Uses of Class
org.tribuo.math.la.DenseVector

Packages that use DenseVector

Package	Description
org.tribuo.classification.sgd.crf	Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
org.tribuo.clustering.kmeans	Provides a multithreaded implementation of K-Means, with a configurable distance function.
org.tribuo.common.sgd	Provides the base classes for models trained with stochastic gradient descent.
org.tribuo.interop.oci	Code for uploading models to Oracle Cloud Infrastructure Data Science, and also for scoring models deployed in Oracle Cloud Infrastructure Data Science.
org.tribuo.math	Contains the implementation of Tribuo's math library, it's gradient descent optimisers, kernels and a set of math related utils.
org.tribuo.math.distributions	A package of statistical distributions.
org.tribuo.math.la	Provides a linear algebra system used for numerical operations in Tribuo.
org.tribuo.math.optimisers.util	Provides some utility tensors for use in gradient optimisers.
org.tribuo.multilabel	Provides classes and infrastructure for working with multi-label classification problems.
org.tribuo.regression.sgd	Provides infrastructure for Stochastic Gradient Descent based regression models.
org.tribuo.regression.sgd.fm	Provides an implementation of factorization machines for regression using Stochastic Gradient Descent.
org.tribuo.regression.sgd.linear	Provides an implementation of linear regression using Stochastic Gradient Descent.
org.tribuo.regression.sgd.objectives	Provides regression loss functions for Stochastic Gradient Descent.
org.tribuo.regression.slm	Provides implementations of sparse linear regression using various forms of regularisation penalty.

Uses of DenseVector in org.tribuo.classification.sgd.crf

Fields in org.tribuo.classification.sgd.crf declared as DenseVector

Modifier and Type	Field	Description
final DenseVector[]	ChainHelper.ChainBPResults.alphas	The alpha values array from forward propagation.
final DenseVector[]	ChainHelper.ChainBPResults.betas	The beta values array from backward propagation.
final DenseVector[]	ChainHelper.ChainCliqueValues.localValues	The per element values.

Methods in org.tribuo.classification.sgd.crf that return DenseVector

Modifier and Type	Method	Description
DenseVector	CRFModel.getFeatureWeights(int featureID)	Get a copy of the weights for feature featureID.
DenseVector	CRFModel.getFeatureWeights(String featureName)	Get a copy of the weights for feature named featureName.
DenseVector	CRFParameters.getFeatureWeights(int id)	Gets a copy of the weights for the specified label id.
DenseVector[]	CRFParameters.getLocalScores(SGDVector[] features)	Generate the local scores (i.e., the linear classifier for each token).
DenseVector[]	CRFParameters.predictMarginals(SGDVector[] features)	Generate a prediction using Belief Propagation.

Methods in org.tribuo.classification.sgd.crf with parameters of type DenseVector

Modifier and Type	Method	Description
static double	ChainHelper.sumLogProbs(DenseVector input)	Sums the log probabilities.

Uses of DenseVector in org.tribuo.clustering.kmeans

Methods in org.tribuo.clustering.kmeans that return DenseVector

Modifier and Type	Method	Description
DenseVector[]	KMeansModel.getCentroidVectors()	Returns a copy of the centroids.

Methods in org.tribuo.clustering.kmeans with parameters of type DenseVector

Modifier and Type	Method	Description
protected void	KMeansTrainer.mStep(ForkJoinPool fjp, DenseVector[] centroidVectors, Map<Integer,List<Integer>> clusterAssignments, SGDVector[] data, double[] weights)	Runs the mStep, writing to the centroidVectors array.

Uses of DenseVector in org.tribuo.common.sgd

Fields in org.tribuo.common.sgd declared as DenseVector

Modifier and Type	Field	Description
final DenseVector	AbstractSGDModel.PredAndActive.prediction	The vector prediction.

Methods in org.tribuo.common.sgd that return DenseVector

Modifier and Type	Method	Description
DenseVector	AbstractFMModel.getBiasesCopy()	Returns a copy of the output dimension biases.
DenseVector	FMParameters.predict(SGDVector example)	Generates an unnormalised prediction by multiplying the weights with the incoming features, adding the bias and adding the feature factors.

Uses of DenseVector in org.tribuo.interop.oci

Methods in org.tribuo.interop.oci with parameters of type DenseVector

Modifier and Type	Method	Description
Prediction<Label>	OCILabelConverter.convertOutput(DenseVector scores, int numValidFeature, Example<Label> example, ImmutableOutputInfo<Label> outputIDInfo)
Prediction<MultiLabel>	OCIMultiLabelConverter.convertOutput(DenseVector scores, int numValidFeature, Example<MultiLabel> example, ImmutableOutputInfo<MultiLabel> outputIDInfo)
Prediction<T>	OCIOutputConverter.convertOutput(DenseVector scores, int numValidFeature, Example<T> example, ImmutableOutputInfo<T> outputIDInfo)	Converts a dense vector into a single prediction of the appropriate type.
Prediction<Regressor>	OCIRegressorConverter.convertOutput(DenseVector scores, int numValidFeature, Example<Regressor> example, ImmutableOutputInfo<Regressor> outputIDInfo)

Uses of DenseVector in org.tribuo.math

Methods in org.tribuo.math that return DenseVector

Modifier and Type	Method	Description
DenseVector	FeedForwardParameters.predict(SGDVector example)	Generates an un-normalized prediction by feeding the features through the parameters.
DenseVector	LinearParameters.predict(SGDVector example)	Generates an unnormalised prediction by leftMultiply'ing the weights with the incoming features.

