| Package | Description |
|---|---|
| gov.sandia.cognition.data.convert.vector |
Provides utilities for doing data type conversion with vectors.
|
| gov.sandia.cognition.framework.learning.converter |
Provides implementations of
CogxelConverters. |
| gov.sandia.cognition.framework.lite |
Provides a lightweight implementation of the Cognitive Framework.
|
| 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.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.delta |
Provides an abstract class for helping to implement variants of the Burrows'
Delta algorithm.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.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.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.semisupervised.valence | |
| 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.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.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 |
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.
|
| gov.sandia.cognition.text.term.vector.weighter.normalize |
Provides term weight normalization algorithms.
|
| gov.sandia.cognition.text.topic |
Provides topic modeling algorithms.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
AbstractToVectorEncoder.evaluate(InputType input)
Converts the given object to a
Vector. |
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractToVectorEncoder.encode(InputType object,
Vector vector) |
void |
DataToVectorEncoder.encode(InputType object,
Vector vector)
Encodes the given object into the given
Vector. |
void |
DataToVectorEncoder.encode(InputType object,
Vector vector,
int startIndex)
Encodes the given object into the given
Vector, starting at the
given index. |
void |
NumberConverterToVectorAdapter.encode(InputType object,
Vector vector,
int index)
Encodes the given object into the vector at the given index by using the
number converter that this object is adapting.
|
void |
UniqueBooleanVectorEncoder.encode(InputType object,
Vector vector,
int startIndex)
Encodes the given object into the given vector at the given starting
index by using a unique boolean encoding, where the given input value is
compared to each of the encoder's values using equality.
|
void |
NumberToVectorEncoder.encode(java.lang.Number number,
Vector vector,
int index)
Encodes the given number into the given vector at the given index.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
CogxelVectorConverter.createEmptyVector()
Creates an empty Vector for the converter, of the proper dimensionality.
|
Vector |
CogxelVectorConverter.fromCogxels(CogxelState cogxels)
Converts from a CogxelState object to an object of type DataType.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Vector> |
CogxelVectorCollectionConverter.fromCogxels(CogxelState cogxels)
Converts from a CogxelState object to an object of type DataType.
|
| Modifier and Type | Method and Description |
|---|---|
void |
CogxelVectorConverter.toCogxels(Vector data,
CogxelState cogxels)
Converts from an object of type DataType to an updated CogxelState.
|
| Modifier and Type | Method and Description |
|---|---|
void |
CogxelVectorCollectionConverter.toCogxels(java.util.Collection<Vector> data,
CogxelState cogxels)
Converts from an object of type DataType to an updated CogxelState.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
PatternRecognizerLite.createEmptyInputVector()
Creates an empty vector to use for input.
|
Vector |
SimplePatternRecognizer.createEmptyInputVector()
Creates an empty vector to use for input.
|
Vector |
SimplePatternRecognizerState.getStateVector()
Gets the state vector stored in the object, which contains the main
state data of the recognizer.
|
Vector |
VectorBasedCognitiveModelInput.getValues()
Getter for values
|
Vector |
PatternRecognizerLite.recognize(CognitiveModuleState state,
Vector inputs)
Computes the recognition.
|
Vector |
SimplePatternRecognizer.recognize(CognitiveModuleState state,
Vector inputs)
Computes the recognition.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
PatternRecognizerLite.recognize(CognitiveModuleState state,
Vector inputs)
Computes the recognition.
|
Vector |
SimplePatternRecognizer.recognize(CognitiveModuleState state,
Vector inputs)
Computes the recognition.
|
void |
SimplePatternRecognizerState.setStateVector(Vector stateVector)
Sets the state vector stored in the state object.
|
protected void |
VectorBasedCognitiveModelInput.setValues(Vector values)
Setter for values
|
| Constructor and Description |
|---|
SimplePatternRecognizerState(java.util.Collection<SemanticLabel> labels,
Vector stateVector)
Creats a new instance of SimplePatternRecognizerState.
|
SimplePatternRecognizerState(java.util.Collection<SemanticLabel> labels,
Vector stateVector,
boolean copyState)
Creats a new instance of SimplePatternRecognizerState.
|
VectorBasedCognitiveModelInput(SemanticIdentifier[] identifiers,
Vector values)
Creates a new instance of VectorBasedCognitiveModelInput
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
VectorizablePerturber.perturbVector(Vector input)
Perturbs the given vector using the underlying random number generator.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
VectorizablePerturber.perturbVector(Vector input)
Perturbs the given vector using the underlying random number generator.
|
| Modifier and Type | Method and Description |
|---|---|
double |
VectorNaiveBayesCategorizer.computeLogPosterior(Vector input,
CategoryType category)
Computes the log-posterior probability that the input belongs to the
given category.
|
double |
VectorNaiveBayesCategorizer.computePosterior(Vector input,
CategoryType category)
Computes the posterior probability that the input belongs to the
given category.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MiniBatchKMeansClusterer<DataType extends Vector>
Approximates k-means clustering by working on random subsets of the
data.
|
static class |
MiniBatchKMeansClusterer.Builder<DataType extends Vector>
Can be used to create custom
MiniBatchKMeansClusterers without
using the big constructor. |
| Modifier and Type | Method and Description |
|---|---|
ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create() |
ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create() |
static PartitionalClusterer<Vector,CentroidCluster<Vector>> |
PartitionalClusterer.create(int numRequestedClusters)
Create a partitional clusterer, using Euclidean distance and a vector
mean centroid cluster creator.
|
static PartitionalClusterer<Vector,CentroidCluster<Vector>> |
PartitionalClusterer.create(int numRequestedClusters)
Create a partitional clusterer, using Euclidean distance and a vector
mean centroid cluster creator.
|
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create(int numClusters,
java.util.Random random)
Creates a new parallelized k-means clustering algorithm for vector data
with the given number of clusters (k) and random number generator.
|
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create(int numClusters,
java.util.Random random)
Creates a new parallelized k-means clustering algorithm for vector data
with the given number of clusters (k) and random number generator.
|
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create(int numClusters,
Semimetric<? super Vector> distanceMetric,
java.util.Random random)
Creates a new parallelized k-means clustering algorithm for vector data
with the given number of clusters (k), distance metric, and random
number generator.
|
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create(int numClusters,
Semimetric<? super Vector> distanceMetric,
java.util.Random random)
Creates a new parallelized k-means clustering algorithm for vector data
with the given number of clusters (k), distance metric, and random
number generator.
|
java.util.Collection<? extends Vector> |
DirichletProcessClustering.getData() |
| Modifier and Type | Method and Description |
|---|---|
protected int[] |
MiniBatchKMeansClusterer.assignDataToClusters(java.util.Collection<? extends Vector> data) |
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> |
KMeansFactory.create(int numClusters,
Semimetric<? super Vector> distanceMetric,
java.util.Random random)
Creates a new parallelized k-means clustering algorithm for vector data
with the given number of clusters (k), distance metric, and random
number generator.
|
java.util.ArrayList<GaussianCluster> |
DirichletProcessClustering.learn(java.util.Collection<? extends Vector> data) |
void |
MiniBatchKMeansClusterer.setData(java.util.Collection<? extends Vector> data)
Set the data to be clustered.
|
MiniBatchKMeansClusterer.Builder<DataType> |
MiniBatchKMeansClusterer.Builder.withCreator(ClusterCreator<MiniBatchCentroidCluster,Vector> creator) |
MiniBatchKMeansClusterer.Builder<DataType> |
MiniBatchKMeansClusterer.Builder.withInitializer(FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer) |
| Constructor and Description |
|---|
Builder(int numClusters,
Semimetric<? super Vector> metric)
Create a mini-batch k-means clusterer builder and set it to
the given number of clusters.
|
DirichletProcessClustering(DirichletProcessMixtureModel<Vector> algorithm)
Creates a new instance of DirichletProcessClustering
|
MiniBatchKMeansClusterer(int numClusters,
int maxIterations,
FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer,
Semimetric<? super Vector> metric,
ClusterCreator<MiniBatchCentroidCluster,Vector> creator,
java.util.Random random)
Creates a new
MiniBatchKMeansClusterer. |
MiniBatchKMeansClusterer(int numClusters,
int maxIterations,
FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer,
Semimetric<? super Vector> metric,
ClusterCreator<MiniBatchCentroidCluster,Vector> creator,
java.util.Random random)
Creates a new
MiniBatchKMeansClusterer. |
MiniBatchKMeansClusterer(int numClusters,
int maxIterations,
FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer,
Semimetric<? super Vector> metric,
ClusterCreator<MiniBatchCentroidCluster,Vector> creator,
java.util.Random random)
Creates a new
MiniBatchKMeansClusterer. |
| Modifier and Type | Method and Description |
|---|---|
CentroidCluster<Vector> |
VectorMeanCentroidClusterCreator.createCluster() |
CentroidCluster<Vector> |
VectorMeanCentroidClusterCreator.createCluster(java.util.Collection<? extends Vector> members) |
| Modifier and Type | Method and Description |
|---|---|
void |
VectorMeanCentroidClusterCreator.addClusterMember(CentroidCluster<Vector> cluster,
Vector member) |
boolean |
VectorMeanCentroidClusterCreator.removeClusterMember(CentroidCluster<Vector> cluster,
Vector member) |
void |
MiniBatchCentroidCluster.updateCluster(Vector dataPoint)
Updates the cluster for the given point.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VectorMeanCentroidClusterCreator.addClusterMember(CentroidCluster<Vector> cluster,
Vector member) |
GaussianCluster |
GaussianClusterCreator.createCluster(java.util.Collection<? extends Vector> members)
Creates a GaussianCluster from a given set of vectors by fitting a
Gaussian to those vectors.
|
CentroidCluster<Vector> |
VectorMeanCentroidClusterCreator.createCluster(java.util.Collection<? extends Vector> members) |
MiniBatchCentroidCluster |
VectorMeanMiniBatchCentroidClusterCreator.createCluster(java.util.Collection<? extends Vector> members) |
boolean |
VectorMeanCentroidClusterCreator.removeClusterMember(CentroidCluster<Vector> cluster,
Vector member) |
void |
MiniBatchCentroidCluster.updateCluster(java.util.Collection<? extends Vector> dataPoints)
Updates the clusters for all the given points.
|
| Constructor and Description |
|---|
GaussianCluster(java.util.Collection<? extends Vector> members,
MultivariateGaussian.PDF gaussian)
Creates a new instance of GaussianCluster.
|
GaussianCluster(int index,
java.util.Collection<? extends Vector> members,
MultivariateGaussian.PDF gaussian)
Creates a new instance of GaussianCluster.
|
MiniBatchCentroidCluster(java.util.Collection<? extends Vector> initialPoints) |
| Modifier and Type | Method and Description |
|---|---|
double |
GaussianClusterDivergenceFunction.evaluate(GaussianCluster first,
Vector second)
Evaluates the divergence between the Gaussian cluster and the given
vector, which is the negative of likelihood that the cluster was
generated.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<GaussianCluster> |
NeighborhoodGaussianClusterInitializer.initializeClusters(int numClusters,
java.util.Collection<? extends Vector> elements) |
| Modifier and Type | Method and Description |
|---|---|
void |
AdaptiveRegularizationOfWeights.update(DefaultConfidenceWeightedBinaryCategorizer target,
Vector input,
boolean label)
Perform an update for the target using the given input and associated
label.
|
void |
ConfidenceWeightedDiagonalDeviation.update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vector input,
boolean label)
Updates the target using the given input and associated label.
|
void |
ConfidenceWeightedDiagonalDeviationProject.update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vector input,
boolean label) |
void |
ConfidenceWeightedDiagonalVariance.update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vector input,
boolean label)
Updates the target using the given input and associated label.
|
void |
ConfidenceWeightedDiagonalVarianceProject.update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vector input,
boolean label) |
| Modifier and Type | Field and Description |
|---|---|
protected java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> |
AbstractDeltaCategorizer.AbstractLearner.trainingSet
The training set.
|
| Modifier and Type | Method and Description |
|---|---|
CategoryType |
BurrowsDeltaCategorizer.evaluate(Vector unknownInput)
Evaluates an unknown input, but does not return the discriminant value.
|
CategoryType |
CosineDeltaCategorizer.evaluate(Vector unknownInput)
Evaluates an unknown input, but does not return the discriminant value.
|
abstract ValueDiscriminantPair<CategoryType,java.lang.Double> |
AbstractDeltaCategorizer.evaluateWithDiscriminant(Vector unknownInput)
This abstract method should implement evaluation aspect of this general
algorithm.
|
ValueDiscriminantPair<CategoryType,java.lang.Double> |
BurrowsDeltaCategorizer.evaluateWithDiscriminant(Vector unknownVector)
This method implements the evaluation aspect of BurrowsDelta.
|
ValueDiscriminantPair<CategoryType,java.lang.Double> |
CosineDeltaCategorizer.evaluateWithDiscriminant(Vector unknownVector)
This method implements the evaluation aspect of CosineDelta.
|
| Modifier and Type | Method and Description |
|---|---|
abstract AbstractDeltaCategorizer<CategoryType> |
AbstractDeltaCategorizer.AbstractLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> trainingSet)
Method that does the training.
|
BurrowsDeltaCategorizer<CategoryType> |
BurrowsDeltaCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> trainingSet) |
CosineDeltaCategorizer<CategoryType> |
CosineDeltaCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> trainingSet) |
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
FactorizationMachine.weights
The weight vector (w) for each dimension.
|
| Modifier and Type | Field and Description |
|---|---|
protected java.util.ArrayList<? extends InputOutputPair<? extends Vector,java.lang.Double>> |
FactorizationMachineAlternatingLeastSquares.dataList
The data in the form that it can be accessed in O(1) as a list.
|
protected java.util.ArrayList<? extends InputOutputPair<? extends Vector,java.lang.Double>> |
FactorizationMachineStochasticGradient.dataList
The input data represented as a list for fast access.
|
protected java.util.ArrayList<Vector> |
FactorizationMachineAlternatingLeastSquares.inputsTransposed
A list representing a transposed form of the matrix of inputs.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
FactorizationMachine.computeParameterGradient(Vector input) |
Vector |
FactorizationMachine.convertToVector() |
Vector |
FactorizationMachine.getWeights()
Gets the weight vector.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
FactorizationMachine.computeParameterGradient(Vector input) |
void |
FactorizationMachine.convertFromVector(Vector parameters) |
double |
FactorizationMachine.evaluateAsDouble(Vector input) |
void |
FactorizationMachine.setWeights(Vector weights)
Sets the weight vector.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
FactorizationMachineStochasticGradient.update(InputOutputPair<? extends Vector,java.lang.Double> example)
Performs a single update of step of the stochastic gradient descent
by updating according to the given example.
|
| Constructor and Description |
|---|
FactorizationMachine(double bias,
Vector weights,
Matrix factors)
Creates a new
FactorizationMachine with the given parameters. |
| Modifier and Type | Method and Description |
|---|---|
Vector |
GradientDescendableApproximator.convertToVector() |
Vector |
GradientDescendableApproximator.evaluate(Vector input) |
| Modifier and Type | Method and Description |
|---|---|
Matrix |
GradientDescendable.computeParameterGradient(Vector input)
Computes the derivative of the function about the input with respect
to the parameters of the function.
|
Matrix |
GradientDescendableApproximator.computeParameterGradient(Vector input)
Computes a forward-differences approximation to the parameter Jacobian
|
static Matrix |
GradientDescendableApproximator.computeParameterGradient(VectorizableVectorFunction function,
Vector input,
double deltaSize)
Computes a forward-differences approximation to the parameter Jacobian
|
void |
GradientDescendableApproximator.convertFromVector(Vector parameters) |
Vector |
GradientDescendableApproximator.evaluate(Vector input) |
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
ParallelHiddenMarkovModel.StateObservationLikelihoodTask.alpha
Alpha at time n.
|
protected Vector |
ParallelHiddenMarkovModel.StateObservationLikelihoodTask.beta
Beta at time n.
|
protected Vector |
MarkovChain.initialProbability
Initial probability Vector over the states.
|
| Modifier and Type | Field and Description |
|---|---|
protected java.util.ArrayList<Vector> |
BaumWelchAlgorithm.sequenceGammas
The list of all gammas from each sequence
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
ParallelHiddenMarkovModel.StateObservationLikelihoodTask.call() |
Vector |
HiddenMarkovModel.computeObservationLikelihoods(ObservationType observation)
Computes the conditionally independent likelihoods
for each state given the observation.
|
protected static Vector |
HiddenMarkovModel.computeStateObservationLikelihood(Vector alpha,
Vector beta,
double scaleFactor)
Computes the probability of the various states at a time instance given
the observation sequence.
