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# Classes and Interfaces in #Neuroph - 153 results found.
NameDescriptionTypePackageFramework
AdalineAdaline neural network architecture with LMS learning rule.Classorg.neuroph.nnetNeuroph
AndPerforms logic AND operation on input vector.Classorg.neuroph.core.inputNeuroph
AntiHebbianLearningA variant of Hebbian learning called Anti-Hebbian learning.Classorg.neuroph.nnet.learningNeuroph
AutoencoderNetworkClassorg.neuroph.nnetNeuroph
BackPropagationBack Propagation learning rule for Multi Layer Perceptron neural networks.Classorg.neuroph.nnet.learningNeuroph
BAMBidirectional Associative MemoryAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.nnetNeuroph
BenchmarkThis class is main benchmark driver.Classorg.neuroph.util.benchmarkNeuroph
BenchmarkSampleThis is an example how to use Neuroph microbenchmarking frameworkAuthor:Zoran Sevarac Classorg.neuroph.util.benchmarkNeuroph
BenchmarkTaskThis class is an abstract base class for specific microbenchmarking tasksAuthor:Zoran Sevarac Classorg.neuroph.util.benchmarkNeuroph
BenchmarkTaskResultsThis class holds benchmarking results, elapsed times for all iterations and various statistics min, max, avg times and standard deviationClassorg.neuroph.util.benchmarkNeuroph
BiasNeuronNeuron with constant high output (1), used as biasAuthor:Zoran Sevarac See Also:Neuron, Classorg.neuroph.nnet.comp.neuronNeuroph
BinaryDeltaRuleDelta rule learning algorithm for perceptrons with step functions.Classorg.neuroph.nnet.learningNeuroph
BinaryHebbianLearningHebbian-like learning algorithm used for Hopfield network.Classorg.neuroph.nnet.learningNeuroph
BufferedDataSetThis class can be used for large training sets, which are partialy read from file during the training.Classorg.neuroph.core.dataNeuroph
ClusterThis class represents a single cluster, with corresponding centroid and assigned vectorsAuthor:Zoran SevaracClassorg.neuroph.nnet.learning.kmeansNeuroph
CompetitiveLayerRepresents layer of competitive neurons, and provides methods for competition.Classorg.neuroph.nnet.comp.layerNeuroph
CompetitiveLearningCompetitive learning rule.Classorg.neuroph.nnet.learningNeuroph
CompetitiveNetworkTwo layer neural network with competitive learning rule.Classorg.neuroph.nnetNeuroph
CompetitiveNeuronProvides neuron behaviour specific for competitive neurons which are used in competitive layers, and networks with competitive learning.Classorg.neuroph.nnet.comp.neuronNeuroph
ConnectionWeighted connection to another neuron.Classorg.neuroph.coreNeuroph
ConnectionFactoryProvides methods to connect neurons by creating Connection objects.Classorg.neuroph.utilNeuroph
ConvolutionalBackpropagationClassorg.neuroph.nnet.learningNeuroph
ConvolutionalLayerConvolutional layer is a special type of layer, used in convolutional neural networks.Classorg.neuroph.nnet.comp.layerNeuroph
ConvolutionalNetworkConvolutional neural network with backpropagation algorithm modified for convolutional networks.Classorg.neuroph.nnetNeuroph
ConvolutionalUtilsUtility functions for convolutional networksAuthor:Boris Fulurija, Zorn SevaracClassorg.neuroph.nnet.compNeuroph
DataSetThis class represents a collection of data rows (DataSetRow instances) used for training and testing neural network.Classorg.neuroph.core.dataNeuroph
DataSetRowThis class represents single data row in a data set.Classorg.neuroph.core.dataNeuroph
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.normNeuroph
DelayedConnectionRepresents the connection between neurons which can delay signal.Classorg.neuroph.nnet.compNeuroph
DelayedNeuronProvides behaviour for neurons with delayed output.Classorg.neuroph.nnet.comp.neuronNeuroph
DifferencePerforms the vector difference operation on input andAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.inputNeuroph
DistortRandomizerThis class provides distort randomization technique, which distorts existing weight values using specified distortion factor.Classorg.neuroph.util.randomNeuroph
DynamicBackPropagationBackpropagation learning rule with dynamic learning rate and momentumAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.nnet.learningNeuroph