Uses of DenseVector in org.tribuo.math.distributions

Methods in org.tribuo.math.distributions that return DenseVector

Modifier and Type	Method	Description
DenseVector	MultivariateNormalDistribution.sampleVector()	Sample a vector from this multivariate normal distribution.

Constructors in org.tribuo.math.distributions with parameters of type DenseVector

Modifier	Constructor	Description
	MultivariateNormalDistribution(DenseVector means, DenseMatrix covariance, long seed)	Constructs a multivariate normal distribution that can be sampled from.
	MultivariateNormalDistribution(DenseVector means, DenseMatrix covariance, long seed, boolean eigenDecomposition)	Constructs a multivariate normal distribution that can be sampled from.

Uses of DenseVector in org.tribuo.math.la

Methods in org.tribuo.math.la that return DenseVector

Modifier and Type	Method	Description
DenseVector	DenseVector.add(SGDVector other)	Adds other to this vector, producing a new DenseVector.
DenseVector	DenseMatrix.columnSum()	Returns a dense vector containing each column sum.
DenseVector	DenseVector.copy()
static DenseVector	DenseVector.createDenseVector(double[] values)	Defensively copies the values before construction.
static <T extends Output<T>> DenseVector	DenseVector.createDenseVector(Example<T> example, ImmutableFeatureMap featureInfo, boolean addBias)	Builds a DenseVector from an Example.
DenseVector	SparseVector.densify()	Returns a dense vector copying this sparse vector.
static DenseVector	DenseVector.deserializeFromProto(int version, String className, com.google.protobuf.Any message)	Deserialization factory.
DenseVector	DenseMatrix.EigenDecomposition.diagonal()	The diagonal vector of the tridiagonal form.
DenseVector	DenseMatrix.EigenDecomposition.eigenvalues()	The vector of eigenvalues, in descending order.
DenseVector	DenseMatrix.gatherAcrossDim1(int[] elements)	Constructs a dense vector by gathering values across dimension 1.
DenseVector	DenseMatrix.gatherAcrossDim2(int[] elements)	Constructs a dense vector by gathering values across dimension 2.
DenseVector	DenseMatrix.getColumn(int index)	Returns a copy of the specified column.
DenseVector	DenseMatrix.EigenDecomposition.getEigenVector(int i)	Returns the dense vector representing the i'th eigenvector.
DenseVector	DenseMatrix.getRow(int i)
DenseVector	DenseMatrix.leftMultiply(SGDVector input)
DenseVector	DenseSparseMatrix.leftMultiply(SGDVector input)
DenseVector	DenseMatrix.EigenDecomposition.offDiagonal()	The off diagonal vector, with the first element set to zero.
DenseVector	DenseMatrix.rightMultiply(SGDVector input)
DenseVector	DenseSparseMatrix.rightMultiply(SGDVector input)	rightMultiply is very inefficient on DenseSparseMatrix due to the storage format.
DenseVector	DenseMatrix.rowSum()
DenseVector	DenseSparseMatrix.rowSum()
DenseVector	Matrix.rowSum()	Generates a DenseVector representing the sum of each row.
DenseVector	DenseVector.scale(double coefficient)
DenseVector	DenseMatrix.CholeskyFactorization.solve(SGDVector vector)	Solves a system of linear equations A * b = y, where y is the input vector, A is the matrix which produced this Cholesky factorization, and b is the returned value.
DenseVector	DenseMatrix.EigenDecomposition.solve(SGDVector vector)	Solves a system of linear equations A * b = y, where y is the input vector, A is the matrix which produced this eigen decomposition, and b is the returned value.
DenseVector	DenseMatrix.LUFactorization.solve(SGDVector vector)	Solves a system of linear equations A * b = y, where y is the input vector, A is the matrix which produced this LU factorization, and b is the returned value.
DenseVector	DenseVector.subtract(SGDVector other)	Subtracts other from this vector, producing a new DenseVector.
protected static DenseVector	DenseVector.unpackProto(org.tribuo.math.protos.DenseTensorProto proto)	Unpacks a DenseTensorProto into a DenseVector.

