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# Classes and Interfaces in #Neuroph - 153 results found.
NameDescriptionTypePackageFrameworkJavaDoc
AdalineAdaline neural network architecture with LMS learning rule.Classorg.neuroph.nnetNeurophjavadoc
AndPerforms logic AND operation on input vector.Classorg.neuroph.core.inputNeurophjavadoc
AntiHebbianLearningA variant of Hebbian learning called Anti-Hebbian learning.Classorg.neuroph.nnet.learningNeurophjavadoc
AutoencoderNetworkClassorg.neuroph.nnetNeurophjavadoc
BackPropagationBack Propagation learning rule for Multi Layer Perceptron neural networks.Classorg.neuroph.nnet.learningNeurophjavadoc
BAMBidirectional Associative MemoryAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.nnetNeurophjavadoc
BenchmarkThis class is main benchmark driver.Classorg.neuroph.util.benchmarkNeurophjavadoc
BenchmarkSampleThis is an example how to use Neuroph microbenchmarking frameworkAuthor:Zoran Sevarac Classorg.neuroph.util.benchmarkNeurophjavadoc
BenchmarkTaskThis class is an abstract base class for specific microbenchmarking tasksAuthor:Zoran Sevarac Classorg.neuroph.util.benchmarkNeurophjavadoc
BenchmarkTaskResultsThis class holds benchmarking results, elapsed times for all iterations and various statistics min, max, avg times and standard deviationClassorg.neuroph.util.benchmarkNeurophjavadoc
BiasNeuronNeuron with constant high output (1), used as biasAuthor:Zoran Sevarac See Also:Neuron, Classorg.neuroph.nnet.comp.neuronNeurophjavadoc
BinaryDeltaRuleDelta rule learning algorithm for perceptrons with step functions.Classorg.neuroph.nnet.learningNeurophjavadoc
BinaryHebbianLearningHebbian-like learning algorithm used for Hopfield network.Classorg.neuroph.nnet.learningNeurophjavadoc
BufferedDataSetThis class can be used for large training sets, which are partialy read from file during the training.Classorg.neuroph.core.dataNeurophjavadoc
ClusterThis class represents a single cluster, with corresponding centroid and assigned vectorsAuthor:Zoran SevaracClassorg.neuroph.nnet.learning.kmeansNeurophjavadoc
CompetitiveLayerRepresents layer of competitive neurons, and provides methods for competition.Classorg.neuroph.nnet.comp.layerNeurophjavadoc
CompetitiveLearningCompetitive learning rule.Classorg.neuroph.nnet.learningNeurophjavadoc
CompetitiveNetworkTwo layer neural network with competitive learning rule.Classorg.neuroph.nnetNeurophjavadoc
CompetitiveNeuronProvides neuron behaviour specific for competitive neurons which are used in competitive layers, and networks with competitive learning.Classorg.neuroph.nnet.comp.neuronNeurophjavadoc
ConnectionWeighted connection to another neuron.Classorg.neuroph.coreNeurophjavadoc
ConnectionFactoryProvides methods to connect neurons by creating Connection objects.Classorg.neuroph.utilNeurophjavadoc
ConvolutionalBackpropagationClassorg.neuroph.nnet.learningNeurophjavadoc
ConvolutionalLayerConvolutional layer is a special type of layer, used in convolutional neural networks.Classorg.neuroph.nnet.comp.layerNeurophjavadoc
ConvolutionalNetworkConvolutional neural network with backpropagation algorithm modified for convolutional networks.Classorg.neuroph.nnetNeurophjavadoc

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ConvolutionalUtilsUtility functions for convolutional networksAuthor:Boris Fulurija, Zorn SevaracClassorg.neuroph.nnet.compNeurophjavadoc
DataSetThis class represents a collection of data rows (DataSetRow instances) used for training and testing neural network.Classorg.neuroph.core.dataNeurophjavadoc
DataSetRowThis class represents single data row in a data set.Classorg.neuroph.core.dataNeurophjavadoc
DecimalScaleNormalizerDecimal scaling normalization method, which normalize data by moving decimal point in regard to max element in training set (by columns) Normalization isClassorg.neuroph.util.data.normNeurophjavadoc
