Uses of Class
org.flag4j.arrays.Shape

Packages that use Shape

Package	Description
org.flag4j.arrays
org.flag4j.arrays.backend
org.flag4j.arrays.backend.field_arrays
org.flag4j.arrays.backend.primitive_arrays
org.flag4j.arrays.backend.ring_arrays
org.flag4j.arrays.backend.semiring_arrays
org.flag4j.arrays.dense	Provides implementations for dense tensors, matrices, and vectors.
org.flag4j.arrays.sparse	Contains implementations for sparse tensors, matrices, and vectors.
org.flag4j.concurrency	Utilities, configurations, and management for multithreaded implementations.
org.flag4j.io	IO for vector, matrix, and tensor objects.
org.flag4j.linalg.decompositions.svd
org.flag4j.linalg.ops
org.flag4j.linalg.ops.dense
org.flag4j.linalg.ops.dense_sparse.coo.field_ops
org.flag4j.linalg.ops.dense_sparse.coo.real
org.flag4j.linalg.ops.dense_sparse.coo.real_complex
org.flag4j.linalg.ops.dense_sparse.coo.real_field_ops
org.flag4j.linalg.ops.dense.field_ops
org.flag4j.linalg.ops.dense.real
org.flag4j.linalg.ops.dense.real_field_ops
org.flag4j.linalg.ops.dense.ring_ops
org.flag4j.linalg.ops.dense.semiring_ops
org.flag4j.linalg.ops.sparse
org.flag4j.linalg.ops.sparse.coo
org.flag4j.linalg.ops.sparse.coo.real
org.flag4j.linalg.ops.sparse.coo.real_complex
org.flag4j.linalg.ops.sparse.coo.ring_ops
org.flag4j.linalg.ops.sparse.coo.semiring_ops
org.flag4j.linalg.ops.sparse.csr
org.flag4j.linalg.ops.sparse.csr.ring_ops
org.flag4j.linalg.ops.sparse.csr.semiring_ops
org.flag4j.linalg.solvers.exact
org.flag4j.rng	This package contains classes for Pseudo-random generation of real/complex numbers, and arrays, vectors, matrices, and tensors.
org.flag4j.util

Uses of Shape in org.flag4j.arrays

Methods in org.flag4j.arrays that return Shape

Modifier and Type	Method	Description
Shape	Shape.flatten()	Flattens this shape to a rank-1 shape with dimension equal to the product of all of this shape's dimensions.
Shape	SmartMatrix.getShape()	Gets the shape of this matrix.
Shape	Shape.permuteAxes(int... axes)	Permutes the axes of this shape.
Shape	SparseMatrixData.shape()	Returns the value of the shape record component.
Shape	SparseTensorData.shape()	Returns the value of the shape record component.
Shape	SparseVectorData.shape()	Returns the value of the shape record component.
Shape	Shape.slice(int startIdx)	Returns a slice of this shape starting from the specified index to the end of this shape's dimensions.
Shape	Shape.slice(int startIdx, int stopIdx)	Returns a slice of this shape from the specified start index to the stop index of this shape's dimensions.
Shape	Shape.swapAxes(int axis1, int axis2)	Swaps two axes of this shape.
Shape	Shape.unsafePermuteAxes(int... axes)	Permutes the axes of this shape.

Constructors in org.flag4j.arrays with parameters of type Shape

Modifier	Constructor	Description
	SparseMatrixData(Shape shape, List<T> data, List<Integer> rowData, List<Integer> colData)	Creates an instance of a SparseMatrixData record class.
	SparseTensorData(Shape shape, List<T> data, List<int[]> indices)	Creates an instance of a SparseTensorData record class.
	SparseVectorData(Shape shape, List<T> data, List<Integer> indices)	Creates an instance of a SparseVectorData record class.

Uses of Shape in org.flag4j.arrays.backend

Fields in org.flag4j.arrays.backend declared as Shape

Modifier and Type	Field	Description
final Shape	AbstractTensor.shape	The shape of this tensor.

Methods in org.flag4j.arrays.backend that return Shape

Modifier and Type	Method	Description
Shape	AbstractTensor.getShape()	Gets the shape of this tensor.
Shape	MatrixMixin.getShape()	Gets the shape of this matrix.

