Uses of Interface
com.aliasi.matrix.Vector

Packages that use Vector

Package	Description
com.aliasi.classify	Classes for classifying data and evaluation.
com.aliasi.crf	Classes and interfaces for conditional random fields.
com.aliasi.dca	Classes for fitting and running discrete choice analysis (DCA) models.
com.aliasi.features	Classes for extracting feature vectors from objects and parsing objects for feature handlers.
com.aliasi.matrix	Classes for matrices and vectors.
com.aliasi.stats	Classes for handling basic statical distributions and estimators.
com.aliasi.test.unit.matrix

Uses of Vector in com.aliasi.classify

Methods in com.aliasi.classify that return types with arguments of type Vector

Modifier and Type	Method and Description
Proximity<Vector>	KnnClassifier.proximity() Returns the proximity measure for this KNN classifier.

Methods in com.aliasi.classify with parameters of type Vector

Modifier and Type	Method and Description
ScoredClassification	BigVectorClassifier.classify(Vector x) Return a scored classification consisting of the top results for the specified vector input.
ConditionalClassification	LogisticRegressionClassifier.classifyVector(Vector v) Returns the classification of the specified vector using the logistic regression model underlying this classifier.

Constructors in com.aliasi.classify with parameters of type Vector

Constructor and Description
BigVectorClassifier(Vector[] termVectors, int maxResults) Construct a big vector classifier with the specified term vectors, maximum number of results, and categories equal to the string representations of the category identifiers.
BigVectorClassifier(Vector[] termVectors, String[] categories, int maxResults) Construct a big vector classifier with the specified term vectors, categories, and maximum number of results.

Constructor parameters in com.aliasi.classify with type arguments of type Vector

Constructor and Description
KnnClassifier(FeatureExtractor<? super E> featureExtractor, int k, Distance<Vector> distance) Construct a k-nearest-neighbor classifier based on the specified feature extractor, specified maximum number of neighbors, and specified distance function.
KnnClassifier(FeatureExtractor<? super E> extractor, int k, Proximity<Vector> proximity, boolean weightByProximity) Construct a k-nearest-neighbor classifier based on the specified feature extractor, specified maximum number of neighbors, specified proximity function, and boolean flag indicating whether or not to weight nearest neighbors by proximity during classification.

Uses of Vector in com.aliasi.crf

Methods in com.aliasi.crf that return Vector

Modifier and Type	Method and Description
Vector[]	ChainCrf.coefficients() Return the coefficient vectors for this CRF.

Constructors in com.aliasi.crf with parameters of type Vector

Constructor and Description
ChainCrf(String[] tags, boolean[] legalTagStarts, boolean[] legalTagEnds, boolean[][] legalTagTransitions, Vector[] coefficients, SymbolTable featureSymbolTable, ChainCrfFeatureExtractor<E> featureExtractor, boolean addInterceptFeature) Construct a conditional random field from the specified tags, feature vector coefficients, symbol table for feature, feature extractors and flag indicating whether to add intercepts or not.
ChainCrf(String[] tags, Vector[] coefficients, SymbolTable featureSymbolTable, ChainCrfFeatureExtractor<E> featureExtractor, boolean addInterceptFeature) Construct a conditional random field from the specified tags, feature vector coefficients, symbol table for feature, feature extractors and flag indicating whether to add intercepts or not.

Uses of Vector in com.aliasi.dca

Methods in com.aliasi.dca that return Vector

Modifier and Type	Method and Description
Vector	DiscreteChooser.coefficients() Return an unmodifiable view of the coefficients underlying this discrete chooser.

Methods in com.aliasi.dca with parameters of type Vector

Modifier and Type	Method and Description
double[]	DiscreteChooser.choiceLogProbs(Vector[] choices) Return an array of (natural) log choice probabilities corresponding to the input array of choices.
double[]	DiscreteChooser.choiceProbs(Vector[] choices) Return an array of choice probabilities corresponding to the input array of choices.
int	DiscreteChooser.choose(Vector[] choices) Returns the most likely choice among the choices in the specified array of vectors.
static DiscreteChooser	DiscreteChooser.estimate(Vector[][] alternativess, int[] choices, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Returns a discrete choice model estimated from the specified training data, prior, and learning parameters.

Constructors in com.aliasi.dca with parameters of type Vector

Constructor and Description
DiscreteChooser(Vector coefficients) Construct a discrete chooser with the specified coefficient vector.

Uses of Vector in com.aliasi.features

Methods in com.aliasi.features that return Vector

Modifier and Type	Method and Description
static Vector	Features.toVector(Map<String,? extends Number> featureVector, SymbolTable table, int numDimensions, boolean addIntercept) Convert the specified feature vector into a sparse float vector using the specified symbol table to encode features as integers.
static Vector	Features.toVectorAddSymbols(Map<String,? extends Number> featureVector, SymbolTable table, int numDimensions, boolean addIntercept) Convert the specified feature vector into a sparse float vector using the specified symbol table to encode features as integers, adding features to the symbol table if necessary.

Uses of Vector in com.aliasi.matrix

Classes in com.aliasi.matrix that implement Vector

Modifier and Type	Class and Description
class	AbstractVector An AbstractVector implements most of a vector's functionality in terms of methods for dimensionality and values.
class	DenseVector A DenseVector is a vector implementation suitable for vectors with primarily non-zero values.
class	SparseFloatVector A SparseFloatVector implements an immutable sparse vector with values represented as single-precision floating point numbers.