DelayedConnectionRepresents the connection between neurons which can delay signal.Classorg.neuroph.nnet.compNeurophjavadoc
DelayedNeuronProvides behaviour for neurons with delayed output.Classorg.neuroph.nnet.comp.neuronNeurophjavadoc
DifferencePerforms the vector difference operation on input andAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.inputNeurophjavadoc
DistortRandomizerThis class provides distort randomization technique, which distorts existing weight values using specified distortion factor.Classorg.neuroph.util.randomNeurophjavadoc
DynamicBackPropagationBackpropagation learning rule with dynamic learning rate and momentumAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.nnet.learningNeurophjavadoc
ElmanNetworkUnder development: Learning rule BackProp Through Time requiredAuthor:zoranSee Also:Serialized FormClassorg.neuroph.nnetNeurophjavadoc
ErrorFunctionInterface for calculating total network error during the learning.Interfaceorg.neuroph.core.learning.errorNeurophjavadoc
FeatureMapsLayerThis class represents an array of feature maps which are 2 dimensional layers (Layer2D instances) and it is base class for Convolution and Pooling layers, Classorg.neuroph.nnet.comp.layerNeurophjavadoc
FileInputAdapterClassorg.neuroph.util.ioNeurophjavadoc
FileOutputAdapterClassorg.neuroph.util.ioNeurophjavadoc
FileUtilsUtility methods for working with files.Classorg.neuroph.utilNeurophjavadoc
Gaussian Gaussian neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
GaussianRandomizerThis class provides Gaussian randomization technique using Box Muller method.Classorg.neuroph.util.randomNeurophjavadoc
GeneralizedHebbianLearningA variant of Hebbian learning called Generalized Hebbian learning.Classorg.neuroph.nnet.learningNeurophjavadoc
HopfieldHopfield neural network.Classorg.neuroph.nnetNeurophjavadoc
HopfieldLearningLearning algorithm for the Hopfield neural network.Classorg.neuroph.nnet.learningNeurophjavadoc
InputAdapterInterface for reading neural network inputs from various data sources.Interfaceorg.neuroph.util.ioNeurophjavadoc
InputFunction Neuron's input function.Classorg.neuroph.core.inputNeurophjavadoc
InputLayerRepresents a layer of input neurons - a typical neural network input layerAuthor:Zoran Sevarac See Also:InputNeuron, Classorg.neuroph.nnet.comp.layerNeurophjavadoc
InputMapsLayerInput layer for convolutional networksAuthor:Boris Fulurija, Zoran SevaracSee Also:Serialized FormClassorg.neuroph.nnet.comp.layerNeurophjavadoc
InputNeuronProvides input neuron behaviour - neuron with input extranaly set, which just transfer that input to output without change.Classorg.neuroph.nnet.comp.neuronNeurophjavadoc

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InputOutputNeuronProvides behaviour specific for neurons which act as input and the output neurons within the same layer.Classorg.neuroph.nnet.comp.neuronNeurophjavadoc
InputStreamAdapterClassorg.neuroph.util.ioNeurophjavadoc
InstarInstar neural network with Instar learning rule.Classorg.neuroph.nnetNeurophjavadoc
InstarLearningHebbian-like learning rule for Instar network.Classorg.neuroph.nnet.learningNeurophjavadoc
IOHelper This class is helper for feeding neural network with data using some InputAdapter and writing network output using OutputAdapterClassorg.neuroph.util.ioNeurophjavadoc
IterativeLearningBase class for all iterative learning algorithms.Classorg.neuroph.core.learningNeurophjavadoc
JDBCInputAdapterClassorg.neuroph.util.ioNeurophjavadoc
JDBCOutputAdapterClassorg.neuroph.util.ioNeurophjavadoc
JordanNetworkUnder development: Learning rule BackProp Through Time requiredAuthor:zoranSee Also:Serialized FormClassorg.neuroph.nnetNeurophjavadoc
KernelKernel used in convolution networks.Classorg.neuroph.nnet.compNeurophjavadoc
