| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.annealing |
Provides the Simulated Annealing algorithm.
|
| gov.sandia.cognition.learning.algorithm.clustering |
Provides clustering algorithms.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.genetic |
Provides a genetic algorithm implementation.
|
| gov.sandia.cognition.learning.algorithm.hmm |
Provides hidden Markov model (HMM) algorithms.
|
| gov.sandia.cognition.learning.algorithm.pca |
Provides implementations of Principle Components Analysis (PCA).
|
| gov.sandia.cognition.learning.algorithm.perceptron |
Provides the Perceptron algorithm and some of its variations.
|
| gov.sandia.cognition.learning.algorithm.perceptron.kernel | |
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.algorithm.root |
Provides algorithms for finding the roots, or zero crossings, of scalar functions.
|
| gov.sandia.cognition.learning.algorithm.svm |
Provides implementations of Support Vector Machine (SVM) learning algorithms.
|
| gov.sandia.cognition.learning.function.vector |
Provides functions that output vectors.
|
| gov.sandia.cognition.statistics.bayesian |
Provides algorithms for computing Bayesian estimates of parameters.
|
| gov.sandia.cognition.statistics.distribution |
Provides statistical distributions.
|
| gov.sandia.cognition.statistics.method |
Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SimulatedAnnealer<CostParametersType,AnnealedType>
The SimulatedAnnealer class implements the simulated annealing algorithm
using the provided cost function and perturbation function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AffinityPropagation<DataType>
The
AffinityPropagation algorithm requires three parameters:
a divergence function, a value to use for self-divergence, and a damping
factor (called lambda in the paper; 0.5 is the default). |
class |
DirichletProcessClustering
Clustering algorithm that wraps Dirichlet Process Mixture Model.
|
class |
KMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
The
KMeansClusterer class implements the standard k-means
(k-centroids) clustering algorithm. |
class |
KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>>
Creates a k-means clustering algorithm that removes clusters that do
not have sufficient membership to pass a simple statistical significance
test.
|
class |
MiniBatchKMeansClusterer<DataType extends Vector>
Approximates k-means clustering by working on random subsets of the
data.
|
class |
OptimizedKMeansClusterer<DataType>
This class implements an optimized version of the k-means algorithm that
makes use of the triangle inequality to compute the same answer as k-means
while using less distance calculations.
|
class |
ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
This is a parallel implementation of the k-means clustering algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFactorizationMachineLearner
An abstract class for learning
FactorizationMachines. |
class |
FactorizationMachineAlternatingLeastSquares
Implements an Alternating Least Squares (ALS) algorithm for learning a
Factorization Machine.
|
class |
FactorizationMachineStochasticGradient
Implements a Stochastic Gradient Descent (SGD) algorithm for learning a
Factorization Machine.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneticAlgorithm<CostParametersType,GenomeType>
The GeneticAlgorithm class implements a generic genetic algorithm
that uses a given cost function to minimize and a given reproduction
function for generating the population.
|
class |
ParallelizedGeneticAlgorithm<CostParametersType,GenomeType>
This is a parallel implementation of the genetic algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBaumWelchAlgorithm<ObservationType,DataType>
Partial implementation of the Baum-Welch algorithm.
|
class |
BaumWelchAlgorithm<ObservationType>
Implements the Baum-Welch algorithm, also known as the "forward-backward
algorithm", the expectation-maximization algorithm, etc for
Hidden Markov Models (HMMs).
|
class |
ParallelBaumWelchAlgorithm<ObservationType>
A Parallelized implementation of some of the methods of the
Baum-Welch Algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneralizedHebbianAlgorithm
Implementation of the Generalized Hebbian Algorithm, also known as
Sanger's Rule, which is a generalization of Oja's Rule.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BatchMultiPerceptron<CategoryType>
Implements a multi-class version of the standard batch Perceptron learning
algorithm.
|
class |
Perceptron
The
Perceptron class implements the standard Perceptron learning
algorithm that learns a binary classifier based on vector input. |
| Modifier and Type | Class and Description |
|---|---|
class |
KernelAdatron<InputType>
The
KernelAdatron class implements an online version of the Support
Vector Machine learning algorithm. |
class |
KernelPerceptron<InputType>
The
KernelPerceptron class implements the kernel version of
the Perceptron algorithm. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
ParameterCostMinimizer<ResultType extends VectorizableVectorFunction>
A anytime algorithm that is used to estimate the locally minimum-cost
parameters of an object.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>>
Partial implementation of ParameterCostMinimizer, based on the algorithms
from the minimization package.
|
class |
AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>>
Partial implementation of ParameterCostMinimizer.
|
class |
FletcherXuHybridEstimation
The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares
parameters.
|
class |
GaussNewtonAlgorithm
Implementation of the Gauss-Newton parameter-estimation procedure.
|
class |
KernelBasedIterativeRegression<InputType>
The
KernelBasedIterativeRegression class implements an online version of
the Support Vector Regression algorithm. |
class |
LeastSquaresEstimator
Abstract implementation of iterative least-squares estimators.
|
class |
LevenbergMarquardtEstimation
Implementation of the nonlinear regression algorithm, known as
Levenberg-Marquardt Estimation (or LMA).
|
class |
ParameterDerivativeFreeCostMinimizer
Implementation of a class of objects that uses a derivative-free
minimization algorithm.
|
class |
ParameterDifferentiableCostMinimizer
This class adapts the unconstrained nonlinear minimization algorithms in
the "minimization" package to the task of estimating locally optimal
(minimum-cost) parameter sets.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBracketedRootFinder
Partial implementation of RootFinder that maintains a bracket on the root.
|
class |
AbstractRootFinder
Partial implementation of RootFinder.
|
class |
RootFinderBisectionMethod
Bisection algorithm for root finding.
|
class |
RootFinderFalsePositionMethod
The false-position algorithm for root finding.
|
class |
RootFinderNewtonsMethod
Newton's method, sometimes called Newton-Raphson method, uses first-order
derivative information to iteratively locate a root.
|
class |
RootFinderRiddersMethod
The root-finding algorithm due to Ridders.
|
class |
RootFinderSecantMethod
The secant algorithm for root finding.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SequentialMinimalOptimization<InputType>
An implementation of the Sequential Minimal Optimization (SMO) algorithm for
training a Support Vector Machine (SVM), which is a kernel-based binary
categorizer.
|
class |
SuccessiveOverrelaxation<InputType>
The
SuccessiveOverrelaxation class implements the Successive
Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM). |
| Modifier and Type | Class and Description |
|---|---|
static class |
GaussianContextRecognizer.Learner
Creates a GaussianContextRecognizer from a Dataset[Vector] using
a BatchClusterer
|
| Modifier and Type | Class and Description |
|---|---|
class |
MetropolisHastingsAlgorithm<ObservationType,ParameterType>
An implementation of the Metropolis-Hastings MCMC algorithm, which is the
most general formulation of MCMC but can be slow.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
MixtureOfGaussians.EMLearner
An Expectation-Maximization based "soft" assignment learner.
|
static class |
MixtureOfGaussians.Learner
A hard-assignment learner for a MixtureOfGaussians
|
static class |
ScalarMixtureDensityModel.EMLearner
An EM learner that estimates a mixture model from data
|
| Modifier and Type | Class and Description |
|---|---|
class |
DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>>
A method of estimating the parameters of a distribution using an arbitrary
CostFunction and FunctionMinimizer algorithm.
|