Class AbstractCooSemiringVector<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>,Y extends Semiring<Y>>

java.lang.Object
org.flag4j.arrays.backend.AbstractTensor<T,Y[],Y>
org.flag4j.arrays.backend.semiring_arrays.AbstractCooSemiringVector<T,U,V,W,Y>
Type Parameters:
T - Type of this vector.
U - Type of equivalent dense vector.
V - Type of matrix equivalent to T.
Y - Type of dense matrix equivalent to U.
Y - Type of the semiring element in this vector.
All Implemented Interfaces:
Serializable, SemiringTensorMixin<T,U,Y>, TensorOverSemiring<T,U,Y[],Y>, VectorMixin<T,V,W,Y>
Direct Known Subclasses:
AbstractCooRingVector, CooSemiringVector
public abstract class AbstractCooSemiringVector<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>,Y extends Semiring<Y>> extends AbstractTensor<T,Y[],Y> implements SemiringTensorMixin<T,U,Y>, VectorMixin<T,V,W,Y>

A sparse vector stored in coordinate list (COO) format. The AbstractTensor.data of this COO vector are elements of a Semiring.

The non-zero data and non-zero indices of a COO vector are mutable but the AbstractTensor.shape and total number of non-zero data is fixed.

Sparse vectors allow for the efficient storage of and ops on large vectors that contain many zero values.

COO vectors are optimized for large hyper-sparse vectors (i.e. vectors which contain almost all zeros relative to the size of the vector).

A sparse COO vector is stored as:

Note: many ops assume that the data of the COO vector are sorted lexicographically. However, this is not explicitly verified. Every operation implemented in this class will preserve the lexicographical sorting.

If indices need to be sorted for any reason, call sortIndices().

See Also:

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    final int[]
    indices
    Indices of the non-zero values of this sparse COO vector.
    final int
    nnz
    The number of non-zero data in this sparse COO vector.
    final int
    size
    The total size of this sparse COO vector (including zero values).
    final double
    sparsity
    The sparsity of this matrix.

    Fields inherited from class org.flag4j.arrays.backend.AbstractTensor

    data, rank, shape

  • Constructor Summary

    Constructors
    Modifier
    Constructor
    Description
    protected
    AbstractCooSemiringVector(Shape shape, Y[] entries, int[] indices)
    Creates a sparse COO semiring vector with the specified data and shape.

  • Method Summary

    Modifier and Type
    Method
    Description
    T
    add(T b)
    Computes the element-wise sum between two tensors of the same shape.
    T
    coalesce()
    Coalesces this sparse COO vector.
    T
    coalesce(BinaryOperator<Y> aggregator)
    Coalesces this sparse COO vector.
    T
    copy()
    Creates a deep copy of this tensor.
    double
    density()
    Gets the density of this matrix as a decimal percentage.
    Y
    dot(T b)
    Computes the dot product between two vectors.
    T
    dropZeros()
    Drops any explicit zeros in this sparse COO vector.
    T
    elemMult(T b)
    Computes the element-wise multiplication of two tensors of the same shape.
    T
    flatten()
    Flattens tensor to single dimension while preserving order of data.
    T
    flatten(int axis)
    Flattens a tensor along the specified axis.
    Y
    get(int idx)
    Gets the element of this vector at the specified index.
    Y
    get(int... target)
    Gets the element of this tensor at the specified indices.
    Y
    getZeroElement()
    Gets the zero element for the field of this vector.
    Y
    inner(T b)
    Computes the inner product between two vectors.
    T
    join(T b)
    Joints specified vector with this vector.
    int
    length()
    Gets the length of a vector.
    Y
    mag()
    Computes the magnitude of this vector.
    abstract W
    makeLikeDenseMatrix(Shape shape, Y... entries)
    Constructs a dense matrix of a similar type as this vector with the specified shape and data.
    abstract U
    makeLikeDenseTensor(Shape shape, Y... entries)
    Constructs a dense vector of a similar type as this vector with the specified shape and data.
    abstract V
    makeLikeMatrix(Shape shape, Y[] entries, int[] rowIndices, int[] colIndices)
    Constructs a COO matrix with the specified shape, non-zero data, and row and column indices.
    abstract T
    makeLikeTensor(Shape shape, List<Y> entries, List<Integer> indices)
    Constructs a COO vector with the specified shape, non-zero data, and non-zero indices.
    abstract T
    makeLikeTensor(Shape shape, Y[] entries, int[] indices)
    Constructs a sparse COO vector of the same type as this vector with the specified non-zero data and indices.
    T
    normalize()
    Normalizes this vector to a unit length vector.
    W
    outer(T b)
    Computes the outer product of two vectors.
    V
    repeat(int n, int axis)
    Repeats a vector n times along a certain axis to create a matrix.
    T
    reshape(Shape newShape)
    Copies and reshapes this tensor.
    T
    set(Y value, int... target)
    Sets the element of this tensor at the specified indices.
    void
    setZeroElement(Y zeroElement)
    Sets the zero element for the field of this tensor.
    void
    sortIndices()
    Sorts the indices of this tensor in lexicographical order while maintaining the associated value for each index.
    double
    sparsity()
    Gets the sparsity of this matrix as a decimal percentage.
    V
    stack(T b, int axis)
    Stacks two vectors along specified axis.
    T
    T(int... axes)
    Computes the transpose of this tensor.
    T
    T(int axis1, int axis2)
    Computes the transpose of a tensor by exchanging axis1 and axis2.
    U
    tensorDot(T src2, int[] aAxes, int[] bAxes)
    Computes the tensor contraction of this tensor with a specified tensor over the specified set of axes.
    T
    tensorTr(int axis1, int axis2)
    Computes the generalized trace of this tensor along the specified axes.
    U
    toDense()
    Converts this sparse COO matrix to an equivalent dense matrix.
    V
    toMatrix(boolean columVector)
    Converts a vector to an equivalent matrix representing either a row or column vector.
    abstract AbstractTensor<?,Y[],Y>
    toTensor()
    Converts this matrix to an equivalent rank 1 tensor.
    abstract AbstractTensor<?,Y[],Y>
    toTensor(Shape newShape)
    Converts this vector to an equivalent tensor with the specified shape.

