| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.bayes |
Provides algorithms for computing Bayesian categorizers.
|
| gov.sandia.cognition.learning.algorithm.clustering.cluster |
Provides implementations of different types of clusters.
|
| gov.sandia.cognition.learning.algorithm.ensemble |
Provides ensemble methods.
|
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.algorithm.tree |
Provides decision tree learning algorithms.
|
| gov.sandia.cognition.learning.data.feature |
Provides data feature extractors.
|
| gov.sandia.cognition.learning.function.categorization |
Provides functions that output a discrete set of categories.
|
| gov.sandia.cognition.learning.function.vector |
Provides functions that output vectors.
|
| gov.sandia.cognition.statistics |
Provides the inheritance hierarchy for general statistical methods and distributions.
|
| 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.montecarlo |
Provides Monte Carlo procedures for numerical integration and sampling.
|
| gov.sandia.cognition.text.spelling |
Provides classes for spelling.
|
| Class and Description |
|---|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |
| UnivariateGaussian.PDF
PDF of the underlying Gaussian.
|
| Class and Description |
|---|
| MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
| Class and Description |
|---|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |
| Class and Description |
|---|
| LogisticDistribution.CDF
CDF of the LogisticDistribution
|
| Class and Description |
|---|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |
| Class and Description |
|---|
| MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
| UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
| Class and Description |
|---|
| BernoulliDistribution
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1-p".
|
| MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
| UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
| Class and Description |
|---|
| MixtureOfGaussians.PDF
PDF of the MixtureOfGaussians
|
| Class and Description |
|---|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |
| Class and Description |
|---|
| BetaDistribution
Computes the Beta-family of probability distributions.
|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |
| GammaDistribution
Class representing the Gamma distribution.
|
| InverseGammaDistribution
Defines an inverse-gamma distribution.
|
| MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
| MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
| MultivariateGaussianInverseGammaDistribution
A distribution where the mean is selected by a multivariate Gaussian and
a variance parameter (either for a univariate Gaussian or isotropic Gaussian)
is determined by an Inverse-Gamma distribution.
|
| MultivariateStudentTDistribution.PDF
PDF of the MultivariateStudentTDistribution
|
| StudentTDistribution
Defines a noncentral Student-t Distribution.
|
| UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
| UnivariateGaussian.PDF
PDF of the underlying Gaussian.
|
| Class and Description |
|---|
| BernoulliDistribution
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1-p".
|
| BetaBinomialDistribution
A Binomial distribution where the binomial parameter, p, is set according
to a Beta distribution instead of a single value.
|
| BetaDistribution
Computes the Beta-family of probability distributions.
|
| BinomialDistribution
Binomial distribution, which is a collection of Bernoulli trials
|
| DirichletDistribution
The Dirichlet distribution is the multivariate generalization of the beta
distribution.
|
| ExponentialDistribution
An Exponential distribution describes the time between events in a poisson
process, resulting in a memoryless distribution.
|
| GammaDistribution
Class representing the Gamma distribution.
|
| MultinomialDistribution
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.
|
| MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
| MultivariatePolyaDistribution
A multivariate Polya Distribution, also known as a Dirichlet-Multinomial
model, is a compound distribution where the parameters of a multinomial
are drawn from a Dirichlet distribution with fixed parameters and a constant
number of trials and then the observations are generated by this
multinomial.
|
| MultivariateStudentTDistribution
Multivariate generalization of the noncentral Student's t-distribution.
|
| NegativeBinomialDistribution
Negative binomial distribution, also known as the Polya distribution,
gives the number of successes of a series of Bernoulli trials before
recording a given number of failures.
|
| NormalInverseGammaDistribution
The normal inverse-gamma distribution is the product of a univariate
Gaussian distribution with an inverse-gamma distribution.
|
| NormalInverseWishartDistribution
The normal inverse Wishart distribution
|
| ParetoDistribution
This class describes the Pareto distribution, sometimes called the Bradford
Distribution.
|
| PoissonDistribution
A Poisson distribution is the limits of what happens when a Bernoulli trial
with "rare" events are sampled on a continuous basis and then binned into
discrete time intervals.
|
| StudentTDistribution
Defines a noncentral Student-t Distribution.
|
| UniformDistribution
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
| UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
| Class and Description |
|---|
| BernoulliDistribution
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1-p".
|
| BernoulliDistribution.CDF
CDF of a Bernoulli distribution.
