Uses of Interface
org.apache.ignite.ml.math.Vector

Packages that use Vector

Package	Description
org.apache.ignite.ml.clustering.kmeans	Contains kMeans clustering algorithm.
org.apache.ignite.ml.knn.classification	Contains main APIs for kNN classification algorithms.
org.apache.ignite.ml.knn.regression	Contains helper classes for kNN regression algorithms.
org.apache.ignite.ml.math	Contains main APIs for distributed code algebra.
org.apache.ignite.ml.math.decompositions	Contains matrix decompositions for distributed code algebra.
org.apache.ignite.ml.math.distances	Contains main APIs for distances.
org.apache.ignite.ml.math.functions	Contains serializable functions for distributed code algebra.
org.apache.ignite.ml.math.impls.matrix	Contains several matrix implementations.
org.apache.ignite.ml.math.impls.storage.matrix	Contains specific implementations for matrix storage models.
org.apache.ignite.ml.math.impls.storage.vector	Contains specific implementations for vector storage models.
org.apache.ignite.ml.math.impls.vector	Contains specific implementations for vectors.
org.apache.ignite.ml.math.util	Some math utils.
org.apache.ignite.ml.nn	Contains neural networks and related classes.
org.apache.ignite.ml.nn.initializers	Contains multilayer perceptron parameters initializers.
org.apache.ignite.ml.optimization	Contains implementations of optimization algorithms and related classes.
org.apache.ignite.ml.optimization.updatecalculators	Contains update calculators.
org.apache.ignite.ml.regressions.linear	Contains various linear regressions.
org.apache.ignite.ml.structures	Contains some internal utility structures.
org.apache.ignite.ml.svm	Contains main APIs for SVM(support vector machines) algorithms.

Uses of Vector in org.apache.ignite.ml.clustering.kmeans

Methods in org.apache.ignite.ml.clustering.kmeans that return Vector

Modifier and Type	Method and Description
Vector[]	KMeansModel.centers() Get centers of clusters.
Vector[]	KMeansModelFormat.getCenters()

Methods in org.apache.ignite.ml.clustering.kmeans with parameters of type Vector

Modifier and Type	Method and Description
Integer	KMeansModel.apply(Vector vec) Predict closest center index for a given vector.

Constructors in org.apache.ignite.ml.clustering.kmeans with parameters of type Vector

Constructor and Description
KMeansModel(Vector[] centers, DistanceMeasure distanceMeasure) Construct KMeans model with given centers and distanceMeasure measure.
KMeansModelFormat(Vector[] centers, DistanceMeasure distance)

Uses of Vector in org.apache.ignite.ml.knn.classification

Methods in org.apache.ignite.ml.knn.classification with parameters of type Vector

Modifier and Type	Method and Description
Double	KNNClassificationModel.apply(Vector v)
protected List<LabeledVector>	KNNClassificationModel.findKNearestNeighbors(Vector v) The main idea is calculation all distance pairs between given vector and all vectors in training set, sorting them and finding k vectors with min distance with the given vector.

Uses of Vector in org.apache.ignite.ml.knn.regression

Methods in org.apache.ignite.ml.knn.regression with parameters of type Vector

Modifier and Type	Method and Description
Double	KNNRegressionModel.apply(Vector v)