KMeansClustering1.Classorg.neuroph.nnet.learning.kmeansNeurophjavadoc
KNearestNeighbour calculate distances to all vectors from list and find minimum vectorClassorg.neuroph.nnet.learning.knnNeurophjavadoc
KohonenKohonen neural network.Classorg.neuroph.nnetNeurophjavadoc
KohonenLearningLearning algorithm for Kohonen network.Classorg.neuroph.nnet.learningNeurophjavadoc
KVectorRepresents feature vector used in k-means clustering algorithmAuthor:Zoran Sevarac, Uros StojkicClassorg.neuroph.nnet.learning.kmeansNeurophjavadoc
Layer Layer of neurons in a neural network.Classorg.neuroph.coreNeurophjavadoc
Layer2D2D Layer provides 2D layout of the neurons in layer.Classorg.neuroph.nnet.comp.layerNeurophjavadoc
LayerFactoryProvides methods to create instance of a Layer with specifed number of neurons and neuron's properties.Classorg.neuroph.utilNeurophjavadoc
LearningEventThis class holds information about the source of some learning event.Classorg.neuroph.core.eventsNeurophjavadoc
LearningEventListenerThis interface is implemented by classes who are listening to learning events (iterations, error etc.Interfaceorg.neuroph.core.eventsNeurophjavadoc
LearningEventTypeenum LearningEventTypeAuthor:Zoran SevaracClassorg.neuroph.core.eventsNeurophjavadoc
LearningRuleBase class for all neural network learning algorithms.Classorg.neuroph.core.learningNeurophjavadoc
LinearLinear neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
LMSLMS learning rule for neural networks.Classorg.neuroph.nnet.learningNeurophjavadoc
Log Log neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc

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MaxPerforms max function on input vectorAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.inputNeurophjavadoc
MaxErrorStopStops learning rule if total network error is below some specified valueAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.stopNeurophjavadoc
MaxIterationsStopStops learning rule if specified number of iterations has been reachedAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.stopNeurophjavadoc
MaxMinNormalizerMaxMin normalization method, which normalize data in regard to min and max elements in training set (by columns) Normalization is done according to formula:Classorg.neuroph.util.data.normNeurophjavadoc
MaxNetMax Net neural network with competitive learning rule.Classorg.neuroph.nnetNeurophjavadoc
MaxNormalizerMax normalization method, which normalize data in regard to max element in training set (by columns) Normalization is done according to formula:Classorg.neuroph.util.data.normNeurophjavadoc
MeanSquaredErrorCommonly used mean squared errorAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.errorNeurophjavadoc
MinPerforms min function on input vectorAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.inputNeurophjavadoc
MomentumBackpropagationBackpropagation learning rule with momentum.Classorg.neuroph.nnet.learningNeurophjavadoc
MultiLayerPerceptronMulti Layer Perceptron neural network with Back propagation learning algorithm.Classorg.neuroph.nnetNeurophjavadoc
MyBenchmarkTaskThis class is example of custom benchmarking task for Multi Layer Perceptorn network Note that this benchmark only measures the speed at implementation level - theClassorg.neuroph.util.benchmarkNeurophjavadoc
NeuralNetwork Base class for artificial neural networks.Classorg.neuroph.coreNeurophjavadoc
NeuralNetworkCODECA CODEC encodes and decodes neural networks, much like the more standard definition of a CODEC encodes and decodes audio/video.Classorg.neuroph.utilNeurophjavadoc
NeuralNetworkEventThis class holds information about the source and type of some neural network event.Classorg.neuroph.core.eventsNeurophjavadoc