|
| BernoulliDistribution.PMF
PMF of the Bernoulli distribution.
|
| BetaBinomialDistribution
A Binomial distribution where the binomial parameter, p, is set according
to a Beta distribution instead of a single value.
|
| BetaBinomialDistribution.CDF
CDF of BetaBinomialDistribution
|
| BetaBinomialDistribution.MomentMatchingEstimator
Estimates the parameters of a Beta-binomial distribution using the matching
of moments, not maximum likelihood.
|
| BetaBinomialDistribution.PMF
PMF of the BetaBinomialDistribution
|
| BetaDistribution
Computes the Beta-family of probability distributions.
|
| BetaDistribution.CDF
CDF of the Beta-family distribution
|
| BetaDistribution.MomentMatchingEstimator
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
|
| BetaDistribution.PDF
Beta distribution probability density function
|
| BinomialDistribution
Binomial distribution, which is a collection of Bernoulli trials
|
| BinomialDistribution.CDF
CDF of the Binomial distribution, which is the probability of getting
up to "x" successes in "N" trials with a Bernoulli probability of "p"
|
| BinomialDistribution.MaximumLikelihoodEstimator
Maximum likelihood estimator of the distribution
|
| BinomialDistribution.PMF
The Probability Mass Function of a binomial distribution.
|
| CategoricalDistribution
The Categorical Distribution is the multivariate generalization of the
Bernoulli distribution, where the outcome of an experiment is a one-of-N
output, where the output is a selector Vector.
|
| CategoricalDistribution.PMF
PMF of the Categorical Distribution
|
| CauchyDistribution
A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes
known as the Lorentz distribution.
|
| CauchyDistribution.CDF
CDF of the CauchyDistribution.
|
| CauchyDistribution.PDF
PDF of the CauchyDistribution.
|
| ChineseRestaurantProcess
A Chinese Restaurant Process is a discrete stochastic processes that
partitions data points to clusters.
|
| ChineseRestaurantProcess.PMF
PMF of the Chinese Restaurant Process
|
| ChiSquareDistribution
Describes a Chi-Square Distribution.
|
| ChiSquareDistribution.CDF
Cumulative Distribution Function (CDF) of a Chi-Square Distribution
|
| ChiSquareDistribution.PDF
PDF of the Chi-Square distribution
|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |
| DefaultDataDistribution.PMF
PMF of the DefaultDataDistribution
|
| DeterministicDistribution
A deterministic distribution that returns samples at a single point.
|
| DeterministicDistribution.CDF
CDF of the deterministic distribution.
|
| DeterministicDistribution.PMF
PMF of the deterministic distribution.
|
| DirichletDistribution
The Dirichlet distribution is the multivariate generalization of the beta
distribution.
|
| DirichletDistribution.PDF
PDF of the Dirichlet distribution.
|
| ExponentialDistribution
An Exponential distribution describes the time between events in a poisson
process, resulting in a memoryless distribution.
|
| ExponentialDistribution.CDF
CDF of the ExponentialDistribution.
|
| ExponentialDistribution.MaximumLikelihoodEstimator
Creates a ExponentialDistribution from data
|
| ExponentialDistribution.PDF
PDF of the ExponentialDistribution.
|
| GammaDistribution
Class representing the Gamma distribution.
|
| GammaDistribution.CDF
CDF of the Gamma distribution
|
| GammaDistribution.MomentMatchingEstimator
Computes the parameters of a Gamma distribution by the
Method of Moments
|
| GammaDistribution.PDF
Closed-form PDF of the Gamma distribution
|
| GeometricDistribution
The geometric distribution models the number of successes before the first
failure occurs under an independent succession of Bernoulli tests.
|
| GeometricDistribution.CDF
CDF of the Geometric distribution
|
| GeometricDistribution.MaximumLikelihoodEstimator
Maximum likelihood estimator of the distribution
|
| GeometricDistribution.PMF
PMF of the Geometric distribution
|
| InverseGammaDistribution
Defines an inverse-gamma distribution.
|
| InverseGammaDistribution.CDF
CDF of the inverseRootFinder-gamma distribution.
|
| InverseGammaDistribution.PDF
PDF of the inverseRootFinder-Gamma distribution.
|
| InverseWishartDistribution
The Inverse-Wishart distribution is the multivariate generalization of the
inverse-gamma distribution.
|
| InverseWishartDistribution.PDF
PDF of the Inverse-Wishart distribution, though I have absolutely no
idea why anybody would evaluate the PDF of an Inverse-Wishart...
