| Package | Description |
|---|---|
| gov.sandia.cognition.algorithm |
Provides general interfaces and implementations for algorithms.
|
| gov.sandia.cognition.collection |
Provides commonly useful collection implementations.
|
| gov.sandia.cognition.graph | |
| gov.sandia.cognition.graph.community | |
| gov.sandia.cognition.graph.inference | |
| gov.sandia.cognition.hash |
Provides hash function implementations.
|
| gov.sandia.cognition.io |
Provides general classes for assisting with input/output operations.
|
| gov.sandia.cognition.learning.algorithm.clustering |
Provides clustering algorithms.
|
| gov.sandia.cognition.learning.algorithm.clustering.cluster |
Provides implementations of different types of clusters.
|
| gov.sandia.cognition.learning.algorithm.clustering.initializer |
Provides implementations of methods for selecting initial clusters.
|
| gov.sandia.cognition.learning.algorithm.confidence |
Provides confidence-weighted categorization algorithms.
|
| gov.sandia.cognition.learning.algorithm.ensemble |
Provides ensemble methods.
|
| gov.sandia.cognition.learning.algorithm.gradient |
Provides gradient based learning algorithms.
|
| gov.sandia.cognition.learning.algorithm.hmm |
Provides hidden Markov model (HMM) 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.matrix |
Provides matrix solving algorithms.
|
| gov.sandia.cognition.learning.algorithm.nearest |
Provides algorithms for Nearest-Neighbor memory-based functions.
|
| 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.algorithm.tree |
Provides decision tree learning algorithms.
|
| gov.sandia.cognition.learning.data.feature |
Provides data feature extractors.
|
| gov.sandia.cognition.learning.experiment |
Provides experiments for validating the performance of learning algorithms.
|
| gov.sandia.cognition.learning.function.categorization |
Provides functions that output a discrete set of categories.
|
| gov.sandia.cognition.learning.function.cost |
Provides cost functions.
|
| gov.sandia.cognition.learning.function.distance |
Provides distance functions.
|
| 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 |
Provides classes for mathematical computation.
|
| gov.sandia.cognition.math.geometry |
Provides classes and interfaces for computational geometry.
|
| gov.sandia.cognition.math.matrix |
Provides interfaces and classes for linear algebra.
|
| gov.sandia.cognition.math.matrix.custom |
Provides a custom linear algebra package implementation for both dense
and sparse classes.
|
| gov.sandia.cognition.math.matrix.decomposition |
Provides matrix decompositions.
|
| gov.sandia.cognition.math.matrix.mtj |
Provides a linear algebra package implementation wrapper using the Matrix
Toolkits for Java (MTJ) library.
|
| gov.sandia.cognition.math.signals |
Provides mathematical signal processing methods.
|
| 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.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.evaluation |
Provides methods for evaluating text processing algorithms.
|
| gov.sandia.cognition.text.spelling |
Provides classes for spelling.
|
| gov.sandia.cognition.text.term.filter |
Provides classes for filtering and transforming terms.
|
| gov.sandia.cognition.text.term.vector.weighter |
Provides term weighting algorithms.
|
| gov.sandia.cognition.text.term.vector.weighter.global |
Provides global term weighting algorithms.
|
| gov.sandia.cognition.text.term.vector.weighter.local |
Provides local term weighting algorithms.
|
| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
AnytimeAlgorithm<ResultType>
The
AnytimeAlgorithm interface defines the functionality of an
iterative algorithm that is stoppable and can return intermediate results. |
| Modifier and Type | Method and Description |
|---|---|
static <ComparableType> |
CollectionUtil.findKthLargest(int k,
java.util.ArrayList<? extends ComparableType> data,
java.util.Comparator<? super ComparableType> comparator)
Returns the set of indices of the data array such that
data[return[0..k-1]] ≤ data[return[k]] ≤ data[return[k+1...N-1]].
|
void |
IntArrayList.randomizeOrder()
Takes the elements of this and alters their order to a random order
(using a variant of Fisher-Yates shuffle called Durstenfeld shuffle).
|
| Modifier and Type | Method and Description |
|---|---|
int[] |
GraphMetrics.computeAllDistancesForNode(int nodeId)
Helper which computes Dijkstra's Algorithm for the input node and returns
all of the distances to all other nodes from this node.
|
void |
GraphMetrics.initializeDegreeAssortativity()
Initialize the degree assortativity for the whole graph.
|
void |
GraphMetrics.initializeNodeTriangles()
Initializes the datastructure for all triangles that all nodes and edges
participate in.
|
void |
GraphMetrics.initializePerEdgeJaccardSimilarity()
Initializes the Jaccard similarity for each edge in the graph.
|
void |
GraphMetrics.initializePerNodeBetweennessCentrality()
Initializes the per-node eccentricity.
|
void |
GraphMetrics.initializePerNodeEccentricity()
Initializes the per-node eccentricity.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CommunityComparisons<NodeNameType>
This class implements various comparisons of partitionings of a graph (or the
resulting communities)
|
class |
Louvain<NodeNameType>
This class performs community detection using the Louvain method.
|
class |
Permanence<NodeNameType> |
class |
PersonalizedPageRank<NodeNameType>
This class can compute PersonalizedPageRank for the input graph and a
specified node, and also can determine a community for any specified node.