|
protected static Vector |
MarkovChain.createUniformInitialProbability(int numStates)
Creates a uniform initial-probability Vector
|
Vector |
MarkovChain.getFutureStateDistribution(Vector current,
int numSteps)
Simulates the Markov chain into the future, given the transition Matrix
and the given current state-probability distribution, for the
given number of time steps into the future.
|
Vector |
MarkovChain.getInitialProbability()
Getter for initialProbability.
|
Vector |
MarkovChain.getSteadyStateDistribution()
Returns the steady-state distribution of the state distribution.
|
protected Vector |
BaumWelchAlgorithm.updateInitialProbabilities(java.util.ArrayList<Vector> firstGammas)
Updates the initial probabilities from sequenceGammas
|
| Modifier and Type | Method and Description |
|---|---|
protected java.util.ArrayList<WeightedValue<Vector>> |
HiddenMarkovModel.computeBackwardProbabilities(java.util.ArrayList<Vector> b,
java.util.ArrayList<WeightedValue<Vector>> alphas)
Computes the backward-probabilities for the given observation likelihoods
and the weights from the alphas.
|
protected WeightedValue<Vector> |
HiddenMarkovModel.computeBackwardProbabilities(Vector beta,
Vector b,
double weight)
Computes the backward probability recursion.
|
protected java.util.ArrayList<WeightedValue<Vector>> |
HiddenMarkovModel.computeForwardProbabilities(java.util.ArrayList<Vector> b,
boolean normalize)
Computes the forward probabilities for the given observation likelihood
sequence.
|
protected WeightedValue<Vector> |
HiddenMarkovModel.computeForwardProbabilities(Vector alpha,
Vector b,
boolean normalize)
Computes the recursive solution to the forward probabilities of the
HMM.
|
protected java.util.ArrayList<Vector> |
HiddenMarkovModel.computeObservationLikelihoods(java.util.Collection<? extends ObservationType> observations)
Computes the conditionally independent likelihoods
for each state given the observation sequence.
|
protected Pair<java.util.ArrayList<java.util.ArrayList<Vector>>,java.util.ArrayList<Matrix>> |
BaumWelchAlgorithm.computeSequenceParameters()
Computes the gammas and A matrices for each sequence.
|
protected java.util.ArrayList<Vector> |
HiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
double scaleFactor)
Computes the probabilities of the various states over time given the
observation sequence.
|
protected java.util.ArrayList<Vector> |
ParallelHiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
double scaleFactor) |
protected Pair<Vector,int[]> |
HiddenMarkovModel.computeViterbiRecursion(Vector delta,
Vector bn)
Computes the Viterbi recursion for a given "delta" and "b"
|
protected Pair<Vector,int[]> |
ParallelHiddenMarkovModel.computeViterbiRecursion(Vector delta,
Vector bn) |
java.util.ArrayList<Vector> |
HiddenMarkovModel.stateBeliefs(java.util.Collection<? extends ObservationType> observations)
Computes the probability distribution over all states for each
observation.
|
| Modifier and Type | Method and Description |
|---|---|
protected WeightedValue<Vector> |
HiddenMarkovModel.computeBackwardProbabilities(Vector beta,
Vector b,
double weight)
Computes the backward probability recursion.
|
protected WeightedValue<Vector> |
HiddenMarkovModel.computeForwardProbabilities(Vector alpha,
Vector b,
boolean normalize)
Computes the recursive solution to the forward probabilities of the
HMM.
|
protected void |
HiddenMarkovModel.computeObservationLikelihoods(ObservationType observation,
Vector b)
Computes the conditionally independent likelihoods
for each state given the observation.
|
protected static Vector |
HiddenMarkovModel.computeStateObservationLikelihood(Vector alpha,
Vector beta,
double scaleFactor)
Computes the probability of the various states at a time instance given
the observation sequence.
|
protected static Matrix |
HiddenMarkovModel.computeTransitions(Vector alphan,
Vector betanp1,
Vector bnp1)
Computes the stochastic transition-probability matrix from the
given probabilities.
|
protected Pair<Vector,int[]> |
HiddenMarkovModel.computeViterbiRecursion(Vector delta,
Vector bn)
Computes the Viterbi recursion for a given "delta" and "b"
|
protected Pair<Vector,int[]> |
ParallelHiddenMarkovModel.computeViterbiRecursion(Vector delta,
Vector bn) |
protected WeightedValue<java.lang.Integer> |
HiddenMarkovModel.findMostLikelyState(int destinationState,
Vector delta)
Finds the most-likely next state given the previous "delta" in the
Viterbi algorithm.
|
Vector |
MarkovChain.getFutureStateDistribution(Vector current,
int numSteps)
Simulates the Markov chain into the future, given the transition Matrix
and the given current state-probability distribution, for the
given number of time steps into the future.
|
void |
MarkovChain.setInitialProbability(Vector initialProbability)
Setter for initialProbability
|
| Modifier and Type | Method and Description |
|---|---|
protected java.util.ArrayList<WeightedValue<Vector>> |
HiddenMarkovModel.computeBackwardProbabilities(java.util.ArrayList<Vector> b,
java.util.ArrayList<WeightedValue<Vector>> alphas)
Computes the backward-probabilities for the given observation likelihoods
and the weights from the alphas.
|
protected java.util.ArrayList<WeightedValue<Vector>> |
HiddenMarkovModel.computeBackwardProbabilities(java.util.ArrayList<Vector> b,
java.util.ArrayList<WeightedValue<Vector>> alphas)
Computes the backward-probabilities for the given observation likelihoods
and the weights from the alphas.
|
protected java.util.ArrayList<WeightedValue<Vector>> |
HiddenMarkovModel.computeForwardProbabilities(java.util.ArrayList<Vector> b,
boolean normalize)
Computes the forward probabilities for the given observation likelihood
sequence.
|
protected java.util.ArrayList<Vector> |
HiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
double scaleFactor)
Computes the probabilities of the various states over time given the
observation sequence.
|
protected java.util.ArrayList<Vector> |
HiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
double scaleFactor)
Computes the probabilities of the various states over time given the
observation sequence.
|
protected java.util.ArrayList<Vector> |
ParallelHiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
double scaleFactor) |
protected java.util.ArrayList<Vector> |
ParallelHiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
double scaleFactor) |
protected Matrix |
HiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
java.util.ArrayList<Vector> b)
Computes the stochastic transition-probability matrix from the
given probabilities.
|
protected Matrix |
HiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
java.util.ArrayList<Vector> b)
Computes the stochastic transition-probability matrix from the
given probabilities.
|
protected Matrix |
HiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
java.util.ArrayList<Vector> b)
Computes the stochastic transition-probability matrix from the
given probabilities.
|
protected Matrix |
ParallelHiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
java.util.ArrayList<Vector> b) |
protected Matrix |
ParallelHiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
java.util.ArrayList<Vector> b) |
protected Matrix |
ParallelHiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas,
java.util.ArrayList<WeightedValue<Vector>> betas,
java.util.ArrayList<Vector> b) |
void |
ParallelBaumWelchAlgorithm.DistributionEstimatorTask.setGammas(java.util.ArrayList<Vector> gammas)
Sets the gamma samples pointer.
|
protected Vector |
BaumWelchAlgorithm.updateInitialProbabilities(java.util.ArrayList<Vector> firstGammas)
Updates the initial probabilities from sequenceGammas
|
protected java.util.ArrayList<ProbabilityFunction<ObservationType>> |
BaumWelchAlgorithm.updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas)
Updates the probability function from the concatenated gammas from
all sequences
|
protected java.util.ArrayList<ProbabilityFunction<ObservationType>> |
ParallelBaumWelchAlgorithm.updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas) |
| Constructor and Description |
|---|
HiddenMarkovModel(Vector initialProbability,
Matrix transitionProbability,
java.util.Collection<? extends ComputableDistribution<ObservationType>> emissionFunctions)
Creates a new instance of ContinuousDensityHiddenMarkovModel
|
MarkovChain(Vector initialProbability,
Matrix transitionProbability)
Creates a new instance of ContinuousDensityHiddenMarkovModel
|
ParallelHiddenMarkovModel(Vector initialProbability,
Matrix transitionProbability,
java.util.Collection<? extends ComputableDistribution<ObservationType>> emissionFunctions)
Creates a new instance of ParallelHiddenMarkovModel
|
| Modifier and Type | Method and Description |
|---|---|
protected Vector |
FunctionMinimizerNelderMead.computeSimplexInputSum()
Computes the sum of input values in the simplex
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<DefaultInputOutputPair<Vector,java.lang.Double>> |
FunctionMinimizerNelderMead.initializeSimplex(InputOutputPair<Vector,java.lang.Double> initialPoint,
double offsetValue)
Initializes the simplex about the initial point
|
| Modifier and Type | Method and Description |
|---|---|
static boolean |
FunctionMinimizerBFGS.BFGSupdateRule(Matrix hessianInverse,
Vector delta,
Vector gamma,
double tolerance)
BFGS Quasi-Newton update rule
|
protected abstract double |
FunctionMinimizerConjugateGradient.computeScaleFactor(Vector gradientCurrent,
Vector gradientPrevious)
Computes the conjugate gradient parameter for the particular update
scheme.
|
protected double |
FunctionMinimizerFletcherReeves.computeScaleFactor(Vector gradientCurrent,
Vector gradientPrevious) |
protected double |
FunctionMinimizerLiuStorey.computeScaleFactor(Vector gradientCurrent,
Vector gradientPrevious) |
protected double |
FunctionMinimizerPolakRibiere.computeScaleFactor(Vector gradientCurrent,
Vector gradientPrevious) |
static boolean |
MinimizationStoppingCriterion.convergence(Vector xnew,
java.lang.Double fxnew,
Vector gradient,
Vector delta,
double tolerance)
Tests for convergence on approximately zero slope and nonmovement along
the x-axis
|
boolean |
FunctionMinimizerBFGS.updateHessianInverse(Matrix hessianInverse,
Vector delta,
Vector gamma) |
protected boolean |
FunctionMinimizerDFP.updateHessianInverse(Matrix hessianInverse,
Vector delta,
Vector gamma) |
protected abstract boolean |
FunctionMinimizerQuasiNewton.updateHessianInverse(Matrix hessianInverse,
Vector delta,
Vector gamma)
The step that makes BFGS/DFP/SR1 different from each other.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<DefaultInputOutputPair<Vector,java.lang.Double>> |
FunctionMinimizerNelderMead.initializeSimplex(InputOutputPair<Vector,java.lang.Double> initialPoint,
double offsetValue)
Initializes the simplex about the initial point
|
| Constructor and Description |
|---|
FunctionMinimizerBFGS(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerBFGS
|
FunctionMinimizerConjugateGradient(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerConjugateGradient
|
FunctionMinimizerDFP(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerBFGS
|
FunctionMinimizerDirectionSetPowell(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerDirectionSetPowell
|
FunctionMinimizerFletcherReeves(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerConjugateGradient
|
FunctionMinimizerGradientDescent(double learningRate,
double momentum,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerGradientDescent
|
FunctionMinimizerLiuStorey(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerLiuStorey
|
FunctionMinimizerPolakRibiere(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerConjugateGradient
|
FunctionMinimizerQuasiNewton(LineMinimizer<?> lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations)
Creates a new instance of FunctionMinimizerBFGS
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
DirectionalVectorToScalarFunction.computeVector(double scaleFactor)
Transforms the scaleFactor into a multidimensional Vector using the
direction
|
Vector |
DirectionalVectorToScalarFunction.getDirection()
Getter for direction
|
Vector |
DirectionalVectorToScalarFunction.getVectorOffset()
Getter for vectorOffset
|
| Modifier and Type | Method and Description |
|---|---|
InputOutputPair<Vector,Vector> |
DirectionalVectorToDifferentiableScalarFunction.getLastGradient()
Getter for lastGradient
|
InputOutputPair<Vector,Vector> |
DirectionalVectorToDifferentiableScalarFunction.getLastGradient()
Getter for lastGradient
|
Evaluator<? super Vector,? extends java.lang.Double> |
DirectionalVectorToScalarFunction.getVectorScalarFunction()
Getter for vectorScalarFunction
|
WeightedInputOutputPair<Vector,java.lang.Double> |
AbstractAnytimeLineMinimizer.minimizeAlongDirection(DirectionalVectorToScalarFunction function,
java.lang.Double functionValue,
Vector gradient) |
WeightedInputOutputPair<Vector,java.lang.Double> |
LineMinimizer.minimizeAlongDirection(DirectionalVectorToScalarFunction function,
java.lang.Double functionValue,
Vector gradient)
Minimizes a Vector function along the direction given by the
DirectionalVectorToScalarFunction.
|
| Modifier and Type | Method and Description |
|---|---|
WeightedInputOutputPair<Vector,java.lang.Double> |
AbstractAnytimeLineMinimizer.minimizeAlongDirection(DirectionalVectorToScalarFunction function,
java.lang.Double functionValue,
Vector gradient) |
WeightedInputOutputPair<Vector,java.lang.Double> |
LineMinimizer.minimizeAlongDirection(DirectionalVectorToScalarFunction function,
java.lang.Double functionValue,
Vector gradient)
Minimizes a Vector function along the direction given by the
DirectionalVectorToScalarFunction.
|
void |
DirectionalVectorToScalarFunction.setDirection(Vector direction)
Setter for direction
|
void |
DirectionalVectorToScalarFunction.setVectorOffset(Vector vectorOffset)
Point to use as input to vectorFunction
|
| Modifier and Type | Method and Description |
|---|---|
void |
DirectionalVectorToDifferentiableScalarFunction.setLastGradient(InputOutputPair<Vector,Vector> lastGradient)
Setter for lastGradient
|
void |
DirectionalVectorToDifferentiableScalarFunction.setLastGradient(InputOutputPair<Vector,Vector> lastGradient)
Setter for lastGradient
|
void |
DirectionalVectorToScalarFunction.setVectorScalarFunction(Evaluator<? super Vector,? extends java.lang.Double> vectorScalarFunction)
Setter for vectorScalarFunction
|
| Constructor and Description |
|---|
DirectionalVectorToDifferentiableScalarFunction(DifferentiableEvaluator<? super Vector,? extends java.lang.Double,Vector> vectorScalarFunction,
Vector vectorOffset,
Vector direction)
Creates a new instance of DirectionalVectorToDifferentiableScalarFunction
|
DirectionalVectorToScalarFunction(Evaluator<? super Vector,? extends java.lang.Double> vectorScalarFunction,
Vector vectorOffset,
Vector direction)
Creates a new function that restricts the vectorFunction to a
particular vector direction
|
| Constructor and Description |
|---|
DirectionalVectorToDifferentiableScalarFunction(DifferentiableEvaluator<? super Vector,? extends java.lang.Double,Vector> vectorScalarFunction,
Vector vectorOffset,
Vector direction)
Creates a new instance of DirectionalVectorToDifferentiableScalarFunction
|
DirectionalVectorToDifferentiableScalarFunction(DifferentiableEvaluator<? super Vector,? extends java.lang.Double,Vector> vectorScalarFunction,
Vector vectorOffset,
Vector direction)
Creates a new instance of DirectionalVectorToDifferentiableScalarFunction
|
DirectionalVectorToScalarFunction(Evaluator<? super Vector,? extends java.lang.Double> vectorScalarFunction,
Vector vectorOffset,
Vector direction)
Creates a new function that restricts the vectorFunction to a
particular vector direction
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
IterativeMatrixSolver.rhs
The right-hand-side vector (b).
|
protected Vector |
IterativeMatrixSolver.x0
The initial guess for the left-hand-side vector (x).