Methods in org.flag4j.arrays.backend with parameters of type Shape

Modifier and Type	Method	Description
abstract T	AbstractTensor.makeLikeTensor(Shape shape, U entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
abstract T	AbstractTensor.reshape(Shape newShape)	Copies and reshapes this tensor.

Constructors in org.flag4j.arrays.backend with parameters of type Shape

Modifier	Constructor	Description
protected	AbstractTensor(Shape shape, U data)	Creates a tensor with the specified data and shape.

Uses of Shape in org.flag4j.arrays.backend.field_arrays

Methods in org.flag4j.arrays.backend.field_arrays with parameters of type Shape

Modifier and Type	Method	Description
abstract AbstractCsrFieldMatrix<?,U,V,W>	AbstractCooFieldMatrix.makeLikeCsrMatrix(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
abstract AbstractCsrFieldMatrix<?,T,?,V>	AbstractDenseFieldMatrix.makeLikeCsrMatrix(Shape shape, V[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix which is of a similar type as this dense matrix.

Constructors in org.flag4j.arrays.backend.field_arrays with parameters of type Shape

Modifier	Constructor	Description
protected	AbstractCooFieldMatrix(Shape shape, W[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
protected	AbstractCooFieldTensor(Shape shape, V[] entries, int[][] indices)	Creates a tensor with the specified data and shape.
protected	AbstractCooFieldVector(Shape shape, Y[] data, int[] indices)	Creates a tensor with the specified data and shape.
protected	AbstractCsrFieldMatrix(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
protected	AbstractDenseFieldMatrix(Shape shape, V[] data)	Creates a tensor with the specified data and shape.
protected	AbstractDenseFieldTensor(Shape shape, V[] entries)	Creates a tensor with the specified data and shape.
protected	AbstractDenseFieldVector(Shape shape, V[] entries)	Creates a tensor with the specified data and shape.

Uses of Shape in org.flag4j.arrays.backend.primitive_arrays

Methods in org.flag4j.arrays.backend.primitive_arrays with parameters of type Shape

Modifier and Type	Method	Description
T	AbstractDenseDoubleTensor.reshape(Shape newShape)	Copies and reshapes this tensor.

Constructors in org.flag4j.arrays.backend.primitive_arrays with parameters of type Shape

Modifier	Constructor	Description
protected	AbstractDenseDoubleTensor(Shape shape, double[] entries)	Creates a tensor with the specified data and shape.
protected	AbstractDoubleTensor(Shape shape, double[] entries)	Creates a tensor with the specified data and shape.

Uses of Shape in org.flag4j.arrays.backend.ring_arrays

Methods in org.flag4j.arrays.backend.ring_arrays with parameters of type Shape

Modifier and Type	Method	Description
abstract AbstractCsrRingMatrix<?,U,V,W>	AbstractCooRingMatrix.makeLikeCsrMatrix(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
abstract AbstractCooRingTensor<?,?,W>	AbstractCooRingMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified shape.

Constructors in org.flag4j.arrays.backend.ring_arrays with parameters of type Shape

Modifier	Constructor	Description
protected	AbstractCooRingMatrix(Shape shape, W[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
protected	AbstractCooRingTensor(Shape shape, V[] data, int[][] indices)	Creates a tensor with the specified data and shape.
protected	AbstractCooRingVector(Shape shape, Y[] entries, int[] indices)	Creates a COO vector with the specified data and shape.
protected	AbstractCsrRingMatrix(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
protected	AbstractDenseRingMatrix(Shape shape, V[] data)	Creates a tensor with the specified data and shape.
protected	AbstractDenseRingTensor(Shape shape, V[] data)	Creates a tensor with the specified data and shape.
protected	AbstractDenseRingVector(Shape shape, V[] data)	Creates a tensor with the specified data and shape.

Uses of Shape in org.flag4j.arrays.backend.semiring_arrays

Methods in org.flag4j.arrays.backend.semiring_arrays that return Shape

Modifier and Type	Method	Description
Shape	TensorOverSemiring.getShape()	Gets the shape of this tensor.

