| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm |
Provides general interfaces for learning algorithms.
|
| gov.sandia.cognition.learning.algorithm.annealing |
Provides the Simulated Annealing algorithm.
|
| gov.sandia.cognition.learning.algorithm.genetic |
Provides a genetic algorithm implementation.
|
| gov.sandia.cognition.learning.function.cost |
Provides cost functions.
|
| gov.sandia.cognition.learning.performance |
Provides performance measures.
|
| gov.sandia.cognition.statistics.method |
Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
|
| Modifier and Type | Method and Description |
|---|---|
CostFunction<? super ResultType,? super CostParametersType> |
BatchCostMinimizationLearner.getCostFunction()
Gets the cost function that the learner is minimizing.
|
| Modifier and Type | Method and Description |
|---|---|
CostFunction<? super AnnealedType,? super CostParametersType> |
SimulatedAnnealer.getCostFunction() |
| Modifier and Type | Method and Description |
|---|---|
void |
SimulatedAnnealer.setCostFunction(CostFunction<? super AnnealedType,? super CostParametersType> cost)
Sets the cost function.
|
| Constructor and Description |
|---|
SimulatedAnnealer(AnnealedType initial,
Perturber<AnnealedType> perturber,
CostFunction<? super AnnealedType,? super CostParametersType> cost)
Creates a new instance of SimulatedAnnealer.
|
SimulatedAnnealer(AnnealedType initial,
Perturber<AnnealedType> perturber,
CostFunction<? super AnnealedType,? super CostParametersType> cost,
int maxIterations)
Creates a new instance of SimulatedAnnealer.
|
SimulatedAnnealer(AnnealedType initial,
Perturber<AnnealedType> perturber,
CostFunction<? super AnnealedType,? super CostParametersType> cost,
int maxIterations,
int maxIterationsWithoutImprovement)
Creates a new instance of SimulatedAnnealer.
|
| Modifier and Type | Method and Description |
|---|---|
CostFunction<? super GenomeType,? super CostParametersType> |
GeneticAlgorithm.getCostFunction() |
| Modifier and Type | Method and Description |
|---|---|
void |
GeneticAlgorithm.setCostFunction(CostFunction<? super GenomeType,? super CostParametersType> cost)
Sets the cost function.
|
| Constructor and Description |
|---|
GeneticAlgorithm(java.util.Collection<GenomeType> initialPopulation,
Reproducer<GenomeType> reproducer,
CostFunction<? super GenomeType,? super CostParametersType> cost)
Creates a new instance of GeneticAlgorithm.
|
GeneticAlgorithm(java.util.Collection<GenomeType> initialPopulation,
Reproducer<GenomeType> reproducer,
CostFunction<? super GenomeType,? super CostParametersType> cost,
int maxIterations)
Creates a new instance of GeneticAlgorithm.
|
GeneticAlgorithm(java.util.Collection<GenomeType> initialPopulation,
Reproducer<GenomeType> reproducer,
CostFunction<? super GenomeType,? super CostParametersType> cost,
int maxIterations,
int maxIterationsWithoutImprovement)
Creates a new instance of GeneticAlgorithm.
|
ParallelizedGeneticAlgorithm(java.util.Collection<GenomeType> initialPopulation,
Reproducer<GenomeType> reproducer,
CostFunction<? super GenomeType,? super CostParametersType> cost,
java.util.concurrent.ThreadPoolExecutor threadPool) |
ParallelizedGeneticAlgorithm(java.util.Collection<GenomeType> initialPopulation,
Reproducer<GenomeType> reproducer,
CostFunction<? super GenomeType,? super CostParametersType> cost,
java.util.concurrent.ThreadPoolExecutor threadPool,
int maxIterations,
int maxIterationsWithoutImprovement) |
| Modifier and Type | Interface and Description |
|---|---|
interface |
DifferentiableCostFunction
The
DifferentiableCostFunction is a cost function that can
be differentiated. |
interface |
ParallelizableCostFunction
Interface describing a cost function that can (largely) be computed in
parallel.
|
interface |
SupervisedCostFunction<InputType,TargetType>
A type of CostFunction normally used in supervised-learning applications.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractCostFunction<EvaluatedType,CostParametersType>
Partial implementation of CostFunction.
|
class |
AbstractParallelizableCostFunction
Partial implementation of the ParallelizableCostFunction
|
class |
AbstractSupervisedCostFunction<InputType,TargetType>
Partial implementation of SupervisedCostFunction
|
class |
ClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
Computes the objective measure for a clustering algorithm, based on the
internal "distortion" of each cluster.
|
class |
EuclideanDistanceCostFunction
The EuclideanDistanceCostFunction class implements a CostFunction that
calculates the Euclidean distance the given Vectorizable and the goal
vector.
|
class |
KolmogorovSmirnovDivergence<DataType extends java.lang.Number>
CostFunction that induces a CDF that most-closely resembles the
target distribution according to the Kolmogorov-Smirnov (K-S) test.
|
class |
MeanL1CostFunction
Cost function that evaluates the mean 1-norm error (absolute value of
difference) weighted by a sample "weight" that is embedded in each sample.
|
class |
MeanSquaredErrorCostFunction
The MeanSquaredErrorCostFunction implements a cost function for functions
that take as input a vector and return a vector.
|
class |
NegativeLogLikelihood<DataType>
CostFunction for computing the maximum likelihood
(because we are minimizing the negative of the log likelihood)
|
class |
ParallelClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
A parallel implementation of ClusterDistortionMeasure.
|
class |
ParallelizedCostFunctionContainer
A cost function that automatically splits a ParallelizableCostFunction
across multiple cores/processors to speed up computation.
|
class |
ParallelNegativeLogLikelihood<DataType>
Parallel implementation of the NegativeLogLikleihood cost function
|
class |
SumSquaredErrorCostFunction
This is the sum-squared error cost function
|
| Modifier and Type | Method and Description |
|---|---|
CostFunction<EvaluatedType,CostParametersType> |
CostFunction.clone() |
| Modifier and Type | Class and Description |
|---|---|
class |
MeanZeroOneErrorEvaluator<InputType,DataType>
The
MeanZeroOneErrorEvaluator class implements a method for
computing the performance of a supervised learner by the mean number of
incorrect values between the target and estimated outputs. |
| Modifier and Type | Field and Description |
|---|---|
protected CostFunction<? super DistributionType,? super java.util.Collection<? extends DataType>> |
DistributionParameterEstimator.DistributionWrapper.costFunction
Cost function to use in the minimization procedure
|
| Constructor and Description |
|---|
DistributionParameterEstimator(DistributionType distribution,
CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction)
Creates a new instance of DistributionParameterEstimator
|
DistributionParameterEstimator(DistributionType distribution,
CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction,
FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper> algorithm)
Creates a new instance of DistributionParameterEstimator
|
DistributionWrapper(DistributionType distribution,
CostFunction<? super DistributionType,? super java.util.Collection<? extends DataType>> costFunction)
Creates a new instance of DistributionWrapper
|