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MatrixVectorMultiplier.evaluate(Vector input)
Returns m times input.
|
Vector |
OverconstrainedMatrixVectorMultiplier.evaluate(Vector input)
Returns m times input.
|
Vector |
IterativeMatrixSolver.getInitialGuess()
Returns the initial guess at "x"
|
Vector |
OverconstrainedMatrixVectorMultiplier.transposeMult(Vector input)
Return A^(T) * input.
|
| Modifier and Type | Method and Description |
|---|---|
protected InputOutputPair<Vector,Vector> |
ConjugateGradientMatrixSolver.completeSolver() |
protected InputOutputPair<Vector,Vector> |
ConjugateGradientMatrixSolver.completeSolver() |
protected InputOutputPair<Vector,Vector> |
ConjugateGradientWithPreconditionerMatrixSolver.completeSolver() |
protected InputOutputPair<Vector,Vector> |
ConjugateGradientWithPreconditionerMatrixSolver.completeSolver() |
protected abstract InputOutputPair<Vector,Vector> |
IterativeMatrixSolver.completeSolver()
Called after the final iteration.
|
protected abstract InputOutputPair<Vector,Vector> |
IterativeMatrixSolver.completeSolver()
Called after the final iteration.
|
protected InputOutputPair<Vector,Vector> |
OverconstrainedConjugateGradientMatrixMinimizer.completeSolver() |
protected InputOutputPair<Vector,Vector> |
OverconstrainedConjugateGradientMatrixMinimizer.completeSolver() |
protected InputOutputPair<Vector,Vector> |
SteepestDescentMatrixSolver.completeSolver() |
protected InputOutputPair<Vector,Vector> |
SteepestDescentMatrixSolver.completeSolver() |
InputOutputPair<Vector,Vector> |
IterativeMatrixSolver.getResult() |
InputOutputPair<Vector,Vector> |
IterativeMatrixSolver.getResult() |
InputOutputPair<Vector,Vector> |
IterativeMatrixSolver.learn(Operator function)
Shell that solves for Ax = b (x0 and rhs passed in on initialization, A
is contained in function).
|
InputOutputPair<Vector,Vector> |
IterativeMatrixSolver.learn(Operator function)
Shell that solves for Ax = b (x0 and rhs passed in on initialization, A
is contained in function).
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MatrixVectorMultiplier.evaluate(Vector input)
Returns m times input.
|
Vector |
OverconstrainedMatrixVectorMultiplier.evaluate(Vector input)
Returns m times input.
|
void |
IterativeMatrixSolver.setInitialGuess(Vector initialGuess)
Sets the initial guess ("x0")
|
Vector |
OverconstrainedMatrixVectorMultiplier.transposeMult(Vector input)
Return A^(T) * input.
|
| Constructor and Description |
|---|
ConjugateGradientMatrixSolver(Vector x0,
Vector rhs)
Initializes a conjugate gradient solver with the minimum values
|
ConjugateGradientMatrixSolver(Vector x0,
Vector rhs,
double tolerance)
Initializes a conjugate gradient solver with some additional parameters
|
ConjugateGradientMatrixSolver(Vector x0,
Vector rhs,
double tolerance,
int maxIterations)
Initializes a conjugate gradient solver with all user-definable parameters
|
ConjugateGradientWithPreconditionerMatrixSolver(Vector x0,
Vector rhs)
Initializes a steepest-descent solver with the minimum values
|
ConjugateGradientWithPreconditionerMatrixSolver(Vector x0,
Vector rhs,
double tolerance)
Initializes a steepest-descent solver with some additional parameters
|
ConjugateGradientWithPreconditionerMatrixSolver(Vector x0,
Vector rhs,
double tolerance,
int maxIterations)
Initializes a steepest-descent solver with all user-definable parameters
|
IterativeMatrixSolver(Vector x0,
Vector rhs)
Initializes a solver with basic necessary values
|
IterativeMatrixSolver(Vector x0,
Vector rhs,
double tolerance)
Initializes a solver with a few more values
|
IterativeMatrixSolver(Vector x0,
Vector rhs,
double tolerance,
int maxIterations)
Inititalizes a solver with all user-definable parameters
|
OverconstrainedConjugateGradientMatrixMinimizer(Vector x0,
Vector rhs)
Initializes a steepest-descent solver with the minimum values
|
OverconstrainedConjugateGradientMatrixMinimizer(Vector x0,
Vector rhs,
double tolerance)
Initializes a steepest-descent solver with some additional parameters
|
OverconstrainedConjugateGradientMatrixMinimizer(Vector x0,
Vector rhs,
double tolerance,
int maxIterations)
Initializes a steepest-descent solver with all user-definable parameters
|
SteepestDescentMatrixSolver(Vector x0,
Vector rhs)
Initializes a steepest-descent solver with the minimum values
|
SteepestDescentMatrixSolver(Vector x0,
Vector rhs,
double tolerance)
Initializes a steepest-descent solver with some additional parameters
|
SteepestDescentMatrixSolver(Vector x0,
Vector rhs,
double tolerance,
int maxIterations)
Initializes a steepest-descent solver with all user-definable parameters
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
KernelPrincipalComponentsAnalysis.Function.evaluate(DataType input) |
Vector |
PrincipalComponentsAnalysisFunction.evaluate(Vector input)
Computes the reduced-dimension representation of the input by
subtracting the mean and mapping it through the dimension-reduction
matrix multiplication
|
Vector |
PrincipalComponentsAnalysisFunction.getMean()
Getter for mean
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
PrincipalComponentsAnalysisFunction.evaluate(Vector input)
Computes the reduced-dimension representation of the input by
subtracting the mean and mapping it through the dimension-reduction
matrix multiplication
|
void |
PrincipalComponentsAnalysisFunction.setMean(Vector mean)
Setter for mean
|
| Modifier and Type | Method and Description |
|---|---|
PrincipalComponentsAnalysisFunction |
ThinSingularValueDecomposition.learn(java.util.Collection<Vector> data)
Creates a PrincipalComponentsAnalysisFunction based on the number of
components and the given data.
|
static PrincipalComponentsAnalysisFunction |
ThinSingularValueDecomposition.learn(java.util.Collection<Vector> data,
int numComponents)
Creates a PrincipalComponentsAnalysisFunction based on the number of
components and the given data.
|
| Constructor and Description |
|---|
PrincipalComponentsAnalysisFunction(Vector mean,
MultivariateDiscriminant dimensionReducer)
Creates a new instance of PrincipalComponentAnalysisFunction
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
AbstractLinearCombinationOnlineLearner.computeDecay(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted,
double update)
Computes the decay scalar for the existing weight vector.
|
protected double |
OnlineShiftingPerceptron.computeDecay(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted,
double update) |
protected double |
AbstractLinearCombinationOnlineLearner.computeRescaling(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted,
double update,
double decay)
Computes the rescaling for the new weight vector.
|
protected abstract double |
AbstractLinearCombinationOnlineLearner.computeUpdate(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted)
Compute the update weight in the linear case.
|
protected double |
OnlineBinaryMarginInfusedRelaxedAlgorithm.computeUpdate(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted) |
double |
OnlinePassiveAggressivePerceptron.computeUpdate(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted) |
double |
OnlinePerceptron.computeUpdate(LinearBinaryCategorizer target,
Vector input,
boolean label,
double predicted) |
double |
OnlineRampPassiveAggressivePerceptron.computeUpdate(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory,
double predicted) |
double |
OnlineShiftingPerceptron.computeUpdate(LinearBinaryCategorizer target,
Vector input,
boolean label,
double predicted) |
protected void |
AbstractLinearCombinationOnlineLearner.initialize(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory)
Initializes the linear binary categorizer.
|
protected void |
OnlineBinaryMarginInfusedRelaxedAlgorithm.initialize(LinearBinaryCategorizer target,
Vector input,
boolean actualCategory) |
void |
AbstractLinearCombinationOnlineLearner.update(LinearBinaryCategorizer target,
Vector input,
boolean label) |
abstract void |
AbstractOnlineLinearBinaryCategorizerLearner.update(LinearBinaryCategorizer target,
Vector input,
boolean output)
The
update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm. |
void |
AggressiveRelaxedOnlineMaximumMarginAlgorithm.update(LinearBinaryCategorizer target,
Vector input,
boolean label) |
void |
Ballseptron.update(LinearBinaryCategorizer target,
Vector input,
boolean label) |
void |
RelaxedOnlineMaximumMarginAlgorithm.update(LinearBinaryCategorizer target,
Vector input,
boolean label) |
void |
Winnow.update(LinearBinaryCategorizer target,
Vector input,
boolean actual) |
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. |
| Modifier and Type | Method and Description |
|---|---|
protected void |
Projectron.applyUpdate(DefaultKernelBinaryCategorizer<InputType> target,
InputType input,
double actual,
double margin,
double kernelInputInput,
double delta,
Vector d)
Apply the update for the Projectron.
|
protected void |
Projectron.LinearSoftMargin.applyUpdate(DefaultKernelBinaryCategorizer<InputType> target,
InputType input,
double actual,
double margin,
double kernelInputInput,
double delta,
Vector d) |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>>
Partial implementation of ParameterCostMinimizer, based on the algorithms
from the minimization package.
|
class |
AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>>
Partial implementation of ParameterCostMinimizer.
|
class |
AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>>
Partial implementation of ParameterCostMinimizer.
|
| Modifier and Type | Field and Description |
|---|---|
static SupervisedCostFunction<Vector,Vector> |
AbstractMinimizerBasedParameterCostMinimizer.DEFAULT_COST_FUNCTION
Default cost function,
SumSquaredErrorCostFunction |
static SupervisedCostFunction<Vector,Vector> |
AbstractMinimizerBasedParameterCostMinimizer.DEFAULT_COST_FUNCTION
Default cost function,
SumSquaredErrorCostFunction |
static FunctionMinimizer<Vector,java.lang.Double,Evaluator<? super Vector,java.lang.Double>> |
ParameterDerivativeFreeCostMinimizer.DEFAULT_FUNCTION_MINIMIZER
Default function minimizer, FunctionMinimizerDirectionSetPowell
|
static FunctionMinimizer<Vector,java.lang.Double,Evaluator<? super Vector,java.lang.Double>> |
ParameterDerivativeFreeCostMinimizer.DEFAULT_FUNCTION_MINIMIZER
Default function minimizer, FunctionMinimizerDirectionSetPowell
|
static FunctionMinimizer<Vector,java.lang.Double,DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>> |
ParameterDifferentiableCostMinimizer.DEFAULT_FUNCTION_MINIMIZER
Default function minimizer,
FunctionMinimizerBFGS with LineMinimizerBacktracking
|
static FunctionMinimizer<Vector,java.lang.Double,DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>> |
ParameterDifferentiableCostMinimizer.DEFAULT_FUNCTION_MINIMIZER
Default function minimizer,
FunctionMinimizerBFGS with LineMinimizerBacktracking
|
static FunctionMinimizer<Vector,java.lang.Double,DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>> |
ParameterDifferentiableCostMinimizer.DEFAULT_FUNCTION_MINIMIZER
Default function minimizer,
FunctionMinimizerBFGS with LineMinimizerBacktracking
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
LogisticRegression.Function.convertToVector() |
Vector |
ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree.differentiate(Vector input) |
Vector |
ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased.differentiate(Vector input) |
| Modifier and Type | Method and Description |
|---|---|
SupervisedCostFunction<Vector,Vector> |
AbstractMinimizerBasedParameterCostMinimizer.getCostFunction() |
SupervisedCostFunction<Vector,Vector> |
AbstractMinimizerBasedParameterCostMinimizer.getCostFunction() |
Evaluator<? super InputType,Vector> |
LinearBasisRegression.getInputToVectorMap()
Getter for inputToVectorMap
|
| Modifier and Type | Method and Description |
|---|---|
void |
LogisticRegression.Function.convertFromVector(Vector parameters) |
Vector |
ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree.differentiate(Vector input) |
Vector |
ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased.differentiate(Vector input) |
java.lang.Double |
ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree.evaluate(Vector input) |
java.lang.Double |
ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased.evaluate(Vector input) |
| Modifier and Type | Method and Description |
|---|---|
ResultType |
AbstractMinimizerBasedParameterCostMinimizer.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data) |
ResultType |
AbstractMinimizerBasedParameterCostMinimizer.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data) |
MultivariateDiscriminantWithBias |
MultivariateLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data) |
MultivariateDiscriminantWithBias |
MultivariateLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data) |
void |
AbstractMinimizerBasedParameterCostMinimizer.setCostFunction(SupervisedCostFunction<Vector,Vector> costFunction)
Setter for costFunction
|
void |
AbstractMinimizerBasedParameterCostMinimizer.setCostFunction(SupervisedCostFunction<Vector,Vector> costFunction)
Setter for costFunction
|
void |
LinearBasisRegression.setInputToVectorMap(Evaluator<? super InputType,Vector> inputToVectorMap)
Setter for inputToVectorMap
|
| Constructor and Description |
|---|
AbstractMinimizerBasedParameterCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super EvaluatorType> algorithm)
Creates a new instance of AbstractMinimizerBasedParameterCostMinimizer
|
AbstractMinimizerBasedParameterCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super EvaluatorType> algorithm,
SupervisedCostFunction<Vector,Vector> costFunction)
Creates a new instance of AbstractMinimizerBasedParameterCostMinimizer
|
AbstractMinimizerBasedParameterCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super EvaluatorType> algorithm,
SupervisedCostFunction<Vector,Vector> costFunction)
Creates a new instance of AbstractMinimizerBasedParameterCostMinimizer
|
AbstractMinimizerBasedParameterCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super EvaluatorType> algorithm,
SupervisedCostFunction<Vector,Vector> costFunction)
Creates a new instance of AbstractMinimizerBasedParameterCostMinimizer
|
LinearBasisRegression(Evaluator<? super InputType,Vector> inputToVectorMap)
Creates a new instance of LinearRegression
|
ParameterCostEvaluatorDerivativeFree(VectorizableVectorFunction internalFunction,
SupervisedCostFunction<Vector,Vector> costFunction)
Creates a new instance of ParameterCostEvaluatorDerivativeFree
|
ParameterCostEvaluatorDerivativeFree(VectorizableVectorFunction internalFunction,
SupervisedCostFunction<Vector,Vector> costFunction)
Creates a new instance of ParameterCostEvaluatorDerivativeFree
|
ParameterDerivativeFreeCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super DifferentiableEvaluator<Vector,java.lang.Double,Vector>> minimizer)
Creates a new instance of ParameterDerivativeFreeCostMinimizer
|
ParameterDerivativeFreeCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super DifferentiableEvaluator<Vector,java.lang.Double,Vector>> minimizer)
Creates a new instance of ParameterDerivativeFreeCostMinimizer
|
ParameterDerivativeFreeCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super DifferentiableEvaluator<Vector,java.lang.Double,Vector>> minimizer)
Creates a new instance of ParameterDerivativeFreeCostMinimizer
|
ParameterDifferentiableCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super DifferentiableEvaluator<Vector,java.lang.Double,Vector>> minimizer)
Creates a new instance of ParameterDerivativeFreeCostMinimizer
|
ParameterDifferentiableCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super DifferentiableEvaluator<Vector,java.lang.Double,Vector>> minimizer)
Creates a new instance of ParameterDerivativeFreeCostMinimizer
|
ParameterDifferentiableCostMinimizer(FunctionMinimizer<Vector,java.lang.Double,? super DifferentiableEvaluator<Vector,java.lang.Double,Vector>> minimizer)
Creates a new instance of ParameterDerivativeFreeCostMinimizer
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MultipartiteValenceMatrix.evaluate(Vector input)
Overrides the default implementation so that L_tilde can be raised to a
power and the diagonal weights can be added implicitly (which is much
faster and memory efficient than the explicit representation).
|
Vector |
MultipartiteValenceMatrix.init()
This method must be called before an instance is passed to an iterative
solver and after all relationships and trusted/weighted elements are
added.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MultipartiteValenceMatrix.evaluate(Vector input)
Overrides the default implementation so that L_tilde can be raised to a
power and the diagonal weights can be added implicitly (which is much
faster and memory efficient than the explicit representation).
