| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.annealing |
Provides the Simulated Annealing algorithm.
|
| 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.clustering.cluster |
Provides implementations of different types of clusters.
|
| gov.sandia.cognition.learning.algorithm.clustering.divergence |
Provides divergence functions for use in clustering.
|
| gov.sandia.cognition.learning.algorithm.confidence |
Provides confidence-weighted categorization algorithms.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.genetic.reproducer |
Provides reproduction functions for use with a Genetic Algorithm.
|
| gov.sandia.cognition.learning.algorithm.gradient |
Provides gradient based learning algorithms.
|
| gov.sandia.cognition.learning.algorithm.nearest |
Provides algorithms for Nearest-Neighbor memory-based functions.
|
| gov.sandia.cognition.learning.algorithm.perceptron |
Provides the Perceptron algorithm and some of its variations.
|
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| 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 |
Provides data set utilities for learning.
|
| gov.sandia.cognition.learning.data.feature |
Provides data feature extractors.
|
| gov.sandia.cognition.learning.function |
Provides function objects for 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.kernel |
Provides kernel 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.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.distribution |
Provides statistical distributions.
|
| gov.sandia.cognition.text.term.relation |
Provides relationships between terms.
|
| gov.sandia.cognition.text.term.vector |
Provides methods for handling documents represented as term vectors.
|
| gov.sandia.cognition.text.term.vector.weighter.local |
Provides local term weighting algorithms.
|
| gov.sandia.cognition.text.topic |
Provides topic modeling algorithms.
|
| Modifier and Type | Method and Description |
|---|---|
Vectorizable |
VectorizablePerturber.perturb(Vectorizable input)
Perturbs the given Vectorizable by cloning it and then operating on
the clone by side-effect.
|
| Modifier and Type | Method and Description |
|---|---|
Vectorizable |
VectorizablePerturber.perturb(Vectorizable input)
Perturbs the given Vectorizable by cloning it and then operating on
the clone by side-effect.
|
| Modifier and Type | Method and Description |
|---|---|
CategoryType |
VectorNaiveBayesCategorizer.evaluate(Vectorizable input) |
DefaultWeightedValueDiscriminant<CategoryType> |
VectorNaiveBayesCategorizer.evaluateWithDiscriminant(Vectorizable input) |
| Modifier and Type | Method and Description |
|---|---|
VectorNaiveBayesCategorizer<CategoryType,DistributionType> |
VectorNaiveBayesCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data) |
VectorNaiveBayesCategorizer<CategoryType,UnivariateGaussian.PDF> |
VectorNaiveBayesCategorizer.BatchGaussianLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data) |
void |
VectorNaiveBayesCategorizer.OnlineLearner.update(VectorNaiveBayesCategorizer<CategoryType,DistributionType> target,
InputOutputPair<? extends Vectorizable,CategoryType> data) |
| Modifier and Type | Class and Description |
|---|---|
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. |
| Modifier and Type | Method and Description |
|---|---|
static PartitionalClusterer<Vectorizable,NormalizedCentroidCluster<Vectorizable>> |
PartitionalClusterer.createSpherical(int numRequestedClusters)
Create a spherical partitional clusterer, using Cosine distance and a
vector mean centroid cluster creator.
|
static PartitionalClusterer<Vectorizable,NormalizedCentroidCluster<Vectorizable>> |
PartitionalClusterer.createSpherical(int numRequestedClusters)
Create a spherical partitional clusterer, using Cosine distance and a
vector mean centroid cluster creator.
|
| Modifier and Type | Method and Description |
|---|---|
NormalizedCentroidCluster<Vectorizable> |
NormalizedCentroidClusterCreator.createCluster() |
NormalizedCentroidCluster<Vectorizable> |
NormalizedCentroidClusterCreator.createCluster(java.util.Collection<? extends Vectorizable> members) |
| Modifier and Type | Method and Description |
|---|---|
void |
NormalizedCentroidClusterCreator.addClusterMember(NormalizedCentroidCluster<Vectorizable> cluster,
Vectorizable member) |
boolean |
NormalizedCentroidClusterCreator.removeClusterMember(NormalizedCentroidCluster<Vectorizable> cluster,
Vectorizable member) |
| Modifier and Type | Method and Description |
|---|---|
void |
NormalizedCentroidClusterCreator.addClusterMember(NormalizedCentroidCluster<Vectorizable> cluster,
Vectorizable member) |
NormalizedCentroidCluster<Vectorizable> |
NormalizedCentroidClusterCreator.createCluster(java.util.Collection<? extends Vectorizable> members) |
boolean |
NormalizedCentroidClusterCreator.removeClusterMember(NormalizedCentroidCluster<Vectorizable> cluster,
Vectorizable member) |
| Modifier and Type | Class and Description |
|---|---|
class |
WithinNormalizedCentroidClusterCosineDivergence<V extends Vectorizable>
This class calculates the total cosine divergence between all members of a
cluster and the cluster's centroid
|
| Modifier and Type | Method and Description |
|---|---|
void |
AdaptiveRegularizationOfWeights.update(DefaultConfidenceWeightedBinaryCategorizer target,
Vectorizable input,
java.lang.Boolean output) |
void |
ConfidenceWeightedDiagonalDeviation.update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vectorizable input,
java.lang.Boolean output) |
void |
ConfidenceWeightedDiagonalVariance.update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vectorizable input,
java.lang.Boolean output) |
| Modifier and Type | Class and Description |
|---|---|
class |
FactorizationMachine
Implements a Factorization Machine.