NeuralNetworkEventListenerThis interface is implemented by classes who are listening to neural network events events (to be defined) NeuralNetworkEvent class holds the information about event.Interfaceorg.neuroph.core.eventsNeurophjavadoc
NeuralNetworkEventTypeenum NeuralNetworkEventTypeAuthor:Zoran SevaracClassorg.neuroph.core.eventsNeurophjavadoc
NeuralNetworkFactoryProvides methods to create various neural networks.Classorg.neuroph.utilNeurophjavadoc
NeuralNetworkTypeenum NeuralNetworkTypeContains neural network types and labels.Classorg.neuroph.utilNeurophjavadoc
NeuroFuzzyPerceptronThe NeuroFuzzyReasoner class represents Neuro Fuzzy Reasoner architecture.Classorg.neuroph.nnetNeurophjavadoc
Neuron Basic general neuron model according to McCulloch-Pitts neuron model.Classorg.neuroph.coreNeurophjavadoc
NeuronFactoryProvides methods to create customized instances of Neurons.Classorg.neuroph.utilNeurophjavadoc
NeuronPropertiesRepresents properties of a neuron.Classorg.neuroph.utilNeurophjavadoc
NeurophClassorg.neuroph.utilNeurophjavadoc
NeurophArrayListResizable-array implementation of the List interface.Classorg.neuroph.utilNeurophjavadoc
NeurophExceptionBase exception type for Neuroph.Classorg.neuroph.core.exceptionsNeurophjavadoc

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NeurophInputExceptionThis exception is thrown when error occurs when reading input using some InputAdapterAuthor:Zoran Sevarac See Also:InputAdapter, Classorg.neuroph.util.ioNeurophjavadoc
NeurophOutputExceptionThis exception is thrown when some error occurs when writing neural network output using some output adapter.Classorg.neuroph.util.ioNeurophjavadoc
NguyenWidrowRandomizerThis class provides NguyenWidrow randmization technique, which gives very good results for Multi Layer Perceptrons trained with back propagation family of learning rules.Classorg.neuroph.util.randomNeurophjavadoc
NormalizerInterface for data set normalization methods.Interfaceorg.neuroph.util.data.normNeurophjavadoc
OjaLearningOja learning rule wich is a modification of unsupervised hebbian learning.Classorg.neuroph.nnet.learningNeurophjavadoc
OrPerforms logic OR operation on input vector.Classorg.neuroph.core.inputNeurophjavadoc
OutputAdapterInterface for writing neural network outputs to some destination.Interfaceorg.neuroph.util.ioNeurophjavadoc
OutputStreamAdapterClassorg.neuroph.util.ioNeurophjavadoc
OutstarOutstar neural network with Outstar learning rule.Classorg.neuroph.nnetNeurophjavadoc
OutstarLearningHebbian-like learning rule for Outstar network.Classorg.neuroph.nnet.learningNeurophjavadoc
PerceptronPerceptron neural network with some LMS based learning algorithm.Classorg.neuroph.nnetNeurophjavadoc
PerceptronLearningPerceptron learning rule for perceptron neural networks.Classorg.neuroph.nnet.learningNeurophjavadoc
PluginBaseBase class for all neural network plugins.Classorg.neuroph.util.pluginsNeurophjavadoc
PoolingLayerPooling layer is a special type of feature maps layer (FeatureMapsLayer) which is used in convolutional networks.Classorg.neuroph.nnet.comp.layerNeurophjavadoc
ProductPerforms multiplication of all input vector elements.Classorg.neuroph.core.inputNeurophjavadoc
PropertiesRepresents a general set of properties for neuroph objectsAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.utilNeurophjavadoc
RampRamp neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
RangeNormalizerThis class does normalization of a data set to specified rangeAuthor:Zoran Sevarac Classorg.neuroph.util.data.normNeurophjavadoc
RangeRandomizerThis class provides ranged weights randomizer, which randomize weights in specified [min, max] range.Classorg.neuroph.util.randomNeurophjavadoc
RBFLearningLearning rule for Radial Basis Function networks.Classorg.neuroph.nnet.learningNeurophjavadoc
RBFNetworkRadial basis function neural network.Classorg.neuroph.nnetNeurophjavadoc
