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.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.regression.sgd	Provides infrastructure for Stochastic Gradient Descent based regression models.
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)
DenseVector[]	CRFParameters.getLocalScores(SparseVector[] features)	Generate the local scores (i.e., the linear classifier for each token).
DenseVector[]	CRFParameters.predictMarginals(SparseVector[] 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.

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

Modifier	Constructor	Description
	ChainBPResults(double logZ, DenseVector[] alphas, DenseVector[] betas, ChainHelper.ChainCliqueValues scores)
	ChainCliqueValues(DenseVector[] localValues, DenseMatrix transitionValues)

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.
protected static DenseVector[]	KMeansTrainer.initialiseCentroids(int centroids, Dataset<ClusterID> examples, ImmutableFeatureMap featureMap, SplittableRandom rng)	Initialisation method called at the start of each train call.

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)

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()
DenseVector	DenseVector.copy()
static DenseVector	DenseVector.createDenseVector(double[] values)	Defensively copies the values before construction.
DenseVector	DenseMatrix.gatherAcrossDim1(int[] elements)
DenseVector	DenseMatrix.gatherAcrossDim2(int[] elements)
DenseVector	DenseMatrix.getColumn(int index)
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)

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.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)	Scores a prediction, returning the loss.
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.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)
com.oracle.labs.mlrg.olcut.util.Pair<Double, SGDVector>	Huber.loss(DenseVector truth, SGDVector prediction)
com.oracle.labs.mlrg.olcut.util.Pair<Double, SGDVector>	SquaredLoss.loss(DenseVector truth, SGDVector prediction)