|
| Modifier and Type | Method and Description |
|---|---|
Vectorizable |
VectorizableCrossoverFunction.crossover(Vectorizable parent1,
Vectorizable parent2)
Crosses over each element of the parent vectors.
|
| Modifier and Type | Method and Description |
|---|---|
Vectorizable |
VectorizableCrossoverFunction.crossover(Vectorizable parent1,
Vectorizable parent2)
Crosses over each element of the parent vectors.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
GradientDescendable
Defines the functionality of an object that is required in order to apply
the gradient descent algorithm to it.
|
interface |
ParameterGradientEvaluator<InputType,OutputType,GradientType>
Interface for computing the derivative of the output with respect to the
parameters for a given input.
|
| 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 |
KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the k-nearest neighbor algorithm.
|
static class |
KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
This is a BatchLearner interface for creating a new KNearestNeighbor
from a given dataset, simply a pass-through to the constructor of
KNearestNeighbor
|
class |
NearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the nearest neighbor algorithm.
|
static class |
NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
This is a BatchLearner interface for creating a new NearestNeighbor
from a given dataset, simply a pass-through to the constructor of
NearestNeighbor
|
| Constructor and Description |
|---|
Learner(int k,
Metric<? super Vectorizable> divergenceFunction,
Summarizer<? super OutputType,? extends OutputType> averager)
Creates a new instance of Learner
|
Learner(Metric<? super Vectorizable> divergenceFunction)
Creates a new instance of Learner
|
| Modifier and Type | Method and Description |
|---|---|
WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> |
OnlineVotedPerceptron.createInitialLearnedObject() |
SupervisedIncrementalLearner<Vectorizable,java.lang.Boolean,LinearBinaryCategorizer> |
LinearizableBinaryCategorizerOnlineLearner.createLinearLearner(VectorFactory<?> vectorFactory)
Creates a new linear learner using the standard learning interfaces
based on this learner and its parameters.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractOnlineLinearBinaryCategorizerLearner.update(LinearBinaryCategorizer target,
Vectorizable input,
java.lang.Boolean output) |
void |
LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target,
Vectorizable input,
boolean output,
VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the
given supervised data.
|
void |
LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target,
Vectorizable input,
java.lang.Boolean output,
VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the
given supervised data.
|
void |
OnlineVotedPerceptron.update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target,
Vectorizable input,
java.lang.Boolean output) |
| Modifier and Type | Method and Description |
|---|---|
static DefaultWeightedValue<LinearBinaryCategorizer> |
OnlineVotedPerceptron.getLastMember(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> ensemble)
Gets the last member in the ensemble.
|
void |
LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target,
InputOutputPair<? extends Vectorizable,java.lang.Boolean> data,
VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the
given supervised data.
|
void |
LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target,
java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Boolean>> data,
VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the
given supervised data.
|
void |
OnlineMultiPerceptron.update(LinearMultiCategorizer<CategoryType> target,
InputOutputPair<? extends Vectorizable,CategoryType> example) |
void |
OnlineMultiPerceptron.UniformUpdate.update(LinearMultiCategorizer<CategoryType> target,
InputOutputPair<? extends Vectorizable,CategoryType> example) |
void |
OnlineMultiPerceptron.ProportionalUpdate.update(LinearMultiCategorizer<CategoryType> target,
InputOutputPair<? extends Vectorizable,CategoryType> example) |
void |
OnlineVotedPerceptron.update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target,
Vector input,
boolean actual)
The
update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm. |
void |
OnlineVotedPerceptron.update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target,
Vectorizable input,
java.lang.Boolean output) |
| Modifier and Type | Interface and Description |
|---|---|
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.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
LogisticRegression.Function
Class that is a linear discriminant, followed by a sigmoid function.
|
| Modifier and Type | Method and Description |
|---|---|
LinearDiscriminantWithBias |
LinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
Computes the linear regression for the given Collection of
InputOutputPairs.
|
| Modifier and Type | Field and Description |
|---|---|
protected java.util.ArrayList<? extends InputOutputPair<? extends Vectorizable,java.lang.Boolean>> |
PrimalEstimatedSubGradient.dataList
The data represented as a list.
|
| Modifier and Type | Field and Description |
|---|---|
protected DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> |
RandomSubVectorThresholdLearner.subLearner
The decider learner for the subspace.
|
| Modifier and Type | Method and Description |
|---|---|
DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> |
RandomSubVectorThresholdLearner.getSubLearner()
Gets the learner used to learn a threshold function over the subspace.
|
| Modifier and Type | Method and Description |
|---|---|
DefaultPair<java.lang.Double,java.lang.Double> |
AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data,
int dimension,
DefaultDataDistribution<OutputType> baseCounts)
Computes the best gain and threshold for a given dimension using the
computeSplitGain method for each potential split point of values for the
given dimension.
|
protected DefaultPair<java.lang.Double,java.lang.Double> |
AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data,
int dimension,
DefaultDataDistribution<OutputType> baseCounts,
java.util.ArrayList<DefaultWeightedValue<OutputType>> values)
Computes the best gain and threshold for a given dimension using the
computeSplitGain method for each potential split point of values for the
given dimension.
|
DefaultPair<java.lang.Double,java.lang.Double> |
VectorThresholdVarianceLearner.computeBestGainThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data,
int dimension,
double baseVariance)
Computes the best information gain-threshold pair for the given
dimension on the given data.
|
VectorElementThresholdCategorizer |
VectorThresholdVarianceLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
Learns a VectorElementThresholdCategorizer from the given data by
picking the vector element and threshold that best maximizes information
gain.
