| Package | Description |
|---|---|
| gov.sandia.cognition.hash |
Provides hash function implementations.
|
| gov.sandia.cognition.learning.algorithm.bayes |
Provides algorithms for computing Bayesian categorizers.
|
| gov.sandia.cognition.learning.algorithm.clustering |
Provides clustering algorithms.
|
| gov.sandia.cognition.learning.algorithm.delta |
Provides an abstract class for helping to implement variants of the Burrows'
Delta algorithm.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization |
Provides minimization algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization.line |
Provides line (scalar) minimization algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization.line.interpolator |
Provides line (scalar) interpolation/extrapolation algorithms that fit an
algebraic function to a (small) collection of data points.
|
| gov.sandia.cognition.learning.algorithm.pca |
Provides implementations of Principle Components Analysis (PCA).
|
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.algorithm.semisupervised.valence | |
| gov.sandia.cognition.math |
Provides classes for mathematical computation.
|
| gov.sandia.cognition.math.geometry |
Provides classes and interfaces for computational geometry.
|
| gov.sandia.cognition.math.matrix.custom |
Provides a custom linear algebra package implementation for both dense
and sparse classes.
|
| gov.sandia.cognition.math.signals |
Provides mathematical signal processing methods.
|
| gov.sandia.cognition.statistics.bayesian |
Provides algorithms for computing Bayesian estimates of parameters.
|
| gov.sandia.cognition.statistics.bayesian.conjugate |
Provides Bayesian estimation routines based on conjugate prior distribution
of parameters of specific conditional distributions.
|
| 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.
|
| gov.sandia.cognition.statistics.montecarlo |
Provides Monte Carlo procedures for numerical integration and sampling.
|
| gov.sandia.cognition.text.term.filter.stem |
Provides stemming algorithms for terms.
|
| gov.sandia.cognition.text.topic |
Provides topic modeling algorithms.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FNV1a32Hash
Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.
|
class |
FNV1a64Hash
Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.
|
class |
MD5Hash
Implementation of the MD5 128-bit (16-byte) cryptographic hash function.
|
class |
Murmur32Hash
Implementation of the MurmurHash2 32-bit (4-byte) non-cryptographic hash
function.
|
class |
SHA1Hash
A Java implementation of the Secure Hash Algorithm, SHA-1, as defined
in FIPS PUB 180-1.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DiscreteNaiveBayesCategorizer<InputType,CategoryType>
Implementation of a Naive Bayes Classifier for Discrete Data.
|
| Modifier and Type | Class and Description |
|---|---|
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. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDeltaCategorizer<CategoryType>
The Burrows Delta algorithm is primarily used for authorship attribution, but
can be used for other applications.
|
class |
BurrowsDeltaCategorizer<CategoryType>
The regular Burrows' Delta algorithm implementation.
|
class |
CosineDeltaCategorizer<CategoryType>
The Cosine Delta algorithm implementation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FactorizationMachine
Implements a Factorization Machine.
|
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 |
FunctionMinimizerBFGS
Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton
nonlinear minimization algorithm.
|
class |
FunctionMinimizerConjugateGradient
Conjugate gradient method is a class of algorithms for finding the
unconstrained local minimum of a nonlinear function.
|
class |
FunctionMinimizerDFP
Implementation of the Davidon-Fletcher-Powell (DFP) formula for a
Quasi-Newton minimization update.
|
class |
FunctionMinimizerDirectionSetPowell
Implementation of the derivative-free unconstrained nonlinear direction-set
minimization algorithm called "Powell's Method" by Numerical Recipes.
|
class |
FunctionMinimizerNelderMead
Implementation of the Downhill Simplex minimization algorithm, also known as
the Nelder-Mead method.
|
class |
FunctionMinimizerPolakRibiere
This is an implementation of the Polack-Ribiere conjugate gradient
minimization procedure.
|
class |
FunctionMinimizerQuasiNewton
This is an abstract implementation of the Quasi-Newton minimization method,
sometimes called "Variable-Metric methods."
This family of minimization algorithms uses first-order gradient information
to find a locally minimum to a scalar function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
WolfeConditions
The Wolfe conditions define a set of sufficient conditions for
"sufficient decrease" in inexact line search.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LineBracketInterpolatorGoldenSection
Interpolates between the two bound points of a LineBracket using the
golden-section step rule, if that step fails, then the interpolator uses
a linear (secant) interpolation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KernelPrincipalComponentsAnalysis<DataType>
An implementation of the Kernel Principal Components Analysis (KPCA)
algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FletcherXuHybridEstimation
The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares
parameters.
|
class |
LevenbergMarquardtEstimation
Implementation of the nonlinear regression algorithm, known as
Levenberg-Marquardt Estimation (or LMA).
|
class |
LinearRegression
Computes the least-squares regression for a LinearCombinationFunction
given a dataset.
|
class |
LogisticRegression
Performs Logistic Regression by means of the iterative reweighted least
squares (IRLS) algorithm, where the logistic function has an explicit bias
term, and a diagonal L2 regularization term.
|
class |
MultivariateLinearRegression
Performs multivariate regression with an explicit bias term, with optional
L2 regularization.
|
interface |
UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>>
A type of Regression algorithm that has a single dependent (output) variable
that we are trying to predict.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultipartiteValenceMatrix
This class implements a semi-supervised learning algorithm for spreading
"valence" across a multi-partite graph.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Semimetric<InputType>
A semimetric is a divergence function that takes inputs from the same
set (domain) and is positive definite and symmetric.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
MathUtil.logGammaFunction(double input)
Computes the logarithm of the Gamma function.