ElmanNetworkUnder development: Learning rule BackProp Through Time requiredAuthor:zoranSee Also:Serialized FormClassorg.neuroph.nnetNeuroph
ErrorFunctionInterface for calculating total network error during the learning.Interfaceorg.neuroph.core.learning.errorNeuroph
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.layerNeuroph
FileInputAdapterClassorg.neuroph.util.ioNeuroph
FileOutputAdapterClassorg.neuroph.util.ioNeuroph
FileUtilsUtility methods for working with files.Classorg.neuroph.utilNeuroph
Gaussian Gaussian neuron transfer function.Classorg.neuroph.core.transferNeuroph
GaussianRandomizerThis class provides Gaussian randomization technique using Box Muller method.Classorg.neuroph.util.randomNeuroph
GeneralizedHebbianLearningA variant of Hebbian learning called Generalized Hebbian learning.Classorg.neuroph.nnet.learningNeuroph
HopfieldHopfield neural network.Classorg.neuroph.nnetNeuroph
HopfieldLearningLearning algorithm for the Hopfield neural network.Classorg.neuroph.nnet.learningNeuroph
InputAdapterInterface for reading neural network inputs from various data sources.Interfaceorg.neuroph.util.ioNeuroph
InputFunction Neuron's input function.Classorg.neuroph.core.inputNeuroph
InputLayerRepresents a layer of input neurons - a typical neural network input layerAuthor:Zoran Sevarac See Also:InputNeuron, Classorg.neuroph.nnet.comp.layerNeuroph
InputMapsLayerInput layer for convolutional networksAuthor:Boris Fulurija, Zoran SevaracSee Also:Serialized FormClassorg.neuroph.nnet.comp.layerNeuroph
InputNeuronProvides input neuron behaviour - neuron with input extranaly set, which just transfer that input to output without change.Classorg.neuroph.nnet.comp.neuronNeuroph
InputOutputNeuronProvides behaviour specific for neurons which act as input and the output neurons within the same layer.Classorg.neuroph.nnet.comp.neuronNeuroph
InputStreamAdapterClassorg.neuroph.util.ioNeuroph
InstarInstar neural network with Instar learning rule.Classorg.neuroph.nnetNeuroph
InstarLearningHebbian-like learning rule for Instar network.Classorg.neuroph.nnet.learningNeuroph
IOHelper This class is helper for feeding neural network with data using some InputAdapter and writing network output using OutputAdapterClassorg.neuroph.util.ioNeuroph
IterativeLearningBase class for all iterative learning algorithms.Classorg.neuroph.core.learningNeuroph
JDBCInputAdapterClassorg.neuroph.util.ioNeuroph
JDBCOutputAdapterClassorg.neuroph.util.ioNeuroph
JordanNetworkUnder development: Learning rule BackProp Through Time requiredAuthor:zoranSee Also:Serialized FormClassorg.neuroph.nnetNeuroph
KernelKernel used in convolution networks.Classorg.neuroph.nnet.compNeuroph
KMeansClustering1.Classorg.neuroph.nnet.learning.kmeansNeuroph
KNearestNeighbour calculate distances to all vectors from list and find minimum vectorClassorg.neuroph.nnet.learning.knnNeuroph
KohonenKohonen neural network.Classorg.neuroph.nnetNeuroph
KohonenLearningLearning algorithm for Kohonen network.Classorg.neuroph.nnet.learningNeuroph
KVectorRepresents feature vector used in k-means clustering algorithmAuthor:Zoran Sevarac, Uros StojkicClassorg.neuroph.nnet.learning.kmeansNeuroph
Layer Layer of neurons in a neural network.Classorg.neuroph.coreNeuroph
Layer2D2D Layer provides 2D layout of the neurons in layer.Classorg.neuroph.nnet.comp.layerNeuroph
LayerFactoryProvides methods to create instance of a Layer with specifed number of neurons and neuron's properties.Classorg.neuroph.utilNeuroph
LearningEventThis class holds information about the source of some learning event.Classorg.neuroph.core.eventsNeuroph
LearningEventListenerThis interface is implemented by classes who are listening to learning events (iterations, error etc.Interfaceorg.neuroph.core.eventsNeuroph
LearningEventTypeenum LearningEventTypeAuthor:Zoran SevaracClassorg.neuroph.core.eventsNeuroph
LearningRuleBase class for all neural network learning algorithms.Classorg.neuroph.core.learningNeuroph
LinearLinear neuron transfer function.Classorg.neuroph.core.transferNeuroph
LMSLMS learning rule for neural networks.Classorg.neuroph.nnet.learningNeuroph