Uses of Vector in org.apache.ignite.ml.math

Methods in org.apache.ignite.ml.math that return Vector

Modifier and Type	Method and Description
Vector	Vector.assign(double val) Assigns given value to all elements of this vector.
Vector	Vector.assign(double[] vals) Assigns values from given array to this vector.
Vector	Vector.assign(IntToDoubleFunction fun) Assigns each vector element to the value generated by given function.
Vector	Vector.assign(Vector vec) Copies values from the argument vector to this one.
Vector	Vector.copy() Creates new copy of this vector.
static Vector	VectorUtils.copyPart(Vector v, int off, int len) Get copy of part of given length of given vector starting from given offset.
Vector	Vector.divide(double x) Creates new vector containing values from this vector divided by the argument.
static Vector	VectorUtils.elementWiseMinus(Vector vec1, Vector vec2) Performs in-place vector subtraction.
static Vector	VectorUtils.elementWiseTimes(Vector vec1, Vector vec2) Performs in-place vector multiplication.
Vector	Matrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns in this matrix.
Vector	Matrix.foldRows(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all rows in this matrix.
static Vector	VectorUtils.fromMap(Map<Integer,Double> val, boolean cp)
Vector	Matrix.getCol(int col) Get a specific row from matrix.
Vector	Matrix.getRow(int row) Get a specific row from matrix.
Vector	Vector.increment(int idx, double val) Increments value at given index.
Vector	Vector.incrementX(int idx, double val) Increments value at given index without checking for index boundaries.
Vector	Vector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	Matrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	Vector.logNormalize() Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.
Vector	Vector.logNormalize(double power) Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.
Vector	Vector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	Vector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	Vector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	Vector.minus(Vector vec) Creates new vector containing element by element difference between this vector and the argument one.
Vector	Vector.normalize() Creates new vector containing the normalized (L_2 norm) values of this vector.
Vector	Vector.normalize(double power) Creates new vector containing the normalized (L_power norm) values of this vector.
static Vector	VectorUtils.num2Vec(int num, int vecSize) Turn number into a local Vector of given size with one-hot encoding.
static Vector	VectorUtils.num2Vec(int num, int vecSize, boolean isDistributed) Turn number into Vector of given size with one-hot encoding.
Vector	Vector.plus(double x) Creates new vector containing sum of each element in this vector and argument.
Vector	Vector.plus(Vector vec) Creates new vector containing element by element sum from both vectors.
Vector	Vector.set(int idx, double val) Sets value.
Vector	Vector.setX(int idx, double val) Sets value without checking for index boundaries.
Vector	Vector.sort() Sorts this vector in ascending order.
Vector	Vector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	Vector.times(Vector vec) Gets a new vector that is an element-wie product of this vector and the argument.
Vector	Matrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	Matrix.viewColumn(int col) Creates new view into matrix column .
Vector	Matrix.viewDiagonal() Creates new view into matrix diagonal.
Vector	Vector.viewPart(int off, int len)
Vector	Matrix.viewRow(int row) Creates new view into matrix row.
static Vector	VectorUtils.zeroesLike(Vector v) Create new vector like given vector initialized by zeroes.
static Vector	VectorUtils.zipWith(Vector v1, Vector v2, IgniteBiFunction<Double,Double,Double> f) Zip two vectors with given binary function (i.e. apply binary function to both vector elementwise and construct vector from results).

Methods in org.apache.ignite.ml.math with parameters of type Vector

Modifier and Type	Method and Description
Vector	Vector.assign(Vector vec) Copies values from the argument vector to this one.
Matrix	Matrix.assignColumn(int col, Vector vec) Assigns values from given vector to the specified column in this matrix.
Matrix	Matrix.assignRow(int row, Vector vec) Assigns values from given vector to the specified row in this matrix.
static void	Blas.axpy(Double a, Vector x, Vector y) Performs y += a * x
static void	Blas.checkCardinality(Matrix a, Vector v) Checks if Matrix A can be multiplied by vector v, if not CardinalityException is thrown.
void	Blas.copy(Vector x, Vector y) Copies Vector x into Vector y.
static Vector	VectorUtils.copyPart(Vector v, int off, int len) Get copy of part of given length of given vector starting from given offset.
Matrix	Vector.cross(Vector vec) Gets the cross product of this vector and the other vector.
double	Vector.dot(Vector vec) Gets dot product of two vectors.
static Double	Blas.dot(Vector x, Vector y) Returns dot product of vectors x and y.
static Vector	VectorUtils.elementWiseMinus(Vector vec1, Vector vec2) Performs in-place vector subtraction.
static Vector	VectorUtils.elementWiseTimes(Vector vec1, Vector vec2) Performs in-place vector multiplication.
<T> T	Vector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
static void	Blas.gemv(double alpha, Matrix a, Vector x, double beta, Vector y) y := alpha * A * x + beta * y.
double	Vector.getDistanceSquared(Vector vec) Get the square of the distance between this vector and the argument vector.
Vector	Vector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	Vector.minus(Vector vec) Creates new vector containing element by element difference between this vector and the argument one.
Vector	Vector.plus(Vector vec) Creates new vector containing element by element sum from both vectors.
static void	Tracer.saveAsCsv(Vector vec, String fmt, String filePath) Saves given vector as CSV file.
static void	Blas.scal(Double a, Vector x) Performs in-place multiplication of vector x by a real scalar a.
static void	Tracer.showAscii(Vector vec)
static void	Tracer.showAscii(Vector vec, IgniteLogger log)
static void	Tracer.showAscii(Vector vec, IgniteLogger log, String fmt)
static void	Tracer.showAscii(Vector vec, String fmt)
static void	Tracer.showHtml(Vector vec) Shows given vector in the browser with D3-based visualization.
static void	Tracer.showHtml(Vector vec, boolean useAsciiFallback) Shows given vector in the browser with D3-based visualization.
static void	Tracer.showHtml(Vector vec, Tracer.ColorMapper cm) Shows given vector in the browser with D3-based visualization.
static void	Tracer.showHtml(Vector vec, Tracer.ColorMapper cm, boolean useAsciiFallback) Shows given vector in the browser with D3-based visualization.
Vector	Vector.times(Vector vec) Gets a new vector that is an element-wie product of this vector and the argument.
Vector	Matrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
static double	VectorUtils.vec2Num(Vector vec) Turn Vector into number by looking at index of maximal element in vector.
static Vector	VectorUtils.zeroesLike(Vector v) Create new vector like given vector initialized by zeroes.
static Vector	VectorUtils.zipWith(Vector v1, Vector v2, IgniteBiFunction<Double,Double,Double> f) Zip two vectors with given binary function (i.e. apply binary function to both vector elementwise and construct vector from results).

