Movement[] movements
double limit
UniverseCell add1
UniverseCell mult
UniverseCell add2
UniverseCellFactory factory
UniverseCell[][] data
UniverseCellFactory cellFactory
svm_parameter param
int nr_class
int l
svm_node[][] SV
double[][] sv_coef
double[] rho
double[] probA
double[] probB
int[] label
int[] nSV
int svm_type
int kernel_type
int degree
double gamma
double coef0
double cache_size
double eps
double C
int nr_weight
int[] weight_label
double[] weight
double nu
double p
int shrinking
int probability
int l
double[] y
svm_node[][] x
Random seedProducer
String label
double min
double max
String label
List<E> parents
List<E> children
Set<E> choices
BayesianTable table
int minimumChoiceIndex
double minimumChoice
int maximumChoiceIndex
double maximumChoice
Map<K,V> eventMap
List<E> events
BayesianQuery query
boolean[] inputPresent
int classificationTarget
double[] classificationProbabilities
boolean calculated
int value
BayesianEvent event
EventType eventType
int compareValue
int sampleSize
int usableSamples
int goodSamples
int totalSamples
BayesianEvent event
List<E> lines
File file
MarketLoader loader
Map<K,V> pointIndex
List<E> descriptions
List<E> points
int inputWindowSize
int predictWindowSize
int lowSequence
int highSequence
int desiredSetSize
int inputNeuronCount
int outputNeuronCount
Date startingPoint
TimeUnit sequenceGrandularity
List<E> sourceColumns
List<E> inputColumns
List<E> outputColumns
NormalizationStrategy normStrategy
CSVFormat format
List<E> unknownValues
Map<K,V> missingHandlers
String name
ColumnType dataType
double low
double high
double mean
double sd
int count
int index
List<E> classes
NormalizationHelper owner
double adjustedScore
double score
Population population
int birthGeneration
Species species
EvolutionaryAlgorithm trainer
int rounds
EvolutionaryAlgorithm trainer
double percent
String name
List<E> species
Genome bestGenome
GenomeFactory genomeFactory
int populationSize
int age
double bestScore
int gensNoImprovement
Genome leader
List<E> members
Population population
EvolutionaryAlgorithm owner
double compatibilityThreshold
int numGensAllowedNoImprovement
int maxNumberOfSpecies
SortGenomesForSpecies sortGenomes
Population population
boolean ignoreExceptions
GenomeComparator bestComparator
GenomeComparator selectionComparator
Population population
CalculateScore scoreFunction
SelectionOperator selection
List<E> adjusters
OperationList operators
GeneticCODEC codec
RandomFactory randomNumberFactory
boolean validationMode
int iteration
int threadCount
int actualThreadCount
Speciation speciation
Throwable reportedError
Genome oldBestGenome
List<E> newPopulation
EvolutionaryOperator champMutation
double eliteRate
int maxTries
Genome bestGenome
ExecutorService taskExecutor
List<E> threadList
RuleHolder rules
int maxOperationErrors
double weight
CalculateScore score
MLEncodable phenotype
double[] pi
double[][] transitionProbability
StateDistribution[] stateDistributions
int[] items
int dimension
double[] mean
Matrix covariance
Matrix covarianceL
Matrix covarianceInv
double covarianceDet
CholeskyDecomposition cd
EncogProgramVariables variables
EncogProgramContext context
ProgramNode rootNode
Map<K,V> extraData
CSVFormat format
FunctionFactory functions
List<E> definedVariables
Map<K,V> map
VariableMapping result
ProgramExtensionTemplate template
EncogProgram owner
ExpressionValue[] data
String stringValue
double floatValue
boolean boolValue
ValueType expressionType
long intValue
int enumType
String name
boolean varValue
int dataSize
NodeType nodeType
int precedence
String signature
List<E> params
ParamTemplate returnValue
EncogProgramContext context
EncogProgramContext context
svm_model model
svm_parameter params
int inputCount
int winner
double a1
double b1
double c1
double d1
double l
double vigilance
int noWinner
BiPolarNeuralData outputF1
BiPolarNeuralData outputF2
int f1Count
int f2Count
Matrix weightsF1toF2
Matrix weightsF2toF1
int inputCount
int instarCount
int outstarCount
int winnerCount
Matrix weightsInputToInstar