Methods in org.flag4j.arrays.backend.semiring_arrays with parameters of type Shape

Modifier and Type	Method	Description
abstract AbstractCooSemiringMatrix<?,U,V,W>	AbstractCsrSemiringMatrix.makeLikeCooMatrix(Shape shape, W[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix of a similar type to this sparse CSR matrix.
protected abstract AbstractCooSemiringMatrix<?,T,?,V>	AbstractDenseSemiringMatrix.makeLikeCooMatrix(Shape shape, V[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix which is of a similar type as this dense matrix.
protected abstract AbstractTensor<?,V[],V>	AbstractDenseSemiringTensor.makeLikeCooTensor(Shape shape, V[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
abstract AbstractCsrSemiringMatrix<?,U,V,W>	AbstractCooSemiringMatrix.makeLikeCsrMatrix(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
abstract AbstractCsrSemiringMatrix<?,T,?,V>	AbstractDenseSemiringMatrix.makeLikeCsrMatrix(Shape shape, V[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix which is of a similar type as this dense matrix.
abstract W	AbstractCooSemiringVector.makeLikeDenseMatrix(Shape shape, Y... entries)	Constructs a dense matrix of a similar type as this vector with the specified shape and data.
abstract U	AbstractCooSemiringMatrix.makeLikeDenseTensor(Shape shape, W[] entries)	Constructs a dense tensor with the specified shape and data which is a similar type to this sparse tensor.
abstract U	AbstractCooSemiringTensor.makeLikeDenseTensor(Shape shape, V[] entries)	Constructs a dense tensor that is a similar type as this sparse COO tensor.
abstract U	AbstractCooSemiringVector.makeLikeDenseTensor(Shape shape, Y... entries)	Constructs a dense vector of a similar type as this vector with the specified shape and data.
abstract U	AbstractCsrSemiringMatrix.makeLikeDenseTensor(Shape shape, W[] entries)	Constructs a dense matrix which is of a similar type to this sparse CSR matrix.
abstract V	AbstractCooSemiringVector.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.
protected abstract U	AbstractDenseSemiringVector.makeLikeMatrix(Shape shape, V[] entries)	Constructs a matrix of similar type to this vector with the specified shape and data.
abstract T	AbstractCooSemiringMatrix.makeLikeTensor(Shape shape, List<W> entries, List<Integer> rowIndices, List<Integer> colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and non-zero indices.
abstract T	AbstractCooSemiringMatrix.makeLikeTensor(Shape shape, W[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO tensor of the same type as this tensor with the specified non-zero data and indices.
abstract T	AbstractCooSemiringTensor.makeLikeTensor(Shape shape, List<V> data, List<int[]> indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
abstract T	AbstractCooSemiringTensor.makeLikeTensor(Shape shape, V[] data, int[][] indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
abstract T	AbstractCooSemiringVector.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	AbstractCooSemiringVector.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.
abstract T	AbstractCsrSemiringMatrix.makeLikeTensor(Shape shape, List<W> entries, List<Integer> rowPointers, List<Integer> colIndices)	Constructs a CSR matrix with the specified shape, non-zero data, and non-zero indices.
abstract T	AbstractCsrSemiringMatrix.makeLikeTensor(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR tensor of the same type as this tensor with the specified non-zero data and indices.
T	TensorOverSemiring.makeLikeTensor(Shape shape, V entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
abstract V	AbstractCooSemiringMatrix.makeLikeVector(Shape shape, W[] entries, int[] indices)	Constructs a sparse COO vector of a similar type to this COO matrix.
protected abstract U	AbstractDenseSemiringMatrix.makeLikeVector(Shape shape, V[] entries)	Constructs a vector of a similar type as this matrix.
T	AbstractCooSemiringMatrix.reshape(Shape newShape)	Copies and reshapes this tensor.
T	AbstractCooSemiringTensor.reshape(Shape newShape)	Copies and reshapes this tensor.
T	AbstractCooSemiringVector.reshape(Shape newShape)	Copies and reshapes this tensor.
T	AbstractCsrSemiringMatrix.reshape(Shape newShape)	Copies and reshapes this tensor.
T	AbstractDenseSemiringTensor.reshape(Shape newShape)	Copies and reshapes this tensor.
abstract AbstractCooSemiringTensor<?,?,W>	AbstractCooSemiringMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified shape.
abstract AbstractTensor<?,Y[],Y>	AbstractCooSemiringVector.toTensor(Shape newShape)	Converts this vector to an equivalent tensor with the specified shape.
AbstractCooSemiringTensor<?,?,W>	AbstractCsrSemiringMatrix.toTensor(Shape shape)	Converts this CSR matrix to an equivalent COO tensor with the specified shape.
abstract AbstractDenseSemiringTensor<?,V>	AbstractDenseSemiringMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified newShape.