Method parameters in org.apache.ignite.ml.math with type arguments of type Vector

Modifier and Type	Method and Description
Vector	Matrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns in this matrix.
Vector	Matrix.foldRows(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all rows in this matrix.

Uses of Vector in org.apache.ignite.ml.math.decompositions

Methods in org.apache.ignite.ml.math.decompositions that return Vector

Modifier and Type	Method and Description
Vector	EigenDecomposition.getImagEigenvalues() Return the imaginary parts of the eigenvalues.
Vector	LUDecomposition.getPivot() Returns the pivot permutation vector.
Vector	EigenDecomposition.getRealEigenValues() Return the real parts of the eigenvalues
protected Vector	DecompositionSupport.likeVector(Matrix matrix) Create the like vector with read-only matrices support.
protected Vector	DecompositionSupport.likeVector(Matrix matrix, int crd) Create the like vector with read-only matrices support.
Vector	QRDecomposition.solve(Vector vec) Least squares solution of A*X = B; returns X.
Vector	CholeskyDecomposition.solve(Vector b) Solve the linear equation A × X = B for matrices A.
Vector	QRDSolver.solve(Vector vec) Least squares solution of A*X = B; returns X.
Vector	LUDecomposition.solve(Vector b)

Methods in org.apache.ignite.ml.math.decompositions with parameters of type Vector

Modifier and Type	Method and Description
Vector	QRDecomposition.solve(Vector vec) Least squares solution of A*X = B; returns X.
Vector	CholeskyDecomposition.solve(Vector b) Solve the linear equation A × X = B for matrices A.
Vector	QRDSolver.solve(Vector vec) Least squares solution of A*X = B; returns X.
Vector	LUDecomposition.solve(Vector b)

Uses of Vector in org.apache.ignite.ml.math.distances

Methods in org.apache.ignite.ml.math.distances with parameters of type Vector

Modifier and Type	Method and Description
double	ManhattanDistance.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	HammingDistance.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	EuclideanDistance.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	DistanceMeasure.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	ManhattanDistance.compute(Vector a, Vector b) Compute the distance between two n-dimensional vectors.
double	HammingDistance.compute(Vector a, Vector b) Compute the distance between two n-dimensional vectors.
double	EuclideanDistance.compute(Vector a, Vector b) Compute the distance between two n-dimensional vectors.
double	DistanceMeasure.compute(Vector a, Vector b) Compute the distance between two n-dimensional vectors.

Uses of Vector in org.apache.ignite.ml.math.functions

Methods in org.apache.ignite.ml.math.functions that return Vector

Modifier and Type	Method and Description
Vector	IgniteDifferentiableVectorToDoubleFunction.differential(Vector pnt) Get function differential at a given point.

Methods in org.apache.ignite.ml.math.functions with parameters of type Vector

Modifier and Type	Method and Description
Vector	IgniteDifferentiableVectorToDoubleFunction.differential(Vector pnt) Get function differential at a given point.

Uses of Vector in org.apache.ignite.ml.math.impls.matrix

Methods in org.apache.ignite.ml.math.impls.matrix that return Vector

Modifier and Type	Method and Description
Vector	AbstractMatrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns in this matrix.
Vector	AbstractMatrix.foldRows(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all rows in this matrix.
Vector	AbstractMatrix.getCol(int col) Get a specific row from matrix.
Vector	SparseBlockDistributedMatrix.getCol(int col) Get a specific row from matrix.
Vector	AbstractMatrix.getRow(int row) Get a specific row from matrix.
Vector	SparseBlockDistributedMatrix.getRow(int row) Get a specific row from matrix.
Vector	TransposedMatrixView.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	DenseLocalOnHeapMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	FunctionMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	SparseLocalOnHeapMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	DenseLocalOffHeapMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	CacheMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	SparseBlockDistributedMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	DiagonalMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	SparseDistributedMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	MatrixView.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	PivotedMatrixView.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	RandomMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	AbstractMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	SparseBlockDistributedMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	SparseDistributedMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	AbstractMatrix.viewColumn(int col) Creates new view into matrix column .
Vector	DiagonalMatrix.viewColumn(int col) Creates new view into matrix column .
Vector	PivotedMatrixView.viewColumn(int col) Creates new view into matrix column .
Vector	AbstractMatrix.viewDiagonal() Creates new view into matrix diagonal.
Vector	AbstractMatrix.viewRow(int row) Creates new view into matrix row.
Vector	DiagonalMatrix.viewRow(int row) Creates new view into matrix row.
Vector	PivotedMatrixView.viewRow(int row) Creates new view into matrix row.