|
| Modifier and Type | Method and Description |
|---|---|
static <NodeNameType> |
CommunityMetrics.computeConductance(DirectedNodeEdgeGraph<NodeNameType> graph,
java.util.Set<NodeNameType> community) |
static <NodeNameType> |
CommunityMetrics.computeModularity(NodePartitioning<NodeNameType> communities,
GraphMetrics<NodeNameType> graphMetrics) |
static <NodeNameType> |
CommunityMetrics.computeOneNodePermanence(GraphMetrics<NodeNameType> metrics,
NodePartitioning<NodeNameType> partitions,
NodeNameType node,
DirectedNodeEdgeGraph<NodeNameType> graph) |
| Modifier and Type | Class and Description |
|---|---|
class |
SumProductBeliefPropagation<LabelType>
This class implements the sum-product belief propagation algorithm for
arbitrary energy functions.
|
class |
SumProductDirectedPropagation<LabelType>
This class implements a Bayes Net -- but only allowing for pairwise influence
along edges.
|
class |
SumProductInferencingAlgorithm<LabelType>
Base class for Sum-Product inferencing algorithms on graphs/energy functions
|
| Modifier and Type | Class and Description |
|---|---|
class |
Eva32Hash
This implements the 32-bit "evahash" due to Robert Jenkins.
|
class |
Eva64Hash
This implements the 64-bit "evahash" due to Robert Jenkins.
|
class |
SHA256Hash
The SHA-2, 256-bit (32 byte) hash function.
|
class |
SHA512Hash
The SHA-2 512-bit (64-byte) hash function.
|
| Modifier and Type | Method and Description |
|---|---|
static boolean |
HashFunctionUtil.isPrime(long num)
Determines if a number is prime or not
|
static long |
HashFunctionUtil.nextPrime(long num)
Returns the next-greater prime, sort of like "ceil" but for primes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XStreamSerializationHandler
Reads and writes Objects in XML format.
|
| 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 |
DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>>
The
DBSCAN algorithm requires three parameters: a distance
metric, a value for neighborhood radius, and a value for the minimum number
of surrounding neighbors for a point to be considered non-noise. |
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.
|
class |
PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>>
The
PartitionalClusterer implements a partitional clustering
algorithm, which is a type of hierarchical clustering algorithm. |
| Modifier and Type | Class and Description |
|---|---|
class |
VectorMeanMiniBatchCentroidClusterCreator
Implementation of
VectorMeanCentroidClusterCreator for mini-batch
clustering. |
| Modifier and Type | Class and Description |
|---|---|
class |
DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
Implements
FixedClusterInitializer that initializes clusters by
first selecting a random point for the first cluster and then randomly
sampling each successive cluster based on the squared minimum distance from
the point to the existing selected clusters. |
| Modifier and Type | Class and Description |
|---|---|
class |
AdaptiveRegularizationOfWeights
An implementation of the Adaptive Regularization of Weights (AROW) algorithm
for online learning of a linear binary categorizer.
|
class |
ConfidenceWeightedDiagonalDeviation
An implementation of the Standard Deviation (Stdev) algorithm for learning
a confidence-weighted categorizer.
|
class |
ConfidenceWeightedDiagonalDeviationProject
An implementation of the Standard Deviation (Stdev) algorithm for learning
a confidence-weighted categorizer.
|
class |
ConfidenceWeightedDiagonalVariance
An implementation of the Variance algorithm for learning a confidence-weighted
linear categorizer.
|
class |
ConfidenceWeightedDiagonalVarianceProject
An implementation of the Variance algorithm for learning a confidence-weighted
linear categorizer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>>
Learns an ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
AdaBoost<InputType>
The
AdaBoost class implements the Adaptive Boosting (AdaBoost)
algorithm formulated by Yoav Freund and Robert Shapire. |
class |
BaggingCategorizerLearner<InputType,CategoryType>
Learns an categorization ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
BaggingRegressionLearner<InputType>
Learns an ensemble for regression by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
IVotingCategorizerLearner<InputType,CategoryType>
Learns an ensemble in a method similar to bagging except that on each
iteration the bag is built from two parts, each sampled from elements from
disjoint sets.
|
class |
MultiCategoryAdaBoost<InputType,CategoryType>
An implementation of a multi-class version of the Adaptive Boosting
(AdaBoost) algorithm, known as AdaBoost.M1.
|
class |
OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
An implementation of an online version of the Bagging algorithm for learning
an ensemble of categorizers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GradientDescendableApproximator
Creates a
radientDescendable from a
VectorizableVectorFunction by estimating the parameter gradient
using a forward-difference approximation of the parameter Jacobian. |
| Modifier and Type | Class and Description |
|---|---|
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 |
HiddenMarkovModel<ObservationType>
A discrete-state Hidden Markov Model (HMM) with either continuous
or discrete observations.
|
class |
MarkovChain
A Markov chain is a random process that has a finite number of states with
random transition probabilities between states at discrete time steps.
|
class |
ParallelBaumWelchAlgorithm<ObservationType>
A Parallelized implementation of some of the methods of the
Baum-Welch Algorithm.