    Methods inherited from class org.flag4j.arrays.backend.AbstractTensor

    getData, getRank, getShape, makeLikeTensor, reshape, sameShape, T, totalEntries

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface org.flag4j.arrays.backend.semiring_arrays.SemiringTensorMixin

    add, addEq, argmax, argmin, isOnes, isZeros, makeEmptyDataArray, max, min, mult, multEq, prod, sum

    Methods inherited from interface org.flag4j.arrays.backend.semiring_arrays.TensorOverSemiring

    getData, getRank, getShape, makeLikeTensor, tensorDot, tensorDot, tensorDot, tensorTr

    Methods inherited from interface org.flag4j.arrays.backend.VectorMixin

    size, stack, toMatrix

  • Field Details

    • indices

      public final int[] indices
      Indices of the non-zero values of this sparse COO vector.

    • nnz

      public final int nnz
      The number of non-zero data in this sparse COO vector.

    • size

      public final int size
      The total size of this sparse COO vector (including zero values).

    • sparsity

      public final double sparsity
      The sparsity of this matrix.

  • Constructor Details

    • AbstractCooSemiringVector

      protected AbstractCooSemiringVector(Shape shape, Y[] entries, int[] indices)
      Creates a sparse COO semiring vector with the specified data and shape.
      Parameters:
      shape - Shape of this vector. Must be rank-1.
      entries - Non-zero entries of this COO vector.
      indices - Non-zero indices of this COO vector.

  • Method Details

    • makeLikeTensor

      public abstract T makeLikeTensor(Shape shape, Y[] entries, int[] indices)
      Constructs a sparse COO vector of the same type as this vector with the specified non-zero data and indices.
      Parameters:
      shape - Shape of the vector to construct.
      entries - Non-zero data of the vector to construct.
      indices - Non-zero row indices of the vector to construct.
      Returns:
      A sparse COO vector of the same type as this vector with the specified non-zero data and indices.

    • makeLikeDenseTensor

      public abstract U makeLikeDenseTensor(Shape shape, Y... entries)
      Constructs a dense vector of a similar type as this vector with the specified shape and data.
      Parameters:
      shape - Shape of the vector to construct.
      entries - Entries of the vector to construct.
      Returns:
      A dense vector of a similar type as this vector with the specified data.

    • makeLikeDenseMatrix

      public abstract W makeLikeDenseMatrix(Shape shape, Y... entries)
      Constructs a dense matrix of a similar type as this vector with the specified shape and data.
      Parameters:
      shape - Shape of the matrix to construct.
      entries - Entries of the matrix to construct.
      Returns:
      A dense matrix of a similar type as this vector with the specified data.