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
PrimalEstimatedSubGradient.update
A vector used to compute the update for the weight vector.
|
| Modifier and Type | Field and Description |
|---|---|
protected VectorFactory<? extends Vector> |
RandomSubVectorThresholdLearner.vectorFactory
The vector factory to use.
|
| Modifier and Type | Method and Description |
|---|---|
static <CategoryType> |
RandomForestFactory.createCategorizationLearner(int ensembleSize,
double baggingFraction,
double dimensionsFraction,
int maxTreeDepth,
int minLeafSize,
java.util.Random random)
Creates a random forest learner for categorization outputs.
|
static <CategoryType> |
RandomForestFactory.createRegressionLearner(int ensembleSize,
double baggingFraction,
double dimensionsFraction,
int maxTreeDepth,
int minLeafSize,
java.util.Random random)
Creates a random forest learner for categorization outputs.
|
VectorFactory<? extends Vector> |
RandomSubVectorThresholdLearner.getVectorFactory()
Gets the vector factory.
|
| Modifier and Type | Method and Description |
|---|---|
void |
RandomSubVectorThresholdLearner.setVectorFactory(VectorFactory<? extends Vector> vectorFactory)
Sets the vector factory.
|
| 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,
VectorFactory<? extends Vector> vectorFactory)
Creates a new
RandomSubVectorThresholdLearner. |
| Modifier and Type | Method and Description |
|---|---|
static java.util.ArrayList<Vector> |
DatasetUtil.appendBias(java.util.Collection<? extends Vector> dataset)
Appends a bias (constant 1.0) to the end of each Vector in the dataset,
the original dataset is unmodified.
|
static java.util.ArrayList<Vector> |
DatasetUtil.appendBias(java.util.Collection<? extends Vector> dataset,
double biasValue)
Appends "biasValue" to the end of each Vector in the dataset,
the original dataset is unmodified.
|
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. |
| Modifier and Type | Method and Description |
|---|---|
static java.util.ArrayList<Vector> |
DatasetUtil.appendBias(java.util.Collection<? extends Vector> dataset)
Appends a bias (constant 1.0) to the end of each Vector in the dataset,
the original dataset is unmodified.
|
static java.util.ArrayList<Vector> |
DatasetUtil.appendBias(java.util.Collection<? extends Vector> dataset,
double biasValue)
Appends "biasValue" to the end of each Vector in the dataset,
the original dataset is unmodified.
|
static Matrix |
DatasetUtil.computeOuterProductDataMatrix(java.util.ArrayList<? extends Vector> data)
Computes the outer-product Matrix of the given set of data:
XXt = [ x1 x2 ...
|
static java.util.ArrayList<java.util.ArrayList<java.lang.Double>> |
DatasetUtil.decoupleVectorDataset(java.util.Collection<? extends Vector> dataset)
Takes a dataset of M-dimensional Vectors and turns it into M
datasets of Doubles
|
static java.util.ArrayList<java.util.ArrayList<InputOutputPair<java.lang.Double,java.lang.Double>>> |
DatasetUtil.decoupleVectorPairDataset(java.util.Collection<? extends InputOutputPair<? extends Vector,? extends Vector>> dataset)
Takes a set of equal-dimension Vector-Vector InputOutputPairs and
turns them into a collection of Double-Double InputOutputPairs.
|
static java.util.ArrayList<java.util.ArrayList<InputOutputPair<java.lang.Double,java.lang.Double>>> |
DatasetUtil.decoupleVectorPairDataset(java.util.Collection<? extends InputOutputPair<? extends Vector,? extends Vector>> dataset)
Takes a set of equal-dimension Vector-Vector InputOutputPairs and
turns them into a collection of Double-Double InputOutputPairs.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
FeatureHashing.evaluate(Vector input) |
Vector |
LinearRegressionCoefficientExtractor.evaluate(Vector input) |
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).
|
Vector |
MultivariateDecorrelator.getMean()
Gets the mean of the underlying Gaussian.
|
| Modifier and Type | Method and Description |
|---|---|
FiniteCapacityBuffer<Vector> |
LinearRegressionCoefficientExtractor.createDefaultState() |
VectorFactory<? extends Vector> |
RandomSubspace.getVectorFactory() |
| Modifier and Type | Method and Description |
|---|---|
Vector |
FeatureHashing.evaluate(Vector input) |
Vector |
LinearRegressionCoefficientExtractor.evaluate(Vector input) |
| Constructor and Description |
|---|
MultivariateDecorrelator(Vector mean,
Matrix covariance)
Creates a new instance of MultivariateDecorrelator with the given
mean and variance.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
LinearCombinationFunction.convertToVector() |
Vector |
LinearCombinationFunction.getCoefficients()
Getter for coefficients
|
| Modifier and Type | Method and Description |
|---|---|
void |
LinearCombinationFunction.convertFromVector(Vector parameters) |
void |
LinearCombinationFunction.setCoefficients(Vector coefficients)
Setter for coefficients
|
| Constructor and Description |
|---|
LinearCombinationFunction(java.util.ArrayList<? extends Evaluator<? super InputType,? extends OutputType>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
DiagonalConfidenceWeightedBinaryCategorizer.variance
The variance values, which is the diagonal of the covariance
matrix.
|
protected Vector |
LinearBinaryCategorizer.weights
The weight vector.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
ScalarThresholdBinaryCategorizer.convertToVector() |
Vector |
AbstractConfidenceWeightedBinaryCategorizer.getMean()
Gets the mean of the categorizer, which is the weight vector.
|
Vector |
ConfidenceWeightedBinaryCategorizer.getMean()
Gets the mean of the categorizer, which is the weight vector.
|
Vector |
DiagonalConfidenceWeightedBinaryCategorizer.getVariance()
Gets the variance vector.
|
Vector |
LinearBinaryCategorizer.getWeights()
Gets the weight vector.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ScalarThresholdBinaryCategorizer.convertFromVector(Vector parameters) |
double |
LinearBinaryCategorizer.evaluateAsDouble(Vector input)
A convenience method for evaluating a Vector object as a double, thus
avoiding the convertToVector call from Vectorizable.
|
double |
LinearMultiCategorizer.evaluateAsDouble(Vector input,
CategoryType category)
Evaluates how much the given input matches the prototype for the given
category.
|
DefaultWeightedValueDiscriminant<CategoryType> |
WinnerTakeAllCategorizer.findBestCategory(Vector output)
Finds the best category (and its output value) from the given vector
of outputs from a vector evaluator.
|
void |
AbstractConfidenceWeightedBinaryCategorizer.setMean(Vector mean)
Sets the mean of the categorizer, which is the weight vector.
|
void |
DiagonalConfidenceWeightedBinaryCategorizer.setVariance(Vector variance)
Sets the variance vector.
|
void |
LinearBinaryCategorizer.setWeights(Vector weights)
Sets the weight vector.
|
| Modifier and Type | Method and Description |
|---|---|
FisherLinearDiscriminantBinaryCategorizer |
FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Boolean>> data) |
static FisherLinearDiscriminantBinaryCategorizer |
FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Boolean>> data,
double defaultCovariance)
Closed-form learning algorithm for a Fisher Linear Discriminant.
|
| Constructor and Description |
|---|
AbstractConfidenceWeightedBinaryCategorizer(Vector mean)
Creates a new
AbstractConfidenceWeightedBinaryCategorizer with
the given mean vector. |
DefaultConfidenceWeightedBinaryCategorizer(Vector mean,
Matrix covariance)
Creates a new
DefaultConfidenceWeightedBinaryCategorizer with
the given mean and covariance. |
FisherLinearDiscriminantBinaryCategorizer(Vector weightVector,
double threshold)
Creates a new of
FisherLinearDiscriminantBinaryCategorizer. |
LinearBinaryCategorizer(Vector weights,
double bias)
Creates a new instance of LinearBinaryCategorizer with the given weights
and bias.
|
| 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.
|
| Modifier and Type | Field and Description |
|---|---|
Vector |
SumSquaredErrorCostFunction.Cache.Jte
Jacobian transpose times Error: J.transpose().times( error )
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
AbstractParallelizableCostFunction.computeParameterGradient(GradientDescendable function) |
Vector |
DifferentiableCostFunction.computeParameterGradient(GradientDescendable function)
Differentiates function with respect to its parameters.
|
Vector |
MeanSquaredErrorCostFunction.computeParameterGradient(GradientDescendable function) |
Vector |
ParallelizedCostFunctionContainer.computeParameterGradient(GradientDescendable function) |
Vector |
ParallelizableCostFunction.computeParameterGradientAmalgamate(java.util.Collection<java.lang.Object> partialResults)
Amalgamates the linear components of the cost gradient function into a
single Vector.
|
Vector |
SumSquaredErrorCostFunction.computeParameterGradientAmalgamate(java.util.Collection<java.lang.Object> partialResults) |
| Modifier and Type | Method and Description |
|---|---|
static SumSquaredErrorCostFunction.Cache |
SumSquaredErrorCostFunction.Cache.compute(GradientDescendable objectToOptimize,
java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data)
Computes often-used parameters of a sum-squared error term
|
static SumSquaredErrorCostFunction.Cache |
SumSquaredErrorCostFunction.Cache.compute(GradientDescendable objectToOptimize,
java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data)
Computes often-used parameters of a sum-squared error term
|
java.lang.Double |
AbstractParallelizableCostFunction.evaluate(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Double |
AbstractParallelizableCostFunction.evaluate(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Double |
ParallelizedCostFunctionContainer.evaluate(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Double |
ParallelizedCostFunctionContainer.evaluate(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Object |
ParallelizableCostFunction.evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator)
Computes the partial (linear) component of the cost function.
|
java.lang.Object |
ParallelizableCostFunction.evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator)
Computes the partial (linear) component of the cost function.
|
java.lang.Object |
SumSquaredErrorCostFunction.evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Object |
SumSquaredErrorCostFunction.evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Double |
MeanL1CostFunction.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
MeanL1CostFunction.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
MeanSquaredErrorCostFunction.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
MeanSquaredErrorCostFunction.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
ParallelizedCostFunctionContainer.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
ParallelizedCostFunctionContainer.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
SumSquaredErrorCostFunction.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
java.lang.Double |
SumSquaredErrorCostFunction.evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) |
void |
ParallelizedCostFunctionContainer.setCostParameters(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> costParameters) |
void |
ParallelizedCostFunctionContainer.setCostParameters(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> costParameters) |
| Constructor and Description |
|---|
Cache(Matrix J,
Matrix JtJ,
Vector Jte,
double parameterCost)
Creates a new instance of Cache
|
EuclideanDistanceCostFunction(Vector goal)
Creates a new instance of EuclideanDistanceCostFunction.
|
GradientPartialSSE(Vector numerator,
java.lang.Double denominator)
Creates a new instance of GradientPartialSSE
|
| Constructor and Description |
|---|
AbstractParallelizableCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> costParameters)
Creates a new instance of AbstractParallelizableCostFunction
|
AbstractParallelizableCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> costParameters)
Creates a new instance of AbstractParallelizableCostFunction
|
MeanL1CostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)
Creates a new instance of MeanL1CostFunction
|
MeanL1CostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)
Creates a new instance of MeanL1CostFunction
|
MeanSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)
Creates a new instance of MeanSquaredErrorCostFunction
|
MeanSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)
Creates a new instance of MeanSquaredErrorCostFunction
|
SubCostEvaluate(ParallelizableCostFunction costFunction,
Evaluator<? super Vector,? extends Vector> evaluator)
Creates a new instance of SubCostEvaluate
|
SubCostEvaluate(ParallelizableCostFunction costFunction,
Evaluator<? super Vector,? extends Vector> evaluator)
Creates a new instance of SubCostEvaluate
|
SumSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)
Creates a new instance of MeanSquaredErrorCostFunction
|
SumSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)
Creates a new instance of MeanSquaredErrorCostFunction
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
WeightedEuclideanDistanceMetric.weights
The weights assigned to each dimension for the distance.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
WeightedEuclideanDistanceMetric.getWeights()
Gets the vector of weights for each dimension.
|
| Modifier and Type | Method and Description |
|---|---|
VectorFactory<? extends Vector> |
DivergencesEvaluator.Learner.getVectorFactory() |
| Modifier and Type | Method and Description |
|---|---|
void |
DivergencesEvaluator.encode(InputType input,
Vector result,
int startIndex) |
void |
WeightedEuclideanDistanceMetric.setWeights(Vector weights)
Sets the vector of weights for each dimension.
|
| Constructor and Description |
|---|
WeightedEuclideanDistanceMetric(Vector weights)
Creates a new
WeightedEuclideanDistanceMetric with the given
weights. |
| Constructor and Description |
|---|
VectorFunctionKernel(VectorFunction function,
Kernel<? super Vector> kernel)
Creates a new VectorFunctionKernel from the given function and kernel.
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
LinearDiscriminant.weightVector
Weight Vector to dot-product with the input
|
| Modifier and Type | Field and Description |
|---|---|
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 |
|---|---|
Vector |
PolynomialFunction.computeParameterGradient(java.lang.Double input) |
Vector |
AtanFunction.convertToVector() |
Vector |
CosineFunction.convertToVector() |
Vector |
LinearDiscriminant.convertToVector() |
Vector |
LinearDiscriminantWithBias.convertToVector() |
Vector |
PolynomialFunction.convertToVector()
Returns the value of the exponent
|
Vector |
ThresholdFunction.convertToVector()
Converts this function into its parameters, which consists of the
threshold value
|
Vector |
VectorEntryFunction.convertToVector()
Converts the index to select into a vector (of length 1).
|
Vector |
VectorFunctionLinearDiscriminant.convertToVector() |
Vector |
LinearVectorScalarFunction.getWeights()
Gets the weight vector.
|
Vector |
LinearDiscriminant.getWeightVector()
Getter for weightVector
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,Vector> |
VectorFunctionLinearDiscriminant.getVectorFunction()
Getter for vectorFunction
|
| Modifier and Type | Method and Description |
|---|---|
void |
AtanFunction.convertFromVector(Vector parameters) |
void |
CosineFunction.convertFromVector(Vector parameters) |
void |
LinearDiscriminant.convertFromVector(Vector parameters) |
void |
LinearDiscriminantWithBias.convertFromVector(Vector parameters) |
void |
PolynomialFunction.convertFromVector(Vector parameters)
Sets the value of the exponent
|
void |
ThresholdFunction.convertFromVector(Vector parameters)
Converts this function from its parameters, which consists of the
threshold value
|
void |
VectorEntryFunction.convertFromVector(Vector parameters)
Converts a vector into the index to select from the vector.
|
void |
VectorFunctionLinearDiscriminant.convertFromVector(Vector parameters) |
double |
LinearVectorScalarFunction.evaluateAsDouble(Vector input)
A convenience method for evaluating a Vector object as a double, thus
avoiding the convertToVector call from Vectorizable.
|
void |
LinearVectorScalarFunction.setWeights(Vector weights)
Sets the weight vector.
|
void |
LinearDiscriminant.setWeightVector(Vector weightVector)
Setter for weightVector
|
| Modifier and Type | Method and Description |
|---|---|
void |
VectorFunctionLinearDiscriminant.setVectorFunction(Evaluator<? super InputType,Vector> vectorFunction)
Setter for vectorFunction
|
| Constructor and Description |
|---|
LinearCombinationScalarFunction(java.util.ArrayList<? extends Evaluator<InputType,java.lang.Double>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
LinearDiscriminant(Vector weightVector)
Creates a new instance of LinearClassifier
|
LinearDiscriminantWithBias(Vector weightVector)
Creates a new instance of LinearClassifier
|
LinearDiscriminantWithBias(Vector weightVector,
double bias)
Creates a new instance of LinearClassifier
|
LinearVectorScalarFunction(Vector weights)
Creates a new instance of LinearVectorScalarFunction.
|
LinearVectorScalarFunction(Vector weights,
double bias)
Creates a new instance of LinearVectorScalarFunction with the given
weights and bias.
|
| 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. |
VectorFunctionLinearDiscriminant(Evaluator<? super InputType,Vector> vectorFunction,
LinearDiscriminant discriminant)
Creates a new instance of VectorFunctionLinearDiscriminant
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
MultivariateDiscriminantWithBias.bias
Bias term that gets added the output of the matrix multiplication.
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.hiddenToOutputBiasWeights
Bias weights to add to each of the output units.
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.inputToHiddenBiasWeights
Bias weights to add to each of the hidden units.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
FeedforwardNeuralNetwork.convertToVector() |
Vector |
GeneralizedLinearModel.convertToVector() |
Vector |
MultivariateDiscriminant.convertToVector()
Creates a row-stacked version of the discriminant.
|
Vector |
MultivariateDiscriminantWithBias.convertToVector() |
Vector |
ThreeLayerFeedforwardNeuralNetwork.convertToVector() |
Vector |
ScalarBasisSet.evaluate(InputType input) |
Vector |
ElementWiseVectorFunction.evaluate(Vector input)
Applies the scalar function to each element of the input Vector
independently of all others, returning a Vector of equal dimension as
the input
|
Vector |
FeedforwardNeuralNetwork.evaluate(Vector input) |
Vector |
GaussianContextRecognizer.evaluate(Vector input) |
Vector |
GeneralizedLinearModel.evaluate(Vector input) |
Vector |
LinearCombinationVectorFunction.evaluate(Vector input) |
Vector |
LinearVectorFunction.evaluate(Vector input) |
Vector |
MultivariateDiscriminant.evaluate(Vector input) |
Vector |
MultivariateDiscriminantWithBias.evaluate(Vector input) |
Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluate(Vector input) |
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.
|
static Vector |
ElementWiseVectorFunction.evaluate(Vector input,
UnivariateScalarFunction function)
Applies the scalar function to each element of the input Vector
independently of all others, returning a Vector of equal dimension as
the input
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluateHiddenLayerActivation(Vector input)
Computes the raw (unsquashed) activation at the hidden layer for the
given input.
