## Uses of Interface
org.apache.mahout.math.Vector

Packages that use Vector
org.apache.mahout.math: Core base classes; Operations on primitive arrays such as sorting, partitioning and permuting.
org.apache.mahout.math.als
org.apache.mahout.math.decomposer
org.apache.mahout.math.decomposer.hebbian
org.apache.mahout.math.decomposer.lanczos
org.apache.mahout.math.function: Core interfaces for functions, comparisons and procedures on objects and primitive data types.
org.apache.mahout.math.random
org.apache.mahout.math.solver
org.apache.mahout.math.ssvd

Uses of Vector in org.apache.mahout.math

Classes in org.apache.mahout.math that implement Vector
`protected static class` `AbstractMatrix.TransposeViewVector`
`class` `AbstractVector` Implementations of generic capabilities like sum of elements and dot products
`class` `Centroid` A centroid is a weighted vector.
`class` `ConstantVector` Implements a vector with all the same values.
`class` `DelegatingVector` A delegating vector provides an easy way to decorate vectors with weights or id's and such while keeping all of the Vector functionality.
`class` `DenseVector` Implements vector as an array of doubles
`class` `MatrixSlice`
`class` `MatrixVectorView` Provides a virtual vector that is really a row or column or diagonal of a matrix.
`class` `NamedVector`
`class` `PermutedVectorView` Provides a permuted view of a vector.
`class` `RandomAccessSparseVector` Implements vector that only stores non-zero doubles
`class` `SequentialAccessSparseVector` Implements vector that only stores non-zero doubles as a pair of parallel arrays (OrderedIntDoubleMapping), one int[], one double[].
`class` `VectorView` Implements subset view of a Vector
`class` `WeightedVector` Decorates a vector with a floating point weight and an index.

Fields in org.apache.mahout.math declared as Vector
`protected Vector` `DelegatingVector.delegate`

