs a VectorSizeMismatchException with the specified detail message and specified cause.
VectorSizeMismatchException(Throwable) - Constructor for exception org.neuroph.core.exceptions.VectorSizeMismatchException
  Constructs a new runtime exception with the specified cause

## W

weight - Variable in class org.neuroph.core.Connection
  Weight for this connection
Weight - 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
weightChange - Variable in class org.neuroph.core.Weight
  Weight change
WeightedSum - Class in org.neuroph.core.input
  Optimized version of weighted input function
WeightedSum() - Constructor for class org.neuroph.core.input.WeightedSum
WeightsRandomizer - Class in org.neuroph.util.random
  Basic weights randomizer, iterates and randomizes all connection weights in network.
WeightsRandomizer() - Constructor for class org.neuroph.util.random.WeightsRandomizer
  Create a new instance of WeightsRandomizer
WeightsRandomizer(Random) - Constructor for class org.neuroph.util.random.WeightsRandomizer
  Create a new instance of WeightsRandomizer with specified random generator If you use the same random generators, you'll get the same random sequences
writeOutput(double[]) - Method in class org.neuroph.util.io.JDBCOutputAdapter
  Writes specified output to table in database
writeOutput(double[]) - Method in interface org.neuroph.util.io.OutputAdapter
  Write neural network output to some destination.
writeOutput(double[]) - Method in class org.neuroph.util.io.OutputStreamAdapter
  Writes specified output to output stream
writeStringToFile(File, String) - Static method in class org.neuroph.util.FileUtils

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