Log Log neuron transfer function.Classorg.neuroph.core.transferNeuroph
MaxPerforms max function on input vectorAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.inputNeuroph
MaxErrorStopStops learning rule if total network error is below some specified valueAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.stopNeuroph
MaxIterationsStopStops learning rule if specified number of iterations has been reachedAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.stopNeuroph
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.normNeuroph
MaxNetMax Net neural network with competitive learning rule.Classorg.neuroph.nnetNeuroph
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.normNeuroph
MeanSquaredErrorCommonly used mean squared errorAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.errorNeuroph
MinPerforms min function on input vectorAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.inputNeuroph
MomentumBackpropagationBackpropagation learning rule with momentum.Classorg.neuroph.nnet.learningNeuroph
MultiLayerPerceptronMulti Layer Perceptron neural network with Back propagation learning algorithm.Classorg.neuroph.nnetNeuroph
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.benchmarkNeuroph
NeuralNetwork Base class for artificial neural networks.Classorg.neuroph.coreNeuroph
NeuralNetworkCODECA CODEC encodes and decodes neural networks, much like the more standard definition of a CODEC encodes and decodes audio/video.Classorg.neuroph.utilNeuroph
NeuralNetworkEventThis class holds information about the source and type of some neural network event.Classorg.neuroph.core.eventsNeuroph
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.eventsNeuroph
NeuralNetworkEventTypeenum NeuralNetworkEventTypeAuthor:Zoran SevaracClassorg.neuroph.core.eventsNeuroph
NeuralNetworkFactoryProvides methods to create various neural networks.Classorg.neuroph.utilNeuroph
NeuralNetworkTypeenum NeuralNetworkTypeContains neural network types and labels.Classorg.neuroph.utilNeuroph
NeuroFuzzyPerceptronThe NeuroFuzzyReasoner class represents Neuro Fuzzy Reasoner architecture.Classorg.neuroph.nnetNeuroph
Neuron Basic general neuron model according to McCulloch-Pitts neuron model.Classorg.neuroph.coreNeuroph
NeuronFactoryProvides methods to create customized instances of Neurons.Classorg.neuroph.utilNeuroph
NeuronPropertiesRepresents properties of a neuron.Classorg.neuroph.utilNeuroph
NeurophClassorg.neuroph.utilNeuroph
NeurophArrayListResizable-array implementation of the List interface.Classorg.neuroph.utilNeuroph
NeurophExceptionBase exception type for Neuroph.Classorg.neuroph.core.exceptionsNeuroph
NeurophInputExceptionThis exception is thrown when error occurs when reading input using some InputAdapterAuthor:Zoran Sevarac See Also:InputAdapter, Classorg.neuroph.util.ioNeuroph
NeurophOutputExceptionThis exception is thrown when some error occurs when writing neural network output using some output adapter.Classorg.neuroph.util.ioNeuroph
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.randomNeuroph
NormalizerInterface for data set normalization methods.Interfaceorg.neuroph.util.data.normNeuroph
OjaLearningOja learning rule wich is a modification of unsupervised hebbian learning.Classorg.neuroph.nnet.learningNeuroph
OrPerforms logic OR operation on input vector.Classorg.neuroph.core.inputNeuroph
OutputAdapterInterface for writing neural network outputs to some destination.Interfaceorg.neuroph.util.ioNeuroph
OutputStreamAdapterClassorg.neuroph.util.ioNeuroph
OutstarOutstar neural network with Outstar learning rule.Classorg.neuroph.nnetNeuroph
OutstarLearningHebbian-like learning rule for Outstar network.Classorg.neuroph.nnet.learningNeuroph
PerceptronPerceptron neural network with some LMS based learning algorithm.Classorg.neuroph.nnetNeuroph
PerceptronLearningPerceptron learning rule for perceptron neural networks.Classorg.neuroph.nnet.learningNeuroph
PluginBaseBase class for all neural network plugins.Classorg.neuroph.util.pluginsNeuroph
PoolingLayerPooling layer is a special type of feature maps layer (FeatureMapsLayer) which is used in convolutional networks.Classorg.neuroph.nnet.comp.layerNeuroph
