Modifier and Type | Method and Description |
---|---|
MLMethod |
EncogAnalyst.getMethod() |
Modifier and Type | Method and Description |
---|---|
void |
EncogAnalyst.setMethod(MLMethod method) |
Modifier and Type | Method and Description |
---|---|
MLMethod |
Cmd.obtainMethod()
Obtain the ML method.
|
Modifier and Type | Method and Description |
---|---|
void |
AnalystEvaluateCSV.process(File outputFile,
MLMethod method)
Process the file.
|
Modifier and Type | Method and Description |
---|---|
static boolean |
EncogCodeGeneration.isSupported(MLMethod method)
Is the specified method supported for code generation?
|
Modifier and Type | Class and Description |
---|---|
class |
BasicUniverse |
Modifier and Type | Interface and Description |
---|---|
interface |
EnsembleML |
Modifier and Type | Class and Description |
---|---|
class |
GenericEnsembleML |
Modifier and Type | Method and Description |
---|---|
MLMethod |
EnsembleMLMethodFactory.createML(int inputs,
int outputs) |
MLMethod |
EnsembleML.getMl() |
MLMethod |
GenericEnsembleML.getMl() |
Modifier and Type | Method and Description |
---|---|
MLTrain |
EnsembleTrainFactory.getTraining(MLMethod method,
MLDataSet trainingData) |
void |
EnsembleMLMethodFactory.reInit(MLMethod ml) |
void |
EnsembleML.setMl(MLMethod newMl)
Set the MLMethod to run
|
void |
GenericEnsembleML.setMl(MLMethod newMl) |
Constructor and Description |
---|
GenericEnsembleML(MLMethod fromML,
String description) |
Modifier and Type | Method and Description |
---|---|
MLMethod |
MultiLayerPerceptronFactory.createML(int inputs,
int outputs) |
Modifier and Type | Method and Description |
---|---|
void |
MultiLayerPerceptronFactory.reInit(MLMethod ml) |
Modifier and Type | Method and Description |
---|---|
MLTrain |
ManhattanPropagationFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
ResilientPropagationFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
ScaledConjugateGradientFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
LevenbergMarquardtFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
BackpropagationFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
Modifier and Type | Method and Description |
---|---|
void |
BasicRandomizer.randomize(MLMethod method)
Randomize the synapses and biases in the basic network based on an array,
modify the array.
|
void |
NguyenWidrowRandomizer.randomize(MLMethod method) |
void |
Randomizer.randomize(MLMethod network)
Randomize the synapses and bias values in the basic network based on an
array, modify the array.
|
Modifier and Type | Interface and Description |
---|---|
interface |
MLAutoAssocation
Defines a MLMethod that can handle autoassocation.
|
interface |
MLClassification
This interface defines a MLMethod that is used for classification.
|
interface |
MLClustering
A machine learning method that is used to break data into clusters.
|
interface |
MLContext
Defines a MLMethod that can hold context.
|
interface |
MLEncodable
Defines a Machine Learning Method that can be encoded to a double array.
|
interface |
MLError
Defines Machine Learning Method that can calculate an error based on a
data set.
|
interface |
MLInput
Defines a MLMethod that accepts input.
|
interface |
MLInputOutput
This is a convenience interface that combines MLInput and MLOutput.
|
interface |
MLOutput
Defines a MLMethod that produces output.
|
interface |
MLProperties
Defines a Machine Learning Method that holds properties.
|
interface |
MLRegression
Defines a Machine Learning Method that supports regression.
|
interface |
MLResettable
Defines a Machine Learning Method that can be reset to an untrained
starting point.
|
interface |
MLStateSequence
A state sequence ML method, for example a Hidden Markov Model.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicML
A class that provides basic property functionality for the MLProperties
interface.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
MethodFactory.factor() |
Modifier and Type | Method and Description |
---|---|
double |
CalculateScore.calculateScore(MLMethod method)
Calculate this network's score.