Constructors in org.flag4j.arrays.backend.semiring_arrays with parameters of type Shape

Modifier	Constructor	Description
protected	AbstractCooSemiringMatrix(Shape shape, W[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
protected	AbstractCooSemiringTensor(Shape shape, V[] data, int[][] indices)	Creates a tensor with the specified data and shape.
protected	AbstractCooSemiringVector(Shape shape, Y[] entries, int[] indices)	Creates a sparse COO semiring vector with the specified data and shape.
protected	AbstractCsrSemiringMatrix(Shape shape, W[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
protected	AbstractDenseSemiringMatrix(Shape shape, V[] entries)	Creates a tensor with the specified data and shape.
protected	AbstractDenseSemiringTensor(Shape shape, V[] data)	Creates a tensor with the specified data and shape.
protected	AbstractDenseSemiringVector(Shape shape, V[] data)	Constructs a dense semiring vector with the specified data and shape.

Uses of Shape in org.flag4j.arrays.dense

Methods in org.flag4j.arrays.dense with parameters of type Shape

Modifier and Type	Method	Description
static CMatrix	CMatrix.I(Shape shape)	Constructs an identity-like matrix of the specified shape.
static <T extends Field<T>> FieldMatrix<T>	FieldMatrix.I(Shape shape, T fieldValue)	Constructs an identity-like matrix of the specified shape.
static Matrix	Matrix.I(Shape shape)	Constructs an identity-like matrix of the specified shape.
static <T extends Ring<T>> RingMatrix<T>	RingMatrix.I(Shape shape, T fieldValue)	Constructs an identity-like matrix of the specified shape.
static <T extends Semiring<T>> SemiringMatrix<T>	SemiringMatrix.I(Shape shape, T fieldValue)	Constructs an identity-like matrix of the specified shape.
protected CooCMatrix	CMatrix.makeLikeCooMatrix(Shape shape, Complex128[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix which is of a similar type as this dense matrix.
CooFieldMatrix<T>	FieldMatrix.makeLikeCooMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix of similar type to this dense matrix.
protected CooRingMatrix<T>	RingMatrix.makeLikeCooMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix which is of a similar type as this dense matrix.
protected CooSemiringMatrix<T>	SemiringMatrix.makeLikeCooMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix which is of a similar type as this dense matrix.
protected CooCMatrix	CMatrix.makeLikeCooTensor(Shape shape, Complex128[] entries, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooCTensor	CTensor.makeLikeCooTensor(Shape shape, Complex128[] entries, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooCVector	CVector.makeLikeCooTensor(Shape shape, Complex128[] entries, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooFieldMatrix<T>	FieldMatrix.makeLikeCooTensor(Shape shape, T[] entries, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooFieldTensor<T>	FieldTensor.makeLikeCooTensor(Shape shape, T[] entries, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooFieldVector<T>	FieldVector.makeLikeCooTensor(Shape shape, T[] entries, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooRingTensor<T>	RingMatrix.makeLikeCooTensor(Shape shape, T[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooRingTensor<T>	RingTensor.makeLikeCooTensor(Shape shape, T[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooRingVector<T>	RingVector.makeLikeCooTensor(Shape shape, T[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooSemiringTensor<T>	SemiringMatrix.makeLikeCooTensor(Shape shape, T[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooSemiringTensor<T>	SemiringTensor.makeLikeCooTensor(Shape shape, T[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
protected CooSemiringVector<T>	SemiringVector.makeLikeCooTensor(Shape shape, T[] data, int[][] indices)	Constructs a sparse COO tensor which is of a similar type as this dense tensor.