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluateOutputFromSquashedHiddenLayerActivation(Vector squashedHiddenActivation)
Evaluates the output from the squashed hidden-layer activation.
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluateSquashedHiddenLayerActivation(Vector hiddenActivation)
Evaluates the squashed hidden-layer activation from its raw activation
value.
|
Vector |
MultivariateDiscriminantWithBias.getBias()
Getter for bias
|
| Modifier and Type | Method and Description |
|---|---|
protected java.util.ArrayList<Vector> |
FeedforwardNeuralNetwork.evaluateAtEachLayer(Vector input)
Returns the activations that occured at each layer
|
java.util.Collection<? extends Vector> |
GaussianContextRecognizer.Learner.getData() |
| Modifier and Type | Method and Description |
|---|---|
static Matrix |
MultivariateDiscriminant.computeParameterGradient(Matrix matrix,
Vector input)
Computes the parameter gradient of the given matrix post-multiplied
by the input Vector
|
Matrix |
DifferentiableFeedforwardNeuralNetwork.computeParameterGradient(Vector input) |
Matrix |
DifferentiableGeneralizedLinearModel.computeParameterGradient(Vector input) |
Matrix |
MultivariateDiscriminant.computeParameterGradient(Vector input) |
Matrix |
MultivariateDiscriminantWithBias.computeParameterGradient(Vector input) |
Matrix |
ThreeLayerFeedforwardNeuralNetwork.computeParameterGradient(Vector input) |
void |
FeedforwardNeuralNetwork.convertFromVector(Vector parameters) |
void |
GeneralizedLinearModel.convertFromVector(Vector parameters) |
void |
MultivariateDiscriminant.convertFromVector(Vector parameters)
Uploads a matrix from a row-stacked vector of parameters.
|
void |
MultivariateDiscriminantWithBias.convertFromVector(Vector parameters) |
void |
ThreeLayerFeedforwardNeuralNetwork.convertFromVector(Vector parameters) |
Matrix |
DifferentiableGeneralizedLinearModel.differentiate(Vector input) |
Matrix |
ElementWiseDifferentiableVectorFunction.differentiate(Vector input) |
Matrix |
LinearVectorFunction.differentiate(Vector input) |
Matrix |
MultivariateDiscriminant.differentiate(Vector input) |
Vector |
ElementWiseVectorFunction.evaluate(Vector input)
Applies the scalar function to each element of the input Vector
independently of all others, returning a Vector of equal dimension as
the input
|
java.lang.Double |
EntropyEvaluator.evaluate(Vector input) |
Vector |
FeedforwardNeuralNetwork.evaluate(Vector input) |
Vector |
GaussianContextRecognizer.evaluate(Vector input) |
Vector |
GeneralizedLinearModel.evaluate(Vector input) |
Vector |
LinearCombinationVectorFunction.evaluate(Vector input) |
Vector |
LinearVectorFunction.evaluate(Vector input) |
Vector |
MultivariateDiscriminant.evaluate(Vector input) |
Vector |
MultivariateDiscriminantWithBias.evaluate(Vector input) |
Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluate(Vector input) |
static Vector |
ElementWiseVectorFunction.evaluate(Vector input,
UnivariateScalarFunction function)
Applies the scalar function to each element of the input Vector
independently of all others, returning a Vector of equal dimension as
the input
|
protected java.util.ArrayList<Vector> |
FeedforwardNeuralNetwork.evaluateAtEachLayer(Vector input)
Returns the activations that occured at each layer
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluateHiddenLayerActivation(Vector input)
Computes the raw (unsquashed) activation at the hidden layer for the
given input.
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluateOutputFromSquashedHiddenLayerActivation(Vector squashedHiddenActivation)
Evaluates the output from the squashed hidden-layer activation.
|
protected Vector |
ThreeLayerFeedforwardNeuralNetwork.evaluateSquashedHiddenLayerActivation(Vector hiddenActivation)
Evaluates the squashed hidden-layer activation from its raw activation
value.
|
void |
MultivariateDiscriminantWithBias.setBias(Vector bias)
Setter for bias
|
| Modifier and Type | Method and Description |
|---|---|
GaussianContextRecognizer |
GaussianContextRecognizer.Learner.learn(java.util.Collection<? extends Vector> data) |
| Constructor and Description |
|---|
LinearCombinationVectorFunction(java.util.ArrayList<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
MultivariateDiscriminantWithBias(Matrix discriminant,
Vector bias)
Creates a new instance of MultivariateDiscriminantWithBias.
|
| Constructor and Description |
|---|
Learner(KMeansClusterer<Vector,GaussianCluster> algorithm)
Creates a new instance of Learner
|
LinearCombinationVectorFunction(java.util.ArrayList<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
LinearCombinationVectorFunction(java.util.ArrayList<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
LinearCombinationVectorFunction(java.util.Collection<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions)
Creates a new instance of LinearCombinationFunction
|
LinearCombinationVectorFunction(java.util.Collection<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions)
Creates a new instance of LinearCombinationFunction
|
SubVectorEvaluator(int inputDimensionality,
int[] subIndices,
VectorFactory<? extends Vector> vectorFactory)
Creates a new
SubVectorEvaluator with the given parameters. |
| Modifier and Type | Method and Description |
|---|---|
Vector |
Combinations.IndexIterator.next() |
| 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.
|
java.util.Iterator<Vector> |
Combinations.iterator() |
| Modifier and Type | Method and Description |
|---|---|
static Matrix |
MultivariateStatisticsUtil.computeVariance(java.util.Collection<? extends Vector> data,
Vector mean)
Computes the variance (second central moment, squared standard deviation)
of a dataset
|
void |
MutableDouble.convertFromVector(Vector parameters) |
void |
MutableInteger.convertFromVector(Vector parameters) |
void |
MutableLong.convertFromVector(Vector parameters) |
static double |
MathUtil.logMultinomialBetaFunction(Vector input)
Evaluates the natural logarithm of the multinomial beta function
for the given input vector.
|
| Modifier and Type | Method and Description |
|---|---|
static Matrix |
MultivariateStatisticsUtil.computeVariance(java.util.Collection<? extends Vector> data)
Computes the variance (second central moment, squared standard deviation)
of a dataset.
|
static Matrix |
MultivariateStatisticsUtil.computeVariance(java.util.Collection<? extends Vector> data,
Vector mean)
Computes the variance (second central moment, squared standard deviation)
of a dataset
|
| Modifier and Type | Class and Description |
|---|---|
class |
SparseVectorFactory<VectorType extends Vector>
Abstract factory class for creating sparse vectors.
|
class |
VectorFactory<VectorType extends Vector>
Abstract factory for creating
Vector objects. |
| Modifier and Type | Interface and Description |
|---|---|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractVector
Abstract implementation of some of the Vector interface, in a storage-free
manner
|
| Modifier and Type | Field and Description |
|---|---|
protected static VectorFactory<? extends Vector> |
VectorFactory.DEFAULT_DENSE_INSTANCE
The default
VectorFactory instance. |
protected static SparseVectorFactory<? extends Vector> |
VectorFactory.DEFAULT_SPARSE_INSTANCE
The default SparseVectorFactory instance.
|
protected VectorFactory<? extends Vector> |
DefaultVectorFactoryContainer.vectorFactory
The vector factory used to create new vectors.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
Vector.clone() |
Vector |
AbstractVector.convertToVector()
Returns this.
|
Vector |
Matrix.convertToVector()
Creates a column-stacked version of this, so that
v(k) = A(i,j) = v(j*M+i)
|
Vector |
Vectorizable.convertToVector()
Converts the object to a vector.
|
Vector |
NumericalDifferentiator.VectorJacobian.differentiate(Vector input) |
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 Vector |
VectorUtil.divideByNorm1(Vector input)
Returns a new vector whose elements are the elements of the original
vector, divided by the 1-norm of the vector (the sum of the absolute
values of the elements).
|
static Vector |
VectorUtil.divideByNorm2(Vector input)
Returns a new vector whose elements are the elements of the original
vector, divided by the 2-norm of the vector (the square root of the sum
of the squared values of the elements).
|
Vector |
AbstractVector.dotDivide(Vector other) |
Vector |
Vector.dotDivide(Vector other)
Element-wise division of
this by other. |
Vector |
Matrix.getColumn(int columnIndex)
Gets the specified column from the zero-based index and returns a
vector that corresponds to that column.
|
Vector |
DefaultTwoVectorEntry.getFirstVector()
Getter for firstVector.
|
Vector |
Matrix.getRow(int rowIndex)
Gets the specified row from the zero-based index and returns a vector
that corresponds to that column
|
Vector |
DefaultTwoVectorEntry.getSecondVector()
Getter for secondVector.
|
static Vector |
VectorUtil.interpolateLinear(Vectorizable first,
Vectorizable second,
double percent)
Performs linear interpolation between two vectors.
|
static Vector |
VectorUtil.interpolateLinear(Vector first,
Vector second,
double percent)
Performs linear interpolation between two vectors.
|
static Vector |
VectorReader.parseVector(java.util.Collection<java.lang.String> tokens)
Parses a
Vector from the given list of element tokens. |
Vector |
VectorReader.read()
Reads a Vector off of the next line in the file
|
Vector |
Matrix.solve(Vector b)
Solves for "x" in the equation: this*x = b
|
Vector |
AbstractVector.stack(Vector other) |
Vector |
Vector.stack(Vector other)
Stacks "other" below "this" and returns the stacked Vector
|
Vector |
Vector.subVector(int minIndex,
int maxIndex)
Gets a subvector of "this", specified by the inclusive indices
|
Vector |
AbstractMatrix.sumOfColumns() |
Vector |
Matrix.sumOfColumns()
Returns a new vector containing the sum across the columns.
|
Vector |
AbstractMatrix.sumOfRows() |
Vector |
Matrix.sumOfRows()
Returns a new vector containing the sum across the rows.
|
Vector |
AbstractVector.times(Matrix matrix) |
Vector |
Vector.times(Matrix matrix)
Premultiplies the matrix by the vector "this"
|
Vector |
AbstractMatrix.times(Vector vector) |
Vector |
Matrix.times(Vector vector)
Returns the column vector from the equation
return = this * vector
|
Vector |
AbstractVector.transform(UnivariateScalarFunction function) |
Vector |
Vector.transform(UnivariateScalarFunction function)
Applies the given function to each of the elements of this vector and
returns a new vector with the result.
|
Vector |
AbstractVector.transform(Vector.IndexValueTransform function) |
Vector |
Vector.transform(Vector.IndexValueTransform function)
Applies the given function to each of the elements of this vector and
returns a new vector with the result.
|
Vector |
AbstractVector.transformNonZeros(UnivariateScalarFunction function) |
Vector |
Vector.transformNonZeros(UnivariateScalarFunction function)
Applies the given function to each of the non-zero elements of this
vector and returns a new vector with the result.
|
Vector |
AbstractVector.transformNonZeros(Vector.IndexValueTransform function) |
Vector |
Vector.transformNonZeros(Vector.IndexValueTransform function)
Applies the given function to each of the non-zero elements of this
vector and returns a new vector with the result.
|
| Modifier and Type | Method and Description |
|---|---|
static SparseVectorFactory<? extends Vector> |
SparseVectorFactory.getDefault()
Gets the default instance.
|
static VectorFactory<? extends Vector> |
VectorFactory.getDefault()
Gets the default implementation of the
VectorFactory. |
static VectorFactory<? extends Vector> |
VectorFactory.getDenseDefault()
Gets the default implementation of
VectorFactory for dense
vectors. |
static SparseVectorFactory<? extends Vector> |
VectorFactory.getSparseDefault()
Gets the default implementation of the
SparseVectorFactory. |
VectorFactory<? extends Vector> |
DefaultVectorFactoryContainer.getVectorFactory()
Gets the vector factory the object to use to create new vectors.
|
VectorFactory<? extends Vector> |
VectorFactoryContainer.getVectorFactory()
Gets the vector factory the object to use to create new vectors.
|
java.util.List<Vector> |
VectorReader.readCollection(boolean mustBeSameSize)
Reads a collection of Vectors from the Reader
|
| Modifier and Type | Method and Description |
|---|---|
static void |
AbstractVector.assertEqualDimensionality(Vector first,
Vector second)
Throws a DimensionalityMismatchException if
first.getDimensionality() != second.getDimensionality(),
otherwise this function has no effect
|
void |
AbstractVector.assertSameDimensionality(Vector other) |
void |
Vector.assertSameDimensionality(Vector other)
Asserts that this vector has the same dimensionality as the given
vector.
|
boolean |
AbstractVector.checkSameDimensionality(Vector other) |
boolean |
Vector.checkSameDimensionality(Vector other)
Determines if
this and other have the same
number of dimensions (size) |
void |
AbstractVector.convertFromVector(Vector parameters)
Assigns the values in the provided vector into this.
|
void |
Matrix.convertFromVector(Vector parameters)
uploads a matrix from a column-stacked vector of parameters, so that
v(k) = A(i,j) = A( k%M, k/M )
|
void |
Vectorizable.convertFromVector(Vector parameters)
Converts the object from a Vector of parameters.
|
abstract VectorType |
VectorFactory.copyVector(Vector m)
Creates a deep copy new Vector given another, argument is unchanged
|
Matrix |
DifferentiableVectorFunction.differentiate(Vector input)
Differentiate the VectorFunction at
input and return
the Jacobian |
Vector |
NumericalDifferentiator.VectorJacobian.differentiate(Vector input) |
Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vector input) |
static Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vector input,
Evaluator<? super Vector,Vector> f)
Static access to the numerical differentiation procedure.
|
static Vector |
VectorUtil.divideByNorm1(Vector input)
Returns a new vector whose elements are the elements of the original
vector, divided by the 1-norm of the vector (the sum of the absolute
values of the elements).
|
static void |
VectorUtil.divideByNorm1Equals(Vector vector)
Divides all of the given elements of the vector by the 1-norm (the sum
of the absolute values of the elements).
|
static Vector |
VectorUtil.divideByNorm2(Vector input)
Returns a new vector whose elements are the elements of the original
vector, divided by the 2-norm of the vector (the square root of the sum
of the squared values of the elements).
|
static void |
VectorUtil.divideByNorm2Equals(Vector vector)
Divides all of the given elements of the vector by the 2-norm (the square
root of the sum of the squared values of the elements).
|
Vector |
AbstractVector.dotDivide(Vector other) |
Vector |
Vector.dotDivide(Vector other)
Element-wise division of
this by other. |
void |
AbstractVector.dotDivideEquals(Vector other) |
void |
Vector.dotDivideEquals(Vector other)
Inline element-wise division of
this by other. |
double |
AbstractVector.dotProduct(Vector other) |
void |
AbstractVector.dotTimesEquals(Vector other) |
boolean |
AbstractVector.equals(Vector other,
double effectiveZero) |
double |
AbstractVector.euclideanDistanceSquared(Vector other) |
protected void |
AbstractMatrix.getColumnInto(int columnIndex,
Vector destinationVector)
Internal function that writes the column onto the destinationVector.
|
protected void |
AbstractMatrix.getRowInto(int rowIndex,
Vector destinationVector)
Internal function that writes the row onto the destinationVector.
|
static Vector |
VectorUtil.interpolateLinear(Vector first,
Vector second,
double percent)
Performs linear interpolation between two vectors.
|
void |
AbstractVector.minusEquals(Vector other) |
Matrix |
AbstractVector.outerProduct(Vector other) |
Matrix |
Vector.outerProduct(Vector other)
Computes the outer matrix product between the two vectors
|
void |
AbstractVector.plusEquals(Vector other) |
Vector3 |
Quaternion.rotate(Vector vector)
Rotates the given vector by this quaternion by performing a
multiplication with this quaternion's rotation matrix: M * v.
|
static int |
VectorUtil.safeGetDimensionality(Vector vector)
Gets the dimensionality of the given vector, if it is not null.
|
void |
AbstractVector.scaledPlusEquals(double scaleFactor,
Vector other) |
void |
AbstractMatrix.setColumn(int columnIndex,
Vector columnVector) |
void |
Matrix.setColumn(int columnIndex,
Vector columnVector)
Sets the specified column from the given columnVector
|
void |
DefaultTwoVectorEntry.setFirstVector(Vector firstVector)
Setter for firstVector.
|
void |
AbstractMatrix.setRow(int rowIndex,
Vector rowVector) |
void |
Matrix.setRow(int rowIndex,
Vector rowVector)
Sets the specified row from the given rowVector
|
void |
DefaultTwoVectorEntry.setSecondVector(Vector secondVector)
Setter for secondVector.