Methods in org.apache.mahout.math that return Vector
`Vector` `Matrix.aggregateColumns(VectorFunction f)` Collects the results of a function applied to each column of a matrix.
`Vector` `AbstractMatrix.aggregateColumns(VectorFunction f)` Collects the results of a function applied to each column of a matrix.
`Vector` `Matrix.aggregateRows(VectorFunction f)` Collects the results of a function applied to each row of a matrix.
`Vector` `AbstractMatrix.aggregateRows(VectorFunction f)` Collects the results of a function applied to each row of a matrix.
`Vector` `DenseVector.assign(DenseVector vector)`
`Vector` `Vector.assign(double value)` Assign the value to all elements of the receiver
`Vector` `NamedVector.assign(double value)`
`Vector` `DenseVector.assign(double value)`
`Vector` `DelegatingVector.assign(double value)`
`Vector` `AbstractVector.assign(double value)`
`Vector` `Vector.assign(double[] values)` Assign the values to the receiver
`Vector` `NamedVector.assign(double[] values)`
`Vector` `DelegatingVector.assign(double[] values)`
`Vector` `AbstractVector.assign(double[] values)`
`Vector` `Vector.assign(DoubleDoubleFunction f, double y)` Apply the function to each element of the receiver, using the y value as the second argument of the DoubleDoubleFunction
`Vector` `NamedVector.assign(DoubleDoubleFunction f, double y)`
`Vector` `DelegatingVector.assign(DoubleDoubleFunction f, double y)`
`Vector` `AbstractVector.assign(DoubleDoubleFunction f, double y)`
`Vector` `Vector.assign(DoubleFunction function)` Apply the function to each element of the receiver
`Vector` `NamedVector.assign(DoubleFunction function)`
`Vector` `DelegatingVector.assign(DoubleFunction function)`
`Vector` `AbstractVector.assign(DoubleFunction f)`
`Vector` `Vector.assign(Vector other)` Assign the other vector values to the receiver
`Vector` `RandomAccessSparseVector.assign(Vector other)`
`Vector` `NamedVector.assign(Vector other)`
`Vector` `DelegatingVector.assign(Vector other)`
`Vector` `AbstractVector.assign(Vector other)`
`Vector` `Vector.assign(Vector other, DoubleDoubleFunction function)` Apply the function to each element of the receiver and the corresponding element of the other argument
`Vector` `NamedVector.assign(Vector other, DoubleDoubleFunction function)`
`Vector` `DelegatingVector.assign(Vector other, DoubleDoubleFunction function)`
`Vector` `AbstractVector.assign(Vector other, DoubleDoubleFunction function)`
`abstract Vector` `VectorBinaryAssign.assign(Vector x, Vector y, DoubleDoubleFunction f)` Main method that applies f to x and y component-wise assigning the results to x.
`Vector` `VectorBinaryAssign.AssignNonzerosIterateThisLookupThat.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateIntersection.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionSequentialMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionSequentialInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionRandomMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionRandomInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateSequentialMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateSequentialInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThisLookupThatMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThisLookupThatInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThatLookupThisMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThatLookupThisInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllLoopMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllLoopInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`static Vector` `VectorBinaryAssign.assignBest(Vector x, Vector y, DoubleDoubleFunction f)` This is the method that should be used when assigning.
`Vector` `VectorView.clone()`
`Vector` `Vector.clone()` Return a copy of the recipient
`Vector` `MatrixVectorView.clone()`
`Vector` `DelegatingVector.clone()`
`Vector` `AbstractVector.clone()`
`Vector` `AbstractMatrix.TransposeViewVector.clone()`
`protected Vector` `AbstractVector.createOptimizedCopy()` Copy the current vector in the most optimum fashion.
`Vector` `Vector.divide(double x)` Return a new vector containing the values of the recipient divided by the argument
`Vector` `NamedVector.divide(double x)`
`Vector` `DelegatingVector.divide(double x)`
`Vector` `AbstractVector.divide(double x)`
`Vector` `NamedVector.getDelegate()`
`Vector` `DelegatingVector.getVector()`
`Vector` `WeightedVector.like()`
`Vector` `Vector.like()` Return an empty vector of the same underlying class as the receiver
`Vector` `PermutedVectorView.like()` Return an empty vector of the same underlying class as the receiver
`Vector` `MatrixVectorView.like()` Return an empty vector of the same underlying class as the receiver