|
VectorElementThresholdCategorizer |
AbstractVectorThresholdMaximumGainLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data) |
VectorElementThresholdCategorizer |
RandomSubVectorThresholdLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data) |
void |
RandomSubVectorThresholdLearner.setSubLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner)
Sets the learner used to learn a threshold function over the subspace.
|
| Constructor and Description |
|---|
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner,
double percentToSample,
int[] dimensionsToConsider,
java.util.Random random,
VectorFactory<? extends Vector> vectorFactory)
Creates a new
RandomSubVectorThresholdLearner. |
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner,
double percentToSample,
java.util.Random random)
Creates a new
RandomSubVectorThresholdLearner. |
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,java.lang.Boolean,VectorElementThresholdCategorizer> subLearner,
double percentToSample,
java.util.Random random,
VectorFactory<? extends Vector> vectorFactory)
Creates a new
RandomSubVectorThresholdLearner. |
| Modifier and Type | Method and Description |
|---|---|
static void |
DatasetUtil.assertDimensionalitiesAllEqual(java.lang.Iterable<? extends Vectorizable> data)
Asserts that all of the dimensionalities of the vectors in the
given set of data are the same.
|
static void |
DatasetUtil.assertInputDimensionalitiesAllEqual(java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,?>> data)
Asserts that all of the dimensionalities of the input vectors in the
given set of input-output pairs are the same.
|
static void |
DatasetUtil.assertInputDimensionalitiesAllEqual(java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,?>> data,
int dimensionality)
Asserts that all of the dimensionalities of the input vectors in the
given set of input-output pairs equal the given dimensionality.
|
static java.util.Collection<Vector> |
DatasetUtil.asVectorCollection(java.util.Collection<? extends Vectorizable> collection)
Takes a collection of
Vectorizable objects and returns a
collection of Vector objects of the same size. |
static int |
DatasetUtil.getDimensionality(java.lang.Iterable<? extends Vectorizable> data)
Gets the dimensionality of the vectors in given set of data.
|
static int |
DatasetUtil.getInputDimensionality(java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,?>> data)
Gets the dimensionality of the input vectors in given set of input-output
pairs.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MultivariateDecorrelator.evaluate(Vectorizable value)
Normalizes the given double value by subtracting the mean and dividing
by the standard deviation (the square root of the variance).
|
| Modifier and Type | Method and Description |
|---|---|
MultivariateDecorrelator |
MultivariateDecorrelator.FullCovarianceLearner.learn(java.util.Collection<? extends Vectorizable> values)
Learns a MultivariateDecorrelator from the given values by
computing the mean and covariance of the dimensions.
|
MultivariateDecorrelator |
MultivariateDecorrelator.DiagonalCovarianceLearner.learn(java.util.Collection<? extends Vectorizable> values)
Learns a MultivariateDecorrelator from the given values by
computing the mean and variance for each dimension separately.
|
SubVectorEvaluator |
RandomSubspace.learn(java.util.Collection<? extends Vectorizable> data) |
static MultivariateDecorrelator |
MultivariateDecorrelator.learnDiagonalCovariance(java.util.Collection<? extends Vectorizable> values,
double defaultCovariance)
Learns a normalization based on a mean and covariance where the
covariance matrix is diagonal.
|
static MultivariateDecorrelator |
MultivariateDecorrelator.learnFullCovariance(java.util.Collection<? extends Vectorizable> values,
double defaultCovariance)
Learns a normalization based on a mean and full covariance matrix from
the given data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearCombinationFunction<InputType,OutputType>
A function whose output is a weighted linear combination of (potentially)
nonlinear basis function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ScalarThresholdBinaryCategorizer
The
ScalarThresholdBinaryCategorizer class implements a binary
categorizer that uses a threshold to categorize a given double. |
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,? extends Vectorizable> |
WinnerTakeAllCategorizer.evaluator
The evaluator that outputs a vector to return.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,? extends Vectorizable> |
WinnerTakeAllCategorizer.getEvaluator()
Gets the wrapped evaluator.
|
| Modifier and Type | Method and Description |
|---|---|
CategoryType |
LinearMultiCategorizer.evaluate(Vectorizable input) |
BernoulliDistribution |
AbstractConfidenceWeightedBinaryCategorizer.evaluateAsBernoulli(Vectorizable input) |
BernoulliDistribution |
ConfidenceWeightedBinaryCategorizer.evaluateAsBernoulli(Vectorizable input)
Returns a Bernoulli distribution over the output of
the distribution of weight vectors times the input, with the
confidence that the categorizer was trained using.
|
double |
LinearBinaryCategorizer.evaluateAsDouble(Vectorizable input)
Categorizes the given input vector as a double by:
weights * input + bias
|
double |
LinearMultiCategorizer.evaluateAsDouble(Vectorizable input,
CategoryType category)
Evaluates how much the given input matches the prototype for the given
category.
|
UnivariateGaussian |
ConfidenceWeightedBinaryCategorizer.evaluateAsGaussian(Vectorizable input)
Returns the univariate Gaussian distribution over the output of
the distribution of weight vectors times the input, with the
confidence that the categorizer was trained using.