|
Vector |
Matrix.solve(Vector b)
Solves for "x" in the equation: this*x = b
|
Vector |
AbstractVector.stack(Vector other) |
Vector |
Vector.stack(Vector other)
Stacks "other" below "this" and returns the stacked Vector
|
Vector |
AbstractMatrix.times(Vector vector) |
Vector |
Matrix.times(Vector vector)
Returns the column vector from the equation
return = this * vector
|
void |
VectorWriter.write(Vector vector)
Writes the given vector to the Java-based writer
|
| Modifier and Type | Method and Description |
|---|---|
static Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vector input,
Evaluator<? super Vector,Vector> f)
Static access to the numerical differentiation procedure.
|
static Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vector input,
Evaluator<? super Vector,Vector> f)
Static access to the numerical differentiation procedure.
|
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 Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,Vector> f,
double h)
Static access to the numerical differentiation procedure.
|
void |
DefaultVectorFactoryContainer.setVectorFactory(VectorFactory<? extends Vector> vectorFactory)
Sets the vector factory for the object to use to create new vectors.
|
void |
VectorWriter.writeCollection(java.lang.Iterable<? extends Vector> vectors)
Writes the collection of vectors to the Java-based writer
|
| Constructor and Description |
|---|
DefaultTwoVectorEntry(Vector firstVector,
Vector secondVector)
Creates a new instance of DefaultTwoVectorEntry.
|
DefaultTwoVectorEntry(Vector firstVector,
Vector secondVector,
int index)
Creates a new instance of DefaultTwoVectorEntry.
|
VectorUnionIterator(Vector first,
Vector second)
Creates a new instance of VectorUnionIterator.
|
| Constructor and Description |
|---|
DefaultVectorFactoryContainer(VectorFactory<? extends Vector> vectorFactory)
Creates a new
DefaultVectorFactoryContainer with the given factory. |
MatrixJacobian(Evaluator<? super Vector,Vector> internalFunction)
Creates a new instance of VectorJacobian
|
MatrixJacobian(Evaluator<? super Vector,Vector> internalFunction)
Creates a new instance of VectorJacobian
|
MatrixJacobian(Evaluator<? super Vector,Vector> internalFunction,
double delta)
Create a new instance of VectorJacobian
|
MatrixJacobian(Evaluator<? super Vector,Vector> internalFunction,
double delta)
Create a new instance of VectorJacobian
|
VectorJacobian(Evaluator<? super Vector,java.lang.Double> internalFunction)
Creates a new instance of VectorJacobian
|
VectorJacobian(Evaluator<? super Vector,java.lang.Double> internalFunction,
double delta)
Create a new instance of VectorJacobian
|
| Modifier and Type | Class and Description |
|---|---|
class |
DenseVector
Our dense vector implementation.
|
class |
SparseVector
Our sparse vector implementation.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
DenseMatrix.convertToVector() |
Vector |
DiagonalMatrix.convertToVector() |
Vector |
SparseMatrix.convertToVector()
Creates a column-stacked version of this, so that
v(k) = A(i,j) = v(j*M+i)
|
Vector |
DenseVector.dotTimes(Vector v) |
Vector |
DenseMatrix.getColumn(int columnIndex) |
Vector |
DiagonalMatrix.getColumn(int columnIndex) |
Vector |
SparseMatrix.getColumn(int columnIndex)
Gets the specified column from the zero-based index and returns a
vector that corresponds to that column.
|
Vector |
DenseMatrix.getRow(int rowIndex) |
Vector |
DiagonalMatrix.getRow(int rowIndex) |
Vector |
SparseMatrix.getRow(int rowIndex)
Gets the specified row from the zero-based index and returns a vector
that corresponds to that column
|
Vector |
SparseVector.minus(Vector v) |
Vector |
SparseVector.plus(Vector v) |
Vector |
DenseMatrix.preTimes(DenseVector vector) |
Vector |
DiagonalMatrix.preTimes(DenseVector vector) |
Vector |
SparseMatrix.preTimes(DenseVector vector)
Type-specific version of pre-times for combining whatever type this is
with the input dense vector.
|
Vector |
DenseMatrix.preTimes(SparseVector vector) |
Vector |
DiagonalMatrix.preTimes(SparseVector vector) |
Vector |
SparseMatrix.preTimes(SparseVector vector)
Type-specific version of pre-times for combining whatever type this is
with the input sparse vector.
|
Vector |
DenseVector.scale(double d) |
Vector |
SparseVector.scale(double d) |
Vector |
DenseMatrix.solve(Vector b) |
Vector |
DiagonalMatrix.solve(Vector b) |
Vector |
SparseMatrix.solve(Vector b)
Solves for "x" in the equation: this*x = b
|
Vector |
DenseVector.stack(DenseVector other) |
Vector |
SparseVector.stack(DenseVector other) |
Vector |
DenseVector.stack(SparseVector other) |
Vector |
SparseVector.stack(SparseVector other) |
Vector |
DenseVector.subVector(int minIndex,
int maxIndex) |
Vector |
SparseVector.subVector(int minIndex,
int maxIndex) |
Vector |
SparseMatrix.sumOfColumns() |
Vector |
SparseMatrix.sumOfRows() |
Vector |
DenseMatrix.times(DenseVector vector) |
Vector |
DiagonalMatrix.times(DenseVector vector) |
Vector |
ParallelSparseMatrix.times(DenseVector vector)
Type-specific version of times for combining whatever type this is with
the input dense vector.
|
Vector |
SparseMatrix.times(DenseVector vector)
Type-specific version of times for combining whatever type this is with
the input dense vector.
|
Vector |
DenseMatrix.times(SparseVector vector) |
Vector |
DiagonalMatrix.times(SparseVector vector) |
Vector |
ParallelSparseMatrix.times(SparseVector vector)
Type-specific version of times for combining whatever type this is with
the input sparse vector.
|
Vector |
SparseMatrix.times(SparseVector vector)
Type-specific version of times for combining whatever type this is with
the input sparse vector.
|
| Modifier and Type | Method and Description |
|---|---|
void |
DenseMatrix.convertFromVector(Vector v) |
void |
DiagonalMatrix.convertFromVector(Vector parameters)
uploads a matrix from a column-stacked vector of parameters, so that
v(k) = A(i,j) = A( k%M, k/M )
|
void |
SparseMatrix.convertFromVector(Vector parameters)
uploads a matrix from a column-stacked vector of parameters, so that
v(k) = A(i,j) = A( k%M, k/M )
|
DenseVector |
CustomDenseVectorFactory.copyVector(Vector m) |
SparseVector |
CustomSparseVectorFactory.copyVector(Vector m) |
Vector |
DenseVector.dotTimes(Vector v) |
Vector |
SparseVector.minus(Vector v) |
Vector |
SparseVector.plus(Vector v) |
Vector |
DenseMatrix.solve(Vector b) |
Vector |
DiagonalMatrix.solve(Vector b) |
Vector |
SparseMatrix.solve(Vector b)
Solves for "x" in the equation: this*x = b
|
| Modifier and Type | Method and Description |
|---|---|
static Vector |
EigenvectorPowerIteration.estimateEigenvector(Vector initial,
Matrix A,
double stoppingThreshold,
int maxIterations)
Estimates the eigenvector corresponding to the largest magnitude
eigenvalue.
|
| Modifier and Type | Method and Description |
|---|---|
static java.util.ArrayList<Vector> |
EigenvectorPowerIteration.estimateEigenvectors(Matrix A,
int numEigenvectors)
Estimates the top eigenvectors of the given matrix using the power
iteration algorithm.
|
static java.util.ArrayList<Vector> |
EigenvectorPowerIteration.estimateEigenvectors(Matrix A,
int numEigenvectors,
double stoppingThreshold,
int maxIterations)
Estimates the top eigenvectors of the given matrix using the power
iteration algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
EigenvectorPowerIteration.estimateEigenvalue(Matrix A,
Vector v)
Finds the eigenvalue associated with the given Matrix and eigenvector.
|
static Vector |
EigenvectorPowerIteration.estimateEigenvector(Vector initial,
Matrix A,
double stoppingThreshold,
int maxIterations)
Estimates the eigenvector corresponding to the largest magnitude
eigenvalue.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMTJVector
Implementation of the Vector interface that relies on MTJ Vectors, but does
not specify sparse or dense storage.
|
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 | Method and Description |
|---|---|
Vector |
AbstractMTJMatrix.convertToVector() |
Vector |
DiagonalMatrixMTJ.getColumn(int columnIndex) |
Vector |
DiagonalMatrixMTJ.getRow(int rowIndex) |
Vector |
AbstractMTJMatrix.solve(AbstractMTJVector b)
Solves for "x" in the equation: this*x = b
|
Vector |
DiagonalMatrixMTJ.solve(AbstractMTJVector b) |
Vector |
AbstractMTJMatrix.solve(Vector b) |
Vector |
DiagonalMatrixMTJ.solve(Vector b) |
Vector |
AbstractMTJVector.times(AbstractMTJMatrix matrix)
Premultiplies the matrix by the vector "this"
|
Vector |
AbstractMTJVector.times(Matrix matrix) |
Vector |
AbstractMTJMatrix.times(Vector vector) |
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractMTJMatrix.convertFromVector(Vector parameters) |
void |
DenseMatrix.convertFromVector(Vector parameters) |
void |
DiagonalMatrixMTJ.convertFromVector(Vector parameters) |
DenseVector |
DenseVectorFactoryMTJ.copyVector(Vector m) |
SparseVector |
SparseVectorFactoryMTJ.copyVector(Vector m) |
double |
AbstractMTJVector.dotProduct(Vector other) |
void |
AbstractMTJVector.dotTimesEquals(Vector other) |
boolean |
DenseVector.equals(Vector other,
double effectiveZero) |
double |
DenseVector.euclideanDistanceSquared(Vector other) |
double |
SparseVector.euclideanDistanceSquared(Vector other) |
void |
AbstractMTJVector.minusEquals(Vector other) |
Matrix |
AbstractMTJVector.outerProduct(Vector other) |
void |
AbstractMTJVector.plusEquals(Vector other) |
void |
AbstractMTJVector.scaledPlusEquals(double scaleFactor,
Vector other) |
Vector |
AbstractMTJMatrix.solve(Vector b) |
Vector |
DiagonalMatrixMTJ.solve(Vector b) |
DenseVector |
DenseVector.stack(Vector other) |
SparseVector |
SparseVector.stack(Vector other) |
Vector |
AbstractMTJMatrix.times(Vector vector) |
| Constructor and Description |
|---|
DenseVector(Vector vector)
Copy constructor for DenseVector
|
SparseVector(Vector vector)
Creates a new copy of SparseVector.
|
Vector1(Vector other)
Creates a new instance of Vector1 with values copied from the given
vector.
|
Vector2(Vector other)
Creates a new instance of Vector2 with values copied from the given
vector.
|
Vector3(Vector other)
Creates a new instance of Vector3 with values copied from the given
vector.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
AutoRegressiveMovingAverageFilter.convertToVector() |
Vector |
LinearDynamicalSystem.convertToVector() |
Vector |
MovingAverageFilter.convertToVector() |
Vector |
LinearDynamicalSystem.createDefaultState() |
Vector |
LinearDynamicalSystem.evaluate(Vector input) |
Vector |
AutoRegressiveMovingAverageFilter.getAutoRegressiveCoefficients()
Getter for autoregressiveCoefficients
|
Vector |
AutoRegressiveMovingAverageFilter.getMovingAverageCoefficients()
Setter for movingAverageCoefficients
|
Vector |
MovingAverageFilter.getMovingAverageCoefficients()
Setter for movingAverageCoefficients
|
| Modifier and Type | Method and Description |
|---|---|
void |
AutoRegressiveMovingAverageFilter.convertFromVector(Vector parameters) |
void |
LinearDynamicalSystem.convertFromVector(Vector parameters) |
void |
MovingAverageFilter.convertFromVector(Vector parameters) |
Vector |
LinearDynamicalSystem.evaluate(Vector input) |
void |
AutoRegressiveMovingAverageFilter.setAutoregressiveCoefficients(Vector autoRegressiveCoefficients)
Setter for autoregressiveCoefficients
|
void |
AutoRegressiveMovingAverageFilter.setMovingAverageCoefficients(Vector movingAverageCoefficients)
Setter for movingAverageCoefficients
|
void |
MovingAverageFilter.setMovingAverageCoefficients(Vector movingAverageCoefficients)
Setter for movingAverageCoefficients
|
| Constructor and Description |
|---|
AutoRegressiveMovingAverageFilter(Vector autoRegressiveCoefficients,
Vector movingAverageCoefficients)
Creates a new instance of AutoRegressiveMovingAverageFilter
|
MovingAverageFilter(Vector movingAverageCoefficients)
Creates a new instance of MovingAverageFilter
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
ChiSquaredSimilarity.getCategorizedVector()
Basic getter for the categorized vector.
|
Vector |
ChiSquaredSimilarity.getTestVector()
Basic getter for the testing vector.
|
| Modifier and Type | Method and Description |
|---|---|
static int |
DiscreteSamplingUtil.sampleIndexFromProbabilities(java.util.Random random,
Vector probabilities)
Samples an random index according to the given vector of probabilities.
|
void |
ChiSquaredSimilarity.setCategorizedVector(Vector newCategorizedVector)
Basic setter for the categorized vector.
|
void |
ChiSquaredSimilarity.setTestVector(Vector newTestVector)
Basic setter for the test vector.
|
| Constructor and Description |
|---|
ChiSquaredSimilarity(Vector categorizedVector,
Vector testingVector)
Basic constructor.
|
TransferEntropy(Vector sourceVector,
Vector destinationVector,
int windowSize)
Creates a new transfer entropy object
|
TransferEntropy(Vector sourceVector,
Vector destinationVector,
int sourceWindowSize,
int destinationWindowSize)
Creates a new transfer entropy object.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>>
A type of regression algorithm maps a Vector space, and the
weights of this Vector space are represented as a posterior distribution
given the observed InputOutputPairs.
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
AbstractKalmanFilter.currentInput
Current input to the model.
|
| Modifier and Type | Field and Description |
|---|---|
protected StatefulEvaluator<Vector,Vector,Vector> |
ExtendedKalmanFilter.motionModel
Model that determines how inputs and the previous state are updated.
|
protected StatefulEvaluator<Vector,Vector,Vector> |
ExtendedKalmanFilter.motionModel
Model that determines how inputs and the previous state are updated.
|
protected StatefulEvaluator<Vector,Vector,Vector> |
ExtendedKalmanFilter.motionModel
Model that determines how inputs and the previous state are updated.
|
protected Evaluator<Vector,Vector> |
ExtendedKalmanFilter.observationModel
Model that determines how the state is observed.
|
protected Evaluator<Vector,Vector> |
ExtendedKalmanFilter.observationModel
Model that determines how the state is observed.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
ExtendedKalmanFilter.ModelJacobianEvaluator.evaluate(Vector input) |
Vector |
AbstractKalmanFilter.getCurrentInput()
Getter for currentInput
|
Vector |
BayesianLinearRegression.IncrementalEstimator.SufficientStatistic.getMean()
Computes the mean of the Gaussian, but involves a matrix
inversion and multiplication, so it's expensive.
|
Vector |
BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic.getMean()
Computes the mean of the Gaussian, but involves a matrix
inversion and multiplication, so it's expensive.
|
Vector |
BayesianLinearRegression.IncrementalEstimator.SufficientStatistic.getZ()
Getter for z
|
Vector |
BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic.getZ()
Getter for z
|
| Modifier and Type | Method and Description |
|---|---|
StatefulEvaluator<Vector,Vector,Vector> |
ExtendedKalmanFilter.getMotionModel()
Getter for motionModel
|
StatefulEvaluator<Vector,Vector,Vector> |
ExtendedKalmanFilter.getMotionModel()
Getter for motionModel
|
StatefulEvaluator<Vector,Vector,Vector> |
ExtendedKalmanFilter.getMotionModel()
Getter for motionModel
|
Evaluator<Vector,Vector> |
ExtendedKalmanFilter.getObservationModel()
Getter for observationModel
|
Evaluator<Vector,Vector> |
ExtendedKalmanFilter.getObservationModel()
Getter for observationModel
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractKalmanFilter.computeMeasurementBelief(MultivariateGaussian belief,
Vector innovation,
Matrix C)
Updates the measurement belief by computing the Kalman gain and
incorporating the innovation into the estimate
|
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.