|
class |
ParallelHiddenMarkovModel<ObservationType>
A Hidden Markov Model with parallelized processing.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<java.lang.Integer> |
HiddenMarkovModel.viterbi(java.util.Collection<? extends ObservationType> observations)
Viterbi algorithm for decoding the most-likely sequence of states
from the HMMs underlying Markov chain for a given observation sequence.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FunctionMinimizerFletcherReeves
This is an implementation of the Fletcher-Reeves conjugate gradient
minimization procedure.
|
class |
FunctionMinimizerLiuStorey
This is an implementation of the Liu-Storey conjugate gradient
minimization procedure.
|
| Modifier and Type | Method and Description |
|---|---|
static boolean |
FunctionMinimizerBFGS.BFGSupdateRule(Matrix hessianInverse,
Vector delta,
Vector gamma,
double tolerance)
BFGS Quasi-Newton update rule
|
protected boolean |
FunctionMinimizerDFP.updateHessianInverse(Matrix hessianInverse,
Vector delta,
Vector gamma) |
| Modifier and Type | Class and Description |
|---|---|
class |
LineMinimizerBacktracking
Implementation of the backtracking line-minimization algorithm.
|
class |
LineMinimizerDerivativeBased
This is an implementation of a line-minimization algorithm proposed by
Fletcher that makes extensive use of first-order derivative information.
|
class |
LineMinimizerDerivativeFree
This is an implementation of a LineMinimizer that does not require
derivative information.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
LineMinimizerDerivativeFree.bracketingStep()
Here's the general idea of derivative-free minimum bracketing:
Given an initial point, a={x,f(x)}, we're looking to find a triplet of points {a,b,c} such that bx is between ax and cx. |
static boolean |
WolfeConditions.evaluateGoldsteinCondition(InputOutputSlopeTriplet originalPoint,
InputOutputPair<java.lang.Double,java.lang.Double> trialPoint,
double slopeCondition)
Evaluates the Goldstein (Armijo) conditions, which is purely a
sufficient decrease condition.
|
static boolean |
WolfeConditions.evaluateStrictCurvatureCondition(double originalSlope,
double trialSlope,
double curvatureCondition)
Evaluates the strict curvature condition.
|
boolean |
LineMinimizerDerivativeFree.sectioningStep() |
| Modifier and Type | Class and Description |
|---|---|
class |
ConjugateGradientMatrixSolver
Implements a matrix solver using Conjugate Gradient.
|
class |
ConjugateGradientWithPreconditionerMatrixSolver
Implements a matrix solver using Conjugate Gradient with a preconditioner.
|
class |
IterativeMatrixSolver<Operator extends MatrixVectorMultiplier>
Base class for all iterative matrix solvers that takes care of most of the
basic iterative logic and the function minimizer interface.
|
class |
MatrixVectorMultiplier
The necessary multiplication wrapper class to match the FunctionMinimizer
interface.
|
class |
MatrixVectorMultiplierDiagonalPreconditioner
Implements a diagonal preconditioner for a matrix-vector multiplier.
|
class |
MatrixVectorMultiplierWithPreconditioner
As various preconditioner operations could be created, this class defines the
interface that must be followed by them.
|
class |
OverconstrainedConjugateGradientMatrixMinimizer
Implements a overconstrained conjugate gradient matrix optimizer.
|
class |
OverconstrainedMatrixVectorMultiplier
Implements an overconstrainted matrix-vector multiplication.
|
class |
SteepestDescentMatrixSolver
Implements a basic Steepest Descent iterative solver for linear systems of
equations.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
KNearestNeighbor<InputType,OutputType>
A generic k-nearest-neighbor classifier.
|
class |
KNearestNeighborExhaustive<InputType,OutputType>
A generic k-nearest-neighbor classifier.
|
class |
KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the k-nearest neighbor algorithm.
|
interface |
NearestNeighbor<InputType,OutputType>
The
NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input. |
class |
NearestNeighborExhaustive<InputType,OutputType>
The
NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input. |
| 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.
|
interface |
PrincipalComponentsAnalysis
Principal Components Analysis is a family of algorithms that map from a
high-dimensional input space to a low-dimensional output space.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AggressiveRelaxedOnlineMaximumMarginAlgorithm
An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm
(AROMMA).
|
class |
Ballseptron
An implementation of the Ballseptron algorithm.
|
class |
BatchMultiPerceptron<CategoryType>
Implements a multi-class version of the standard batch Perceptron learning
algorithm.
|
class |
OnlineBinaryMarginInfusedRelaxedAlgorithm
An implementation of the binary MIRA algorithm.
|
static class |
OnlineMultiPerceptron.ProportionalUpdate<CategoryType>
Variant of a multi-category Perceptron that performs a proportional
weight update on all categories that are scored higher than the true
category such that the weights sum to 1.0 and are proportional how much
larger the score was for each incorrect category than the true category.
|
static class |
OnlineMultiPerceptron.UniformUpdate<CategoryType>
Variant of a multi-category Perceptron that performs a uniform weight
update on all categories that are scored higher than the true category
such that the weights are equal and sum to -1.
|
class |
OnlinePassiveAggressivePerceptron
An implementation of the Passive-Aggressive algorithm for learning a linear
binary categorizer.
|
class |
OnlinePerceptron
An online version of the classic Perceptron algorithm.
|
class |
OnlineRampPassiveAggressivePerceptron
An implementation of the Ramp Loss Passive Aggressive Perceptron (PA^R) from
the referenced paper.
|
class |
OnlineShiftingPerceptron
An implementation of the Shifting Perceptron algorithm.