ResilientPropagationResilient Propagation learning rule used for Multi Layer Perceptron neural networks.Classorg.neuroph.nnet.learningNeurophjavadoc
SamplingInterface for data set sampling methods.Interfaceorg.neuroph.util.data.sampleNeurophjavadoc
SgnSgn neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
Sigmoid Sigmoid neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc

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SigmoidDeltaRuleDelta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.Classorg.neuroph.nnet.learningNeurophjavadoc
SimulatedAnnealingLearningThis class implements a simulated annealing learning rule for supervised neural networks.Classorg.neuroph.nnet.learningNeurophjavadoc
Sin Sin neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
SmallErrorChangeStopStops learning rule if error change has been too small for specified numberAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.stopNeurophjavadoc
StepStep neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
StopConditionInterface for learning rule stop condition.Interfaceorg.neuroph.core.learning.stopNeurophjavadoc
StopwatchA class to help benchmark code, it simulates a real stop watch.Classorg.neuroph.util.benchmarkNeurophjavadoc
SubSamplingThis class provides subsampling of a data set, and creates two subsets of a given data set - for training and testing.Classorg.neuroph.util.data.sampleNeurophjavadoc
SumPerforms summing of all input vector elements.Classorg.neuroph.core.inputNeurophjavadoc
SumSqrCalculates squared sum of all input vector elements.Classorg.neuroph.core.inputNeurophjavadoc
SupervisedHebbianLearningSupervised hebbian learning rule.Classorg.neuroph.nnet.learningNeurophjavadoc
SupervisedHebbianNetworkHebbian neural network with supervised Hebbian learning algorithm.Classorg.neuroph.nnetNeurophjavadoc
SupervisedLearningBase class for all supervised learning algorithms.Classorg.neuroph.core.learningNeurophjavadoc
Tanh Tanh neuron transfer function.Classorg.neuroph.core.transferNeurophjavadoc
ThresholdNeuronProvides behaviour for neurons with threshold.Classorg.neuroph.nnet.comp.neuronNeurophjavadoc
TrainingSetImportHandles training set importsAuthor:Zoran Sevarac, Ivan Nedeljkovic, Kokanovic RadosClassorg.neuroph.utilNeurophjavadoc
TransferFunctionAbstract base class for all neuron tranfer functions.Classorg.neuroph.core.transferNeurophjavadoc
TransferFunctionTypeenum TransferFunctionTypeContains transfer functions types and labels.Classorg.neuroph.utilNeurophjavadoc
TrapezoidFuzzy trapezoid neuron tranfer function.Classorg.neuroph.core.transferNeurophjavadoc
UnsupervisedHebbianLearningUnsupervised hebbian learning rule.Classorg.neuroph.nnet.learningNeurophjavadoc
UnsupervisedHebbianNetworkHebbian neural network with unsupervised Hebbian learning algorithm.Classorg.neuroph.nnetNeurophjavadoc
UnsupervisedLearningBase class for all unsupervised learning algorithms.Classorg.neuroph.core.learningNeurophjavadoc
URLInputAdapterClassorg.neuroph.util.ioNeurophjavadoc
URLOutputAdapterClassorg.neuroph.util.ioNeurophjavadoc
VectorParserProvides methods to parse strings as Integer or Double vectors.Classorg.neuroph.utilNeurophjavadoc

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VectorSizeMismatchExceptionThrown to indicate that vector size does not match the network input or training element size.Classorg.neuroph.core.exceptionsNeurophjavadoc
WeightNeuron connection weight.Classorg.neuroph.coreNeurophjavadoc
WeightedSumOptimized version of weighted input functionAuthor:Zoran SevaracSee Also:Serialized FormClassorg.neuroph.core.inputNeurophjavadoc
WeightsRandomizerBasic weights randomizer, iterates and randomizes all connection weights in network.Classorg.neuroph.util.randomNeurophjavadoc

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