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

Packages that use SparseVector

Package	Description
org.tribuo.classification.explanations.lime	Provides an implementation of LIME (Locally Interpretable Model Explanations).
org.tribuo.classification.sgd	Provides infrastructure for Stochastic Gradient Descent for classification problems.
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.tree	Provides common functionality for building decision trees, irrespective of the predicted Output.
org.tribuo.common.xgboost	Provides abstract classes for interfacing with XGBoost abstracting away all the Output dependent parts.
org.tribuo.interop	This package contains the abstract implementation of an external model trained by something outside of Tribuo.
org.tribuo.interop.onnx	This package contains a Tribuo wrapper around the ONNX Runtime.
org.tribuo.interop.tensorflow	Provides an interface to Tensorflow, allowing the training of non-sequential models using any supported Tribuo output type.
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.kernel	Provides a Kernel interface for Mercer kernels, along with implementations of standard kernels.
org.tribuo.math.la	Provides a linear algebra system used for numerical operations in Tribuo.
org.tribuo.math.util	Provides math related util classes.
org.tribuo.regression.impl	Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
org.tribuo.regression.sgd	Provides infrastructure for Stochastic Gradient Descent based regression models.
org.tribuo.regression.slm	Provides implementations of sparse linear regression using various forms of regularisation penalty.

Uses of SparseVector in org.tribuo.classification.explanations.lime

Methods in org.tribuo.classification.explanations.lime with parameters of type SparseVector

Modifier and Type	Method and Description
static double	LIMEBase.measureDistance(ImmutableFeatureMap fMap, long numTrainingExamples, SparseVector input, SparseVector sample) Measures the distance between an input point and a sampled point.
static Example<Label>	LIMEBase.samplePoint(Random rng, ImmutableFeatureMap fMap, long numTrainingExamples, SparseVector input) Samples a single example from the supplied feature map and input vector.

Uses of SparseVector in org.tribuo.classification.sgd

Fields in org.tribuo.classification.sgd declared as SparseVector

Modifier and Type	Field and Description
SparseVector[]	Util.ExampleArray.features
SparseVector[][]	Util.SequenceExampleArray.features

Methods in org.tribuo.classification.sgd with parameters of type SparseVector

Modifier and Type	Method and Description
static Util.SequenceExampleArray	Util.shuffle(SparseVector[][] features, int[][] labels, double[] weights, SplittableRandom rng) Shuffles a sequence of features, labels and weights, returning a tuple of the shuffled values.
static Util.ExampleArray	Util.shuffle(SparseVector[] features, int[] labels, double[] weights, SplittableRandom rng) Shuffles the features, labels and weights returning a tuple of the shuffled inputs.
static void	Util.shuffleInPlace(SparseVector[][] features, int[][] labels, double[] weights, SplittableRandom rng) In place shuffle used for sequence problems.
static void	Util.shuffleInPlace(SparseVector[] features, int[] labels, double[] weights, int[] indices, SplittableRandom rng) In place shuffle of the features, labels, weights and indices.
static void	Util.shuffleInPlace(SparseVector[] features, int[] labels, double[] weights, SplittableRandom rng) In place shuffle of the features, labels and weights.

Constructors in org.tribuo.classification.sgd with parameters of type SparseVector

Constructor and Description
ExampleArray(SparseVector[] features, int[] labels, double[] weights)
SequenceExampleArray(SparseVector[][] features, int[][] labels, double[] weights)

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

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

Modifier and Type	Method and Description
static <T extends Output<T>> SparseVector[]	CRFModel.convert(SequenceExample<T> example, ImmutableFeatureMap featureIDMap) Converts a SequenceExample into an array of SparseVectors suitable for CRF prediction.