CsrCMatrix	CMatrix.makeLikeCsrMatrix(Shape shape, Complex128[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix which is of a similar type as this dense matrix.
CsrFieldMatrix<T>	FieldMatrix.makeLikeCsrMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of similar type to this dense matrix.
CsrRingMatrix<T>	RingMatrix.makeLikeCsrMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix which is of a similar type as this dense matrix.
CsrSemiringMatrix<T>	SemiringMatrix.makeLikeCsrMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix which is of a similar type as this dense matrix.
CMatrix	CVector.makeLikeMatrix(Shape shape, Complex128[] entries)	Constructs a matrix of similar type to this vector with the specified shape and data.
FieldMatrix<T>	FieldVector.makeLikeMatrix(Shape shape, T[] entries)	Constructs a matrix of similar type to this vector with the specified shape and data.
protected RingMatrix<T>	RingVector.makeLikeMatrix(Shape shape, T[] entries)	Constructs a matrix of similar type to this vector with the specified shape and data.
protected SemiringMatrix<T>	SemiringVector.makeLikeMatrix(Shape shape, T[] entries)	Constructs a matrix of similar type to this vector with the specified shape and data.
CMatrix	CMatrix.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CTensor	CTensor.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CVector	CVector.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
FieldMatrix<T>	FieldMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
FieldTensor<T>	FieldTensor.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
FieldVector<T>	FieldVector.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
Matrix	Matrix.makeLikeTensor(Shape shape, double[] data)	Constructs a tensor of the same type as this tensor with the given the shape and data.
RingMatrix<T>	RingMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
RingTensor<T>	RingTensor.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
RingVector<T>	RingVector.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
SemiringMatrix<T>	SemiringMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
SemiringTensor<T>	SemiringTensor.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
SemiringVector<T>	SemiringVector.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
Tensor	Tensor.makeLikeTensor(Shape shape, double[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
Vector	Vector.makeLikeTensor(Shape shape, double[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
protected CVector	CMatrix.makeLikeVector(Shape shape, Complex128[] entries)	Constructs a vector of a similar type as this matrix.
protected FieldVector<T>	FieldMatrix.makeLikeVector(Shape shape, T[] entries)	Constructs a vector of a similar type as this matrix.
protected RingVector<T>	RingMatrix.makeLikeVector(Shape shape, T[] entries)	Constructs a vector of a similar type as this matrix.
protected SemiringVector<T>	SemiringMatrix.makeLikeVector(Shape shape, T[] entries)	Constructs a vector of a similar type as this matrix.
FieldMatrix<T>	FieldTensor.toMatrix(Shape matShape)	Converts this tensor to a matrix with the specified shape.
RingMatrix<T>	RingTensor.toMatrix(Shape matShape)	Converts this tensor to a matrix with the specified shape.
SemiringMatrix<T>	SemiringTensor.toMatrix(Shape matShape)	Converts this tensor to a matrix with the specified shape.
Matrix	Tensor.toMatrix(Shape shape)	Converts this tensor to an equivalent matrix with the specified shape.
CTensor	CMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified newShape.
FieldTensor<T>	FieldMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified newShape.
RingTensor<T>	RingMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified newShape.
SemiringTensor<T>	SemiringMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified newShape.