Methods in org.tribuo.math.la with parameters of type DenseVector

Modifier and Type	Method	Description
void	DenseMatrix.rowScaleInPlace(DenseVector scalingCoefficients)
void	DenseSparseMatrix.rowScaleInPlace(DenseVector scalingCoefficients)
void	Matrix.rowScaleInPlace(DenseVector scalingCoefficients)	Scales each row by the appropriate value in the DenseVector.
void	DenseVector.setElements(DenseVector other)	Sets all the elements of this vector to be the same as other.

Constructors in org.tribuo.math.la with parameters of type DenseVector

Modifier	Constructor	Description
protected	DenseVector(DenseVector other)	Copy constructor.

Uses of DenseVector in org.tribuo.math.optimisers.util

Subclasses of DenseVector in org.tribuo.math.optimisers.util

Modifier and Type	Class	Description
class	ShrinkingVector	A subclass of DenseVector which shrinks the value every time a new value is added.

Methods in org.tribuo.math.optimisers.util that return DenseVector

Modifier and Type	Method	Description
DenseVector	ShrinkingVector.convertToDense()
DenseVector	ShrinkingMatrix.leftMultiply(SGDVector input)

Constructors in org.tribuo.math.optimisers.util with parameters of type DenseVector

Modifier	Constructor	Description
	ShrinkingVector(DenseVector v, double baseRate, boolean scaleShrinking)	Constructs a shrinking vector copy of the supplied dense matrix.
	ShrinkingVector(DenseVector v, double baseRate, double lambda)	Constructs a shrinking vector copy of the supplied dense vector.

Uses of DenseVector in org.tribuo.multilabel

Methods in org.tribuo.multilabel that return DenseVector

Modifier and Type	Method	Description
DenseVector	MultiLabel.convertToDenseVector(ImmutableOutputInfo<MultiLabel> info)	Converts this MultiLabel into a DenseVector using the indices from the output info.

Uses of DenseVector in org.tribuo.regression.sgd

Methods in org.tribuo.regression.sgd with parameters of type DenseVector

Modifier and Type	Method	Description
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	RegressionObjective.loss(DenseVector truth, SGDVector prediction)	Deprecated. In 4.1 to move to the new name, lossAndGradient.
default com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	RegressionObjective.lossAndGradient(DenseVector truth, SGDVector prediction)
static void	Util.shuffleInPlace(SparseVector[] features, DenseVector[] regressors, double[] weights, int[] indices, SplittableRandom rng)	In place shuffle of the features, labels and weights.
static void	Util.shuffleInPlace(SparseVector[] features, DenseVector[] regressors, double[] weights, SplittableRandom rng)	In place shuffle of the features, labels and weights.

Uses of DenseVector in org.tribuo.regression.sgd.fm

Methods in org.tribuo.regression.sgd.fm that return DenseVector

Modifier and Type	Method	Description
protected DenseVector	FMRegressionTrainer.getTarget(ImmutableOutputInfo<Regressor> outputInfo, Regressor output)

Methods in org.tribuo.regression.sgd.fm that return types with arguments of type DenseVector

Modifier and Type	Method	Description
protected SGDObjective<DenseVector>	FMRegressionTrainer.getObjective()

Uses of DenseVector in org.tribuo.regression.sgd.linear

Methods in org.tribuo.regression.sgd.linear that return DenseVector

Modifier and Type	Method	Description
protected DenseVector	LinearSGDTrainer.getTarget(ImmutableOutputInfo<Regressor> outputInfo, Regressor output)

Methods in org.tribuo.regression.sgd.linear that return types with arguments of type DenseVector

Modifier and Type	Method	Description
protected SGDObjective<DenseVector>	LinearSGDTrainer.getObjective()

Uses of DenseVector in org.tribuo.regression.sgd.objectives

Methods in org.tribuo.regression.sgd.objectives with parameters of type DenseVector

Modifier and Type	Method	Description
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	AbsoluteLoss.loss(DenseVector truth, SGDVector prediction)	Deprecated.
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	Huber.loss(DenseVector truth, SGDVector prediction)	Deprecated.
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	SquaredLoss.loss(DenseVector truth, SGDVector prediction)	Deprecated.
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	AbsoluteLoss.lossAndGradient(DenseVector truth, SGDVector prediction)
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	Huber.lossAndGradient(DenseVector truth, SGDVector prediction)
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	SquaredLoss.lossAndGradient(DenseVector truth, SGDVector prediction)

Uses of DenseVector in org.tribuo.regression.slm

Methods in org.tribuo.regression.slm that return DenseVector

Modifier and Type	Method	Description
protected DenseVector	LARSLassoTrainer.newWeights(org.tribuo.regression.slm.SLMTrainer.SLMState state)
protected DenseVector	LARSTrainer.newWeights(org.tribuo.regression.slm.SLMTrainer.SLMState state)
protected DenseVector	SLMTrainer.newWeights(org.tribuo.regression.slm.SLMTrainer.SLMState state)	Computes the new feature weights.