`Vector` `DelegatingVector.like()`
`Vector` `ConstantVector.like()` Return an empty vector of the same underlying class as the receiver
`Vector` `AbstractMatrix.TransposeViewVector.like()`
`Vector` `AbstractMatrix.TransposeViewVector.like(int cardinality)`
`Vector` `Vector.logNormalize()` Return a new vector containing the log(1 + entry)/ L_2 norm values of the recipient
`Vector` `NamedVector.logNormalize()`
`Vector` `DelegatingVector.logNormalize()`
`Vector` `AbstractVector.logNormalize()`
`Vector` `Vector.logNormalize(double power)` Return a new Vector with a normalized value calculated as log_power(1 + entry)/ L_power norm.
`Vector` `NamedVector.logNormalize(double power)`
`Vector` `DelegatingVector.logNormalize(double power)`
`Vector` `AbstractVector.logNormalize(double power)`
`Vector` `AbstractVector.logNormalize(double power, double normLength)`
`Vector` `Vector.minus(Vector x)` Return a new vector containing the element by element difference of the recipient and the argument
`Vector` `NamedVector.minus(Vector x)`
`Vector` `DelegatingVector.minus(Vector that)`
`Vector` `AbstractVector.minus(Vector that)`
`static Vector` `Algebra.mult(Matrix m, Vector v)`
`protected Vector` `AbstractMatrix.TransposeViewVector.newVector(int cardinality)`
`Vector` `Vector.normalize()` Return a new vector containing the normalized (L_2 norm) values of the recipient
`Vector` `NamedVector.normalize()`
`Vector` `DelegatingVector.normalize()`
`Vector` `AbstractVector.normalize()`
`Vector` `Vector.normalize(double power)` Return a new Vector containing the normalized (L_power norm) values of the recipient.
`Vector` `NamedVector.normalize(double power)`
`Vector` `DelegatingVector.normalize(double power)`
`Vector` `AbstractVector.normalize(double power)`
`Vector` `Vector.plus(double x)` Return a new vector containing the sum of each value of the recipient and the argument
`Vector` `NamedVector.plus(double x)`
`Vector` `DelegatingVector.plus(double x)`
`Vector` `AbstractVector.plus(double x)`
`Vector` `Vector.plus(Vector x)` Return a new vector containing the element by element sum of the recipient and the argument
`Vector` `NamedVector.plus(Vector x)`
`Vector` `DelegatingVector.plus(Vector x)`
`Vector` `AbstractVector.plus(Vector that)`
`Vector` `Vector.times(double x)` Return a new vector containing the product of each value of the recipient and the argument
`Vector` `NamedVector.times(double x)`
`Vector` `DelegatingVector.times(double x)`
`Vector` `AbstractVector.times(double x)`
`Vector` `VectorIterable.times(Vector v)` Return a new vector with cardinality equal to getNumRows() of this matrix which is the matrix product of the recipient and the argument
`Vector` `Vector.times(Vector x)` Return a new vector containing the element-wise product of the recipient and the argument
`Vector` `NamedVector.times(Vector x)`
`Vector` `DelegatingVector.times(Vector x)`
`Vector` `AbstractVector.times(Vector that)`
`Vector` `AbstractMatrix.times(Vector v)`
`Vector` `VectorIterable.timesSquared(Vector v)` Convenience method for producing this.transpose().times(this.times(v)), which can be implemented with only one pass over the matrix, without making the transpose() call (which can be expensive if the matrix is sparse)
`Vector` `AbstractMatrix.timesSquared(Vector v)`
`Vector` `MatrixSlice.vector()`
`Vector` `SparseColumnMatrix.viewColumn(int column)`
`Vector` `PivotedMatrix.viewColumn(int column)` Return the column at the given index
`Vector` `MatrixView.viewColumn(int column)`
`Vector` `Matrix.viewColumn(int column)` Return a reference to a column.
`Vector` `AbstractMatrix.viewColumn(int column)` Returns a view of a row.
`Vector` `Matrix.viewDiagonal()` Returns a reference to the diagonal of a matrix.
`Vector` `DiagonalMatrix.viewDiagonal()` Provides a view of the diagonal of a matrix.
`Vector` `AbstractMatrix.viewDiagonal()` Provides a view of the diagonal of a matrix.
`Vector` `VectorView.viewPart(int offset, int length)`
`Vector` `Vector.viewPart(int offset, int length)` Return a new vector containing the subset of the recipient
`Vector` `NamedVector.viewPart(int offset, int length)`
`Vector` `DenseVector.viewPart(int offset, int length)`
`Vector` `DelegatingVector.viewPart(int offset, int length)`
`Vector` `AbstractVector.viewPart(int offset, int length)`
`Vector` `SparseRowMatrix.viewRow(int row)`
`Vector` `SparseMatrix.viewRow(int row)`
`Vector` `PivotedMatrix.viewRow(int row)` Return the row at the given index
`Vector` `MatrixView.viewRow(int row)`
`Vector` `Matrix.viewRow(int row)` Return a reference to a row.
`Vector` `FileBasedSparseBinaryMatrix.viewRow(int rowIndex)` Returns a view of a row.
`Vector` `DenseMatrix.viewRow(int row)`
`Vector` `AbstractMatrix.viewRow(int row)` Returns a view of a row.