Matrix weightsInstarToOutstar
int inputCount
int[] layerCounts
int[] layerContextCount
int[] layerFeedCounts
int[] layerIndex
double[] layerOutput
double[] layerSums
int outputCount
int[] weightIndex
double[] weights
ActivationFunction[] activationFunctions
int[] contextTargetOffset
int[] contextTargetSize
double[] biasActivation
int beginTraining
int endTraining
boolean isLimited
double connectionLimit
boolean hasContext
RadialBasisFunction[] rbf
FreeformNeuron contextSource
FreeformLayer inputLayer
FreeformLayer outputLayer
FreeformConnectionFactory connectionFactory
FreeformLayerFactory layerFactory
FreeformNeuronFactory neuronFactory
InputSummationFactory summationFactory
ActivationFunction activationFunction
List<E> inputs
double sum
double weight
FreeformNeuron source
FreeformNeuron target
boolean recurrent
double[] tempTraining
InputSummation inputSummation
List<E> outputConnections
double activation
boolean bias
double[] tempTraining
FreeformNetwork network
MLDataSet training
int iterationCount
double error
Set<E> visited
boolean fixFlatSopt
int batchSize
int dimensions
List<E> inputNodes
List<E> outputNodes
List<E> hiddenNodes
List<E> links
int currentNeuronNumber
int activationCycles
SubstrateNode source
SubstrateNode target
NEATLink[] links
ActivationFunction[] activationFunctions
double[] preActivation
double[] postActivation
int outputIndex
int inputCount
int outputCount
int activationCycles
boolean hasRelaxed
double relaxationThreshold
int activationCycles
GenerateID geneIDGenerate
GenerateID innovationIDGenerate
NEATInnovationList innovations
double weightRange
Genome cachedBestGenome
NEATNetwork bestNetwork
int inputCount
int outputCount
double survivalRate
Substrate substrate
ChooseObject<T> activationFunctions
GeneticCODEC codec
double initialConnectionDensity
RandomFactory randomNumberFactory
NEATPopulation population
Map<K,V> list
NEATNeuronType neuronType
ActivationFunction activationFunction
NeuralStructure structure
BasicNetwork network
List<E> layers
BasicNetwork network
double connectionLimit
boolean connectionLimited
FlatNetwork flat
int inputCount
int outputCount
PNNKernelType kernel
PNNOutputMode outputMode
boolean trained
double error
int[] confusion
double[] deriv
double[] deriv2
int exclude
boolean separateClass
double[] sigma
BasicMLDataSet samples
int[] countPer
double[] priors
FlatNetworkRBF flat
Matrix weights
double temperature
double[] threshold
int annealCycles
int runCycles
BiPolarNeuralData currentState
double[] weights
int neuronCount
JLabel labelError
JLabel labelIterations
JLabel labelTime
JButton buttonStop
boolean shouldStop
Image image
Downsample downsampler
int height
int width
boolean findBounds
double hi
double lo
double actualHigh
double actualLow
double normalizedHigh
double normalizedLow
NormalizationAction action
String name
List<E> classes
Equilateral eq
Map<K,V> lookup
char decimal
char separator
NumberFormat numberFormatter
List<E> inputFields
List<E> outputFields
Set<E> groups
List<E> segregators
NormalizationStorage storage
int recordCount
int currentIndex
CSVFormat csvFormat
int lastReport
double min
double max
double currentValue
boolean usedForNetworkInput
File file
int offset
String resourceName
int offset
NeuralDataSet data
int offset
MLDataPair pair
Iterator<E> iterator
InputFieldMLDataSet field
Collection<E> fields
InputField sourceField
OutputFieldGroup group
InputField sourceField
InputField field
double low
double high
InputField sourceField
double catchAll
List<E> ranges
double low
double high
InputField inputField
List<E> items
Equilateral equilateral
int currentValue
double high
double low
DataNormalization normalization
InputField target
int count
Map<K,V> runningCounts
InputField sourceField
boolean include
Collection<E> ranges
DataNormalization normalization
int currentIndex
DataNormalization normalization
String resourceName
int inputCount
int idealCount
MLDataSet dataset
List<E> list
RandomChoice chooser
Object obj
double probability
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