|
| KolmogorovDistribution
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
| KolmogorovDistribution.CDF
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
| LaplaceDistribution
A Laplace distribution, sometimes called a double exponential distribution.
|
| LaplaceDistribution.CDF
CDF of the Laplace distribution.
|
| LaplaceDistribution.MaximumLikelihoodEstimator
Estimates the ML parameters of a Laplace distribution from a
Collection of Numbers.
|
| LaplaceDistribution.PDF
The PDF of a Laplace Distribution.
|
| LinearMixtureModel
A linear mixture of RandomVariables, with a prior probability distribution.
|
| LogisticDistribution
A implementation of the scalar logistic distribution, which measures the
log-odds of a binary event.
|
| LogisticDistribution.CDF
CDF of the LogisticDistribution
|
| LogisticDistribution.PDF
PDF of the LogisticDistribution
|
| LogNormalDistribution
Log-Normal distribution PDF and CDF implementations.
|
| LogNormalDistribution.CDF
CDF of the Log-Normal Distribution
|
| LogNormalDistribution.MaximumLikelihoodEstimator
Maximum Likelihood Estimator of a log-normal distribution.
|
| LogNormalDistribution.PDF
PDF of a Log-normal distribution
|
| MixtureOfGaussians.PDF
PDF of the MixtureOfGaussians
|
| MultinomialDistribution
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.
|
| MultinomialDistribution.Domain
Allows the iteration through the set of subsets.
|
| MultinomialDistribution.PMF
Probability Mass Function of the Multinomial Distribution.
|
| MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
| MultivariateGaussian.MaximumLikelihoodEstimator
Computes the Maximum Likelihood Estimate of the MultivariateGaussian
given a set of Vectors
|
| MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
| MultivariateGaussian.SufficientStatistic
Implements the sufficient statistics of the MultivariateGaussian.
|
| MultivariateGaussian.SufficientStatisticCovarianceInverse
Implements the sufficient statistics of the MultivariateGaussian while
estimating the inverse of the covariance matrix.
|
| MultivariateGaussian.WeightedMaximumLikelihoodEstimator
Computes the Weighted Maximum Likelihood Estimate of the
MultivariateGaussian given a weighted set of Vectors
|
| MultivariateGaussianInverseGammaDistribution
A distribution where the mean is selected by a multivariate Gaussian and
a variance parameter (either for a univariate Gaussian or isotropic Gaussian)
is determined by an Inverse-Gamma distribution.
|
| MultivariateMixtureDensityModel
A LinearMixtureModel of multivariate distributions with associated PDFs.
|
| MultivariateMixtureDensityModel.PDF
PDF of the MultivariateMixtureDensityModel
|
| MultivariatePolyaDistribution
A multivariate Polya Distribution, also known as a Dirichlet-Multinomial
model, is a compound distribution where the parameters of a multinomial
are drawn from a Dirichlet distribution with fixed parameters and a constant
number of trials and then the observations are generated by this
multinomial.
|
| MultivariatePolyaDistribution.PMF
PMF of the MultivariatePolyaDistribution
|
| MultivariateStudentTDistribution
Multivariate generalization of the noncentral Student's t-distribution.
|
| MultivariateStudentTDistribution.PDF
PDF of the MultivariateStudentTDistribution
|
| NegativeBinomialDistribution
Negative binomial distribution, also known as the Polya distribution,
gives the number of successes of a series of Bernoulli trials before
recording a given number of failures.
|
| NegativeBinomialDistribution.CDF
CDF of the NegativeBinomialDistribution
|
| NegativeBinomialDistribution.MaximumLikelihoodEstimator
Maximum likelihood estimator of the distribution
|
| NegativeBinomialDistribution.PMF
PMF of the NegativeBinomialDistribution.
|
| NormalInverseGammaDistribution
The normal inverse-gamma distribution is the product of a univariate
Gaussian distribution with an inverse-gamma distribution.
|
| NormalInverseGammaDistribution.PDF
PDF of the NormalInverseGammaDistribution
|
| NormalInverseWishartDistribution
The normal inverse Wishart distribution
|
| NormalInverseWishartDistribution.PDF
PDF of the normal inverse-Wishart distribution.
|
| ParetoDistribution
This class describes the Pareto distribution, sometimes called the Bradford
Distribution.