Methods in org.apache.ignite.ml.math.impls.matrix with parameters of type Vector

Modifier and Type	Method and Description
Matrix	AbstractMatrix.assignColumn(int col, Vector vec) Assigns values from given vector to the specified column in this matrix.
Matrix	AbstractMatrix.assignRow(int row, Vector vec) Assigns values from given vector to the specified row in this matrix.
Vector	AbstractMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	SparseBlockDistributedMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	SparseDistributedMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.

Method parameters in org.apache.ignite.ml.math.impls.matrix with type arguments of type Vector

Modifier and Type	Method and Description
Vector	AbstractMatrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns in this matrix.
Vector	AbstractMatrix.foldRows(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all rows in this matrix.

Constructors in org.apache.ignite.ml.math.impls.matrix with parameters of type Vector

Constructor and Description
DiagonalMatrix(Vector diagonal)

Uses of Vector in org.apache.ignite.ml.math.impls.storage.matrix

Methods in org.apache.ignite.ml.math.impls.storage.matrix that return Vector

Modifier and Type	Method and Description
Vector	DiagonalMatrixStorage.diagonal()

Constructors in org.apache.ignite.ml.math.impls.storage.matrix with parameters of type Vector

Constructor and Description
DiagonalMatrixStorage(Vector diagonal)

Uses of Vector in org.apache.ignite.ml.math.impls.storage.vector

Methods in org.apache.ignite.ml.math.impls.storage.vector that return Vector

Modifier and Type	Method and Description
Vector	SingleElementVectorDelegateStorage.delegate()

Constructors in org.apache.ignite.ml.math.impls.storage.vector with parameters of type Vector

Constructor and Description
SingleElementVectorDelegateStorage(Vector vec, int idx)

Uses of Vector in org.apache.ignite.ml.math.impls.vector

Classes in org.apache.ignite.ml.math.impls.vector that implement Vector

Modifier and Type	Class and Description
class	AbstractReadOnlyVector This class provides a helper implementation of the read-only implementation of Vector interface to minimize the effort required to implement it.
class	AbstractVector This class provides a helper implementation of the Vector interface to minimize the effort required to implement it.
class	CacheVector<K,V> Vector based on existing cache and index and value mapping functions.
class	ConstantVector Constant value, read-only vector.
class	DelegatingVector Convenient class that can be used to add decorations to an existing vector.
class	DenseLocalOffHeapVector Implementation for Vector assuming dense logic and local offheap JVM storage.
class	DenseLocalOnHeapVector Basic implementation for vector.
class	FunctionVector Implementation of Vector that maps vector element index to java.util.function interfaces.
class	MapWrapperVector Vector wrapping a given map.
class	MatrixVectorView Row or column vector view off the matrix.
class	PivotedVectorView Pivoted (index mapped) view over another vector.
class	RandomVector Random vector.
class	SingleElementVector Read-write vector holding a single non-zero value at some index.
class	SingleElementVectorView Single value vector view over another vector.
class	SparseBlockDistributedVector Sparse distributed vector implementation based on data grid.
class	SparseDistributedVector Sparse distributed vector implementation based on data grid.
class	SparseLocalOffHeapVector Implementation for Vector assuming sparse logic and local offheap JVM storage.
class	SparseLocalVector Local on-heap sparse vector based on hash map storage.
class	VectorBlockEntry Block for SparseBlockDistributedVector.
class	VectorView Implements the partial view into the parent Vector.