Constructors in org.flag4j.arrays.dense with parameters of type Shape

Modifier	Constructor	Description
	CMatrix(Shape shape)	Creates a zero matrix with the specified shape.
	CMatrix(Shape shape, double[] entries)	Constructs a complex matrix with specified shape and data.
	CMatrix(Shape shape, Double fillValue)	Creates matrix with the specified shape filled with fillValue.
	CMatrix(Shape shape, Complex128 fillValue)	Creates a complex matrix with the specified shape filled with fillValue.
	CMatrix(Shape shape, Complex128[] entries)	Creates a complex matrix with the specified data and shape.
	CTensor(Shape shape)	Creates a zero tensor with the specified shape.
	CTensor(Shape shape, double fillValue)	Creates a tensor with the specified shape and filled with fillValue.
	CTensor(Shape shape, double[] entries)	Creates a tensor with the specified data and shape.
	CTensor(Shape shape, String fillValue)	Creates a tensor with the specified shape and filled with fillValue.
	CTensor(Shape shape, String[] entries)	Creates a tensor with the specified data and shape.
	CTensor(Shape shape, Complex128 fillValue)	Creates a tensor with the specified shape and filled with fillValue.
	CTensor(Shape shape, Complex128[] entries)	Creates a tensor with the specified data and shape.
	CTensor(Shape shape, Complex64 fillValue)	Creates a tensor with the specified shape and filled with fillValue.
	CTensor(Shape shape, Complex64[] entries)	Creates a tensor with the specified data and shape.
	CVector(Shape shape)	Constructs a zero vector with the specified shape.
	CVector(Shape shape, Complex128[] data)	Constructs a dense complex vector with the given shape and entries.
	FieldMatrix(Shape shape, T fillValue)	Creates a dense field matrix with the specified data and filled with filledValue.
	FieldMatrix(Shape shape, T[] entries)	Creates a dense field matrix with the specified data and shape.
	FieldTensor(Shape shape, T fillValue)	Creates a tensor with the specified shape filled with fillValue.
	FieldTensor(Shape shape, T[] entries)	Creates a tensor with the specified data and shape.
	FieldVector(Shape shape, T[] entries)	Constructs a dense complex vector with the given shape and entries.
	Matrix(Shape shape)	Creates a real dense matrix with specified shape filled with zeros.
	Matrix(Shape shape, double value)	Creates a real dense matrix with specified shape filled with a specific value.
	Matrix(Shape shape, double... entries)	Creates a tensor with the specified data and shape.
	RingMatrix(Shape shape, T fillValue)	Creates a dense ring matrix with the specified data and filled with filledValue.
	RingMatrix(Shape shape, T[] entries)	Creates a tensor with the specified data and shape.
	RingTensor(Shape shape, T fillValue)	Creates a dense ring tensor with the specified data and filled with filledValue.
	RingTensor(Shape shape, T[] data)	Creates a tensor with the specified data and shape.
	RingVector(Shape shape, T[] data)	Creates a ring vector with the specified data and shape.
	SemiringMatrix(Shape shape, T fillValue)	Creates a dense semiring matrix with the specified data and filled with filledValue.
	SemiringMatrix(Shape shape, T[] entries)	Creates a tensor with the specified data and shape.
	SemiringTensor(Shape shape, T fillValue)	Creates a dense semiring tensor with the specified data and filled with filledValue.
	SemiringTensor(Shape shape, T[] data)	Creates a tensor with the specified data and shape.
	SemiringVector(Shape shape, T[] data)	Creates a semiring vector with the specified data and shape.
	Tensor(Shape shape)	Creates a zero tensor with the shape.
	Tensor(Shape shape, double fillValue)	Creates a tensor with the specified shape filled with fillValue.
	Tensor(Shape shape, double... entries)	Creates a tensor with the specified data and shape.
	Tensor(Shape shape, int... entries)	Creates a tensor with the specified data and shape.
	Tensor(Shape shape, Double[] entries)	Creates a tensor with the specified data and shape.
	Tensor(Shape shape, Integer[] entries)	Creates a tensor with the specified data and shape.
	Vector(Shape shape)	Creates a vector of the specified shape filled with zeros.
	Vector(Shape shape, double fillValue)	Creates a vector of specified size filled with a specified value.
	Vector(Shape shape, double[] entries)	Creates a tensor with the specified data and shape.

Uses of Shape in org.flag4j.arrays.sparse

Fields in org.flag4j.arrays.sparse declared as Shape

Modifier and Type	Field	Description
final Shape	PermutationMatrix.shape	Shape of this permutation matrix.

Methods in org.flag4j.arrays.sparse that return Shape

Modifier and Type	Method	Description
Shape	SymmTriDiag.getShape()	Gets the shape of this symmetric tri-diagonal matrix.