|
Modifier and Type | Class and Description |
---|---|
class |
BayesianNetwork
The Bayesian Network is a machine learning method that is based on
probability, and particularly Bayes' Rule.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainBayesian.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
DataFold.getMethod() |
Modifier and Type | Method and Description |
---|---|
void |
DataFold.setMethod(MLMethod method) |
Modifier and Type | Method and Description |
---|---|
MLMethod |
GenomeAsPhenomeCODEC.decode(Genome genome)
Decode the specified genome into a phenome.
|
MLMethod |
GeneticCODEC.decode(Genome genome)
Decode the specified genome into a phenome.
|
Modifier and Type | Method and Description |
---|---|
Genome |
GenomeAsPhenomeCODEC.encode(MLMethod phenotype)
Attempt to build a genome from a phenome.
|
Genome |
GeneticCODEC.encode(MLMethod phenotype)
Attempt to build a genome from a phenome.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Genome
A genome is the basic blueprint for creating an phenome (organism) in Encog.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicGenome
A basic abstract genome.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Population
Defines a population of genomes.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicPopulation
Defines the basic functionality for a population of genomes.
|
Modifier and Type | Method and Description |
---|---|
double |
EmptyScoreFunction.calculateScore(MLMethod phenotype)
Calculate this network's score.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainEA.getMethod() |
Modifier and Type | Method and Description |
---|---|
MLMethod |
MLMethodFactory.create(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
MLTrainFactory.create(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
FeedforwardFactory.create(String architecture,
int input,
int output)
Create a feed forward network.
|
MLMethod |
PNNFactory.create(String architecture,
int input,
int output)
Create a PNN network.
|
MLMethod |
SVMFactory.create(String architecture,
int input,
int output)
Create the SVM.
|
MLMethod |
RBFNetworkFactory.create(String architecture,
int input,
int output)
Create a RBF network.
|
MLMethod |
SRNFactory.create(String architecture,
int input,
int output)
Create the SRN.
|
MLMethod |
EPLFactory.create(String architecture,
int input,
int output)
Create a feed forward network.
|
MLMethod |
NEATFactory.create(String architecture,
int input,
int output)
Create a NEAT population.
|
MLMethod |
BayesianFactory.create(String architecture,
int input,
int output)
Create a bayesian network.
|
MLMethod |
SOMFactory.create(String architecture,
int input,
int output)
Create a SOM.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
GeneticFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an annealing trainer.
|
MLTrain |
SVMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a SVM trainer.
|
MLTrain |
ClusterSOMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a cluster SOM trainer.
|
MLTrain |
LMAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a LMA trainer.
|
MLTrain |
NEATGAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an NEAT GA trainer.
|
MLTrain |
PSOFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a PSO trainer.
|
MLTrain |
QuickPropFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a quick propagation trainer.
|
MLTrain |
TrainBayesianFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a K2 trainer.
|
MLTrain |
RBFSVDFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a RBF-SVD trainer.
|
MLTrain |
AnnealFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an annealing trainer.
|
MLTrain |
SCGFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a SCG trainer.
|
MLTrain |
NeighborhoodSOMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a LMA trainer.
|
MLTrain |
BackPropFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a backpropagation trainer.
|
MLTrain |
PNNTrainFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a PNN trainer.
|
MLTrain |
NelderMeadFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a Nelder Mead trainer.
|
MLTrain |
ManhattanFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a Manhattan trainer.
|
MLTrain |
SVMSearchFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a SVM trainer.
|
MLTrain |
EPLGAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an EPL GA trainer.
|
MLTrain |
RPROPFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a RPROP trainer.
|
Modifier and Type | Method and Description |
---|---|
double |
MultiObjectiveFitness.calculateScore(MLMethod method)
Calculate this network's score.
|
Modifier and Type | Class and Description |
---|---|
class |
GaussianFitting |
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainGaussian.getMethod() |
Modifier and Type | Class and Description |
---|---|
class |
LinearRegression |
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainLinearRegression.getMethod() |
Modifier and Type | Class and Description |
---|---|
class |
MLMethodGenome
Implements a genome that allows a feedforward neural network to be trained
using a genetic algorithm.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
MLEncodableCODEC.decode(Genome genome)
Decode the specified genome into a phenome.
|
MLMethod |
MLMethodGeneticAlgorithm.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
Genome |
MLEncodableCODEC.encode(MLMethod phenotype)
Attempt to build a genome from a phenome.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ArrayGenome
An array genome represents an array of "something", this allows array
operators such as crossover and mutate to work on the genome.