ProductPerforms multiplication of all input vector elements.Classorg.neuroph.core.inputNeuroph
PropertiesRepresents a general set of properties for neuroph objectsAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.utilNeuroph
RampRamp neuron transfer function.Classorg.neuroph.core.transferNeuroph
RangeNormalizerThis class does normalization of a data set to specified rangeAuthor:Zoran Sevarac Classorg.neuroph.util.data.normNeuroph
RangeRandomizerThis class provides ranged weights randomizer, which randomize weights in specified [min, max] range.Classorg.neuroph.util.randomNeuroph
RBFLearningLearning rule for Radial Basis Function networks.Classorg.neuroph.nnet.learningNeuroph
RBFNetworkRadial basis function neural network.Classorg.neuroph.nnetNeuroph
ResilientPropagationResilient Propagation learning rule used for Multi Layer Perceptron neural networks.Classorg.neuroph.nnet.learningNeuroph
SamplingInterface for data set sampling methods.Interfaceorg.neuroph.util.data.sampleNeuroph
SgnSgn neuron transfer function.Classorg.neuroph.core.transferNeuroph
Sigmoid Sigmoid neuron transfer function.Classorg.neuroph.core.transferNeuroph
SigmoidDeltaRuleDelta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.Classorg.neuroph.nnet.learningNeuroph
SimulatedAnnealingLearningThis class implements a simulated annealing learning rule for supervised neural networks.Classorg.neuroph.nnet.learningNeuroph
Sin Sin neuron transfer function.Classorg.neuroph.core.transferNeuroph
SmallErrorChangeStopStops learning rule if error change has been too small for specified numberAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.core.learning.stopNeuroph
StepStep neuron transfer function.Classorg.neuroph.core.transferNeuroph
StopConditionInterface for learning rule stop condition.Interfaceorg.neuroph.core.learning.stopNeuroph
StopwatchA class to help benchmark code, it simulates a real stop watch.Classorg.neuroph.util.benchmarkNeuroph
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.sampleNeuroph
SumPerforms summing of all input vector elements.Classorg.neuroph.core.inputNeuroph
SumSqrCalculates squared sum of all input vector elements.Classorg.neuroph.core.inputNeuroph
SupervisedHebbianLearningSupervised hebbian learning rule.Classorg.neuroph.nnet.learningNeuroph
SupervisedHebbianNetworkHebbian neural network with supervised Hebbian learning algorithm.Classorg.neuroph.nnetNeuroph
SupervisedLearningBase class for all supervised learning algorithms.Classorg.neuroph.core.learningNeuroph
Tanh Tanh neuron transfer function.Classorg.neuroph.core.transferNeuroph
ThresholdNeuronProvides behaviour for neurons with threshold.Classorg.neuroph.nnet.comp.neuronNeuroph
TrainingSetImportHandles training set importsAuthor:Zoran Sevarac, Ivan Nedeljkovic, Kokanovic RadosClassorg.neuroph.utilNeuroph
TransferFunctionAbstract base class for all neuron tranfer functions.Classorg.neuroph.core.transferNeuroph
TransferFunctionTypeenum TransferFunctionTypeContains transfer functions types and labels.Classorg.neuroph.utilNeuroph
TrapezoidFuzzy trapezoid neuron tranfer function.Classorg.neuroph.core.transferNeuroph
UnsupervisedHebbianLearningUnsupervised hebbian learning rule.Classorg.neuroph.nnet.learningNeuroph
UnsupervisedHebbianNetworkHebbian neural network with unsupervised Hebbian learning algorithm.Classorg.neuroph.nnetNeuroph
UnsupervisedLearningBase class for all unsupervised learning algorithms.Classorg.neuroph.core.learningNeuroph
URLInputAdapterClassorg.neuroph.util.ioNeuroph
URLOutputAdapterClassorg.neuroph.util.ioNeuroph
VectorParserProvides methods to parse strings as Integer or Double vectors.Classorg.neuroph.utilNeuroph
VectorSizeMismatchExceptionThrown to indicate that vector size does not match the network input or training element size.Classorg.neuroph.core.exceptionsNeuroph
WeightNeuron connection weight.Classorg.neuroph.coreNeuroph
WeightedSumOptimized version of weighted input functionAuthor:Zoran SevaracSee Also:Serialized FormClassorg.neuroph.core.inputNeuroph
WeightsRandomizerBasic weights randomizer, iterates and randomizes all connection weights in network.Classorg.neuroph.util.randomNeuroph