|
UnivariateGaussian |
DefaultConfidenceWeightedBinaryCategorizer.evaluateAsGaussian(Vectorizable input) |
UnivariateGaussian |
DiagonalConfidenceWeightedBinaryCategorizer.evaluateAsGaussian(Vectorizable input) |
DefaultWeightedValueDiscriminant<CategoryType> |
LinearMultiCategorizer.evaluateWithDiscriminant(Vectorizable input) |
protected double |
VectorElementThresholdCategorizer.evaluateWithoutThreshold(Vectorizable input) |
| Modifier and Type | Method and Description |
|---|---|
void |
WinnerTakeAllCategorizer.setEvaluator(Evaluator<? super InputType,? extends Vectorizable> evaluator)
Sets the wrapped evaluator.
|
| Constructor and Description |
|---|
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,Evaluator<? super InputType,? extends Vectorizable>> learner)
Creates a new learner adapter with the given internal learner.
|
WinnerTakeAllCategorizer(Evaluator<? super InputType,? extends Vectorizable> evaluator,
java.util.Set<CategoryType> categories)
Creates a new
WinnerTakesAllCategorizer. |
| Modifier and Type | Method and Description |
|---|---|
Vectorizable |
EuclideanDistanceCostFunction.getCostParameters() |
| Modifier and Type | Method and Description |
|---|---|
java.lang.Double |
EuclideanDistanceCostFunction.evaluate(Vectorizable target)
Evaluates the Euclidean distance between the provided target and the
goal.
|
void |
EuclideanDistanceCostFunction.setCostParameters(Vectorizable costParameters) |
| Modifier and Type | Method and Description |
|---|---|
double |
ChebyshevDistanceMetric.evaluate(Vectorizable first,
Vectorizable second) |
double |
CosineDistanceMetric.evaluate(Vectorizable first,
Vectorizable second)
Evaluates the cosine distance between the two given vectors.
|
double |
EuclideanDistanceMetric.evaluate(Vectorizable first,
Vectorizable second)
Evaluates the Euclidean distance between the two given vectors.
|
double |
EuclideanDistanceSquaredMetric.evaluate(Vectorizable first,
Vectorizable second)
The evaluates the squared Euclidean distance between the two given
vectors.
|
double |
ManhattanDistanceMetric.evaluate(Vectorizable first,
Vectorizable second)
Evaluates the Manhattan distance between the two given vectors.
|
double |
MinkowskiDistanceMetric.evaluate(Vectorizable first,
Vectorizable second) |
double |
WeightedEuclideanDistanceMetric.evaluate(Vectorizable first,
Vectorizable second)
Evaluates the weighted Euclidean distance between two vectors.
|
| Modifier and Type | Method and Description |
|---|---|
double |
LinearKernel.evaluate(Vectorizable x,
Vectorizable y)
Evaluates the linear kernel by taking the inner product of the two
vectors.
|
double |
PolynomialKernel.evaluate(Vectorizable x,
Vectorizable y)
This kernel just evaluates the polynomial kernel between the two given
vectors, which is: (x dot y + c)^d.
|
double |
RadialBasisKernel.evaluate(Vectorizable x,
Vectorizable y)
Evaluates the following kernel between the two given vectors:
exp( -||x - y||^2 / (2 * sigma^2))
|
double |
SigmoidKernel.evaluate(Vectorizable x,
Vectorizable y)
Evaluates the sigmoid kernel between the two given vectors, which is:
tanh(kappa * (x dot y) + c)
|
double |
VectorFunctionKernel.evaluate(Vectorizable x,
Vectorizable y)
Evaluates the kernel on the given inputs by first applying the vector
function to each input vector and then evaluating the kernel on the
results of the vector function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AtanFunction
Returns the element-wise arctangent of the input vector, compressed between
-maxMagnitude and maxMagnitude (instead of just -PI/2 and PI/2)
|
class |
CosineFunction
A closed-form cosine function.
|
class |
LinearCombinationScalarFunction<InputType>
A weighted linear combination of scalar functions.
|
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.
|
class |
PolynomialFunction
A single polynomial term specified by a real-valued exponent.
|
class |
ThresholdFunction
Maps the input space onto the set {LOW_VALUE,HIGH_VALUE}.
|
class |
VectorEntryFunction
An evaluator that returns the value of an input vector at a specified index.
|
class |
VectorFunctionLinearDiscriminant<InputType>
This class takes a function that maps a generic InputType to a Vector.
|
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,? extends Vectorizable> |
VectorFunctionToScalarFunction.vectorFunction
The function that takes a given input and outputs a 1-dimensional
vector.
|
protected BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> |
VectorFunctionToScalarFunction.Learner.vectorLearner
The supervised learner that learns on vectors as outputs.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,? extends Vectorizable> |
VectorFunctionToScalarFunction.getVectorFunction()
Gets the vector function with a one-dimensional output that is being
converted to a scalar function.
|
| Modifier and Type | Method and Description |
|---|---|
double |
LinearDiscriminant.evaluateAsDouble(Vectorizable input) |
double |
LinearDiscriminantWithBias.evaluateAsDouble(Vectorizable input) |
double |
LinearVectorScalarFunction.evaluateAsDouble(Vectorizable input)
Evaluate the given input vector as a double by:
weights * input + bias
|
double |
VectorEntryFunction.evaluateAsDouble(Vectorizable input)
Returns the vector value at the specified index.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VectorFunctionToScalarFunction.setVectorFunction(Evaluator<? super InputType,? extends Vectorizable> vectorFunction)
Sets the vector function with a one-dimensional output that is being
converted to a scalar function.