|
| ParetoDistribution.CDF
CDF of the Pareto Distribution.
|
| ParetoDistribution.PDF
PDF of the ParetoDistribution
|
| PoissonDistribution
A Poisson distribution is the limits of what happens when a Bernoulli trial
with "rare" events are sampled on a continuous basis and then binned into
discrete time intervals.
|
| PoissonDistribution.CDF
CDF of the PoissonDistribution
|
| PoissonDistribution.MaximumLikelihoodEstimator
Creates a PoissonDistribution from data
|
| PoissonDistribution.PMF
PMF of the PoissonDistribution.
|
| ScalarDataDistribution
A Data Distribution that uses Doubles as its keys, making it a univariate
distribution
|
| ScalarDataDistribution.CDF
CDF of the ScalarDataDistribution, maintains the keys/domain in
sorted order (TreeMap), so it's slower than it's peers.
|
| ScalarDataDistribution.Estimator
Estimator for a ScalarDataDistribution
|
| ScalarDataDistribution.PMF
PMF of the ScalarDataDistribution
|
| ScalarMixtureDensityModel
ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density
model.
|
| ScalarMixtureDensityModel.CDF
CDFof the SMDM
|
| ScalarMixtureDensityModel.PDF
PDF of the SMDM
|
| SnedecorFDistribution
CDF of the Snedecor F-distribution (also known as Fisher F-distribution,
Fisher-Snedecor F-distribution, or just plain old F-distribution).
|
| SnedecorFDistribution.CDF
CDF of the F-distribution.
|
| StudentizedRangeDistribution
Implementation of the Studentized Range distribution, which defines the
population correction factor when performing multiple comparisons.
|
| StudentizedRangeDistribution.CDF
CDF of the StudentizedRangeDistribution
|
| StudentTDistribution
Defines a noncentral Student-t Distribution.
|
| StudentTDistribution.CDF
Evaluator that computes the Cumulative Distribution Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
| StudentTDistribution.MaximumLikelihoodEstimator
Estimates the parameters of the Student-t distribution from the given
data, where the degrees of freedom are estimated from the Kurtosis
of the sample data.
|
| StudentTDistribution.PDF
Evaluator that computes the Probability Density Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
| UniformDistribution
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
| UniformDistribution.CDF
Cumulative Distribution Function of a uniform
|
| UniformDistribution.MaximumLikelihoodEstimator
Maximum Likelihood Estimator of a uniform distribution.
|
| UniformDistribution.PDF
Probability density function of a Uniform Distribution
|
| UniformIntegerDistribution
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
| UniformIntegerDistribution.CDF
Implements the cumulative distribution function for the discrete
uniform distribution.
|
| UniformIntegerDistribution.MaximumLikelihoodEstimator
Implements a maximum likelihood estimator for the discrete uniform
distribution.
|
| UniformIntegerDistribution.PMF
Probability mass function of a discrete uniform distribution.
|
| UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
| UnivariateGaussian.CDF
CDF of the underlying Gaussian.
|
| UnivariateGaussian.ErrorFunction
Gaussian Error Function, useful for computing the cumulative distribution
function for a Gaussian.
|
| UnivariateGaussian.ErrorFunction.Inverse
Inverse of the ErrorFunction
|
| UnivariateGaussian.MaximumLikelihoodEstimator
Creates a UnivariateGaussian from data
|
| UnivariateGaussian.PDF
PDF of the underlying Gaussian.
|
| UnivariateGaussian.SufficientStatistic
Captures the sufficient statistics of a UnivariateGaussian, which are
the values to estimate the mean and variance.
|
| WeibullDistribution
Describes a Weibull distribution, which is often used to describe the
mortality, lifespan, or size distribution of objects.
|
| WeibullDistribution.CDF
CDF of the Weibull distribution
|
| WeibullDistribution.PDF
PDF of the Weibull distribution
|
| YuleSimonDistribution
The Yule-Simon distribution is a model of preferential attachment, such as
a model of the number of groups follows a power-law distribution
(Zipf's Law).
|
| YuleSimonDistribution.CDF
CDF of the Yule-Simon Distribution
|
| YuleSimonDistribution.PMF
PMF of the Yule-Simon Distribution
|
| Class and Description |
|---|
| MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
| UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
| UnivariateGaussian.PDF
PDF of the underlying Gaussian.
|
| Class and Description |
|---|
| DefaultDataDistribution
A default implementation of
ScalarDataDistribution that uses a
backing map. |