Methods in org.apache.ignite.ml.math.impls.vector that return Vector

Modifier and Type	Method and Description
Vector	AbstractVector.assign(double val) Assigns given value to all elements of this vector.
Vector	SparseBlockDistributedVector.assign(double val) Assigns given value to all elements of this vector.
Vector	SparseDistributedVector.assign(double val) Assigns given value to all elements of this vector.
Vector	CacheVector.assign(double val) Assigns given value to all elements of this vector.
Vector	DelegatingVector.assign(double val) Assigns given value to all elements of this vector.
Vector	AbstractVector.assign(double[] vals) Assigns values from given array to this vector.
Vector	DelegatingVector.assign(double[] vals) Assigns values from given array to this vector.
Vector	AbstractVector.assign(IntToDoubleFunction fun) Assigns each vector element to the value generated by given function.
Vector	DelegatingVector.assign(IntToDoubleFunction fun) Assigns each vector element to the value generated by given function.
Vector	AbstractVector.assign(Vector vec) Copies values from the argument vector to this one.
Vector	DenseLocalOffHeapVector.assign(Vector vec) Copies values from the argument vector to this one.
Vector	DelegatingVector.assign(Vector vec) Copies values from the argument vector to this one.
Vector	AbstractVector.copy() Creates new copy of this vector.
Vector	PivotedVectorView.copy() Creates new copy of this vector.
Vector	MatrixVectorView.copy() Creates new copy of this vector.
Vector	SingleElementVectorView.copy() Creates new copy of this vector.
Vector	AbstractReadOnlyVector.copy() Creates new copy of this vector.
Vector	VectorView.copy() Creates new copy of this vector.
Vector	DelegatingVector.copy() Creates new copy of this vector.
Vector	ConstantVector.copy() Creates new copy of this vector.
Vector	AbstractVector.divide(double x) Creates new vector containing values from this vector divided by the argument.
Vector	SparseBlockDistributedVector.divide(double d) Return the same matrix with updates values (broken contract).
Vector	SparseDistributedVector.divide(double d) Return the same matrix with updates values (broken contract).
Vector	AbstractReadOnlyVector.divide(double x) Creates new vector containing values from this vector divided by the argument.
Vector	CacheVector.divide(double x) Creates new vector containing values from this vector divided by the argument.
Vector	DelegatingVector.divide(double x) Creates new vector containing values from this vector divided by the argument.
Vector	PivotedVectorView.getBaseVector()
Vector	DelegatingVector.getVector() Get the delegating vector
Vector	AbstractVector.increment(int idx, double val) Increments value at given index.
Vector	DelegatingVector.increment(int idx, double val) Increments value at given index.
Vector	AbstractVector.incrementX(int idx, double val) Increments value at given index without checking for index boundaries.
Vector	DelegatingVector.incrementX(int idx, double val) Increments value at given index without checking for index boundaries.
Vector	PivotedVectorView.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	SparseBlockDistributedVector.like(int size) Creates new empty vector of the same underlying class but of different cardinality.
Vector	MatrixVectorView.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	SparseDistributedVector.like(int size) Creates new empty vector of the same underlying class but of different cardinality.
Vector	DenseLocalOffHeapVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	SparseLocalVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	SingleElementVectorView.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	SingleElementVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	SparseLocalOffHeapVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	VectorView.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	CacheVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	MapWrapperVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	FunctionVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	DelegatingVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	ConstantVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	DenseLocalOnHeapVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	RandomVector.like(int crd) Creates new empty vector of the same underlying class but of different cardinality.
Vector	AbstractVector.logNormalize() Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.
Vector	AbstractReadOnlyVector.logNormalize() Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.
Vector	DelegatingVector.logNormalize() Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.
Vector	AbstractVector.logNormalize(double power) Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.
Vector	AbstractReadOnlyVector.logNormalize(double power) Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.
Vector	DelegatingVector.logNormalize(double power) Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.
Vector	AbstractVector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	AbstractReadOnlyVector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	CacheVector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	DelegatingVector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	AbstractVector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	SparseBlockDistributedVector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	SparseDistributedVector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	AbstractReadOnlyVector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	CacheVector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	DelegatingVector.map(IgniteDoubleFunction<Double> fun) Maps all values in this vector through a given function.
Vector	AbstractVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	AbstractReadOnlyVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	DelegatingVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	AbstractVector.minus(Vector vec) Creates new vector containing element by element difference between this vector and the argument one.
Vector	DelegatingVector.minus(Vector vec) Creates new vector containing element by element difference between this vector and the argument one.
Vector	AbstractVector.normalize() Creates new vector containing the normalized (L_2 norm) values of this vector.
Vector	DelegatingVector.normalize() Creates new vector containing the normalized (L_2 norm) values of this vector.
Vector	AbstractVector.normalize(double power) Creates new vector containing the normalized (L_power norm) values of this vector.
Vector	DelegatingVector.normalize(double power) Creates new vector containing the normalized (L_power norm) values of this vector.
Vector	AbstractVector.plus(double x) Creates new vector containing sum of each element in this vector and argument.
Vector	SparseBlockDistributedVector.plus(double x) Return the same matrix with updates values (broken contract).
Vector	SparseDistributedVector.plus(double x) Return the same matrix with updates values (broken contract).
Vector	CacheVector.plus(double x) Creates new vector containing sum of each element in this vector and argument.
Vector	DelegatingVector.plus(double x) Creates new vector containing sum of each element in this vector and argument.
Vector	AbstractVector.plus(Vector vec) Creates new vector containing element by element sum from both vectors.
Vector	DelegatingVector.plus(Vector vec) Creates new vector containing element by element sum from both vectors.
Vector	AbstractVector.set(int idx, double val) Sets value.
Vector	DelegatingVector.set(int idx, double val) Sets value.
Vector	AbstractVector.setX(int idx, double val) Sets value without checking for index boundaries.
Vector	DelegatingVector.setX(int idx, double val) Sets value without checking for index boundaries.
Vector	AbstractVector.sort() Sorts this vector in ascending order.
Vector	DelegatingVector.sort() Sorts this vector in ascending order.
Vector	AbstractVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	PivotedVectorView.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	SparseBlockDistributedVector.times(double x) Return the same matrix with updates values (broken contract).
Vector	SparseDistributedVector.times(double x) Return the same matrix with updates values (broken contract).
Vector	DenseLocalOffHeapVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	SparseLocalVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	SingleElementVectorView.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	AbstractReadOnlyVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	CacheVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	FunctionVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	DelegatingVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	AbstractVector.times(Vector vec) Gets a new vector that is an element-wie product of this vector and the argument.
Vector	DelegatingVector.times(Vector vec) Gets a new vector that is an element-wie product of this vector and the argument.
Vector	AbstractVector.viewPart(int off, int len)
Vector	DelegatingVector.viewPart(int off, int len)