|
| Constructor and Description |
|---|
Learner(BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> vectorLearner)
Creates a new
VectorFunctionToScalarFunction.Learner. |
VectorFunctionToScalarFunction(Evaluator<? super InputType,? extends Vectorizable> vectorFunction)
Creates a new instance of
VectorFunctionToScalarFunction. |
| Modifier and Type | Class and Description |
|---|---|
class |
DifferentiableFeedforwardNeuralNetwork
A feedforward neural network that can have an arbitrary number of layers,
and an arbitrary differentiable squashing (activation) function assigned to
each layer.
|
class |
DifferentiableGeneralizedLinearModel
A GradientDescenable version of a GeneralizedLinearModel, in
other words, a GeneralizedLinearModel where the squashing
function is differentiable
|
class |
FeedforwardNeuralNetwork
A feedforward neural network that can have an arbitrary number of layers,
and an arbitrary squashing (activation) function assigned to each layer.
|
class |
GeneralizedLinearModel
A VectorizableVectorFunction that is a matrix multiply followed by a
VectorFunction...
|
class |
LinearCombinationVectorFunction
A weighted linear combination of scalar functions.
|
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 |
ThreeLayerFeedforwardNeuralNetwork
This is a "standard" feedforward neural network with a single hidden
layer.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
SubVectorEvaluator.evaluate(Vectorizable input) |
Vector |
VectorizableVectorConverter.evaluate(Vectorizable input)
Evaluates the given input by converting it to a vector by calling the
proper method on the given
Vectorizable. |
Vector |
VectorizableVectorConverterWithBias.evaluate(Vectorizable input)
Evaluates the given vectorizable input by converting it to a vector
and then creating a new vector of one extra dimensionality and adding a
single element with bias 1.0 to the end.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MutableDouble
A mutable object containing a double.
|
class |
MutableInteger
A mutable object containing an integer.
|
class |
MutableLong
A mutable object containing a long.
|
| Modifier and Type | Method and Description |
|---|---|
static Pair<Vector,Matrix> |
MultivariateStatisticsUtil.computeMeanAndCovariance(java.lang.Iterable<? extends Vectorizable> data)
Computes the mean and unbiased covariance Matrix of a multivariate
data set.
|
static Pair<Vector,Matrix> |
MultivariateStatisticsUtil.computeWeightedMeanAndCovariance(java.lang.Iterable<? extends WeightedValue<? extends Vectorizable>> data)
Computes the mean and biased covariance Matrix of a multivariate
weighted data set.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KDTree<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
Implementation of a kd-tree.
|
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.
|
protected static class |
KDTree.Neighborhood<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
A Collection of nearby pairs.
|
protected class |
KDTree.Neighborhood.Neighbor<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
Holds neighbor information used during the evaluate method and is put
into a priority queue.
|
class |
Quadtree<DataType extends Vectorizable>
Implements the quadtree region-partitioning algorithm and data structure.
|
| Modifier and Type | Method and Description |
|---|---|
static <VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>> |
KDTree.createBalanced(java.util.Collection<? extends PairType> points)
Creates a balanced KDTree based on the given collection of Pairs.
|
| Modifier and Type | Method and Description |
|---|---|
int |
KDTree.PairFirstVectorizableIndexComparator.compare(Pair<? extends Vectorizable,?> o1,
Pair<? extends Vectorizable,?> o2) |
int |
KDTree.PairFirstVectorizableIndexComparator.compare(Pair<? extends Vectorizable,?> o1,
Pair<? extends Vectorizable,?> o2) |
| Modifier and Type | Interface and Description |
|---|---|
interface |
VectorInputEvaluator<InputType extends Vectorizable,OutputType>
An interface for an evaluator that takes a vector of a fixed dimensionality.
|
interface |
VectorOutputEvaluator<InputType,OutputType extends Vectorizable>
An interface for an evaluator that produces a vector of a fixed
dimensionality.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DiagonalMatrix
Interface describing a diagonal matrix.
|
interface |
Matrix
Defines the base functionality for all implementations of a Matrix
|
interface |
Quaternion
Interface for a mathematical quaternion, which represents rotations using
four dimensions.
|
interface |
Vector
The
Vector interface defines the operations that are expected
on a mathematical vector. |
interface |
Vector1D
An interface for a 1-dimensional vector.
|
interface |
Vector2D
An interface for a 2-dimensional vector.
|
interface |
Vector3D
An interface for a 3-dimensional vector.
|
interface |
VectorizableDifferentiableVectorFunction
A VectorizableVectorFunction that also define a derivative
(this is needed for GradientDescendable).
|
interface |
VectorizableVectorFunction
The VectorizableVectorFunction interface defines a useful interface for
doing machine learning, which is a function that takes and returns vectors
and also is parameterizable as a vector.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMatrix
Abstract implementation of some low-hanging functions in the Matrix
interface.
|
class |
AbstractVector
Abstract implementation of some of the Vector interface, in a storage-free
manner
|
| Modifier and Type | Method and Description |
|---|---|
Vectorizable |
Vectorizable.clone() |
| Modifier and Type | Method and Description |
|---|---|
int |
VectorizableIndexComparator.compare(Vectorizable o1,
Vectorizable o2) |
MatrixType |
MatrixFactory.copyColumnVectors(Vectorizable... columns)
Creates a new matrix by copying the given set of column vectors.
|
MatrixType |
MatrixFactory.copyRowVectors(Vectorizable... rows)
Creates a new matrix by copying the given set of row vectors.
|
MatrixType |
MatrixFactory.createDiagonal(Vectorizable diagonal)
Creates a new square matrix whose number of rows and columns match the
dimensionality of the given vector.
|
static Vector |
NumericalDifferentiator.VectorJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,java.lang.Double> f)
Static access to the numerical differentiation procedure.