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleArrayGenome
A genome made up of continuous doubles.
|
class |
IntegerArrayGenome
A genome that is an array of discrete integer values.
|
Modifier and Type | Class and Description |
---|---|
class |
HiddenMarkovModel
A Hidden Markov Model (HMM) is a Machine Learning Method that allows for
predictions to be made about the hidden states and observations of a given
system over time.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
BaseBaumWelch.getMethod() |
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainKMeans.getMethod() |
Modifier and Type | Class and Description |
---|---|
class |
KMeansClustering
This class performs a basic K-Means clustering.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
EncogModel.createMethod()
Create the selected method.
|
MLMethod |
EncogModel.crossvalidate(int k,
boolean shuffle)
Crossvalidate and fit.
|
Modifier and Type | Method and Description |
---|---|
double |
EncogModel.calculateError(MLMethod method,
MLDataSet data)
Calculate the error for the given method and dataset.
|
Modifier and Type | Class and Description |
---|---|
class |
EncogProgram
Holds an Encog Programming Language (EPL) program.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
PrgCODEC.decode(Genome genome)
Decode the specified genome into a phenome.
|
Modifier and Type | Method and Description |
---|---|
Genome |
PrgCODEC.encode(MLMethod phenotype)
Attempt to build a genome from a phenome.
|
Modifier and Type | Class and Description |
---|---|
class |
PrgPopulation
A population that contains EncogProgram's.
|
Modifier and Type | Method and Description |
---|---|
double |
ZeroEvalScoreFunction.calculateScore(MLMethod genome)
Calculate this network's score.
|
Modifier and Type | Class and Description |
---|---|
class |
SVM
This is a network that is backed by one or more Support Vector Machines
(SVM).
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
SVMTrain.getMethod()
Get the current best machine learning method from the training.
|
MLMethod |
SVMSearchTrain.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
MLTrain.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Class and Description |
---|---|
class |
ART
Adaptive Resonance Theory (ART) is a form of neural network developed
by Stephen Grossberg and Gail Carpenter.
|
class |
ART1
Implements an ART1 neural network.
|
Modifier and Type | Class and Description |
---|---|
class |
BAM
Bidirectional associative memory (BAM) is a type of neural network
developed by Bart Kosko in 1988.
|
Modifier and Type | Class and Description |
---|---|
class |
CPN
Counterpropagation Neural Networks (CPN) were developed by Professor
Robert Hecht-Nielsen in 1987.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainOutstar.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Class and Description |
---|---|
class |
FreeformNetwork
Implements a freefrom neural network.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
FreeformPropagationTraining.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Class and Description |
---|---|
class |
HyperNEATGenome
A HyperNEAT genome.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
HyperNEATCODEC.decode(Genome genome)
Decode the specified genome into a phenome.
|
MLMethod |
HyperNEATCODEC.decode(NEATPopulation pop,
Substrate substrate,
Genome genome) |
Modifier and Type | Method and Description |
---|---|
Genome |
HyperNEATCODEC.encode(MLMethod phenotype) |
Modifier and Type | Class and Description |
---|---|
class |
NEATNetwork
NEAT networks relieve the programmer of the need to define the hidden layer
structure of the neural network.
|
class |
NEATPopulation
A population for a NEAT or HyperNEAT system.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
NEATCODEC.decode(Genome genome)
Decode the specified genome into a phenome.
|
Modifier and Type | Method and Description |
---|---|
Genome |
NEATCODEC.encode(MLMethod phenotype)
This method is not currently implemented.