    • makeLikeTensor

      public abstract T makeLikeTensor(Shape shape, List<Y> entries, List<Integer> indices)
      Constructs a COO vector with the specified shape, non-zero data, and non-zero indices.
      Parameters:
      shape - Shape of the vector.
      entries - Non-zero values of the vector.
      indices - Indices of the non-zero values in the vector.
      Returns:
      A COO vector of the same type as this vector with the specified shape, non-zero data, and non-zero indices.

    • makeLikeMatrix

      public abstract V makeLikeMatrix(Shape shape, Y[] entries, int[] rowIndices, int[] colIndices)
      Constructs a COO matrix with the specified shape, non-zero data, and row and column indices.
      Parameters:
      shape - Shape of the matrix to construct.
      entries - Non-zero data of the matrix.
      rowIndices - Row indices of the matrix.
      colIndices - Column indices of the matrix.
      Returns:
      A COO matrix of similar type as this vector with the specified shape, non-zero data, and non-zero row/col indices.

    • sparsity

      public double sparsity()
      Gets the sparsity of this matrix as a decimal percentage. That is, the percentage of data in this matrix that are zero.
      Returns:
      The sparsity of this matrix as a decimal percentage.
      See Also:

    • density

      public double density()
      Gets the density of this matrix as a decimal percentage. That is, the percentage of data in this matrix that are non-zero.
      Returns:
      The density of this matrix as a decimal percentage.
      See Also:

    • sortIndices

      public void sortIndices()
      Sorts the indices of this tensor in lexicographical order while maintaining the associated value for each index.

    • get

      public Y get(int... target)
      Gets the element of this tensor at the specified indices.
      Specified by:
      get in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Parameters:
      target - Indices of the element to get.
      Returns:
      The element of this tensor at the specified indices.
      Throws:
      IndexOutOfBoundsException - If any {target} are not within this tensor.

    • T

      public T T(int axis1, int axis2)
      Computes the transpose of a tensor by exchanging axis1 and axis2.
      Specified by:
      T in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Parameters:
      axis1 - First axis to exchange.
      axis2 - Second axis to exchange.
      Returns:
      The transpose of this tensor according to the specified axes.
      Throws:
      IndexOutOfBoundsException - If either axis1 or axis2 are out of bounds for the rank of this tensor.
      See Also:

    • T

      public T T(int... axes)
      Computes the transpose of this tensor. That is, permutes the axes of this tensor so that it matches the permutation specified by axes.
      Specified by:
      T in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Parameters:
      axes - Permutation of tensor axis. If the tensor has rank N, then this must be an array of length N which is a permutation of {0, 1, 2, ..., N-1}.
      Returns:
      The transpose of this tensor with its axes permuted by the axes array.
      Throws:
      IllegalArgumentException - If axes is not a permutation of {1, 2, 3, ... N-1}.
      See Also:

    • copy

      public T copy()
      Creates a deep copy of this tensor.
      Specified by:
      copy in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Returns:
      A deep copy of this tensor.

    • set

      public T set(Y value, int... target)
      Sets the element of this tensor at the specified indices.
      Specified by:
      set in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Parameters:
      value - New value to set the specified index of this tensor to.
      target - Indices of the element to set.
      Returns:
      If this tensor is dense, a reference to this tensor is returned. If this tensor is sparse, a copy of this tensor with the updated value is returned.
      Throws:
      IndexOutOfBoundsException - If indices is not within the bounds of this tensor.

    • flatten

      public T flatten()
      Flattens tensor to single dimension while preserving order of data.
      Specified by:
      flatten in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Returns:
      The flattened tensor.
      See Also:

    • flatten

      public T flatten(int axis)
      Flattens a tensor along the specified axis. Unlike flatten()
      Specified by:
      flatten in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Parameters:
      axis - Axis along which to flatten tensor.
      Throws:
      ArrayIndexOutOfBoundsException - If the axis is not positive or larger than this.{@link #getRank()}-1.
      See Also:

    • reshape

      public T reshape(Shape newShape)
      Copies and reshapes this tensor.
      Specified by:
      reshape in class AbstractTensor<T extends AbstractCooSemiringVector<T,U,V,W,Y>,Y extends Semiring<Y>[],Y extends Semiring<Y>>
      Parameters:
      newShape - New shape for the tensor.
      Returns:
      A copy of this tensor with the new shape.
      Throws:
      TensorShapeException - If newShape is not broadcastable to this.shape.