|
static Vector |
NumericalDifferentiator.VectorJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,java.lang.Double> f,
double h)
Static access to the numerical differentiation procedure.
|
static Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,Vector> f,
double h)
Static access to the numerical differentiation procedure.
|
static Vector |
VectorUtil.interpolateLinear(Vectorizable first,
Vectorizable second,
double percent)
Performs linear interpolation between two vectors.
|
static int |
VectorUtil.safeGetDimensionality(Vectorizable vector)
Gets the dimensionality of the given vector, if it is not null.
|
| Modifier and Type | Method and Description |
|---|---|
static void |
VectorUtil.assertDimensionalitiesAllEqual(java.lang.Iterable<? extends Vectorizable> data,
int dimensionality)
Asserts that all of the dimensionalities of the vectors in the
given set of data equal the given dimensionality.
|
MatrixType |
MatrixFactory.copyColumnVectors(java.util.Collection<? extends Vectorizable> columns)
Creates a new matrix by copying the given set of column vectors.
|
MatrixType |
MatrixFactory.copyRowVectors(java.util.Collection<? extends Vectorizable> rows)
Creates a new matrix by copying the given set of row vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DenseMatrix
A dense matrix implementation.
|
class |
DenseVector
Our dense vector implementation.
|
class |
DiagonalMatrix
Diagonal matrices are a special case, but a rather common one with very quick
and simple solutions to multiplications, inverses, etc.
|
class |
ParallelSparseMatrix
A sparse matrix implementation.
|
class |
SparseMatrix
A sparse matrix implementation.
|
class |
SparseVector
Our sparse vector implementation.
|
| 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 |
AbstractSparseMatrix
Implements some generic operations that any sparse-matrix representation
must do.
|
class |
DiagonalMatrixMTJ
A diagonal matrix that wraps MTJ's BandMatrix class.
|
class |
SparseColumnMatrix
A sparse matrix, represented as a collection of sparse column vectors.
|
class |
SparseRowMatrix
A sparse matrix, represented as a collection of sparse row vectors.
|
class |
Vector1
Implements a one-dimensional MTJ
DenseVector. |
class |
Vector2
Implements a two-dimensional MTJ
DenseVector. |
class |
Vector3
Implements a three-dimensional
DenseVector. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
DiscreteTimeFilter<StateType extends CloneableSerializable>
A discrete-time filter.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AutoRegressiveMovingAverageFilter
A type of filter using a moving-average calculation.
|
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 |
MovingAverageFilter
A type of filter using a moving-average calculation.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ClosedFormComputableDiscreteDistribution<DataType>
A discrete, closed-form Distribution with a PMF.
|
interface |
ClosedFormComputableDistribution<DataType>
A closed-form Distribution that also has an associated distribution function.
|
interface |
ClosedFormCumulativeDistributionFunction<DomainType extends java.lang.Number>
Functionality of a cumulative distribution function that's defined with
closed-form parameters.
|
interface |
ClosedFormDiscreteUnivariateDistribution<DomainType extends java.lang.Number>
A ClosedFormUnivariateDistribution that is also a DiscreteDistribution
|
interface |
ClosedFormDistribution<DataType>
Defines a distribution that is described a parameterized mathematical
equation.
|
interface |
ClosedFormUnivariateDistribution<NumberType extends java.lang.Number>
Defines the functionality associated with a closed-form scalar distribution.
|
interface |
ProbabilityDensityFunction<DataType>
Defines a probability density function.
|
interface |
SmoothCumulativeDistributionFunction
This defines a CDF that has an associated derivative, which is its PDF.
|
interface |
SmoothUnivariateDistribution
A closed-form scalar distribution that is also smooth.
|
interface |
UnivariateProbabilityDensityFunction
A PDF that takes doubles as input.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClosedFormIntegerDistribution
An abstract class for closed-form integer distributions.
|
class |
AbstractClosedFormSmoothUnivariateDistribution
Partial implementation of SmoothUnivariateDistribution
|
class |
AbstractClosedFormUnivariateDistribution<NumberType extends java.lang.Number>
Partial implementation of a ClosedFormUnivariateDistribution.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super Vectorizable,? extends ClosedFormDistribution<OutputType>> |
BayesianRegression.createPredictiveDistribution(PosteriorType posterior)
Creates the predictive distribution of outputs given the weight posterior
|
| Modifier and Type | Method and Description |
|---|---|
UnivariateGaussian |
BayesianLinearRegression.createConditionalDistribution(Vectorizable input,
Vector weights)
Creates the distribution from which the outputs are generated, given
the weights and the input to consider.
|
Distribution<OutputType> |
BayesianRegression.createConditionalDistribution(Vectorizable input,
Vector weights)
Creates the distribution from which the outputs are generated, given
the weights and the input to consider.
|
UnivariateGaussian |
BayesianRobustLinearRegression.createConditionalDistribution(Vectorizable input,
Vector weights)
Creates the distribution from which the outputs are generated, given
the weights and the input to consider.