Methods in org.apache.mahout.math with parameters of type Vector
`void` `DenseVector.addAll(Vector v)`
`double` `Vector.aggregate(Vector other, DoubleDoubleFunction aggregator, DoubleDoubleFunction combiner)` Generalized inner product - take two vectors, iterate over them both, using the combiner to combine together (and possibly map in some way) each pair of values, which are then aggregated with the previous accumulated value in the combiner.
`double` `NamedVector.aggregate(Vector other, DoubleDoubleFunction aggregator, DoubleDoubleFunction combiner)`
`double` `DelegatingVector.aggregate(Vector other, DoubleDoubleFunction aggregator, DoubleDoubleFunction combiner)`
`double` `AbstractVector.aggregate(Vector other, DoubleDoubleFunction aggregator, DoubleDoubleFunction combiner)`
`abstract double` `VectorBinaryAggregate.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)` Main method that applies fc to x and y component-wise aggregating the results with fa.
`double` `VectorBinaryAggregate.AggregateNonzerosIterateThisLookupThat.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateNonzerosIterateThatLookupThis.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateIterateIntersection.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateIterateUnionSequential.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateIterateUnionRandom.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllIterateSequential.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllIterateThisLookupThat.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllIterateThatLookupThis.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllLoop.aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`static double` `VectorBinaryAggregate.aggregateBest(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)` This is the method that should be used when aggregating.
`Vector` `Vector.assign(Vector other)` Assign the other vector values to the receiver
`Vector` `RandomAccessSparseVector.assign(Vector other)`
`Vector` `NamedVector.assign(Vector other)`
`Vector` `DelegatingVector.assign(Vector other)`
`Vector` `AbstractVector.assign(Vector other)`
`Vector` `Vector.assign(Vector other, DoubleDoubleFunction function)` Apply the function to each element of the receiver and the corresponding element of the other argument
`Vector` `NamedVector.assign(Vector other, DoubleDoubleFunction function)`
`Vector` `DelegatingVector.assign(Vector other, DoubleDoubleFunction function)`
`Vector` `AbstractVector.assign(Vector other, DoubleDoubleFunction function)`
`abstract Vector` `VectorBinaryAssign.assign(Vector x, Vector y, DoubleDoubleFunction f)` Main method that applies f to x and y component-wise assigning the results to x.
`Vector` `VectorBinaryAssign.AssignNonzerosIterateThisLookupThat.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateIntersection.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionSequentialMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionSequentialInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionRandomMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignIterateUnionRandomInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateSequentialMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateSequentialInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThisLookupThatMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThisLookupThatInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThatLookupThisMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllIterateThatLookupThisInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllLoopMergeUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`Vector` `VectorBinaryAssign.AssignAllLoopInplaceUpdates.assign(Vector x, Vector y, DoubleDoubleFunction f)`
`static Vector` `VectorBinaryAssign.assignBest(Vector x, Vector y, DoubleDoubleFunction f)` This is the method that should be used when assigning.
`Matrix` `UpperTriangular.assignColumn(int column, Vector other)`
`Matrix` `SparseRowMatrix.assignColumn(int column, Vector other)`
`Matrix` `SparseMatrix.assignColumn(int column, Vector other)`
`Matrix` `SparseColumnMatrix.assignColumn(int column, Vector other)`
`Matrix` `RandomTrinaryMatrix.assignColumn(int column, Vector other)`
`Matrix` `PivotedMatrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `MatrixView.assignColumn(int column, Vector other)`
`Matrix` `Matrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `FileBasedSparseBinaryMatrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `FileBasedMatrix.assignColumn(int column, Vector other)` Assign the other vector values to the column of the receiver
`Matrix` `DiagonalMatrix.assignColumn(int column, Vector other)`
`Matrix` `DenseMatrix.assignColumn(int column, Vector other)`
`Matrix` `UpperTriangular.assignRow(int row, Vector other)`
`Matrix` `SparseRowMatrix.assignRow(int row, Vector other)`
`Matrix` `SparseMatrix.assignRow(int row, Vector other)`
`Matrix` `SparseColumnMatrix.assignRow(int row, Vector other)`
`Matrix` `RandomTrinaryMatrix.assignRow(int row, Vector other)`
`Matrix` `PivotedMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `MatrixView.assignRow(int row, Vector other)`
`Matrix` `Matrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `FileBasedSparseBinaryMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `FileBasedMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `DiagonalMatrix.assignRow(int row, Vector other)` Assign the other vector values to the row of the receiver
`Matrix` `DenseMatrix.assignRow(int row, Vector other)`
`static Centroid` `Centroid.create(int key, Vector initialValue)`
`Matrix` `Vector.cross(Vector other)` Return the cross product of the receiver and the other vector
`Matrix` `NamedVector.cross(Vector other)`
`Matrix` `DelegatingVector.cross(Vector other)`
`Matrix` `AbstractVector.cross(Vector other)`
`double` `Vector.dot(Vector x)` Return the dot product of the recipient and the argument
`double` `NamedVector.dot(Vector x)`
`double` `DenseVector.dot(Vector x)`
`double` `DelegatingVector.dot(Vector x)`
`double` `AbstractVector.dot(Vector x)`
`abstract double` `VectorBinaryAssign.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)` Estimates the cost of using this algorithm to compute the assignment.