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

Modifier and Type	Method and Description
ChainHelper.ChainCliqueValues	CRFParameters.getCliqueValues(SparseVector[] features) Generates the local scores and tuples them with the label - label transition weights.
DenseVector[]	CRFParameters.getLocalScores(SparseVector[] features) Generate the local scores (i.e., the linear classifier for each token).
int[]	CRFParameters.predict(SparseVector[] features) Generate a prediction using Viterbi.
List<Double>	CRFParameters.predictConfidenceUsingCBP(SparseVector[] features, List<Chunk> chunks) This predicts per chunk confidence using the constrained forward backward algorithm from Culotta and McCallum 2004.
DenseVector[]	CRFParameters.predictMarginals(SparseVector[] features) Generate a prediction using Belief Propagation.
com.oracle.labs.mlrg.olcut.util.Pair<Double,Tensor[]>	CRFParameters.valueAndGradient(SparseVector[] features, int[] labels) Generates predictions based on the input features and labels, then scores those predictions to produce a loss for the example and a gradient update.

Uses of SparseVector in org.tribuo.clustering.kmeans

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

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

Uses of SparseVector in org.tribuo.common.tree

Methods in org.tribuo.common.tree with parameters of type SparseVector

Modifier and Type	Method and Description
Node<T>	SplitNode.getNextNode(SparseVector e) Return the appropriate child node.
Node<T>	Node.getNextNode(SparseVector example) Returns the next node in the tree based on the supplied example, or null if it's a leaf.
Node<T>	LeafNode.getNextNode(SparseVector e)
Node<T>	AbstractTrainingNode.getNextNode(SparseVector example)

Uses of SparseVector in org.tribuo.common.xgboost

Methods in org.tribuo.common.xgboost with parameters of type SparseVector

Modifier and Type	Method and Description
protected ml.dmlc.xgboost4j.java.DMatrix	XGBoostExternalModel.convertFeatures(SparseVector input)
protected static ml.dmlc.xgboost4j.java.DMatrix	XGBoostTrainer.convertSparseVector(SparseVector vector) Used when predicting with an externally trained XGBoost model.

Method parameters in org.tribuo.common.xgboost with type arguments of type SparseVector

Modifier and Type	Method and Description
protected ml.dmlc.xgboost4j.java.DMatrix	XGBoostExternalModel.convertFeaturesList(List<SparseVector> input)
protected static ml.dmlc.xgboost4j.java.DMatrix	XGBoostTrainer.convertSparseVectors(List<SparseVector> vectors) Used when predicting with an externally trained XGBoost model.

Uses of SparseVector in org.tribuo.interop

Methods in org.tribuo.interop with parameters of type SparseVector

Modifier and Type	Method and Description
protected abstract U	ExternalModel.convertFeatures(SparseVector input) Converts from a SparseVector using the external model's indices into the ingestion format for the external model.

Method parameters in org.tribuo.interop with type arguments of type SparseVector

Modifier and Type	Method and Description
protected abstract U	ExternalModel.convertFeaturesList(List<SparseVector> input) Converts from a list of SparseVector using the external model's indices into the ingestion format for the external model.

Uses of SparseVector in org.tribuo.interop.onnx

Methods in org.tribuo.interop.onnx with parameters of type SparseVector

Modifier and Type	Method and Description
protected ai.onnxruntime.OnnxTensor	ONNXExternalModel.convertFeatures(SparseVector input)
ai.onnxruntime.OnnxTensor	ImageTransformer.transform(ai.onnxruntime.OrtEnvironment env, SparseVector vector)
ai.onnxruntime.OnnxTensor	ExampleTransformer.transform(ai.onnxruntime.OrtEnvironment env, SparseVector vector) Converts a SparseVector representing the features into a OnnxTensor.
ai.onnxruntime.OnnxTensor	DenseTransformer.transform(ai.onnxruntime.OrtEnvironment env, SparseVector vector)

Method parameters in org.tribuo.interop.onnx with type arguments of type SparseVector

Modifier and Type	Method and Description
protected ai.onnxruntime.OnnxTensor	ONNXExternalModel.convertFeaturesList(List<SparseVector> input)
ai.onnxruntime.OnnxTensor	ImageTransformer.transform(ai.onnxruntime.OrtEnvironment env, List<SparseVector> vectors)
ai.onnxruntime.OnnxTensor	ExampleTransformer.transform(ai.onnxruntime.OrtEnvironment env, List<SparseVector> vectors) Converts a list of SparseVectors representing a batch of features into a OnnxTensor.
ai.onnxruntime.OnnxTensor	DenseTransformer.transform(ai.onnxruntime.OrtEnvironment env, List<SparseVector> vectors)