|
UnivariateGaussian.PDF |
BayesianLinearRegression.PredictiveDistribution.evaluate(Vectorizable input) |
StudentTDistribution |
BayesianRobustLinearRegression.PredictiveDistribution.evaluate(Vectorizable input) |
| Modifier and Type | Method and Description |
|---|---|
MultivariateGaussian.PDF |
BayesianLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) |
MultivariateGaussian.PDF |
BayesianLinearRegression.IncrementalEstimator.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) |
MultivariateGaussianInverseGammaDistribution |
BayesianRobustLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) |
MultivariateGaussianInverseGammaDistribution |
BayesianRobustLinearRegression.IncrementalEstimator.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) |
void |
BayesianLinearRegression.IncrementalEstimator.update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target,
InputOutputPair<? extends Vectorizable,java.lang.Double> data) |
void |
BayesianLinearRegression.IncrementalEstimator.update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target,
java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) |
void |
BayesianRobustLinearRegression.IncrementalEstimator.update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target,
InputOutputPair<? extends Vectorizable,java.lang.Double> data) |
void |
BayesianRobustLinearRegression.IncrementalEstimator.update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target,
java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) |
void |
BayesianLinearRegression.IncrementalEstimator.SufficientStatistic.update(InputOutputPair<? extends Vectorizable,java.lang.Double> value) |
void |
BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic.update(InputOutputPair<? extends Vectorizable,java.lang.Double> value) |
| 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".
|
static class |
BernoulliDistribution.CDF
CDF of a Bernoulli distribution.
|
static class |
BernoulliDistribution.PMF
PMF of the Bernoulli distribution.
|
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.CDF
CDF of BetaBinomialDistribution
|
static class |
BetaBinomialDistribution.PMF
PMF of the BetaBinomialDistribution
|
class |
BetaDistribution
Computes the Beta-family of probability distributions.
|
static class |
BetaDistribution.CDF
CDF of the Beta-family distribution
|
static class |
BetaDistribution.PDF
Beta distribution probability density function
|
class |
BinomialDistribution
Binomial distribution, which is a collection of Bernoulli trials
|
static class |
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"
|
static class |
BinomialDistribution.PMF
The Probability Mass Function of a binomial distribution.
|
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.
|
static class |
CategoricalDistribution.PMF
PMF of the Categorical Distribution
|
class |
CauchyDistribution
A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes
known as the Lorentz distribution.
|
static class |
CauchyDistribution.CDF
CDF of the CauchyDistribution.
|
static class |
CauchyDistribution.PDF
PDF of the CauchyDistribution.
|
class |
ChineseRestaurantProcess
A Chinese Restaurant Process is a discrete stochastic processes that
partitions data points to clusters.
|
static class |
ChineseRestaurantProcess.PMF
PMF of the Chinese Restaurant Process
|
class |
ChiSquareDistribution
Describes a Chi-Square Distribution.
|
static class |
ChiSquareDistribution.CDF
Cumulative Distribution Function (CDF) of a Chi-Square Distribution
|
static class |
ChiSquareDistribution.PDF
PDF of the Chi-Square distribution
|
class |
DeterministicDistribution
A deterministic distribution that returns samples at a single point.
|
static class |
DeterministicDistribution.CDF
CDF of the deterministic distribution.
|
static class |
DeterministicDistribution.PMF
PMF of the deterministic distribution.
|
class |
DirichletDistribution
The Dirichlet distribution is the multivariate generalization of the beta
distribution.
|
static class |
DirichletDistribution.PDF
PDF of the Dirichlet distribution.
|
class |
ExponentialDistribution
An Exponential distribution describes the time between events in a poisson
process, resulting in a memoryless distribution.
|
static class |
ExponentialDistribution.CDF
CDF of the ExponentialDistribution.
|
static class |
ExponentialDistribution.PDF
PDF of the ExponentialDistribution.
|
class |
GammaDistribution
Class representing the Gamma distribution.
|
static class |
GammaDistribution.CDF
CDF of the Gamma distribution
|
static class |
GammaDistribution.PDF
Closed-form PDF of 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.
|
static class |
GeometricDistribution.CDF
CDF of the Geometric distribution
|
static class |
GeometricDistribution.PMF
PMF of the Geometric distribution
|
class |
InverseGammaDistribution
Defines an inverse-gamma distribution.
|
static class |
InverseGammaDistribution.CDF
CDF of the inverseRootFinder-gamma distribution.
|
static class |
InverseGammaDistribution.PDF
PDF of the inverseRootFinder-Gamma distribution.
|
class |
InverseWishartDistribution
The Inverse-Wishart distribution is the multivariate generalization of the
inverse-gamma distribution.
|
static class |
InverseWishartDistribution.PDF
PDF of the Inverse-Wishart distribution, though I have absolutely no
idea why anybody would evaluate the PDF of an Inverse-Wishart...
|
class |
KolmogorovDistribution
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
static class |
KolmogorovDistribution.CDF
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.
|
static class |
LaplaceDistribution.CDF
CDF of the Laplace distribution.
|
static class |
LaplaceDistribution.PDF
The PDF of a Laplace Distribution.
|
class |
LogisticDistribution
A implementation of the scalar logistic distribution, which measures the
log-odds of a binary event.
|
static class |
LogisticDistribution.CDF
CDF of the LogisticDistribution
|
static class |
LogisticDistribution.PDF
PDF of the LogisticDistribution
|
class |
LogNormalDistribution
Log-Normal distribution PDF and CDF implementations.
|
static class |
LogNormalDistribution.CDF
CDF of the Log-Normal Distribution
|
static class |
LogNormalDistribution.PDF
PDF of a Log-normal distribution
|
static class |
MixtureOfGaussians.PDF
PDF of the MixtureOfGaussians
|
class |
MultinomialDistribution
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.
|
static class |
MultinomialDistribution.PMF
Probability Mass Function of the Multinomial Distribution.
|
class |
MultivariateGaussian
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
static class |
MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
class |
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.
|
class |
MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>
A LinearMixtureModel of multivariate distributions with associated PDFs.