|
Vector |
ExtendedKalmanFilter.ModelJacobianEvaluator.evaluate(Vector input) |
abstract void |
AbstractKalmanFilter.measure(MultivariateGaussian belief,
Vector observation)
Integrates a measurement into the system, refining the current
belief of the state of the system
|
void |
ExtendedKalmanFilter.measure(MultivariateGaussian belief,
Vector observation) |
void |
KalmanFilter.measure(MultivariateGaussian belief,
Vector observation) |
void |
AbstractKalmanFilter.setCurrentInput(Vector currentInput)
Setter for currentInput
|
void |
AbstractKalmanFilter.update(MultivariateGaussian belief,
Vector observation) |
| Modifier and Type | Method and Description |
|---|---|
MultivariateGaussian.PDF |
DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater.createClusterPosterior(java.lang.Iterable<? extends Vector> values,
java.util.Random random) |
MultivariateGaussian.PDF |
DirichletProcessMixtureModel.MultivariateMeanUpdater.createClusterPosterior(java.lang.Iterable<? extends Vector> values,
java.util.Random random) |
MultivariateStudentTDistribution.PDF |
DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater.createPriorPredictive(java.lang.Iterable<? extends Vector> data) |
MultivariateGaussian.PDF |
DirichletProcessMixtureModel.MultivariateMeanUpdater.createPriorPredictive(java.lang.Iterable<? extends Vector> data) |
void |
ExtendedKalmanFilter.setMotionModel(StatefulEvaluator<Vector,Vector,Vector> motionModel)
Setter for motionModel
|
void |
ExtendedKalmanFilter.setMotionModel(StatefulEvaluator<Vector,Vector,Vector> motionModel)
Setter for motionModel
|
void |
ExtendedKalmanFilter.setMotionModel(StatefulEvaluator<Vector,Vector,Vector> motionModel)
Setter for motionModel
|
void |
ExtendedKalmanFilter.setObservationModel(Evaluator<Vector,Vector> observationModel)
Setter for observationModel
|
void |
ExtendedKalmanFilter.setObservationModel(Evaluator<Vector,Vector> observationModel)
Setter for observationModel
|
| Constructor and Description |
|---|
AbstractKalmanFilter(Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of AbstractKalmanFilter
|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
| Constructor and Description |
|---|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MultinomialBayesianEstimator.Parameter.getValue() |
Vector |
MultivariateGaussianMeanBayesianEstimator.Parameter.getValue() |
Vector |
UnivariateGaussianMeanVarianceBayesianEstimator.Parameter.getValue() |
| Modifier and Type | Method and Description |
|---|---|
MultinomialDistribution |
MultinomialBayesianEstimator.createConditionalDistribution(Vector parameter) |
MultivariateGaussian |
MultivariateGaussianMeanBayesianEstimator.createConditionalDistribution(Vector parameter) |
void |
MultinomialBayesianEstimator.Parameter.setValue(Vector value) |
void |
MultivariateGaussianMeanBayesianEstimator.Parameter.setValue(Vector value) |
void |
UnivariateGaussianMeanVarianceBayesianEstimator.Parameter.setValue(Vector value) |
void |
MultinomialBayesianEstimator.update(DirichletDistribution belief,
Vector value) |
void |
MultivariateGaussianMeanBayesianEstimator.update(MultivariateGaussian updater,
Vector data) |
void |
MultivariateGaussianMeanCovarianceBayesianEstimator.update(NormalInverseWishartDistribution target,
Vector data) |
| Modifier and Type | Method and Description |
|---|---|
void |
MultivariateGaussianMeanBayesianEstimator.update(MultivariateGaussian target,
java.lang.Iterable<? extends Vector> data) |
void |
MultivariateGaussianMeanCovarianceBayesianEstimator.update(NormalInverseWishartDistribution prior,
java.lang.Iterable<? extends Vector> data) |
| Constructor and Description |
|---|
MultinomialBayesianEstimator(BayesianParameter<Vector,MultinomialDistribution,DirichletDistribution> parameter)
Creates a new instance
|
MultivariateGaussianMeanBayesianEstimator(BayesianParameter<Vector,MultivariateGaussian,MultivariateGaussian> parameter)
Creates a new instance
|
UnivariateGaussianMeanVarianceBayesianEstimator(BayesianParameter<Vector,UnivariateGaussian,NormalInverseGammaDistribution> parameter)
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
|
| Modifier and Type | Class and Description |
|---|---|
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
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
MultivariateStudentTDistribution.mean
Mean, or noncentrality parameter, of the distribution
|
protected Vector |
CategoricalDistribution.parameters
Parameters of the multinomial distribution, must be at least
2-dimensional and each element must be nonnegative.
|
protected Vector |
DirichletDistribution.parameters
Parameters of the Dirichlet distribution, must be at least 2-dimensional
and each element must be positive.
|
protected Vector |
MultivariatePolyaDistribution.parameters
Parameters of the Dirichlet distribution, must be at least 2-dimensional
and each element must be positive.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
BernoulliDistribution.convertToVector() |
Vector |
BetaBinomialDistribution.convertToVector() |
Vector |
BetaDistribution.convertToVector()
Gets the parameters of the distribution
|
Vector |
BinomialDistribution.convertToVector() |
Vector |
CategoricalDistribution.convertToVector() |
Vector |
CauchyDistribution.convertToVector() |
Vector |
ChiSquareDistribution.convertToVector()
Returns the parameter of the chi-square PDF
|
Vector |
ChineseRestaurantProcess.convertToVector() |
Vector |
DeterministicDistribution.convertToVector() |
Vector |
DirichletDistribution.convertToVector() |
Vector |
ExponentialDistribution.convertToVector() |
Vector |
GammaDistribution.convertToVector()
Gets the parameters of the distribution
|
Vector |
GeometricDistribution.convertToVector() |
Vector |
InverseGammaDistribution.convertToVector() |
Vector |
InverseWishartDistribution.convertToVector() |
Vector |
KolmogorovDistribution.convertToVector() |
Vector |
LaplaceDistribution.convertToVector() |
Vector |
LogNormalDistribution.convertToVector()
Returns a 2-dimensional Vector with ( logNormalMean logNormalVariance )
|
Vector |
LogisticDistribution.convertToVector() |
Vector |
MultinomialDistribution.convertToVector() |
Vector |
MultivariateGaussian.convertToVector() |
Vector |
MultivariateGaussianInverseGammaDistribution.convertToVector() |
Vector |
MultivariateMixtureDensityModel.convertToVector() |
Vector |
MultivariatePolyaDistribution.convertToVector() |
Vector |
MultivariateStudentTDistribution.convertToVector() |
Vector |
NegativeBinomialDistribution.convertToVector() |
Vector |
NormalInverseGammaDistribution.convertToVector() |
Vector |
NormalInverseWishartDistribution.convertToVector() |
Vector |
ParetoDistribution.convertToVector() |
Vector |
PoissonDistribution.convertToVector() |
Vector |
ScalarMixtureDensityModel.convertToVector() |
Vector |
SnedecorFDistribution.convertToVector() |
Vector |
StudentTDistribution.convertToVector()
Returns the parameters of this PDF, which is a
1-dimensional Vector containing the degrees of freedom
|
Vector |
StudentizedRangeDistribution.convertToVector() |
Vector |
UniformDistribution.convertToVector() |
Vector |
UniformIntegerDistribution.convertToVector() |
Vector |
UnivariateGaussian.convertToVector() |
Vector |
WeibullDistribution.convertToVector() |
Vector |
YuleSimonDistribution.convertToVector() |
Vector |
CategoricalDistribution.getMean() |
Vector |
ChineseRestaurantProcess.getMean() |
Vector |
DirichletDistribution.getMean() |
Vector |
MultinomialDistribution.getMean() |
Vector |
MultivariateGaussian.getMean() |
Vector |
MultivariateGaussian.SufficientStatistic.getMean()
Getter for mean
|
Vector |
MultivariateGaussian.SufficientStatisticCovarianceInverse.getMean()
Getter for mean
|
Vector |
MultivariateGaussianInverseGammaDistribution.getMean() |
Vector |
MultivariateMixtureDensityModel.getMean() |
Vector |
MultivariatePolyaDistribution.getMean() |
Vector |
MultivariateStudentTDistribution.getMean() |
Vector |
NormalInverseGammaDistribution.getMean() |
Vector |
CategoricalDistribution.getParameters()
Getter for parameters
|
Vector |
DirichletDistribution.getParameters()
Getter for parameters
|
Vector |
MultinomialDistribution.getParameters()
Getter for parameters
|
Vector |
MultivariatePolyaDistribution.getParameters()
Getter for parameters
|
Vector |
MultinomialDistribution.Domain.MultinomialIterator.next() |
Vector |
ChineseRestaurantProcess.sample(java.util.Random random) |
Vector |
DirichletDistribution.sample(java.util.Random random) |
static Vector |
MultivariateGaussian.sample(Vector mean,
Matrix covarianceSquareRoot,
java.util.Random random)
Returns a single draw from the Gaussian with the given mean and
covariance.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.Set<Vector> |
CategoricalDistribution.getDomain() |
java.util.Set<Vector> |
ChineseRestaurantProcess.getDomain() |
java.util.Iterator<Vector> |
MultinomialDistribution.Domain.iterator() |
static java.util.ArrayList<Vector> |
MultivariateGaussian.sample(Vector mean,
Matrix covarianceSquareRoot,
java.util.Random random,
int sampleCount)
Returns a collection of draws this Gaussian with the given mean and
covariance.
|
| Modifier and Type | Method and Description |
|---|---|
double[] |
MultivariateMixtureDensityModel.PDF.computeRandomVariableLikelihoods(Vector input)
Computes the likelihoods of the underlying distributions
|
double[] |
MultivariateMixtureDensityModel.PDF.computeRandomVariableProbabilities(Vector input)
Computes the probability distribution that the input was generated by
the underlying distributions
|
double |
MixtureOfGaussians.PDF.computeWeightedZSquared(Vector input)
Computes the weighted z-value (deviate) of the given input.
|
double |
MultivariateGaussian.computeZSquared(Vector input)
Computes the z value squared, such that p(x) = coefficient *
exp{-0.5*z^2}
|
void |
BernoulliDistribution.convertFromVector(Vector parameters) |
void |
BetaBinomialDistribution.convertFromVector(Vector parameters) |
void |
BetaDistribution.convertFromVector(Vector parameters)
Sets the parameters of the distribution
|
void |
BinomialDistribution.convertFromVector(Vector parameters) |
void |
CategoricalDistribution.convertFromVector(Vector parameters) |
void |
CauchyDistribution.convertFromVector(Vector parameters) |
void |
ChiSquareDistribution.convertFromVector(Vector parameters)
Sets the parameter of the chi-square PDF
|
void |
ChineseRestaurantProcess.convertFromVector(Vector parameters) |
void |
DeterministicDistribution.convertFromVector(Vector parameters) |
void |
DirichletDistribution.convertFromVector(Vector parameters) |
void |
ExponentialDistribution.convertFromVector(Vector parameters) |
void |
GammaDistribution.convertFromVector(Vector parameters)
Sets the parameters of the distribution
|
void |
GeometricDistribution.convertFromVector(Vector parameters) |
void |
InverseGammaDistribution.convertFromVector(Vector parameters) |
void |
InverseWishartDistribution.convertFromVector(Vector parameters) |
void |
KolmogorovDistribution.convertFromVector(Vector parameters) |
void |
LaplaceDistribution.convertFromVector(Vector parameters) |
void |
LogNormalDistribution.convertFromVector(Vector parameters)
Sets the parameters of the distribution from a
2-dimensional Vector with ( logNormalMean logNormalVariance )
|
void |
LogisticDistribution.convertFromVector(Vector parameters) |
void |
MultinomialDistribution.convertFromVector(Vector parameters) |
void |
MultivariateGaussian.convertFromVector(Vector parameters) |
void |
MultivariateGaussianInverseGammaDistribution.convertFromVector(Vector parameters) |
void |
MultivariateMixtureDensityModel.convertFromVector(Vector parameters) |
void |
MultivariatePolyaDistribution.convertFromVector(Vector parameters) |
void |
MultivariateStudentTDistribution.convertFromVector(Vector parameters) |
void |
NegativeBinomialDistribution.convertFromVector(Vector parameters) |
void |
NormalInverseGammaDistribution.convertFromVector(Vector parameters) |
void |
NormalInverseWishartDistribution.convertFromVector(Vector parameters) |
void |
ParetoDistribution.convertFromVector(Vector parameters) |
void |
PoissonDistribution.convertFromVector(Vector parameters) |
void |
ScalarMixtureDensityModel.convertFromVector(Vector parameters) |
void |
SnedecorFDistribution.convertFromVector(Vector parameters) |
void |
StudentTDistribution.convertFromVector(Vector parameters)
Sets the parameters of this PDF, which must be a
1-dimensional Vector containing the degrees of freedom
|
void |
StudentizedRangeDistribution.convertFromVector(Vector parameters) |
void |
UniformDistribution.convertFromVector(Vector parameters) |
void |
UniformIntegerDistribution.convertFromVector(Vector parameters) |
void |
UnivariateGaussian.convertFromVector(Vector parameters) |
void |
WeibullDistribution.convertFromVector(Vector parameters) |
void |
YuleSimonDistribution.convertFromVector(Vector parameters) |
java.lang.Double |
CategoricalDistribution.PMF.evaluate(Vector input) |
java.lang.Double |
ChineseRestaurantProcess.PMF.evaluate(Vector input) |
java.lang.Double |
DirichletDistribution.PDF.evaluate(Vector input)
Evaluates the Dirichlet PDF about the given input.
|
java.lang.Double |
MultinomialDistribution.PMF.evaluate(Vector input) |
java.lang.Double |
MultivariateGaussian.PDF.evaluate(Vector input) |
java.lang.Double |
MultivariateMixtureDensityModel.PDF.evaluate(Vector input) |
java.lang.Double |
MultivariatePolyaDistribution.PMF.evaluate(Vector input) |
java.lang.Double |
MultivariateStudentTDistribution.PDF.evaluate(Vector input) |
java.lang.Double |
NormalInverseGammaDistribution.PDF.evaluate(Vector input) |
int |
MultivariateMixtureDensityModel.PDF.getMostLikelyRandomVariable(Vector input)
Gets the index of the most-likely distribution, given the input.
|
double |
CategoricalDistribution.PMF.logEvaluate(Vector input) |
double |
ChineseRestaurantProcess.PMF.logEvaluate(Vector input) |
double |
DirichletDistribution.PDF.logEvaluate(Vector input) |
double |
MultinomialDistribution.PMF.logEvaluate(Vector input) |
double |
MultivariateGaussian.PDF.logEvaluate(Vector input) |
double |
MultivariateMixtureDensityModel.PDF.logEvaluate(Vector input) |
double |
MultivariatePolyaDistribution.PMF.logEvaluate(Vector input) |
double |
MultivariateStudentTDistribution.PDF.logEvaluate(Vector input) |
double |
NormalInverseGammaDistribution.PDF.logEvaluate(Vector input) |
static Matrix |
InverseWishartDistribution.sample(java.util.Random random,
Vector mean,
Matrix covarianceSqrt,
int degreesOfFreedom)
Creates a single sample covariance matrix inverse from the given
parameters.
|
static Vector |
MultivariateGaussian.sample(Vector mean,
Matrix covarianceSquareRoot,
java.util.Random random)
Returns a single draw from the Gaussian with the given mean and
covariance.
|
static java.util.ArrayList<Vector> |
MultivariateGaussian.sample(Vector mean,
Matrix covarianceSquareRoot,
java.util.Random random,
int sampleCount)
Returns a collection of draws this Gaussian with the given mean and
covariance.
|
static void |
MultivariateGaussian.sampleInto(Vector mean,
Matrix covarianceSquareRoot,
java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output)
Performs a collection of draws this Gaussian with the given mean and
covariance.
|
void |
MultivariateGaussian.setMean(Vector mean)
Sets the mean vector.
|
void |
MultivariateStudentTDistribution.setMean(Vector mean)
Setter for mean
|
void |
CategoricalDistribution.setParameters(Vector parameters)
Setter for parameters
|
void |
DirichletDistribution.setParameters(Vector parameters)
Setter for parameters
|
void |
MultinomialDistribution.setParameters(Vector parameters)
Setter for parameters
|
void |
MultivariatePolyaDistribution.setParameters(Vector parameters)
Setter for parameters
|
void |
MultivariateGaussian.SufficientStatistic.update(Vector value) |
void |
MultivariateGaussian.SufficientStatisticCovarianceInverse.update(Vector value) |
| Modifier and Type | Method and Description |
|---|---|
MixtureOfGaussians.PDF |
MixtureOfGaussians.Learner.learn(java.util.Collection<? extends Vector> data) |
MultivariateGaussian.PDF |
MultivariateGaussian.MaximumLikelihoodEstimator.learn(java.util.Collection<? extends Vector> data)
Computes the Gaussian that estimates the maximum likelihood of
generating the given set of samples.