|
class |
OnlineVotedPerceptron
An online version of the Voted-Perceptron algorithm.
|
class |
Perceptron
The
Perceptron class implements the standard Perceptron learning
algorithm that learns a binary classifier based on vector input. |
class |
RelaxedOnlineMaximumMarginAlgorithm
An implementation of the Relaxed Online Maximum Margin Algorithm
(ROMMA).
|
class |
Winnow
An implementation of the Winnow incremental learning algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Forgetron<InputType>
An implementation of the "self-tuned" Forgetron algorithm, which is an online
budgeted kernel binary categorizer learner.
|
static class |
Forgetron.Basic<InputType>
An implementation of the "basic" Forgetron algorithm, which is an online
budgeted kernel binary categorizer learner.
|
static class |
Forgetron.Greedy<InputType>
An implementation of the "greedy" Forgetron algorithm, which is an online
budgeted kernel binary categorizer learner.
|
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. |
class |
OnlineKernelRandomizedBudgetPerceptron<InputType>
An implementation of a fixed-memory kernel Perceptron algorithm.
|
class |
Projectron<InputType>
An implementation of the Projectron algorithm, which is an online kernel
binary categorizer learner that has a budget parameter tuned by the eta
parameter.
|
static class |
Projectron.LinearSoftMargin<InputType>
An implementation of the Projectron++ algorithm, which is an online
kernel binary categorizer learner that has a budget parameter tuned by
the eta parameter.
|
class |
Stoptron<InputType>
An online, budgeted, kernel version of the Perceptron algorithm that stops
learning once it has reached its budget.
|
| Modifier and Type | Class and Description |
|---|---|
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 |
LinearBasisRegression<InputType>
Computes the least-squares regression for a LinearCombinationFunction
given a dataset.
|
class |
LocallyWeightedFunction<InputType,OutputType>
LocallyWeightedFunction is a generalization of the k-nearest neighbor
concept, also known as "Instance-Based Learning", "Memory-Based Learning",
"Nonparametric Regression", "Case-Based Regression", or
"Kernel-Based Regression".
|
interface |
MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>>
A regression algorithm that maps one or more independent (input) variables
onto multiple output variables.
|
interface |
Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
A supervised learning algorithm that attempts to interpolate/extrapolate
inputs given a training set of input/output pairs.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
RootFinder
Defines the functionality of a root-finding algorithm.
|
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 |
PrimalEstimatedSubGradient
An implementation of the Primal Estimated Sub-Gradient Solver (PEGASOS)
algorithm for learning a linear support vector machine (SVM).
|
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 |
|---|---|
class |
RandomForestFactory
A factory class for creating Random Forest learners.
|
class |
VectorThresholdGiniImpurityLearner<OutputType>
Learns vector thresholds based on the Gini impurity measure.
|
class |
VectorThresholdHellingerDistanceLearner<OutputType>
A categorization tree decision function learner on vector data that learns a
vector value threshold function using the Hellinger distance.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FeatureHashing
Implements a function that applies vector feature hashing.
|
class |
RandomSubspace
Selects a random subspace from the given vector, which is a random set of
indices.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LearnerValidationExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType>
The
LearnerValidationExperiment class implements an experiment where
a supervised machine learning algorithm is evaluated by applying it to a set
of folds created from a given set of data. |
| Modifier and Type | Class and Description |
|---|---|
class |
FisherLinearDiscriminantBinaryCategorizer
A Fisher Linear Discriminant classifier, which creates an optimal linear
separating plane between two Gaussian classes of different covariances.
|
class |
MaximumAPosterioriCategorizer<ObservationType,CategoryType>
Categorizer that returns the category with the highest posterior likelihood
for a given observation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
Computes the objective measure for a clustering algorithm, based on the
internal "distortion" of each cluster.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ChebyshevDistanceMetric
An implementation of the Chebyshev distance, which is the absolute value of
the largest difference between two vectors in a single dimension.
|
class |
MinkowskiDistanceMetric
An implementation of the Minkowski distance metric.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LeakyRectifiedLinearFunction
A leaky rectified linear unit.
|
class |
RectifiedLinearFunction
A rectified linear unit, which is the maximum of its input or 0.
|
class |
SigmoidFunction
An implementation of a sigmoid squashing function.
|
class |
SoftPlusFunction
A smoothed approximation for rectified linear unit.
|
| Modifier and Type | Method and Description |
|---|---|
static java.lang.Double[] |
PolynomialFunction.Quadratic.roots(double q0,
double q1,
double q2)
Finds the roots of the quadratic equation using the quadratic
formula.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneralizedLinearModel
A VectorizableVectorFunction that is a matrix multiply followed by a
VectorFunction...
|
class |
ThreeLayerFeedforwardNeuralNetwork
This is a "standard" feedforward neural network with a single hidden
layer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ComplexNumber
Represents a complex number in a rectangular manner, explicitly storing
the real and imaginary portions: real + j*imaginary
|
interface |
EuclideanRing<RingType extends EuclideanRing<RingType>>
Defines something similar to a Euclidean ring from abstract algebra.
|
interface |
Field<FieldType extends Field<FieldType>>
Defines something similar to a mathematical field.
|
class |
LentzMethod
This class implements Lentz's method for evaluating continued fractions.
