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#Org.neuroph.util Classes and Interfaces - 46 results found.
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
ConnectionFactoryProvides methods to connect neurons by creating Connection objects.Classorg.neuroph.utilNeuroph
DecimalScaleNormalizerDecimal scaling normalization method, which normalize data by moving decimal point in regard to max element in training set (by columns) Normalization
DistortRandomizerThis class provides distort randomization technique, which distorts existing weight values using specified distortion factor.Classorg.neuroph.util.randomNeuroph
FileUtilsUtility methods for working with files.Classorg.neuroph.utilNeuroph
GaussianRandomizerThis class provides Gaussian randomization technique using Box Muller method.Classorg.neuroph.util.randomNeuroph
InputAdapterInterface for reading neural network inputs from various data sources.Interfaceorg.neuroph.util.ioNeuroph
IOHelper This class is helper for feeding neural network with data using some InputAdapter and writing network output using OutputAdapterClassorg.neuroph.util.ioNeuroph
LayerFactoryProvides methods to create instance of a Layer with specifed number of neurons and neuron's properties.Classorg.neuroph.utilNeuroph
MaxMinNormalizerMaxMin normalization method, which normalize data in regard to min and max elements in training set (by columns) Normalization is done according to
MaxNormalizerMax normalization method, which normalize data in regard to max element in training set (by columns) Normalization is done according to
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
NeuralNetworkCODECA CODEC encodes and decodes neural networks, much like the more standard definition of a CODEC encodes and decodes audio/video.Classorg.neuroph.utilNeuroph
NeuralNetworkFactoryProvides methods to create various neural networks.Classorg.neuroph.utilNeuroph
NeuralNetworkTypeenum NeuralNetworkTypeContains neural network types and labels.Classorg.neuroph.utilNeuroph
NeuronFactoryProvides methods to create customized instances of Neurons.Classorg.neuroph.utilNeuroph
NeuronPropertiesRepresents properties of a neuron.Classorg.neuroph.utilNeuroph
NeurophArrayListResizable-array implementation of the List interface.Classorg.neuroph.utilNeuroph
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
OutputAdapterInterface for writing neural network outputs to some destination.Interfaceorg.neuroph.util.ioNeuroph
PluginBaseBase class for all neural network plugins.Classorg.neuroph.util.pluginsNeuroph
PropertiesRepresents a general set of properties for neuroph objectsAuthor:Zoran Sevarac See Also:Serialized FormClassorg.neuroph.utilNeuroph
RangeNormalizerThis class does normalization of a data set to specified rangeAuthor:Zoran Sevarac
RangeRandomizerThis class provides ranged weights randomizer, which randomize weights in specified [min, max] range.Classorg.neuroph.util.randomNeuroph
SamplingInterface for data set sampling
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
TrainingSetImportHandles training set importsAuthor:Zoran Sevarac, Ivan Nedeljkovic, Kokanovic RadosClassorg.neuroph.utilNeuroph
TransferFunctionTypeenum TransferFunctionTypeContains transfer functions types and labels.Classorg.neuroph.utilNeuroph
VectorParserProvides methods to parse strings as Integer or Double vectors.Classorg.neuroph.utilNeuroph
WeightsRandomizerBasic weights randomizer, iterates and randomizes all connection weights in network.Classorg.neuroph.util.randomNeuroph