|
static MultivariateGaussian.PDF |
MultivariateGaussian.MaximumLikelihoodEstimator.learn(java.util.Collection<? extends Vector> data,
double defaultCovariance)
Computes the Gaussian that estimates the maximum likelihood of
generating the given set of samples.
|
MultivariateGaussian.PDF |
MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends Vector>> data)
Computes the Gaussian that estimates the maximum likelihood of
generating the given set of weighted samples.
|
static MultivariateGaussian.PDF |
MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends Vector>> data,
double defaultCovariance)
Computes the Gaussian that estimates the maximum likelihood of
generating the given set of weighted samples.
|
void |
CategoricalDistribution.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
ChineseRestaurantProcess.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
DirichletDistribution.sampleInto(java.util.Random random,
int numSamples,
java.util.Collection<? super Vector> output) |
void |
MultinomialDistribution.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
MultivariateGaussian.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
MultivariateGaussianInverseGammaDistribution.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
MultivariatePolyaDistribution.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
MultivariateStudentTDistribution.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
void |
NormalInverseGammaDistribution.sampleInto(java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output) |
static void |
MultivariateGaussian.sampleInto(Vector mean,
Matrix covarianceSquareRoot,
java.util.Random random,
int sampleCount,
java.util.Collection<? super Vector> output)
Performs a collection of draws this Gaussian with the given mean and
covariance.
|
| Constructor and Description |
|---|
CategoricalDistribution(Vector parameters)
Creates a new instance of CategoricalDistribution
|
DirichletDistribution(Vector parameters)
Creates a new instance of DirichletDistribution
|
MultinomialDistribution(Vector parameters,
int numTrials)
Creates a new instance of MultinomialDistribution
|
MultivariateGaussian(Vector mean,
Matrix covariance)
Creates a new instance of MultivariateGaussian.
|
MultivariatePolyaDistribution(Vector parameters,
int numTrials)
Creates a new instance of MultivariatePolyaDistribution
|
MultivariateStudentTDistribution(double degreesOfFreedom,
Vector mean,
Matrix precision)
Creates a distribution with the given dimensionality.
|
PDF(double degreesOfFreedom,
Vector mean,
Matrix precision)
Creates a distribution with the given dimensionality.
|
PDF(Vector parameters)
Creates a new instance of PDF
|
PDF(Vector mean,
Matrix covariance)
Creates a new instance of MultivariateGaussian.
|
PMF(Vector parameters)
Creates a new instance of CategoricalDistribution
|
PMF(Vector parameters,
int numTrials)
Creates a new instance of MultinomialDistribution.PMF
|
PMF(Vector parameters,
int numTrials)
Creates a new instance of MultivariatePolyaDistribution
|
| Constructor and Description |
|---|
Learner(KMeansClusterer<Vector,GaussianCluster> algorithm)
Creates a new Learner
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
DistributionParameterEstimator.DistributionWrapper.differentiate(Vector input) |
| Modifier and Type | Method and Description |
|---|---|
Vector |
DistributionParameterEstimator.DistributionWrapper.differentiate(Vector input) |
java.lang.Double |
DistributionParameterEstimator.DistributionWrapper.evaluate(Vector input) |
| Constructor and Description |
|---|
DistributionParameterEstimator(DistributionType distribution,
CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction,
FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper> algorithm)
Creates a new instance of DistributionParameterEstimator
|
| Modifier and Type | Method and Description |
|---|---|
static UnivariateGaussian |
MultivariateCumulativeDistributionFunction.compute(Vector input,
Distribution<Vector> distribution,
java.util.Random random,
double probabilityTolerance)
Computes a multi-variate cumulative distribution for a given input
according to the given distribution.
|
| Modifier and Type | Method and Description |
|---|---|
static UnivariateGaussian |
MultivariateCumulativeDistributionFunction.compute(Vector input,
Distribution<Vector> distribution,
java.util.Random random,
double probabilityTolerance)
Computes a multi-variate cumulative distribution for a given input
according to the given distribution.
|
MultivariateGaussian.PDF |
MultivariateMonteCarloIntegrator.getMean(java.util.Collection<? extends Vector> samples) |
MultivariateGaussian.PDF |
MultivariateMonteCarloIntegrator.getMean(java.util.List<? extends WeightedValue<? extends Vector>> samples) |
<SampleType> |
MultivariateMonteCarloIntegrator.integrate(java.util.Collection<? extends SampleType> samples,
Evaluator<? super SampleType,? extends Vector> expectationFunction) |
<SampleType> |
MultivariateMonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples,
Evaluator<? super SampleType,? extends Vector> expectationFunction) |
| Modifier and Type | Field and Description |
|---|---|
protected SimilarityFunction<? super Vector,? super Vector> |
TermVectorSimilarityNetworkCreator.similarityFunction
The similarity function between term vectors used to determine the
similarity between two terms.
|
protected SimilarityFunction<? super Vector,? super Vector> |
TermVectorSimilarityNetworkCreator.similarityFunction
The similarity function between term vectors used to determine the
similarity between two terms.
|
| Modifier and Type | Method and Description |
|---|---|
SimilarityFunction<? super Vector,? super Vector> |
TermVectorSimilarityNetworkCreator.getSimilarityFunction()
Gets the similarity function between term vectors used to determine the
similarity between two terms.
|
SimilarityFunction<? super Vector,? super Vector> |
TermVectorSimilarityNetworkCreator.getSimilarityFunction()
Gets the similarity function between term vectors used to determine the
similarity between two terms.
|
| Modifier and Type | Method and Description |
|---|---|
void |
TermVectorSimilarityNetworkCreator.setSimilarityFunction(SimilarityFunction<? super Vector,? super Vector> similarityFunction)
Sets the similarity function between term vectors used to determine the
similarity between two terms.
|
void |
TermVectorSimilarityNetworkCreator.setSimilarityFunction(SimilarityFunction<? super Vector,? super Vector> similarityFunction)
Sets the similarity function between term vectors used to determine the
similarity between two terms.
|
| Constructor and Description |
|---|
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction)
Creates a new
TermVectorSimilarityNetworkCreator. |
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction)
Creates a new
TermVectorSimilarityNetworkCreator. |
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction,
double effectiveZero)
Creates a new
TermVectorSimilarityNetworkCreator. |
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction,
double effectiveZero)
Creates a new
TermVectorSimilarityNetworkCreator. |
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction,
double effectiveZero,
MatrixFactory<? extends Matrix> matrixFactory)
Creates a new
TermVectorSimilarityNetworkCreator. |
TermVectorSimilarityNetworkCreator(SimilarityFunction<? super Vector,? super Vector> similarityFunction,
double effectiveZero,
MatrixFactory<? extends Matrix> matrixFactory)
Creates a new
TermVectorSimilarityNetworkCreator. |
| Modifier and Type | Method and Description |
|---|---|
Vector |
BagOfWordsTransform.convertToVector(java.lang.Iterable<? extends Termable> terms)
Converts a given list of terms to a vector by counting the occurrence of
each term.
|
static Vector |
BagOfWordsTransform.convertToVector(java.lang.Iterable<? extends Termable> terms,
TermIndex termIndex,
VectorFactory<?> vectorFactory)
Converts a given list of terms to a vector by counting the occurrence of
each term.
|
Vector |
BagOfWordsTransform.convertToVector(java.lang.Iterable<? extends Termable> terms,
VectorFactory<?> vectorFactory)
Converts a given list of terms to a vector by counting the occurrence of
each term.
|
Vector |
BagOfWordsTransform.evaluate(java.lang.Iterable<? extends Termable> terms) |
| Modifier and Type | Method and Description |
|---|---|
void |
VectorSpaceModel.add(Vector document)
Adds a document to the model.
|
boolean |
VectorSpaceModel.remove(Vector document)
Removes the document from the model.
|
| Constructor and Description |
|---|
BagOfWordsTransform(TermIndex termIndex,
VectorFactory<? extends Vector> vectorFactory)
Creates a new
BagOfWordsTransform with the given term index. |
| Modifier and Type | Method and Description |
|---|---|
Vector |
CompositeLocalGlobalTermWeighter.evaluate(Vector document) |
| Modifier and Type | Method and Description |
|---|---|
Vector |
CompositeLocalGlobalTermWeighter.evaluate(Vector document) |
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
DominanceGlobalTermWeighter.dominance
A vector caching the global dominance weight of the document collection.
|
protected Vector |
EntropyGlobalTermWeighter.entropy
A vector caching the global entropy weight of the document collection.
|
protected Vector |
InverseDocumentFrequencyGlobalTermWeighter.inverseDocumentFrequency
The (cached) value of the inverse document frequency.
|
protected Vector |
AbstractFrequencyBasedGlobalTermWeighter.termDocumentFrequencies
The vector containing the number of documents that each term occurs in.
|
protected Vector |
AbstractEntropyBasedGlobalTermWeighter.termEntropiesSum
A vector containing the sum of the entropy term (f_ij * log(f_ij)) over
each document in the collection for each term.
|
protected Vector |
AbstractFrequencyBasedGlobalTermWeighter.termGlobalFrequencies
A vector containing the total number of times that each term occurred
in the document set.
|
| Modifier and Type | Field and Description |
|---|---|
protected VectorFactory<? extends Vector> |
AbstractGlobalTermWeighter.vectorFactory
The vector factory.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
DominanceGlobalTermWeighter.getDominance()
Gets the dominance weight (global weight) vector for all of the terms.
|
Vector |
EntropyGlobalTermWeighter.getEntropy()
Gets the entropy weight (global weight) vector for all of the terms.
|
Vector |
DominanceGlobalTermWeighter.getGlobalWeights() |
Vector |
EntropyGlobalTermWeighter.getGlobalWeights() |
Vector |
GlobalTermWeighter.getGlobalWeights()
Gets the current vector of global weights.
|
Vector |
InverseDocumentFrequencyGlobalTermWeighter.getGlobalWeights() |
Vector |
InverseDocumentFrequencyGlobalTermWeighter.getInverseDocumentFrequency()
Gets the inverse-document-frequency (IDF) global weight values.
|
Vector |
AbstractFrequencyBasedGlobalTermWeighter.getTermDocumentFrequencies()
Gets the vector containing the number of documents that each term
appears in.
|
Vector |
AbstractEntropyBasedGlobalTermWeighter.getTermEntropiesSum()
Gets the vector containing the sum of term the entropies.
|
Vector |
AbstractFrequencyBasedGlobalTermWeighter.getTermGlobalFrequencies()
Gets the vector containing the number of times that each term appears.
|
| Modifier and Type | Method and Description |
|---|---|
VectorFactory<? extends Vector> |
AbstractGlobalTermWeighter.getVectorFactory()
Gets the vector factory.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractEntropyBasedGlobalTermWeighter.add(Vector counts) |
void |
AbstractFrequencyBasedGlobalTermWeighter.add(Vector counts) |
void |
DominanceGlobalTermWeighter.add(Vector counts) |
void |
EntropyGlobalTermWeighter.add(Vector counts) |
void |
InverseDocumentFrequencyGlobalTermWeighter.add(Vector counts) |
boolean |
AbstractEntropyBasedGlobalTermWeighter.remove(Vector counts) |
boolean |
AbstractFrequencyBasedGlobalTermWeighter.remove(Vector counts) |
boolean |
DominanceGlobalTermWeighter.remove(Vector counts) |
boolean |
EntropyGlobalTermWeighter.remove(Vector counts) |
boolean |
InverseDocumentFrequencyGlobalTermWeighter.remove(Vector counts) |
protected void |
DominanceGlobalTermWeighter.setDominance(Vector dominance)
Sets the cached dominance weight vector.
|
protected void |
EntropyGlobalTermWeighter.setEntropy(Vector entropy)
Sets the cached entropy weight vector.
|
protected void |
InverseDocumentFrequencyGlobalTermWeighter.setInverseDocumentFrequency(Vector inverseDocumentFrequency)
Sets the cached inverse-document-frequency (IDF) global weight values.
|
protected void |
AbstractFrequencyBasedGlobalTermWeighter.setTermDocumentFrequencies(Vector termDocumentFrequencies)
Sets the vector containing the number of documents that each term
appears in.
|
protected void |
AbstractEntropyBasedGlobalTermWeighter.setTermEntropiesSum(Vector termEntropiesSum)
Sets the vector containing the sum of the term entropies.
|
protected void |
AbstractFrequencyBasedGlobalTermWeighter.setTermGlobalFrequencies(Vector termGlobalFrequencies)
Gets the vector containing the number of times that each term appears.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractGlobalTermWeighter.setVectorFactory(VectorFactory<? extends Vector> vectorFactory)
Sets the vector factory.
|
| Constructor and Description |
|---|
AbstractEntropyBasedGlobalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
AbstractEntropyBasedGlobalTermWeighter. |
AbstractFrequencyBasedGlobalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
AbstractCountingBasedGlobalTermWeighter. |
AbstractGlobalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
AbstractGlobalTermWeighter. |
DominanceGlobalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
DominanceGlobalTermWeighter. |
EntropyGlobalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
EntropyGlobalTermWeighter. |
InverseDocumentFrequencyGlobalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
InverseDocumentFrequencyGlobalTermWeighter. |
| Modifier and Type | Method and Description |
|---|---|
Vector |
BinaryLocalTermWeighter.computeLocalWeights(Vector counts) |
Vector |
LocalTermWeighter.computeLocalWeights(Vector document)
Computes the new local weights for a given document.
|
Vector |
LogLocalTermWeighter.computeLocalWeights(Vector counts) |
Vector |
NormalizedLogLocalTermWeighter.computeLocalWeights(Vector counts) |
Vector |
TermFrequencyLocalTermWeighter.computeLocalWeights(Vector counts) |
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 |
BinaryLocalTermWeighter.computeLocalWeights(Vector counts) |
Vector |
LocalTermWeighter.computeLocalWeights(Vector document)
Computes the new local weights for a given document.
|
Vector |
LogLocalTermWeighter.computeLocalWeights(Vector counts) |
Vector |
NormalizedLogLocalTermWeighter.computeLocalWeights(Vector counts) |
Vector |
TermFrequencyLocalTermWeighter.computeLocalWeights(Vector counts) |
| Constructor and Description |
|---|
AbstractLocalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
AbstractLocalTermWeighter. |
BinaryLocalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
BinaryLocalTermWeighter. |
LogLocalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
LogLocalTermWeighter. |
NormalizedLogLocalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
NormalizedLogLocalTermWeighter |
TermFrequencyLocalTermWeighter(VectorFactory<? extends Vector> vectorFactory)
Creates a new
LogLocalTermWeighter. |
| Modifier and Type | Method and Description |
|---|---|
void |
TermWeightNormalizer.normalizeWeights(Vector weights,
Vector counts,
Vector globalWeights)
Normalizes the given weight vector.
|
void |
UnitTermWeightNormalizer.normalizeWeights(Vector weights,
Vector counts,
Vector globalWeights) |
| Modifier and Type | Field and Description |
|---|---|
protected VectorFactory<? extends Vector> |
ProbabilisticLatentSemanticAnalysis.vectorFactory
The vector factory.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
LatentSemanticAnalysis.Transform.evaluate(Vectorizable input) |
Vector |
ProbabilisticLatentSemanticAnalysis.Result.evaluate(Vectorizable input) |
Vector |
LatentSemanticAnalysis.Transform.getTermVector(int i)
Gets the i-th orthogonal term vector that makes up the basis for
the transform.
|
| Modifier and Type | Method and Description |
|---|---|
VectorFactory<? extends Vector> |
ProbabilisticLatentSemanticAnalysis.getVectorFactory()
Gets the vector factory to use.
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
ProbabilisticLatentSemanticAnalysis.Result.step(Vector query,
Matrix pLatentGivenQueryTerm,
Vector pQueryGivenLatent)
Take a step of the expectation-maximization algorithm for computing
the probability of the query given each latent variable.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ProbabilisticLatentSemanticAnalysis.setVectorFactory(VectorFactory<? extends Vector> vectorFactory)
Sets the vector factory to use.
|
| Constructor and Description |
|---|
DocumentSampleTask(Vector v,
int doc,
int occ)
Creates a new instance of DocumentSampleTask
|