`double` `VectorBinaryAssign.AssignNonzerosIterateThisLookupThat.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignIterateIntersection.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignIterateUnionSequentialMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignIterateUnionSequentialInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignIterateUnionRandomMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignIterateUnionRandomInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllIterateSequentialMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllIterateSequentialInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllIterateThisLookupThatMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllIterateThisLookupThatInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllIterateThatLookupThisMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllIterateThatLookupThisInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllLoopMergeUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`double` `VectorBinaryAssign.AssignAllLoopInplaceUpdates.estimateCost(Vector x, Vector y, DoubleDoubleFunction f)`
`abstract double` `VectorBinaryAggregate.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)` Estimates the cost of using this algorithm to compute the aggregation.
`double` `VectorBinaryAggregate.AggregateNonzerosIterateThisLookupThat.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateNonzerosIterateThatLookupThis.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateIterateIntersection.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateIterateUnionSequential.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateIterateUnionRandom.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllIterateSequential.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllIterateThisLookupThat.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllIterateThatLookupThis.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`double` `VectorBinaryAggregate.AggregateAllLoop.estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`static VectorBinaryAssign` `VectorBinaryAssign.getBestOperation(Vector x, Vector y, DoubleDoubleFunction f)` The best operation is the least expensive valid one.
`static VectorBinaryAggregate` `VectorBinaryAggregate.getBestOperation(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)` The best operation is the least expensive valid one.
`double` `VectorView.getDistanceSquared(Vector v)`
`double` `Vector.getDistanceSquared(Vector v)` Get the square of the distance between this vector and the other vector.
`double` `NamedVector.getDistanceSquared(Vector v)`
`double` `DelegatingVector.getDistanceSquared(Vector v)`
`double` `AbstractVector.getDistanceSquared(Vector that)`
`abstract boolean` `VectorBinaryAssign.isValid(Vector x, Vector y, DoubleDoubleFunction f)` Returns true iff we can use this algorithm to apply f to x and y component-wise and assign the result to x.
`boolean` `VectorBinaryAssign.AssignNonzerosIterateThisLookupThat.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignNonzerosIterateThatLookupThisMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignIterateIntersection.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignIterateUnionSequentialMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignIterateUnionSequentialInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignIterateUnionRandomMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignIterateUnionRandomInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllIterateSequentialMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllIterateSequentialInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllIterateThisLookupThatMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllIterateThisLookupThatInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllIterateThatLookupThisMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllIterateThatLookupThisInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllLoopMergeUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`boolean` `VectorBinaryAssign.AssignAllLoopInplaceUpdates.isValid(Vector x, Vector y, DoubleDoubleFunction f)`
`abstract boolean` `VectorBinaryAggregate.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)` Returns true iff we can use this algorithm to apply fc to x and y component-wise and aggregate the result using fa.
`boolean` `VectorBinaryAggregate.AggregateNonzerosIterateThisLookupThat.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateNonzerosIterateThatLookupThis.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateIterateIntersection.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateIterateUnionSequential.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateIterateUnionRandom.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateAllIterateSequential.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateAllIterateThisLookupThat.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateAllIterateThatLookupThis.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`boolean` `VectorBinaryAggregate.AggregateAllLoop.isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc)`
`Vector` `Vector.minus(Vector x)` Return a new vector containing the element by element difference of the recipient and the argument
`Vector` `NamedVector.minus(Vector x)`
`Vector` `DelegatingVector.minus(Vector that)`
`Vector` `AbstractVector.minus(Vector that)`
`static Vector` `Algebra.mult(Matrix m, Vector v)`
`Vector` `Vector.plus(Vector x)` Return a new vector containing the element by element sum of the recipient and the argument
`Vector` `NamedVector.plus(Vector x)`
`Vector` `DelegatingVector.plus(Vector x)`
`Vector` `AbstractVector.plus(Vector that)`
`static WeightedVector` `WeightedVector.project(Vector v, Vector projection)`
`static WeightedVector` `WeightedVector.project(Vector v, Vector projection, int index)`
`Vector` `VectorIterable.times(Vector v)` Return a new vector with cardinality equal to getNumRows() of this matrix which is the matrix product of the recipient and the argument
`Vector` `Vector.times(Vector x)` Return a new vector containing the element-wise product of the recipient and the argument
`Vector` `NamedVector.times(Vector x)`
`Vector` `DelegatingVector.times(Vector x)`
`Vector` `AbstractVector.times(Vector that)`
`Vector` `AbstractMatrix.times(Vector v)`
`Vector` `VectorIterable.timesSquared(Vector v)` Convenience method for producing this.transpose().times(this.times(v)), which can be implemented with only one pass over the matrix, without making the transpose() call (which can be expensive if the matrix is sparse)
`Vector` `AbstractMatrix.timesSquared(Vector v)`
`void` `Centroid.update(Vector v)`
`void` `Centroid.update(Vector other, double wy)`