Uses of SparseVector in org.tribuo.interop.tensorflow

Methods in org.tribuo.interop.tensorflow with parameters of type SparseVector

Modifier and Type	Method and Description
protected org.tensorflow.Tensor<?>	TensorflowExternalModel.convertFeatures(SparseVector input)
org.tensorflow.Tensor<?>	ImageTransformer.transform(SparseVector vector)
org.tensorflow.Tensor<?>	ExampleTransformer.transform(SparseVector vector) Converts a SparseVector representing the features into a Tensor.
org.tensorflow.Tensor<?>	DenseTransformer.transform(SparseVector vector)

Method parameters in org.tribuo.interop.tensorflow with type arguments of type SparseVector

Modifier and Type	Method and Description
protected org.tensorflow.Tensor<?>	TensorflowExternalModel.convertFeaturesList(List<SparseVector> input)
org.tensorflow.Tensor<?>	ImageTransformer.transform(List<SparseVector> vectors)
org.tensorflow.Tensor<?>	ExampleTransformer.transform(List<SparseVector> vectors) Converts a list of SparseVectors representing a batch of features into a Tensor.
org.tensorflow.Tensor<?>	DenseTransformer.transform(List<SparseVector> vectors)

Uses of SparseVector in org.tribuo.math

Methods in org.tribuo.math with parameters of type SparseVector

Modifier and Type	Method and Description
Tensor[]	LinearParameters.gradients(com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> score, SparseVector features) Generate the gradients for a particular feature vector given the loss and the per output gradients.
SGDVector	LinearParameters.predict(SparseVector example) Generates an unnormalised prediction by leftMultiply'ing the weights with the incoming features.

Uses of SparseVector in org.tribuo.math.kernel

Methods in org.tribuo.math.kernel with parameters of type SparseVector

Modifier and Type	Method and Description
double	Sigmoid.similarity(SparseVector a, SparseVector b)
double	RBF.similarity(SparseVector a, SparseVector b)
double	Polynomial.similarity(SparseVector a, SparseVector b)
double	Linear.similarity(SparseVector a, SparseVector b)
double	Kernel.similarity(SparseVector first, SparseVector second) Calculates the similarity between two SparseVectors.

Uses of SparseVector in org.tribuo.math.la

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

Modifier and Type	Method and Description
SparseVector	SparseVector.copy()
static <T extends Output<T>> SparseVector	SparseVector.createSparseVector(Example<T> example, ImmutableFeatureMap featureInfo, boolean addBias) Builds a SparseVector from an Example.
static SparseVector	SparseVector.createSparseVector(int dimension, int[] indices, double[] values) Defensively copies the input, and checks that the indices are sorted.
static SparseVector	SparseVector.createSparseVector(int dimension, Map<Integer,Double> indexMap) Builds a SparseVector from a map.
SparseVector	DenseSparseMatrix.getRow(int i)
SparseVector	SparseVector.scale(double coefficient)
SparseVector	DenseVector.sparsify() Generates a SparseVector representation from this dense vector, removing all values with absolute value below VectorTuple.DELTA.
SparseVector	DenseVector.sparsify(double tolerance) Generates a SparseVector representation from this dense vector, removing all values with absolute value below the supplied tolerance.
static <T extends Output<T>> SparseVector[]	SparseVector.transpose(Dataset<T> dataset) Converts a dataset of row-major examples into an array of column-major sparse vectors.
static <T extends Output<T>> SparseVector[]	SparseVector.transpose(Dataset<T> dataset, ImmutableFeatureMap fMap) Converts a dataset of row-major examples into an array of column-major sparse vectors.
static SparseVector[]	SparseVector.transpose(SparseVector[] input) Transposes an array of sparse vectors from row-major to column-major or vice versa.