    • join

      public T join(T b)
      Joints specified vector with this vector. That is, creates a vector of length this.length() + b.length() containing first the elements of this vector followed by the elements of b.
      Specified by:
      join in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Vector to join with this vector.
      Returns:
      A vector resulting from joining the specified vector with this vector.

    • inner

      public Y inner(T b)

      Computes the inner product between two vectors.

      Note: this method is distinct from dot(AbstractCooSemiringVector). The inner product is equivalent to the dot product of this tensor with the conjugation of b.

      Specified by:
      inner in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Second vector in the inner product.
      Returns:
      The inner product between this vector and the vector b.
      Throws:
      IllegalArgumentException - If this vector and vector b do not have the same number of data.
      See Also:

    • dot

      public Y dot(T b)

      Computes the dot product between two vectors.

      Note: this method is distinct from inner(AbstractCooSemiringVector). The inner product is equivalent to the dot product of this tensor with the conjugation of b.

      Specified by:
      dot in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Second vector in the dot product.
      Returns:
      The dot product between this vector and the vector b.
      Throws:
      IllegalArgumentException - If this vector and vector b do not have the same number of data.
      See Also:

    • length

      public int length()

      Gets the length of a vector. Same as VectorMixin.size().

      WARNING: This method will throw a ArithmeticException if the total number of data in this vector is greater than the maximum integer. In this case, the true size of this vector can still be found by calling shape.totalEntries() on this vector.

      Specified by:
      length in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Returns:
      The length, i.e. the number of data, in this vector.
      Throws:
      ArithmeticException - If the total number of data in this vector is greater than the maximum integer.

    • repeat

      public V repeat(int n, int axis)
      Repeats a vector n times along a certain axis to create a matrix.
      Specified by:
      repeat in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      n - Number of times to repeat vector.
      axis - Axis along which to repeat vector:
      • If axis=0, then the vector will be treated as a row vector and stacked vertically n times.
      • If axis=1 then the vector will be treated as a column vector and stacked horizontally n times.
      Returns:
      A matrix whose rows/columns are this vector repeated.

    • stack

      public V stack(T b, int axis)

      Stacks two vectors along specified axis.

      Stacking two vectors of length n along axis 0 stacks the vectors as if they were row vectors resulting in a 2-by-n matrix.

      Stacking two vectors of length n along axis 1 stacks the vectors as if they were column vectors resulting in a n-by-2 matrix.

      Specified by:
      stack in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Vector to stack with this vector.
      axis - Axis along which to stack vectors. If axis=0, then vectors are stacked as if they are row vectors. If axis=1, then vectors are stacked as if they are column vectors.
      Returns:
      The result of stacking this vector and the vector b.
      Throws:
      IllegalArgumentException - If the number of data in this vector is different from the number of data in the vector b.
      IllegalArgumentException - If axis is not either 0 or 1.

    • outer

      public W outer(T b)
      Computes the outer product of two vectors.
      Specified by:
      outer in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Second vector in the outer product.
      Returns:
      The result of the vector outer product between this vector and b.
      Throws:
      IllegalArgumentException - If the two vectors do not have the same number of data.

    • toMatrix

      public V toMatrix(boolean columVector)
      Converts a vector to an equivalent matrix representing either a row or column vector.
      Specified by:
      toMatrix in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      columVector - Flag indicating whether to convert this vector to a matrix representing a row or column vector:

      If true, the vector will be converted to a matrix representing a column vector.

      If false, The vector will be converted to a matrix representing a row vector.

      Returns:
      A matrix equivalent to this vector.

    • add

      public T add(T b)
      Computes the element-wise sum between two tensors of the same shape.
      Specified by:
      add in interface TensorOverSemiring<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Second tensor in the element-wise sum.
      Returns:
      The sum of this tensor with b.
      Throws:
      TensorShapeException - If this tensor and b do not have the same shape.

    • elemMult

      public T elemMult(T b)
      Computes the element-wise multiplication of two tensors of the same shape.
      Specified by:
      elemMult in interface TensorOverSemiring<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      b - Second tensor in the element-wise product.
      Returns:
      The element-wise product between this tensor and b.
      Throws:
      IllegalArgumentException - If this tensor and b do not have the same shape.