Methods in com.aliasi.matrix that return Vector

Modifier and Type	Method and Description
Vector	SparseFloatVector.add(Vector v)
Vector	Vector.add(Vector v) Returns a new vector that is the reuslt of adding this vector to the specified vector.
Vector	AbstractVector.add(Vector v) Returns the result of adding the specified vector to this vector.
Vector	DenseVector.add(Vector v)
Vector	Matrix.columnVector(int column) Returns the vector of values in the specified column.
Vector	AbstractMatrix.columnVector(int column) Returns the specified column of this matrix.
Vector	Matrix.rowVector(int row) Returns the vector of values in the specified row.
Vector	AbstractMatrix.rowVector(int row) Returns the specified row of this matrix.
static Vector	Matrices.unmodifiableVector(Vector v) Returns an unmodifiable view of the specified vector.

Methods in com.aliasi.matrix with parameters of type Vector

Modifier and Type	Method and Description
Vector	SparseFloatVector.add(Vector v)
Vector	Vector.add(Vector v) Returns a new vector that is the reuslt of adding this vector to the specified vector.
Vector	AbstractVector.add(Vector v) Returns the result of adding the specified vector to this vector.
Vector	DenseVector.add(Vector v)
double	SparseFloatVector.cosine(Vector v)
double	Vector.cosine(Vector v) Returns the cosine product of this vector with the specified vector.
double	AbstractVector.cosine(Vector v) Returns the cosine of this vector and the specified vector.
double	TaxicabDistance.distance(Vector v1, Vector v2) Returns the taxicab distance between the specified pair of vectors.
double	CosineDistance.distance(Vector v1, Vector v2) Returns one minus the proximity of the vectors.
double	EuclideanDistance.distance(Vector v1, Vector v2) Returns the Euclidean distance between the specified pair of vectors.
double	MinkowskiDistance.distance(Vector v1, Vector v2) Returns the Minkowski distance between the specified pair of vectors.
double	SparseFloatVector.dotProduct(Vector v)
double	Vector.dotProduct(Vector v) Returns the dot product of this vector with the specified vector.
double	AbstractVector.dotProduct(Vector v) Returns the dot product (inner product) of this vector with the specified vector.
double	DenseVector.dotProduct(Vector v)
void	SparseFloatVector.increment(double scale, Vector v) This operation is not supported for sparse vectors.
void	Vector.increment(double scale, Vector v) Adds the specified vector multiplied by the specified scalar to this vector.
void	AbstractVector.increment(double scale, Vector v) Adds the specified vector multiplied by the specified scalar to this vector.
void	DenseVector.increment(double scale, Vector v) Adds the specified vector multiplied by the specified scalar to this vector.
double	CosineDistance.proximity(Vector v1, Vector v2) Returns the cosine between the specified vectors.
double	KernelFunction.proximity(Vector v1, Vector v2) Return the result of applying the kernel function to the specified pair of vectors.
double	GaussianRadialBasisKernel.proximity(Vector v1, Vector v2) Returns the result of applying this Guassian radial basis kernel to the specified vectors.
double	HyperbolicTangentKernel.proximity(Vector v1, Vector v2) Returns the result of applying the hyperbolic tangent kernel function to to the specified vectors.
double	DotProductKernel.proximity(Vector v1, Vector v2) Returns the dot product of the input vectors.
double	PolynomialKernel.proximity(Vector v1, Vector v2) Returns the result of applying the polynomial kernel of this class's degree to the specified vectors.
static double[]	Matrices.toArray(Vector v) Returns the content of the specified vector as an array.
static Vector	Matrices.unmodifiableVector(Vector v) Returns an unmodifiable view of the specified vector.

Constructors in com.aliasi.matrix with parameters of type Vector

Constructor and Description
DenseVector(Vector v) Cosntruct a deep copy of the specified vector as a dense vector.

Uses of Vector in com.aliasi.stats

Methods in com.aliasi.stats that return Vector

Modifier and Type	Method and Description
Vector[]	LogisticRegression.weightVectors() Returns an array of views of the weight vectors used for this regression model.

Methods in com.aliasi.stats with parameters of type Vector

Modifier and Type	Method and Description
double[]	LogisticRegression.classify(Vector x) Returns an array of conditional probabilities indexed by outcomes for the specified input vector.
void	LogisticRegression.classify(Vector x, double[] ysHat) Fills the specified array with the conditional probabilities indexed by outcomes for the specified input vector.
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, AnnealingSchedule annealingSchedule, Reporter reporter, double minImprovement, int minEpochs, int maxEpochs) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, int blockSize, LogisticRegression hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegression> handler, Reporter reporter) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.
static double	LogisticRegression.log2Likelihood(Vector[] inputs, int[] cats, LogisticRegression regression) Returns the log (base 2) likelihood of the specified inputs with the specified categories using the specified regression model.
double	RegressionPrior.log2Prior(Vector beta) Returns the log (base 2) prior density for a specified coefficient vector (up to an additive constant).
double	RegressionPrior.log2Prior(Vector[] betas) Returns the log (base 2) prior density for the specified array of coefficient vectors (up to an additive constant).

Constructors in com.aliasi.stats with parameters of type Vector

Constructor and Description
LogisticRegression(Vector weightVector) Construct a binomial logistic regression model with the specified parameter vector.
LogisticRegression(Vector[] weightVectors) Construct a multinomial logistic regression model with the specified weight vectors.

Uses of Vector in com.aliasi.test.unit.matrix

Classes in com.aliasi.test.unit.matrix that implement Vector

Modifier and Type	Class and Description
static class	AbstractVectorTest.MutVect
static class	AbstractVectorTest.Vector1
static class	AbstractVectorTest.Vector2