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

Modifier and Type	Method and Description
static DenseSparseMatrix	DenseSparseMatrix.createFromSparseVectors(SparseVector[] values) Defensively copies the values.
int[]	SparseVector.difference(SparseVector other) Generates an array of the indices that are active in this vector but are not present in other.
int[]	SparseVector.intersection(SparseVector other) Generates an array of the indices that are active in both this vector and other
static SparseVector[]	SparseVector.transpose(SparseVector[] input) Transposes an array of sparse vectors from row-major to column-major or vice versa.

Constructor parameters in org.tribuo.math.la with type arguments of type SparseVector

Constructor and Description
DenseSparseMatrix(List<SparseVector> values)

Uses of SparseVector in org.tribuo.math.util

Methods in org.tribuo.math.util that return SparseVector

Modifier and Type	Method and Description
static SparseVector	HeapMerger.merge(List<SparseVector> vectors, int dimension, int[] indicesBuffer, double[] valuesBuffer) Merges a list of sparse vectors into a single sparse vector, summing the values.
SparseVector	Merger.merge(SparseVector[] inputs) Merges an array of SparseVector into a single SparseVector.
SparseVector	MatrixHeapMerger.merge(SparseVector[] inputs)
SparseVector	HeapMerger.merge(SparseVector[] inputs)

Methods in org.tribuo.math.util with parameters of type SparseVector

Modifier and Type	Method and Description
SparseVector	Merger.merge(SparseVector[] inputs) Merges an array of SparseVector into a single SparseVector.
SparseVector	MatrixHeapMerger.merge(SparseVector[] inputs)
SparseVector	HeapMerger.merge(SparseVector[] inputs)

Method parameters in org.tribuo.math.util with type arguments of type SparseVector

Modifier and Type	Method and Description
static SparseVector	HeapMerger.merge(List<SparseVector> vectors, int dimension, int[] indicesBuffer, double[] valuesBuffer) Merges a list of sparse vectors into a single sparse vector, summing the values.

Uses of SparseVector in org.tribuo.regression.impl

Methods in org.tribuo.regression.impl that return SparseVector

Modifier and Type	Method and Description
protected SparseVector	SkeletalIndependentRegressionSparseModel.createFeatures(Example<Regressor> example) Creates the feature vector.
protected SparseVector	SkeletalIndependentRegressionModel.createFeatures(Example<Regressor> example) Creates the feature vector.

Methods in org.tribuo.regression.impl with parameters of type SparseVector

Modifier and Type	Method and Description
protected abstract Regressor.DimensionTuple	SkeletalIndependentRegressionSparseModel.scoreDimension(int dimensionIdx, SparseVector features) Makes a prediction for a single dimension.
protected abstract Regressor.DimensionTuple	SkeletalIndependentRegressionModel.scoreDimension(int dimensionIdx, SparseVector features) Makes a prediction for a single dimension.
protected abstract T	SkeletalIndependentRegressionTrainer.trainDimension(double[] outputs, SparseVector[] features, float[] weights, SplittableRandom rng) Trains a single dimension of the possibly multiple dimensions.
protected abstract T	SkeletalIndependentRegressionSparseTrainer.trainDimension(double[] outputs, SparseVector[] features, float[] weights, SplittableRandom rng) Trains a single dimension of the possibly multiple dimensions.

Uses of SparseVector in org.tribuo.regression.sgd

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

Modifier and Type	Method and Description
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 SparseVector in org.tribuo.regression.slm

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

Modifier and Type	Method and Description
protected SparseVector	SparseLinearModel.createFeatures(Example<Regressor> example) Creates the feature vector.

Methods in org.tribuo.regression.slm that return types with arguments of type SparseVector

Modifier and Type	Method and Description
Map<String,SparseVector>	SparseLinearModel.getWeights() Gets a copy of the model parameters.

Methods in org.tribuo.regression.slm with parameters of type SparseVector

Modifier and Type	Method and Description
protected Regressor.DimensionTuple	SparseLinearModel.scoreDimension(int dimensionIdx, SparseVector features)