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.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.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.linear	Provides an implementation of linear regression using Stochastic Gradient Descent.
org.tribuo.regression.sgd.objectives	Provides regression loss functions for Stochastic Gradient Descent.

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
final DenseVector[]	ChainHelper.ChainBPResults.betas
final DenseVector[]	ChainHelper.ChainCliqueValues.localValues

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, SparseVector[] 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

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.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 the 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.
DenseVector	DenseMatrix.gatherAcrossDim1(int[] elements)
DenseVector	DenseMatrix.gatherAcrossDim2(int[] elements)
DenseVector	DenseMatrix.getColumn(int index)	Returns a copy of the specified column.
DenseVector	DenseMatrix.getRow(int i)
DenseVector	DenseMatrix.leftMultiply(SGDVector input)
DenseVector	DenseSparseMatrix.leftMultiply(SGDVector input)
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	DenseVector.subtract(SGDVector other)	Subtracts other from this vector, producing a new 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)
	ShrinkingVector(DenseVector v, double baseRate, double lambda)

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

Subinterfaces with type arguments of type DenseVector in org.tribuo.regression.sgd

Modifier and Type	Interface	Description
interface	RegressionObjective	An interface for regression objectives.

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.linear

Subclasses with type arguments of type DenseVector in org.tribuo.regression.sgd.linear

Modifier and Type	Class	Description
class	LinearSGDTrainer	A trainer for a linear regression model which uses SGD.

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)