|
static class |
MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>>
PDF of the MultivariateMixtureDensityModel
|
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.
|
static class |
MultivariatePolyaDistribution.PMF
PMF of the MultivariatePolyaDistribution
|
class |
MultivariateStudentTDistribution
Multivariate generalization of the noncentral Student's t-distribution.
|
static class |
MultivariateStudentTDistribution.PDF
PDF of the MultivariateStudentTDistribution
|
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.
|
static class |
NegativeBinomialDistribution.CDF
CDF of the NegativeBinomialDistribution
|
static class |
NegativeBinomialDistribution.PMF
PMF of the NegativeBinomialDistribution.
|
class |
NormalInverseGammaDistribution
The normal inverse-gamma distribution is the product of a univariate
Gaussian distribution with an inverse-gamma distribution.
|
static class |
NormalInverseGammaDistribution.PDF
PDF of the NormalInverseGammaDistribution
|
class |
NormalInverseWishartDistribution
The normal inverse Wishart distribution
|
static class |
NormalInverseWishartDistribution.PDF
PDF of the normal inverse-Wishart distribution.
|
class |
ParetoDistribution
This class describes the Pareto distribution, sometimes called the Bradford
Distribution.
|
static class |
ParetoDistribution.CDF
CDF of the Pareto Distribution.
|
static class |
ParetoDistribution.PDF
PDF of the ParetoDistribution
|
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.
|
static class |
PoissonDistribution.CDF
CDF of the PoissonDistribution
|
static class |
PoissonDistribution.PMF
PMF of the PoissonDistribution.
|
class |
ScalarMixtureDensityModel
ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density
model.
|
static class |
ScalarMixtureDensityModel.CDF
CDFof the SMDM
|
static class |
ScalarMixtureDensityModel.PDF
PDF of the SMDM
|
class |
SnedecorFDistribution
CDF of the Snedecor F-distribution (also known as Fisher F-distribution,
Fisher-Snedecor F-distribution, or just plain old F-distribution).
|
static class |
SnedecorFDistribution.CDF
CDF of the F-distribution.
|
class |
StudentizedRangeDistribution
Implementation of the Studentized Range distribution, which defines the
population correction factor when performing multiple comparisons.
|
static class |
StudentizedRangeDistribution.CDF
CDF of the StudentizedRangeDistribution
|
class |
StudentTDistribution
Defines a noncentral Student-t Distribution.
|
static class |
StudentTDistribution.CDF
Evaluator that computes the Cumulative Distribution Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
static class |
StudentTDistribution.PDF
Evaluator that computes the Probability Density Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
class |
UniformDistribution
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
static class |
UniformDistribution.CDF
Cumulative Distribution Function of a uniform
|
static class |
UniformDistribution.PDF
Probability density function 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.CDF
Implements the cumulative distribution function for the discrete
uniform distribution.
|
static class |
UniformIntegerDistribution.PMF
Probability mass function of a discrete uniform distribution.
|
class |
UnivariateGaussian
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
static class |
UnivariateGaussian.CDF
CDF of the underlying Gaussian.
|
static class |
UnivariateGaussian.CDF.Inverse
Inverts the CumulativeDistribution function.
|
static class |
UnivariateGaussian.PDF
PDF of the underlying Gaussian.
|
class |
WeibullDistribution
Describes a Weibull distribution, which is often used to describe the
mortality, lifespan, or size distribution of objects.
|
static class |
WeibullDistribution.CDF
CDF of the Weibull distribution
|
static class |
WeibullDistribution.PDF
PDF of the Weibull distribution
|
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).
|
static class |
YuleSimonDistribution.CDF
CDF of the Yule-Simon Distribution
|
static class |
YuleSimonDistribution.PMF
PMF of the Yule-Simon Distribution
|
| Modifier and Type | Method and Description |
|---|---|
MatrixBasedTermSimilarityNetwork |
TermVectorSimilarityNetworkCreator.create(java.util.Collection<? extends Vectorizable> documents,
TermIndex termIndex)
Creates a new similarity network between the terms in the given
documents.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractVectorSpaceModel.add(Vectorizable document) |
void |
VectorSpaceModel.add(Vectorizable document)
Adds a document to the model.
|
double |
CosineSimilarityFunction.evaluate(Vectorizable from,
Vectorizable to) |
boolean |
AbstractVectorSpaceModel.remove(Vectorizable document) |
boolean |
VectorSpaceModel.remove(Vectorizable document)
Removes the document from the model.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractVectorSpaceModel.addAll(java.lang.Iterable<? extends Vectorizable> documents) |
void |
VectorSpaceModel.addAll(java.lang.Iterable<? extends Vectorizable> documents)
Adds all of the given documents to the model.
|
boolean |
AbstractVectorSpaceModel.removeAll(java.lang.Iterable<? extends Vectorizable> documents) |
boolean |
VectorSpaceModel.removeAll(java.lang.Iterable<? extends Vectorizable> documents)
Removes all of the given documents from the model.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
AbstractLocalTermWeighter.computeLocalWeights(Vectorizable document) |
Vector |
LocalTermWeighter.computeLocalWeights(Vectorizable document)
Computes the new local weights for a given document.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
LatentSemanticAnalysis.Transform.evaluate(Vectorizable input) |
Vector |
ProbabilisticLatentSemanticAnalysis.Result.evaluate(Vectorizable input) |
| Modifier and Type | Method and Description |
|---|---|
LatentSemanticAnalysis.Transform |
LatentSemanticAnalysis.learn(java.util.Collection<? extends Vectorizable> documents) |