|
static double |
MathUtil.lowerIncompleteGammaFunction(double a,
double x)
Computes the Lower incomplete gamma function.
|
static double |
MathUtil.regularizedIncompleteBetaFunction(double a,
double b,
double x)
Computes the regularized incomplete Beta function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KDTree<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
Implementation of a kd-tree.
|
| Modifier and Type | Method and Description |
|---|---|
ComplexNumber |
DenseMatrix.logDeterminant() |
| Modifier and Type | Class and Description |
|---|---|
class |
LinearDynamicalSystem
A generic Linear Dynamical System of the form
x_n = A*x_(n-1) + B*u_n y_n = C*x_n, where x_(n-1) is the previous state, x_n is the current state, u_n is the current input, y_n is the current output, A is the system matrix, B is the input-gain matrix, and C is the output-selector matrix |
class |
PIDController
This class defines a Proportional-plus-Integral-plus-Derivative set-point
controller.
|
| Modifier and Type | Method and Description |
|---|---|
protected static ComplexNumber[] |
FourierTransform.cooleyTukeyFFT(java.util.ArrayList<ComplexNumber> data)
Computes the Cooley-Tukey Radix-2 Fast Fourier Transform (FFT).
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>
A type of estimation procedure based on Bayes's rule, which allows us
to estimate the uncertainty of parameters given a set of observations
that we are given.
|
class |
BayesianLinearRegression
Computes a Bayesian linear estimator for a given feature function
and a set of observed data.
|
interface |
BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>>
A type of regression algorithm maps a Vector space, and the
weights of this Vector space are represented as a posterior distribution
given the observed InputOutputPairs.
|
class |
BayesianRobustLinearRegression
Computes a Bayesian linear estimator for a given feature function given
a set of InputOutputPair observed values.
|
class |
DirichletProcessMixtureModel<ObservationType>
An implementation of Dirichlet Process clustering, which estimates the
number of clusters and the centroids of the clusters from a set of
data.
|
class |
GaussianProcessRegression<InputType>
Gaussian Process Regression, is also known as Kriging, is a nonparametric
method to interpolate and extrapolate using Bayesian regression, where
the expressiveness of the estimator can grow with the data.
|
class |
KalmanFilter
A Kalman filter estimates the state of a dynamical system corrupted with
white Gaussian noise with observations that are corrupted with white
Gaussian noise.
|
interface |
MarkovChainMonteCarlo<ObservationType,ParameterType>
Defines the functionality of a Markov chain Monte Carlo algorithm.
|
interface |
ParticleFilter<ObservationType,ParameterType>
A particle filter aims to estimate a sequence of hidden parameters
based on observed data using point-mass estimates of the posterior
distribution.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>>
A Bayesian Estimator that makes use of conjugate priors, which is a
mathematical trick when the conditional and the prior result a posterior
that is the same type as the prior.
|
class |
ExponentialBayesianEstimator
Conjugate prior Bayesian estimator of the "rate" parameter of an
Exponential distribution using the conjugate prior Gamma distribution.
|
class |
MultivariateGaussianMeanCovarianceBayesianEstimator
Performs robust estimation of both the mean and covariance of a
MultivariateGaussian conditional distribution using the conjugate prior
Normal-Inverse-Wishart distribution.
|
class |
UnivariateGaussianMeanVarianceBayesianEstimator
Computes the mean and variance of a univariate Gaussian using the
conjugate prior NormalInverseGammaDistribution
|
| Modifier and Type | Class and Description |
|---|---|
class |
ChineseRestaurantProcess
A Chinese Restaurant Process is a discrete stochastic processes that
partitions data points to clusters.
|
class |
InverseWishartDistribution
The Inverse-Wishart distribution is the multivariate generalization of the
inverse-gamma distribution.
|
class |
MultivariateStudentTDistribution
Multivariate generalization of the noncentral Student's t-distribution.
|
class |
NormalInverseGammaDistribution
The normal inverse-gamma distribution is the product of a univariate
Gaussian distribution with an inverse-gamma distribution.
|
class |
StudentizedRangeDistribution
Implementation of the Studentized Range distribution, which defines the
population correction factor when performing multiple comparisons.
|
class |
StudentTDistribution
Defines a noncentral Student-t Distribution.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ConvexReceiverOperatingCharacteristic
Computes the convex hull of the Receiver Operating Characteristic (ROC),
which a mathematician might call a "concave down" function.
|
interface |
MultipleHypothesisComparison<TreatmentData>
Describes the functionality of an algorithm for accepting or rejecting
multiple null hypothesis at the same time.
|
class |
ShafferStaticCorrection
The Shaffer Static Correction uses logical relationships to tighten up the
Bonferroni/Sidak corrections when performing pairwise multiple hypothesis
comparisons.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
MonteCarloIntegrator<OutputType>
Monte Carlo integration is a way of compute the integral of a function using
samples from another.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PorterEnglishStemmingFilter
A term filter that uses the Porter Stemming algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LatentDirichletAllocationVectorGibbsSampler
A Gibbs sampler for performing Latent Dirichlet Allocation (LDA).
|
class |
LatentSemanticAnalysis
Implements the Latent Semantic Analysis (LSA) algorithm using Singular Value
Decomposition (SVD).
|
class |
ProbabilisticLatentSemanticAnalysis
An implementation of the Probabilistic Latent Semantic Analysis (PLSA)
algorithm.
|