|
interface |
Metric<EvaluatedType>
A metric is a non-negative function that satisfies the following properties
g(x, y) + g(y, z) >= g(x, z)
g(x, y) == g(y, x)
g(x, x) == 0.
|
interface |
Ring<RingType extends Ring<RingType>>
Defines something similar to a mathematical ring.
|
| Modifier and Type | Method and Description |
|---|---|
static int |
MathUtil.binomialCoefficient(int N,
int k)
Returns the binomial coefficient for "N choose k".
|
static int |
MathUtil.checkedAdd(int a,
int b)
Safely checks for underflow/overflow before adding two integers.
|
static int |
MathUtil.checkedMultiply(int a,
int b)
Safely checks for overflow before multiplying two integers.
|
static double |
UnivariateStatisticsUtil.computeCorrelation(java.util.Collection<? extends java.lang.Number> data1,
java.util.Collection<? extends java.lang.Number> data2)
Computes the correlation coefficient in a single pass.
|
static double |
UnivariateStatisticsUtil.computeEntropy(java.lang.Iterable<? extends java.lang.Number> data)
Computes the information-theoretic entropy of the PMF in bits (base 2).
|
static double |
UnivariateStatisticsUtil.computeKurtosis(java.util.Collection<? extends java.lang.Number> data)
Computes the biased excess kurtosis of the given dataset.
|
static double |
UnivariateStatisticsUtil.computeMean(java.lang.Iterable<? extends java.lang.Number> data)
Computes the arithmetic mean (average, expectation, first central moment)
of a dataset
|
static Pair<java.lang.Double,java.lang.Double> |
UnivariateStatisticsUtil.computeMeanAndVariance(java.lang.Iterable<? extends java.lang.Number> data)
Computes the mean and unbiased variance of a Collection of data using
the one-pass approach.
|
static double |
UnivariateStatisticsUtil.computeSkewness(java.util.Collection<? extends java.lang.Number> data)
Computes the unbiased skewness of the dataset.
|
static double |
UnivariateStatisticsUtil.computeWeightedKurtosis(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data)
Computes the biased excess kurtosis of the given dataset.
|
static double |
UnivariateStatisticsUtil.computeWeightedMean(java.lang.Iterable<? extends WeightedValue<? extends java.lang.Number>> data)
Computes the arithmetic mean (average, expectation, first central moment)
of a dataset.
|
static Pair<java.lang.Double,java.lang.Double> |
UnivariateStatisticsUtil.computeWeightedMeanAndVariance(java.lang.Iterable<? extends WeightedValue<? extends java.lang.Number>> data)
Computes the mean and unbiased variance of a Collection of data using
the one-pass approach.
|
protected static double |
MathUtil.incompleteBetaContinuedFraction(double a,
double b,
double x)
Evaluates the continued fraction of the incomplete beta function.
|
static double |
MathUtil.incompleteGammaContinuedFraction(double a,
double x)
Returns the incomplete Gamma function using the continued
fraction expansion evaluation using Lentz's method
|
protected static double |
MathUtil.incompleteGammaSeriesExpansion(double a,
double x)
Computes the series expansion approximation to the incomplete
gamma function.
|
static double |
MathUtil.log1MinusExp(double x)
Computes log(1 - exp(x)).
|
static double |
MathUtil.log1PlusExp(double x)
Computes log(1 + exp(x)).
|
static double |
MathUtil.logBetaFunction(double a,
double b)
Compute the natural logarithm of the Beta Function.
|
static double |
MathUtil.logMultinomialBetaFunction(Vector input)
Evaluates the natural logarithm of the multinomial beta function
for the given input vector.
|
| Modifier and Type | Class and Description |
|---|---|
protected static class |
KDTree.InOrderKDTreeIterator<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
Iterates through the KDTree using "inorder", also known as "symmetric
traversal", of the tree.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.Iterator<PairType> |
KDTree.iterator()
Iterates through the KDTree using "inorder", also known as "symmetric
traversal", of the tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NumericalDifferentiator<InputType,OutputType,DerivativeType>
Automatically differentiates a function by the method of forward differences.
|
interface |
VectorSpace<VectorType extends VectorSpace<VectorType,?>,EntryType extends VectorSpace.Entry>
In the Foundry, a VectorSpace is a type of Ring that we can perform
Vector-like operations on: norm, distances between Vectors, etc.
|
| Modifier and Type | Method and Description |
|---|---|
double |
VectorSpace.norm(double power)
Returns the p-norm of the Vector with the given power.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NativeBlasHandler
This class provides a uniform interface between working with a native-coded
BLAS package or the jBLAS package.
|
class |
ParallelSparseMatrix
A sparse matrix implementation.
|
class |
SparseMatrix
A sparse matrix implementation.
|
| Modifier and Type | Method and Description |
|---|---|
DenseMatrix.QR |
DenseMatrix.qrDecompose()
Leverage LAPACK to compute the QR Decomposition of this.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
EigenDecomposition
Performs a right eigendecomposition for symmetric or asymmetric matrices
|
class |
EigenvectorPowerIteration
Implementation of the Eigenvector Power Iteration algorithm.
|
interface |
SingularValueDecomposition
Interface that describes the operations of all SingularValueDecompositions
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMTJMatrix
Relies on internal MTJ matrix to do some of the heavy lifting, without
assuming that the underlying matrix is Dense or Sparse
|
class |
AbstractMTJVector
Implementation of the Vector interface that relies on MTJ Vectors, but does
not specify sparse or dense storage.