|
Modifier and Type | Class and Description |
---|---|
class |
NEATGenome
Implements a NEAT genome.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ContainsFlat
Interface that specifies that a machine learning method contains a
flat network.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicNetwork
This class implements a neural network.
|
Modifier and Type | Method and Description |
---|---|
static void |
NetworkCODEC.arrayToNetwork(double[] array,
MLMethod network)
Use an array to populate the memory of the neural network.
|
static int |
NetworkCODEC.networkSize(MLMethod network)
Determine the network size.
|
static double[] |
NetworkCODEC.networkToArray(MLMethod network)
Convert to an array.
|
Modifier and Type | Method and Description |
---|---|
double |
TrainingSetScore.calculateScore(MLMethod method)
Calculate the score for the network.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
CrossTraining.getMethod()
Get the current best machine learning method from the training.
|
Constructor and Description |
---|
CrossTraining(MLMethod network,
FoldedDataSet training)
Construct a cross trainer.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
LevenbergMarquardtTraining.getMethod() |
Modifier and Type | Method and Description |
---|---|
MLMethod |
NelderMeadTraining.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainBasicPNN.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
Propagation.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
NeuralPSO.getMethod() |
Modifier and Type | Method and Description |
---|---|
MLMethod |
TrainAdaline.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
NeuralNetworkPattern.generate()
Generate the specified neural network.
|
MLMethod |
ART1Pattern.generate()
Generate the neural network.
|
MLMethod |
BAMPattern.generate() |
MLMethod |
ElmanPattern.generate()
Generate the Elman neural network.
|
MLMethod |
JordanPattern.generate()
Generate a Jordan neural network.
|
MLMethod |
RadialBasisPattern.generate()
Generate the RBF network.
|
MLMethod |
BoltzmannPattern.generate()
Generate the network.
|
MLMethod |
ADALINEPattern.generate()
Generate the network.
|
MLMethod |
FeedForwardPattern.generate()
Generate the feedforward neural network.
|
MLMethod |
SVMPattern.generate() |
MLMethod |
SOMPattern.generate()
Generate the RSOM network.
|
MLMethod |
CPNPattern.generate()
Generate the network.
|
MLMethod |
HopfieldPattern.generate()
Generate the Hopfield neural network.
|
MLMethod |
PNNPattern.generate()
Generate the RSOM network.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractPNN
Abstract class to build PNN networks upon.
|
class |
BasicPNN
This class implements either a:
Probabilistic Neural Network (PNN)
General Regression Neural Network (GRNN)
To use a PNN specify an output mode of classification, to make use of a GRNN
specify either an output mode of regression or un-supervised autoassociation.
|
Modifier and Type | Class and Description |
---|---|
class |
RBFNetwork
RBF neural network.
|
Modifier and Type | Class and Description |
---|---|
class |
SOM
A self organizing map neural network.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
BasicTrainSOM.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
SOMClusterCopyTraining.getMethod()
Get the current best machine learning method from the training.
|
Modifier and Type | Class and Description |
---|---|
class |
BoltzmannMachine
Implements a Boltzmann machine.
|
class |
HopfieldNetwork
Implements a Hopfield network.
|
class |
ThermalNetwork
The thermal network forms the base class for Hopfield and Boltzmann machines.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
EncogPluginService1.createMethod(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
EncogPluginService1.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
Modifier and Type | Method and Description |
---|---|
MLMethod |
SystemActivationPlugin.createMethod(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method.
|
MLMethod |
SystemTrainingPlugin.createMethod(String methodType,
String architecture,
int input,
int output) |
MLMethod |
SystemMethodsPlugin.createMethod(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
SystemActivationPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
MLTrain |
SystemTrainingPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args) |
MLTrain |
SystemMethodsPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
Modifier and Type | Method and Description |
---|---|
static void |
EncogUtility.trainToError(MLMethod method,
MLDataSet dataSet,
double error)
Train the method, to a specific error, send the output to the console.
|
Modifier and Type | Method and Description |
---|---|
static void |
ValidateNetwork.validateMethodToData(MLMethod method,
MLDataSet training) |
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