Methods in org.apache.ignite.ml.math.impls.vector with parameters of type Vector

Modifier and Type	Method and Description
Vector	AbstractVector.assign(Vector vec) Copies values from the argument vector to this one.
Vector	DenseLocalOffHeapVector.assign(Vector vec) Copies values from the argument vector to this one.
Vector	DelegatingVector.assign(Vector vec) Copies values from the argument vector to this one.
protected void	AbstractVector.checkCardinality(Vector vec)
Matrix	AbstractVector.cross(Vector vec) Gets the cross product of this vector and the other vector.
Matrix	AbstractReadOnlyVector.cross(Vector vec) Gets the cross product of this vector and the other vector.
Matrix	DelegatingVector.cross(Vector vec) Gets the cross product of this vector and the other vector.
double	AbstractVector.dot(Vector vec) Gets dot product of two vectors.
double	DelegatingVector.dot(Vector vec) Gets dot product of two vectors.
<T> T	AbstractVector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
<T> T	DelegatingVector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
double	AbstractVector.getDistanceSquared(Vector vec) Get the square of the distance between this vector and the argument vector.
double	DelegatingVector.getDistanceSquared(Vector vec) Get the square of the distance between this vector and the argument vector.
Vector	AbstractVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	AbstractReadOnlyVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	DelegatingVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	AbstractVector.minus(Vector vec) Creates new vector containing element by element difference between this vector and the argument one.
Vector	DelegatingVector.minus(Vector vec) Creates new vector containing element by element difference between this vector and the argument one.
Vector	AbstractVector.plus(Vector vec) Creates new vector containing element by element sum from both vectors.
Vector	DelegatingVector.plus(Vector vec) Creates new vector containing element by element sum from both vectors.
Vector	AbstractVector.times(Vector vec) Gets a new vector that is an element-wie product of this vector and the argument.
Vector	DelegatingVector.times(Vector vec) Gets a new vector that is an element-wie product of this vector and the argument.

Constructors in org.apache.ignite.ml.math.impls.vector with parameters of type Vector

Constructor and Description
DelegatingVector(Vector dlg)
PivotedVectorView(Vector vec, int[] pivot)
PivotedVectorView(Vector vec, int[] pivot, int[] unpivot)
SingleElementVectorView(Vector vec, int idx)
VectorBlockEntry(Vector v)
VectorView(Vector parent, int off, int len)

Uses of Vector in org.apache.ignite.ml.math.util

Methods in org.apache.ignite.ml.math.util that return Vector

Modifier and Type	Method and Description
static Vector	MatrixUtil.likeVector(Matrix matrix) Create the like vector with read-only matrices support.
static Vector	MatrixUtil.likeVector(Matrix matrix, int crd) Create the like vector with read-only matrices support.
static Vector	MatrixUtil.zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by column-by-column with specified function.
static Vector	MatrixUtil.zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by row-by-row with specified function.
static Vector	MatrixUtil.zipWith(Vector v1, Vector v2, IgniteTriFunction<Double,Double,Integer,Double> f) Zip two vectors with given tri-function taking as third argument position in vector (i.e. apply binary function to both vector elementwise and construct vector from results).

Methods in org.apache.ignite.ml.math.util with parameters of type Vector

Modifier and Type	Method and Description
static DenseLocalOnHeapVector	MatrixUtil.localCopyOf(Vector vec) TODO: IGNITE-5723, rewrite in a more optimal way.
static Vector	MatrixUtil.zipWith(Vector v1, Vector v2, IgniteTriFunction<Double,Double,Integer,Double> f) Zip two vectors with given tri-function taking as third argument position in vector (i.e. apply binary function to both vector elementwise and construct vector from results).