|
class |
DenseMatrix
Matrix that represents all its entries using a fixed-size storage scheme,
based on MTJ's DenseMatrix storage class.
|
class |
DenseVector
A generally useful vector representation that allocates a fixed-size
underlying vector, based on MTJ's DenseVector
|
class |
DiagonalMatrixMTJ
A diagonal matrix that wraps MTJ's BandMatrix class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AutoRegressiveMovingAverageFilter
A type of filter using a moving-average calculation.
|
interface |
DiscreteTimeFilter<StateType extends CloneableSerializable>
A discrete-time filter.
|
class |
FourierTransform
Computes the Fast Fourier Transform, or brute-force discrete Fourier
transform, of a discrete input sequence.
|
class |
MovingAverageFilter
A type of filter using a moving-average calculation.
|
| Modifier and Type | Method and Description |
|---|---|
protected static ComplexNumber[] |
FourierTransform.discreteFourierTransformComplex(java.util.ArrayList<ComplexNumber> data)
Computes the brute-force discrete Fourier transform of the input data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ChiSquaredSimilarity
A class for computing the chi-squared similarity between two vectors.
|
interface |
CumulativeDistributionFunction<NumberType extends java.lang.Number>
Functionality of a cumulative distribution function.
|
class |
KullbackLeiblerDivergence<DomainType>
A class used for measuring how similar two distributions are using Kullback--Leibler Divergence.
|
interface |
ProbabilityDensityFunction<DataType>
Defines a probability density function.
|
interface |
ProbabilityMassFunction<DataType>
The
ProbabilityMassFunction interface defines the functionality of
a probability mass function. |
interface |
RandomVariable<DataType>
Describes the functionality of a random variable.
|
interface |
SufficientStatistic<DataType,DistributionType>
Sufficient statistics are the data which are sufficient to store all
information to create an underlying parameter, such as a Distribution.
|
class |
TransferEntropy
A class for calculating the transfer entropy of two vectors.
|
class |
UnivariateRandomVariable
This is an implementation of a RandomVariable for scalar distributions.
|
| Modifier and Type | Method and Description |
|---|---|
static <DataType> double |
ProbabilityMassFunctionUtil.getEntropy(ProbabilityMassFunction<DataType> pmf)
Computes the information-theoretic entropy of the PMF in bits.
|
ConfidenceInterval |
UnivariateRandomVariable.getSamplingError(double confidence)
Gets the 95% confidence interval estimated sampling error associated
with this empirical random variable.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AdaptiveRejectionSampling
Samples form a univariate distribution using the method of adaptive
rejection sampling, which is a very efficient method that iteratively
improves the rejection and acceptance envelopes in response to additional
points.
|
class |
BayesianCredibleInterval
A Bayesian credible interval defines a bound that a scalar parameter is
within the given interval.
|
class |
BayesianLinearRegression.PredictiveDistribution
Creates the predictive distribution for the likelihood of a given point.
|
class |
ConditionalProbability<DataType>
A class for finding the conditional probability of two elements, or one element and a collection of other elements.
|
class |
ExtendedKalmanFilter
Implements the Extended Kalman Filter (EKF), which is an extension of the
Kalman filter that allows nonlinear motion and observation models.
|
class |
GaussianProcessRegression.PredictiveDistribution
Predictive distribution for Gaussian Process Regression.
|
class |
ImportanceSampling<ObservationType,ParameterType>
Importance sampling is a Monte Carlo inference technique where we sample
from an easy distribution over the hidden variables (parameters) and then
weight the result by the ratio of the likelihood of the parameters given
the evidence and the likelihood of generating the parameters.
|
class |
MetropolisHastingsAlgorithm<ObservationType,ParameterType>
An implementation of the Metropolis-Hastings MCMC algorithm, which is the
most general formulation of MCMC but can be slow.
|
interface |
RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>>
A recursive Bayesian estimator is an estimation method that uses the
previous belief of the system parameter and a single observation to refine
the estimate of the system parameter.
|
class |
RejectionSampling<ObservationType,ParameterType>
Rejection sampling is a method of inferring hidden parameters by using
an easy-to-sample-from distribution (times a scale factor) that envelopes
another distribution that is difficult to sample from.
|
class |
SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType>
An implementation of the standard Sampling Importance Resampling
particle filter.
|
| Modifier and Type | Method and Description |
|---|---|
static <ObservationType> |
BayesianUtil.deviance(ComputableDistribution<ObservationType> conditional,
java.lang.Iterable<? extends ObservationType> observations)
Computes the deviance of the model, which is
-2log(p(observations|parameter)).
|
static <ObservationType,ParameterType> |
BayesianUtil.expectedDeviance(BayesianParameter<ParameterType,? extends ComputableDistribution<ObservationType>,?> predictiveDistribution,
java.lang.Iterable<? extends ObservationType> observations,
java.util.Random random,
int numSamples)
Computes the expected deviance of the model by sampling parameters from
the posterior and then computing the deviance using the conditional
distribution.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BernoulliBayesianEstimator
A Bayesian estimator for the parameter of a BernoulliDistribution using
the conjugate prior BetaDistribution.
