| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.bayes |
Provides algorithms for computing Bayesian categorizers.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.perceptron |
Provides the Perceptron algorithm and some of its variations.
|
| 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.scalar |
Provides functions that output real numbers.
|
| gov.sandia.cognition.learning.function.vector |
Provides functions that output vectors.
|
| gov.sandia.cognition.math.signals |
Provides mathematical signal processing methods.
|
| gov.sandia.cognition.statistics.distribution |
Provides statistical distributions.
|
| gov.sandia.cognition.text.topic |
Provides topic modeling algorithms.
|
| Modifier and Type | Class and Description |
|---|---|
class |
VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
A naive Bayesian categorizer that takes an input vector and applies an
independent scalar probability density function to each one.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FactorizationMachine
Implements a Factorization Machine.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
OnlineShiftingPerceptron.LinearResult
This is the result learned by the shifting perceptron.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultivariateDecorrelator
Decorrelates a data using a mean and full or diagonal covariance matrix.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ConfidenceWeightedBinaryCategorizer
Interface for a confidence-weighted binary categorizer, which defines a
distribution over linear binary categorizers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractConfidenceWeightedBinaryCategorizer
Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
|
class |
DefaultConfidenceWeightedBinaryCategorizer
A default implementation of the
ConfidenceWeightedBinaryCategorizer
that stores a full mean and covariance matrix. |
class |
DiagonalConfidenceWeightedBinaryCategorizer
A confidence-weighted linear predictor with a diagonal covariance,
which is stored as a vector.
|
class |
LinearBinaryCategorizer
The
LinearBinaryCategorizer class implements a binary
categorizer that is implemented by a linear function. |
class |
LinearMultiCategorizer<CategoryType>
A multi-category version of the LinearBinaryCategorizer that keeps a separate
LinearBinaryCategorizer for each category.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearDiscriminant
LinearDiscriminant takes the dot product between the weight Vector and
the input Vector.
|
class |
LinearDiscriminantWithBias
A LinearDiscriminant with an additional bias term that gets added to the
output of the dot product.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DifferentiableGeneralizedLinearModel
A GradientDescenable version of a GeneralizedLinearModel, in
other words, a GeneralizedLinearModel where the squashing
function is differentiable
|
class |
GaussianContextRecognizer
Uses a MixtureOfGaussians to compute the probability of the different
constituent MultivariateGaussians (that is, the contexts)
|
class |
GeneralizedLinearModel
A VectorizableVectorFunction that is a matrix multiply followed by a
VectorFunction...
|
class |
MultivariateDiscriminant
Allows learning algorithms (vectorizing, differentiating) on a matrix*vector
multiply.
|
class |
MultivariateDiscriminantWithBias
A multivariate discriminant (matrix multiply) plus a constant vector
that gets added to the output of the discriminant.
|
class |
SubVectorEvaluator
Extracts the given set of indices from an input vector to create a new
vector containing the input vector's elements at those indices.
|
class |
ThreeLayerFeedforwardNeuralNetwork
This is a "standard" feedforward neural network with a single hidden
layer.
|
| 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 |
| Modifier and Type | Class and Description |
|---|---|
static class |
CategoricalDistribution.PMF
PMF of the Categorical Distribution
|
static class |
DirichletDistribution.PDF
PDF of the Dirichlet distribution.
|
static class |
MultinomialDistribution.PMF
Probability Mass Function of the Multinomial Distribution.
|
static class |
MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
static class |
MultivariatePolyaDistribution.PMF
PMF of the MultivariatePolyaDistribution
|
static class |
MultivariateStudentTDistribution.PDF
PDF of the MultivariateStudentTDistribution
|
static class |
NormalInverseGammaDistribution.PDF
PDF of the NormalInverseGammaDistribution
|
| Modifier and Type | Class and Description |
|---|---|
static class |
LatentSemanticAnalysis.Transform
The result from doing latent semantic analysis (LSA).
|
static class |
ProbabilisticLatentSemanticAnalysis.Result
The dimensionality transform created by probabilistic latent semantic
analysis.
|