Constructors in org.apache.mahout.math with parameters of type Vector
`Centroid(int key, Vector initialValue)`
`Centroid(int key, Vector initialValue, double weight)`
`DelegatingVector(Vector v)`
`DenseSymmetricMatrix(Vector data)`
`DenseVector(Vector vector)` Copy-constructor (for use in turning a sparse vector into a dense one, for example)
`DiagonalMatrix(Vector values)`
`MatrixSlice(Vector v, int index)`
`NamedVector(Vector delegate, String name)`
`PermutedVectorView(Vector vector, int[] pivot)`
`PermutedVectorView(Vector vector, int[] pivot, int[] unpivot)`
`RandomAccessSparseVector(Vector other)`
`SequentialAccessSparseVector(Vector other)`
`SparseRowMatrix(int rows, int columns, Vector[] rowVectors)` Construct a sparse matrix starting with the provided row vectors.
`SparseRowMatrix(int rows, int columns, Vector[] vectors, boolean shallowCopy, boolean randomAccess)`
`UpperTriangular(Vector data)`
`VectorView(Vector vector, int offset, int cardinality)`
`WeightedVector(Vector v, double weight, int index)`
`WeightedVector(Vector v, Vector projection, int index)`

Uses of Vector in org.apache.mahout.math.als

Methods in org.apache.mahout.math.als that return Vector
`static Vector` `AlternatingLeastSquaresSolver.solve(Iterable<Vector> featureVectors, Vector ratingVector, double lambda, int numFeatures)`
`Vector` `ImplicitFeedbackAlternatingLeastSquaresSolver.solve(Vector ratings)`

Methods in org.apache.mahout.math.als with parameters of type Vector
`static Vector` `AlternatingLeastSquaresSolver.solve(Iterable<Vector> featureVectors, Vector ratingVector, double lambda, int numFeatures)`
`Vector` `ImplicitFeedbackAlternatingLeastSquaresSolver.solve(Vector ratings)`

Method parameters in org.apache.mahout.math.als with type arguments of type Vector
`static Vector` `AlternatingLeastSquaresSolver.solve(Iterable<Vector> featureVectors, Vector ratingVector, double lambda, int numFeatures)`

Constructor parameters in org.apache.mahout.math.als with type arguments of type Vector
`ImplicitFeedbackAlternatingLeastSquaresSolver(int numFeatures, double lambda, double alpha, OpenIntObjectHashMap<Vector> Y)`

Uses of Vector in org.apache.mahout.math.decomposer

Methods in org.apache.mahout.math.decomposer with parameters of type Vector
`protected EigenStatus` `AsyncEigenVerifier.innerVerify(VectorIterable corpus, Vector vector)`
`EigenStatus` `SingularVectorVerifier.verify(VectorIterable eigenMatrix, Vector vector)`
`EigenStatus` `SimpleEigenVerifier.verify(VectorIterable corpus, Vector vector)`
`EigenStatus` `AsyncEigenVerifier.verify(VectorIterable corpus, Vector vector)`

Uses of Vector in org.apache.mahout.math.decomposer.hebbian

Methods in org.apache.mahout.math.decomposer.hebbian that return Vector
`Vector` `TrainingState.currentTrainingProjection()`
`Vector` `TrainingState.getHelperVector()`
`Vector` `TrainingState.mostRecentEigen()`

Methods in org.apache.mahout.math.decomposer.hebbian with parameters of type Vector
`protected boolean` `HebbianSolver.hasNotConverged(Vector currentPseudoEigen, Matrix corpus, TrainingState state)` Uses the `SingularVectorVerifier` to check for convergence
`void` `TrainingState.setHelperVector(Vector helperVector)`
`void` `HebbianUpdater.update(Vector pseudoEigen, Vector trainingVector, TrainingState currentState)`
`void` `EigenUpdater.update(Vector pseudoEigen, Vector trainingVector, TrainingState currentState)`
`protected EigenStatus` `HebbianSolver.verify(Matrix corpus, Vector currentPseudoEigen)`