|
class |
MultinomialBayesianEstimator
A Bayesian estimator for the parameters of a MultinomialDistribution using
its conjugate prior distribution, the DirichletDistribution.
|
class |
MultivariateGaussianMeanBayesianEstimator
Bayesian estimator for the mean of a MultivariateGaussian using its conjugate
prior, which is also a MultivariateGaussian.
|
class |
PoissonBayesianEstimator
A Bayesian estimator for the parameter of a PoissonDistribution using
the conjugate prior GammaDistribution.
|
class |
UniformDistributionBayesianEstimator
A Bayesian estimator for a conditional Uniform(0,theta) distribution using
its conjugate prior Pareto distribution.
|
class |
UnivariateGaussianMeanBayesianEstimator
Bayesian estimator for the mean of a UnivariateGaussian using its conjugate
prior, which is also a UnivariateGaussian.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BernoulliDistribution
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1-p".
|
class |
BetaBinomialDistribution
A Binomial distribution where the binomial parameter, p, is set according
to a Beta distribution instead of a single value.
|
static class |
BetaBinomialDistribution.MomentMatchingEstimator
Estimates the parameters of a Beta-binomial distribution using the matching
of moments, not maximum likelihood.
|
class |
BetaDistribution
Computes the Beta-family of probability distributions.
|
static class |
BetaDistribution.MomentMatchingEstimator
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
|
static class |
BetaDistribution.WeightedMomentMatchingEstimator
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
|
class |
BinomialDistribution
Binomial distribution, which is a collection of Bernoulli trials
|
class |
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.
|
class |
CauchyDistribution
A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes
known as the Lorentz distribution.
|
class |
ChiSquareDistribution
Describes a Chi-Square Distribution.
|
class |
DeterministicDistribution
A deterministic distribution that returns samples at a single point.
|
class |
DirichletDistribution
The Dirichlet distribution is the multivariate generalization of the beta
distribution.
|
class |
ExponentialDistribution
An Exponential distribution describes the time between events in a poisson
process, resulting in a memoryless distribution.
|
class |
GammaDistribution
Class representing the Gamma distribution.
|
class |
GeometricDistribution
The geometric distribution models the number of successes before the first
failure occurs under an independent succession of Bernoulli tests.
|
class |
InverseGammaDistribution
Defines an inverse-gamma distribution.
|
static class |
InverseWishartDistribution.MultivariateGammaFunction
Multivariate generalization of the Gamma function.
|
class |
KolmogorovDistribution
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
class |
LaplaceDistribution
A Laplace distribution, sometimes called a double exponential distribution.
|
class |
LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>>
A linear mixture of RandomVariables, with a prior probability distribution.
|
class |
LogisticDistribution
A implementation of the scalar logistic distribution, which measures the
log-odds of a binary event.
|
class |
LogNormalDistribution
Log-Normal distribution PDF and CDF implementations.
|
class |
MixtureOfGaussians
Creates a probability density function (pdf) comprising of a collection of
MultivariateGaussian and corresponding prior probability distribution that
a particular MultivariateGaussian generates observations.
|
static class |
MixtureOfGaussians.EMLearner
An Expectation-Maximization based "soft" assignment learner.
|
class |
MultinomialDistribution
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.
|
class |
MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
static class |
MultivariateGaussian.SufficientStatisticCovarianceInverse
Implements the sufficient statistics of the MultivariateGaussian while
estimating the inverse of the covariance matrix.
|
class |
MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>
A LinearMixtureModel of multivariate distributions with associated PDFs.
|
class |
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.
|
class |
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.
|
class |
NormalInverseWishartDistribution
The normal inverse Wishart distribution
|
class |
ParetoDistribution
This class describes the Pareto distribution, sometimes called the Bradford
Distribution.
|
class |
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.
|
class |
ScalarMixtureDensityModel
ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density
model.
|
class |
SnedecorFDistribution
CDF of the Snedecor F-distribution (also known as Fisher F-distribution,
Fisher-Snedecor F-distribution, or just plain old F-distribution).
|
class |
UniformDistribution
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
static class |
UniformDistribution.MaximumLikelihoodEstimator
Maximum Likelihood Estimator of a uniform distribution.
|
class |
UniformIntegerDistribution
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
static class |
UniformIntegerDistribution.MaximumLikelihoodEstimator
Implements a maximum likelihood estimator for the discrete uniform
distribution.
|
class |
UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
static class |
UnivariateGaussian.ErrorFunction
Gaussian Error Function, useful for computing the cumulative distribution
function for a Gaussian.
|
static class |
UnivariateGaussian.SufficientStatistic
Captures the sufficient statistics of a UnivariateGaussian, which are
the values to estimate the mean and variance.
|
class |
WeibullDistribution
Describes a Weibull distribution, which is often used to describe the
mortality, lifespan, or size distribution of objects.
|
class |
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).
|
| Modifier and Type | Method and Description |
|---|---|
static double |
KolmogorovDistribution.CDF.evaluate(double input)
This is the probks() function from Numerical Recipes in C, pp.
|
double |
ScalarDataDistribution.getVariance() |
double |
ScalarMixtureDensityModel.getVariance() |
double |
ExponentialDistribution.sampleAsDouble(java.util.Random random) |
| Modifier and Type | Class and Description |
|---|---|
class |
AdjustedPValueStatistic
A multiple-comparison statistic derived from a single adjusted p-value.