Method parameters in org.apache.ignite.ml.math.util with type arguments of type Vector

Modifier and Type	Method and Description
static DenseLocalOnHeapMatrix	MatrixUtil.fromList(List<Vector> vecs, boolean entriesAreRows)
static Vector	MatrixUtil.zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by column-by-column with specified function.
static Vector	MatrixUtil.zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by column-by-column with specified function.
static Vector	MatrixUtil.zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by row-by-row with specified function.
static Vector	MatrixUtil.zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by row-by-row with specified function.

Uses of Vector in org.apache.ignite.ml.nn

Fields in org.apache.ignite.ml.nn declared as Vector

Modifier and Type	Field and Description
protected Vector	MLPLayer.biases Biases vector.

Methods in org.apache.ignite.ml.nn that return Vector

Modifier and Type	Method and Description
Vector	MultilayerPerceptron.biases(int layerIdx) Get biases of layer with given index.
Vector	MultilayerPerceptron.differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch) Compose function in the following way: feed output of this model as input to second argument to loss function.
Vector	MultilayerPerceptron.parameters() Get parameters vector.
protected Vector	MultilayerPerceptron.paramsAsVector(List<MLPLayer> layersParams) Flatten this MLP parameters as vector.

Methods in org.apache.ignite.ml.nn with parameters of type Vector

Modifier and Type	Method and Description
MultilayerPerceptron	MultilayerPerceptron.setBiases(int layerIdx, Vector bias) Sets the biases of layer with a given index.
MultilayerPerceptron	MultilayerPerceptron.setParameters(Vector vector) Set parameters.

Method parameters in org.apache.ignite.ml.nn with type arguments of type Vector

Modifier and Type	Method and Description
Vector	MultilayerPerceptron.differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch) Compose function in the following way: feed output of this model as input to second argument to loss function.

Constructors in org.apache.ignite.ml.nn with parameters of type Vector

Constructor and Description
MLPLayer(Matrix weights, Vector biases) Construct MLPLayer from weights and biases.

Constructor parameters in org.apache.ignite.ml.nn with type arguments of type Vector

Constructor and Description
MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed) Constructs a new instance of multilayer perceptron trainer.
MLPTrainer(MLPArchitecture arch, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed) Constructs a new instance of multilayer perceptron trainer.

Uses of Vector in org.apache.ignite.ml.nn.initializers

Methods in org.apache.ignite.ml.nn.initializers with parameters of type Vector

Modifier and Type	Method and Description
void	RandomInitializer.initBiases(Vector biases) In-place change values of vector representing vectors.
void	MLPInitializer.initBiases(Vector biases) In-place change values of vector representing vectors.

Uses of Vector in org.apache.ignite.ml.optimization

Fields in org.apache.ignite.ml.optimization with type parameters of type Vector

Modifier and Type	Field and Description
static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>	LossFunctions.MSE Mean squared error loss function.

Methods in org.apache.ignite.ml.optimization that return Vector

Modifier and Type	Method and Description
Vector	SimpleUpdater.compute(Vector oldWeights, Vector oldGradient, Vector weights, Vector gradient, int iteration)
Vector	Updater.compute(Vector oldWeights, Vector oldGradient, Vector weights, Vector gradient, int iteration)
Vector	BarzilaiBorweinUpdater.compute(Vector oldWeights, Vector oldGradient, Vector weights, Vector gradient, int iteration)
Vector	SmoothParametrized.differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch) Compose function in the following way: feed output of this model as input to second argument to loss function.

Methods in org.apache.ignite.ml.optimization with parameters of type Vector

Modifier and Type	Method and Description
Vector	SimpleUpdater.compute(Vector oldWeights, Vector oldGradient, Vector weights, Vector gradient, int iteration)
Vector	Updater.compute(Vector oldWeights, Vector oldGradient, Vector weights, Vector gradient, int iteration)
Vector	BarzilaiBorweinUpdater.compute(Vector oldWeights, Vector oldGradient, Vector weights, Vector gradient, int iteration)

Method parameters in org.apache.ignite.ml.optimization with type arguments of type Vector

Modifier and Type	Method and Description
Vector	SmoothParametrized.differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch) Compose function in the following way: feed output of this model as input to second argument to loss function.