Uses of Vector in org.apache.mahout.math.decomposer.lanczos

Fields in org.apache.mahout.math.decomposer.lanczos with type parameters of type Vector
`protected Map<Integer,Vector>` `LanczosState.basis`
`protected Map<Integer,Vector>` `LanczosState.singularVectors`

Methods in org.apache.mahout.math.decomposer.lanczos that return Vector
`Vector` `LanczosState.getBasisVector(int i)`
`Vector` `LanczosState.getRightSingularVector(int i)`

Methods in org.apache.mahout.math.decomposer.lanczos with parameters of type Vector
`protected static double` `LanczosSolver.calculateScaleFactor(Vector nextVector)`
`protected static void` `LanczosSolver.orthoganalizeAgainstAllButLast(Vector nextVector, LanczosState state)`
`void` `LanczosState.setBasisVector(int i, Vector basisVector)`
`void` `LanczosState.setRightSingularVector(int i, Vector vector)`

Constructors in org.apache.mahout.math.decomposer.lanczos with parameters of type Vector
`LanczosState(VectorIterable corpus, int desiredRank, Vector initialVector)`

Uses of Vector in org.apache.mahout.math.function

Methods in org.apache.mahout.math.function with parameters of type Vector
`double` `VectorFunction.apply(Vector f)`

Uses of Vector in org.apache.mahout.math.random

Methods in org.apache.mahout.math.random that return Vector
`Vector` `MultiNormal.getScale()`
`Vector` `MultiNormal.sample()`

Constructors in org.apache.mahout.math.random with parameters of type Vector
`MultiNormal(double radius, Vector mean)`
`MultiNormal(Matrix a, Vector mean)` Constructs a sampler with non-trivial scale matrix and mean.
`MultiNormal(Vector diagonal)` Constructs a sampler with diagonal scale matrix.
`MultiNormal(Vector diagonal, Vector mean)` Constructs a sampler with diagonal scale matrix and (potentially) non-zero mean.

Uses of Vector in org.apache.mahout.math.solver

Methods in org.apache.mahout.math.solver that return Vector
`Vector` `EigenDecomposition.getImagEigenvalues()` Return the imaginary parts of the eigenvalues
`Vector` `EigenDecomposition.getRealEigenvalues()` Return the real parts of the eigenvalues
`Vector` `Preconditioner.precondition(Vector v)` Preconditions the specified vector.
`Vector` `JacobiConditioner.precondition(Vector v)`
`Vector` `LSMR.solve(Matrix A, Vector b)`
`Vector` `ConjugateGradientSolver.solve(VectorIterable a, Vector b)` Solves the system Ax = b with default termination criteria.
`Vector` `ConjugateGradientSolver.solve(VectorIterable a, Vector b, Preconditioner precond)` Solves the system Ax = b with default termination criteria using the specified preconditioner.
`Vector` `ConjugateGradientSolver.solve(VectorIterable a, Vector b, Preconditioner preconditioner, int maxIterations, double maxError)` Solves the system Ax = b, where A is a linear operator and b is a vector.

Methods in org.apache.mahout.math.solver with parameters of type Vector
`Vector` `Preconditioner.precondition(Vector v)` Preconditions the specified vector.
`Vector` `JacobiConditioner.precondition(Vector v)`
`Vector` `LSMR.solve(Matrix A, Vector b)`
`Vector` `ConjugateGradientSolver.solve(VectorIterable a, Vector b)` Solves the system Ax = b with default termination criteria.
`Vector` `ConjugateGradientSolver.solve(VectorIterable a, Vector b, Preconditioner precond)` Solves the system Ax = b with default termination criteria using the specified preconditioner.
`Vector` `ConjugateGradientSolver.solve(VectorIterable a, Vector b, Preconditioner preconditioner, int maxIterations, double maxError)` Solves the system Ax = b, where A is a linear operator and b is a vector.

Uses of Vector in org.apache.mahout.math.ssvd

Methods in org.apache.mahout.math.ssvd that return Vector
`Vector` `SequentialBigSvd.getSingularValues()`

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