    • tensorDot

      public U tensorDot(T src2, int[] aAxes, int[] bAxes)
      Computes the tensor contraction of this tensor with a specified tensor over the specified set of axes. That is, computes the sum of products between the two tensors along the specified set of axes.
      Specified by:
      tensorDot in interface TensorOverSemiring<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      src2 - Tensor to contract with this tensor.
      aAxes - Axes along which to compute products for this tensor.
      bAxes - Axes along which to compute products for src2 tensor.
      Returns:
      The tensor dot product over the specified axes.
      Throws:
      IllegalArgumentException - If the two tensors shapes do not match along the specified axes pairwise in aAxes and bAxes.
      IllegalArgumentException - If aAxes and bAxes do not match in length, or if any of the axes are out of bounds for the corresponding tensor.

    • tensorTr

      public T tensorTr(int axis1, int axis2)

      Computes the generalized trace of this tensor along the specified axes.

      The generalized tensor trace is the sum along the diagonal values of the 2D sub-arrays of this tensor specified by axis1 and axis2. The shape of the resulting tensor is equal to this tensor with the axis1 and axis2 removed.

      Specified by:
      tensorTr in interface TensorOverSemiring<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      axis1 - First axis for 2D sub-array.
      axis2 - Second axis for 2D sub-array.
      Returns:
      The generalized trace of this tensor along axis1 and axis2.
      Throws:
      IndexOutOfBoundsException - If the two axes are not both larger than zero and less than this tensors rank.
      IllegalArgumentException - If axis1 == axis2 or this.shape.get(axis1) != this.shape.get(axis1) (i.e. the axes are equal or the tensor does not have the same length along the two axes.)

    • getZeroElement

      public Y getZeroElement()
      Gets the zero element for the field of this vector.
      Returns:
      The zero element for the field of this vector. If it could not be determined during construction of this object and has not been set explicitly by setZeroElement(Semiring) then null will be returned.

    • setZeroElement

      public void setZeroElement(Y zeroElement)
      Sets the zero element for the field of this tensor.
      Parameters:
      zeroElement - The zero element of this tensor.
      Throws:
      IllegalArgumentException - If zeroElement is not an additive identity for the semiring.

    • toDense

      public U toDense()
      Converts this sparse COO matrix to an equivalent dense matrix.
      Returns:
      A dense matrix equivalent to this sparse COO matrix.

    • toTensor

      public abstract AbstractTensor<?,Y[],Y> toTensor()
      Converts this matrix to an equivalent rank 1 tensor.
      Returns:
      A tensor which is equivalent to this matrix.

    • toTensor

      public abstract AbstractTensor<?,Y[],Y> toTensor(Shape newShape)
      Converts this vector to an equivalent tensor with the specified shape.
      Parameters:
      newShape - New shape for the tensor. Can be any rank but must be broadcastable to this.shape.
      Returns:
      A tensor equivalent to this matrix which has been reshaped to newShape

    • normalize

      public T normalize()
      Normalizes this vector to a unit length vector.
      Specified by:
      normalize in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Returns:
      This vector normalized to a unit length.

    • mag

      public Y mag()
      Computes the magnitude of this vector.
      Specified by:
      mag in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Returns:
      The magnitude of this vector.

    • get

      public Y get(int idx)
      Gets the element of this vector at the specified index.
      Specified by:
      get in interface VectorMixin<T extends AbstractCooSemiringVector<T,U,V,W,Y>,U extends AbstractDenseSemiringVector<U,W,Y>,V extends AbstractCooSemiringMatrix<V,W,T,Y>,W extends AbstractDenseSemiringMatrix<W,U,Y>>
      Parameters:
      idx - Index of the element to get within this vector.
      Returns:
      The element of this vector at index idx.

    • coalesce

      public T coalesce()
      Coalesces this sparse COO vector. An uncoalesced vector is a sparse vector with multiple data for a single index. This method will ensure that each index only has one non-zero value by summing duplicated data. If another form of aggregation other than summing is desired, use coalesce(BinaryOperator).
      Returns:
      A new coalesced sparse COO vector which is equivalent to this COO vector.
      See Also:

    • coalesce

      public T coalesce(BinaryOperator<Y> aggregator)
      Coalesces this sparse COO vector. An uncoalesced vector is a sparse vector with multiple data for a single index. This method will ensure that each index only has one non-zero value by aggregating duplicated data using aggregator.
      Parameters:
      aggregator - Custom aggregation function to combine multiple.
      Returns:
      A new coalesced sparse COO vector which is equivalent to this COO vector.
      See Also:

    • dropZeros

      public T dropZeros()
      Drops any explicit zeros in this sparse COO vector.
      Returns:
      A copy of this COO vector with any explicitly stored zeros removed.