Methods in org.flag4j.arrays.sparse with parameters of type Shape

Modifier and Type	Method	Description
Tensor	CooTensor.makeDenseTensor(Shape shape, double[] entries)	Makes a dense tensor with the specified shape and data which is a similar type to this sparse tensor.
CooCMatrix	CsrCMatrix.makeLikeCooMatrix(Shape shape, Complex128[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix of a similar type to this sparse CSR matrix.
CooFieldMatrix<T>	CsrFieldMatrix.makeLikeCooMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix of a similar type to this sparse CSR matrix.
CooRingMatrix<T>	CsrRingMatrix.makeLikeCooMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix of a similar type to this sparse CSR matrix.
CooSemiringMatrix<T>	CsrSemiringMatrix.makeLikeCooMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO matrix of a similar type to this sparse CSR matrix.
CsrCMatrix	CooCMatrix.makeLikeCsrMatrix(Shape shape, Complex128[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
CsrFieldMatrix<T>	CooFieldMatrix.makeLikeCsrMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
CsrRingMatrix<T>	CooRingMatrix.makeLikeCsrMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
AbstractCsrSemiringMatrix<?,SemiringMatrix<T>,CooSemiringVector<T>,T>	CooSemiringMatrix.makeLikeCsrMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR matrix of a similar type to this sparse COO matrix.
CMatrix	CooCVector.makeLikeDenseMatrix(Shape shape, Complex128... entries)	Constructs a dense matrix of a similar type as this vector with the specified shape and data.
FieldMatrix<T>	CooFieldVector.makeLikeDenseMatrix(Shape shape, T... entries)	Constructs a dense matrix of a similar type as this vector with the specified shape and data.
RingMatrix<T>	CooRingVector.makeLikeDenseMatrix(Shape shape, T... entries)	Constructs a dense matrix of a similar type as this vector with the specified shape and data.
SemiringMatrix<T>	CooSemiringVector.makeLikeDenseMatrix(Shape shape, T... entries)	Constructs a dense matrix of a similar type as this vector with the specified shape and data.
CMatrix	CooCMatrix.makeLikeDenseTensor(Shape shape, Complex128[] entries)	Constructs a dense tensor with the specified shape and data which is a similar type to this sparse tensor.
CTensor	CooCTensor.makeLikeDenseTensor(Shape shape, Complex128[] entries)	Constructs a dense tensor that is a similar type as this sparse COO tensor.
CVector	CooCVector.makeLikeDenseTensor(Shape shape, Complex128... entries)	Constructs a dense vector of a similar type as this vector with the specified shape and data.
FieldMatrix<T>	CooFieldMatrix.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense tensor with the specified shape and data which is a similar type to this sparse tensor.
FieldTensor<T>	CooFieldTensor.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense tensor that is a similar type as this sparse COO tensor.
FieldVector<T>	CooFieldVector.makeLikeDenseTensor(Shape shape, T... entries)	Constructs a dense vector of a similar type as this vector with the specified shape and data.
RingMatrix<T>	CooRingMatrix.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense tensor with the specified shape and data which is a similar type to this sparse tensor.
RingTensor<T>	CooRingTensor.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense tensor that is a similar type as this sparse COO tensor.
RingVector<T>	CooRingVector.makeLikeDenseTensor(Shape shape, T... entries)	Constructs a dense vector of a similar type as this vector with the specified shape and data.
SemiringMatrix<T>	CooSemiringMatrix.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense tensor with the specified shape and data which is a similar type to this sparse tensor.
SemiringTensor<T>	CooSemiringTensor.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense tensor that is a similar type as this sparse COO tensor.
SemiringVector<T>	CooSemiringVector.makeLikeDenseTensor(Shape shape, T... entries)	Constructs a dense vector of a similar type as this vector with the specified shape and data.
CMatrix	CsrCMatrix.makeLikeDenseTensor(Shape shape, Complex128[] entries)	Constructs a dense matrix which is of a similar type to this sparse CSR matrix.
FieldMatrix<T>	CsrFieldMatrix.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense matrix which is of a similar type to this sparse CSR matrix.
RingMatrix<T>	CsrRingMatrix.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense matrix which is of a similar type to this sparse CSR matrix.
SemiringMatrix<T>	CsrSemiringMatrix.makeLikeDenseTensor(Shape shape, T[] entries)	Constructs a dense matrix which is of a similar type to this sparse CSR matrix.
CooCMatrix	CooCVector.makeLikeMatrix(Shape shape, Complex128[] entries, int[] rowIndices, int[] colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and row and column indices.
CooFieldMatrix<T>	CooFieldVector.makeLikeMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and row and column indices.