|
interface |
BlockExperimentComparison<DataType>
Implements a null-hypothesis multiple-comparison test from a block-design
experiment.
|
class |
HolmCorrection
The Holm correction is a uniformly tighter bound than the Bonferroni/Sidak
correction by first sorting the pair-wide p-values and then adjusting the
p-values by the number of remaining hypotheses.
|
class |
ImportanceSampling
Importance sampling is a technique for estimating properties of
a target distribution, while only having samples generated from an
"importance" distribution rather than the target distribution.
|
class |
InverseTransformSampling
Inverse transform sampling is a method by which one can sample from an
arbitrary distribution using only a uniform random-number generator and
the ability to empirically invert the CDF.
|
class |
MarkovInequality
Implementation of the Markov Inequality hypothesis test.
|
class |
ReceiverOperatingCharacteristic
Class that describes a Receiver Operating Characteristic (usually called an
"ROC Curve").
|
| Modifier and Type | Method and Description |
|---|---|
static double |
ReceiverOperatingCharacteristic.Statistic.computeAreaUnderCurveTopLeft(java.util.Collection<ReceiverOperatingCharacteristic.DataPoint> points)
Computes the Area Under Curve for an x-axis sorted Collection
of ROC points using the top-left rectangle method for numerical
integration.
|
static double |
ReceiverOperatingCharacteristic.Statistic.computeAreaUnderCurveTrapezoid(java.util.Collection<ReceiverOperatingCharacteristic.DataPoint> points)
Computes the Area Under Curve for an x-axis sorted Collection
of ROC points using the top-left rectangle method for numerical
integration.
|
ConfidenceInterval |
GaussianConfidence.computeConfidenceInterval(double mean,
double variance,
int numSamples,
double confidence) |
static ConfidenceInterval |
BernoulliConfidence.computeConfidenceInterval(double bernoulliParameter,
int numSamples,
double confidence)
Computes the ConfidenceInterval for the Bernoulli parameter based on
the given data and the desired level of confidence.
|
static int |
BernoulliConfidence.computeSampleSize(double accuracy,
double confidence)
Computes the number of samples needed to estimate the Bernoulli parameter
"p" (mean) within "accuracy" with probability at least "confidence".
|
AnalysisOfVarianceOneWay.Statistic |
AnalysisOfVarianceOneWay.evaluateNullHypothesis(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data) |
static <DomainType extends java.lang.Number> |
KolmogorovSmirnovConfidence.evaluateNullHypothesis(java.util.Collection<? extends DomainType> data1,
CumulativeDistributionFunction<DomainType> function)
This is the standard K-S test for determining if the given data were
generated by the given CDF.
|
KolmogorovSmirnovConfidence.Statistic |
KolmogorovSmirnovConfidence.evaluateNullHypothesis(java.util.Collection<? extends java.lang.Number> data1,
java.util.Collection<? extends java.lang.Number> data2)
This is the standard K-S test for two distributions of data.
|
StudentTConfidence.Statistic |
StudentTConfidence.evaluateNullHypothesis(java.util.Collection<? extends java.lang.Number> data1,
java.util.Collection<? extends java.lang.Number> data2)
Computes a paired Student-t test for the given data.
|
static double |
KolmogorovSmirnovConfidence.Statistic.KSsignificance(double Ne,
double D)
Computes the significance of the K-S test from the given degrees of
freedom and D-statistic.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ImportanceSampler<DataType>
Importance sampling is a technique for estimating properties of
a target distribution, while only having samples generated from an
"importance" distribution rather than the target distribution.
|
interface |
MonteCarloSampler<DataType,SampleType,FunctionType extends Evaluator<? super DataType,java.lang.Double>>
A sampling technique based on the Monte Carlo method.
|
class |
MultivariateCumulativeDistributionFunction
Utility class for multivariate cumulative distribution functions (CDF).
|
| Modifier and Type | Class and Description |
|---|---|
class |
DefaultPrecisionRecallPair
A default implementation of the
PrecisionRecallPair interface. |
| Modifier and Type | Class and Description |
|---|---|
class |
SimpleStatisticalSpellingCorrector
A simple statistical spelling corrector based on word counts that looks at
possible one and two-character edits.
|
| Modifier and Type | Field and Description |
|---|---|
static int |
TermLengthFilter.DEFAULT_MAXIMUM_LENGTH
The default maximum length is 28.
|
| Modifier and Type | Method and Description |
|---|---|
static CompositeLocalGlobalTermWeighter |
CommonTermWeighterFactory.createLogEntropyWeighter()
Creates a log-entropy weighting scheme.
|
static CompositeLocalGlobalTermWeighter |
CommonTermWeighterFactory.createTFIDFWeighter()
Creates a term-frequency inverse-document-frequency (TF-IDF) weighting
scheme but without any normalization.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EntropyGlobalTermWeighter
Implements the entropy global term weighting scheme.
|
class |
InverseDocumentFrequencyGlobalTermWeighter
Implements the inverse-document-frequency (IDF) term global weighting scheme.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LogLocalTermWeighter
Implements the log-based local term weighting scheme.
|
class |
NormalizedLogLocalTermWeighter
Implements a normalized version of the log local weighter.
|
class |
TermFrequencyLocalTermWeighter
Local weighting for term frequency.
|