Uses of Vector in org.apache.ignite.ml.optimization.updatecalculators

Fields in org.apache.ignite.ml.optimization.updatecalculators declared as Vector

Modifier and Type	Field and Description
protected Vector	RPropParameterUpdate.deltas Previous iteration parameters deltas.
protected Vector	RPropParameterUpdate.prevIterationGradient Previous iteration model partial derivatives by parameters.
protected Vector	RPropParameterUpdate.prevIterationUpdates Previous iteration parameters updates.
protected Vector	NesterovParameterUpdate.prevIterationUpdates Previous step weights updates.
protected Vector	RPropParameterUpdate.updatesMask Updates mask (values by which updateCache is multiplied).

Fields in org.apache.ignite.ml.optimization.updatecalculators with type parameters of type Vector

Modifier and Type	Field and Description
protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>	SimpleGDUpdateCalculator.loss Loss function.
protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>	RPropUpdateCalculator.loss Loss function.

Methods in org.apache.ignite.ml.optimization.updatecalculators that return Vector

Modifier and Type	Method and Description
Vector	SimpleGDParameterUpdate.gradient() Get gradient.
Vector	NesterovParameterUpdate.prevIterationUpdates() Get previous step parameters updates.
Vector	RPropParameterUpdate.updatesMask() Get updates mask (values by which updateCache is multiplied).

Methods in org.apache.ignite.ml.optimization.updatecalculators with parameters of type Vector

Modifier and Type	Method and Description
RPropParameterUpdate	RPropParameterUpdate.setDeltas(Vector deltas) Set previous iteration deltas.
NesterovParameterUpdate	NesterovParameterUpdate.setPreviousUpdates(Vector updates) Set previous step parameters updates.
RPropParameterUpdate	RPropParameterUpdate.setUpdatesMask(Vector updatesMask) Set updates mask (values by which updateCache is multiplied).

Method parameters in org.apache.ignite.ml.optimization.updatecalculators with type arguments of type Vector

Modifier and Type	Method and Description
NesterovParameterUpdate	NesterovUpdateCalculator.init(M mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss) Initializes the update calculator.
P	ParameterUpdateCalculator.init(M mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss) Initializes the update calculator.
SimpleGDParameterUpdate	SimpleGDUpdateCalculator.init(SmoothParametrized mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss) Initializes the update calculator.
RPropParameterUpdate	RPropUpdateCalculator.init(SmoothParametrized mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss) Initializes the update calculator.

Constructors in org.apache.ignite.ml.optimization.updatecalculators with parameters of type Vector

Constructor and Description
NesterovParameterUpdate(Vector prevIterationUpdates) Construct NesterovParameterUpdate.
RPropParameterUpdate(Vector prevIterationUpdates, Vector prevIterationGradient, Vector deltas, Vector updatesMask) Construct instance of this class by given parameters.
SimpleGDParameterUpdate(Vector gradient) Construct instance of this class.

Uses of Vector in org.apache.ignite.ml.regressions.linear

Methods in org.apache.ignite.ml.regressions.linear that return Vector

Modifier and Type	Method and Description
Vector	LinearRegressionModel.getWeights()

Methods in org.apache.ignite.ml.regressions.linear with parameters of type Vector

Modifier and Type	Method and Description
Double	LinearRegressionModel.apply(Vector input)

Constructors in org.apache.ignite.ml.regressions.linear with parameters of type Vector

Constructor and Description
LinearRegressionModel(Vector weights, double intercept)

Uses of Vector in org.apache.ignite.ml.structures

Classes in org.apache.ignite.ml.structures with type parameters of type Vector

Modifier and Type	Class and Description
class	DatasetRow<V extends Vector> Class to keep one observation in dataset.
class	LabeledVector<V extends Vector,L> Class for vector with label.
class	LabeledVectorDouble<V extends Vector> Labeled vector specialized to double label.

Fields in org.apache.ignite.ml.structures declared as Vector

Modifier and Type	Field and Description
protected V	DatasetRow.vector Vector.

Methods in org.apache.ignite.ml.structures that return Vector

Modifier and Type	Method and Description
static Vector	LabeledDataset.emptyVector(int size, boolean isDistributed)
Vector	Dataset.features(int idx) Get the features.

Uses of Vector in org.apache.ignite.ml.svm

Methods in org.apache.ignite.ml.svm that return Vector

Modifier and Type	Method and Description
Vector	SVMLinearBinaryClassificationModel.weights() Gets the weights.

Methods in org.apache.ignite.ml.svm with parameters of type Vector

Modifier and Type	Method and Description
Double	SVMLinearBinaryClassificationModel.apply(Vector input)
Double	SVMLinearMultiClassClassificationModel.apply(Vector input)
SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationModel.withWeights(Vector weights) Set up the weights.

Constructors in org.apache.ignite.ml.svm with parameters of type Vector

Constructor and Description
SVMLinearBinaryClassificationModel(Vector weights, double intercept)