CooRingMatrix<T>	CooRingVector.makeLikeMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and row and column indices.
CooSemiringMatrix<T>	CooSemiringVector.makeLikeMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and row and column indices.
CooCMatrix	CooCMatrix.makeLikeTensor(Shape shape, List<Complex128> entries, List<Integer> rowIndices, List<Integer> colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and non-zero indices.
CooCMatrix	CooCMatrix.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooCMatrix	CooCMatrix.makeLikeTensor(Shape shape, Complex128[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO tensor of the same type as this tensor with the specified non-zero data and indices.
CooCTensor	CooCTensor.makeLikeTensor(Shape shape, List<Complex128> entries, List<int[]> indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooCTensor	CooCTensor.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooCTensor	CooCTensor.makeLikeTensor(Shape shape, Complex128[] entries, int[][] indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooCVector	CooCVector.makeLikeTensor(Shape shape, List<Complex128> entries, List<Integer> indices)	Constructs a COO vector with the specified shape, non-zero data, and non-zero indices.
CooCVector	CooCVector.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooCVector	CooCVector.makeLikeTensor(Shape shape, Complex128[] entries, int[] indices)	Constructs a sparse COO vector of the same type as this vector with the specified non-zero data and indices.
CooFieldMatrix<T>	CooFieldMatrix.makeLikeTensor(Shape shape, List<T> entries, List<Integer> rowIndices, List<Integer> colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and non-zero indices.
CooFieldMatrix<T>	CooFieldMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooFieldMatrix<T>	CooFieldMatrix.makeLikeTensor(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO tensor of the same type as this tensor with the specified non-zero data and indices.
CooFieldTensor<T>	CooFieldTensor.makeLikeTensor(Shape shape, List<T> entries, List<int[]> indices)	Constructs a sparse tensor of the same type as this tensor with the given the shape, non-zero data, and non-zero indices.
CooFieldTensor<T>	CooFieldTensor.makeLikeTensor(Shape shape, T[] entries)	Constructs a sparse tensor of the same type as this tensor with the same indices as this sparse tensor and with the provided the shape and data.
CooFieldTensor<T>	CooFieldTensor.makeLikeTensor(Shape shape, T[] entries, int[][] indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooFieldVector<T>	CooFieldVector.makeLikeTensor(Shape shape, List<T> entries, List<Integer> indices)	Constructs a COO vector with the specified shape, non-zero data, and non-zero indices.
CooFieldVector<T>	CooFieldVector.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooFieldVector<T>	CooFieldVector.makeLikeTensor(Shape shape, T[] entries, int[] indices)	Constructs a sparse COO vector of the same type as this vector with the specified non-zero data and indices.
CooMatrix	CooMatrix.makeLikeTensor(Shape shape, double[] entries)	Constructs a sparse COO matrix of the same type as this tensor with the given the shape and data and indices copied from this matrix.
CooRingMatrix<T>	CooRingMatrix.makeLikeTensor(Shape shape, List<T> entries, List<Integer> rowIndices, List<Integer> colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and non-zero indices.
CooRingMatrix<T>	CooRingMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooRingMatrix<T>	CooRingMatrix.makeLikeTensor(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO tensor of the same type as this tensor with the specified non-zero data and indices.
CooRingTensor<T>	CooRingTensor.makeLikeTensor(Shape shape, List<T> data, List<int[]> indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooRingTensor<T>	CooRingTensor.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooRingTensor<T>	CooRingTensor.makeLikeTensor(Shape shape, T[] data, int[][] indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooRingVector<T>	CooRingVector.makeLikeTensor(Shape shape, List<T> entries, List<Integer> indices)	Constructs a COO vector with the specified shape, non-zero data, and non-zero indices.
CooRingVector<T>	CooRingVector.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooRingVector<T>	CooRingVector.makeLikeTensor(Shape shape, T[] entries, int[] indices)	Constructs a sparse COO vector of the same type as this vector with the specified non-zero data and indices.
CooSemiringMatrix<T>	CooSemiringMatrix.makeLikeTensor(Shape shape, List<T> entries, List<Integer> rowIndices, List<Integer> colIndices)	Constructs a COO matrix with the specified shape, non-zero data, and non-zero indices.
CooSemiringMatrix<T>	CooSemiringMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooSemiringMatrix<T>	CooSemiringMatrix.makeLikeTensor(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Constructs a sparse COO tensor of the same type as this tensor with the specified non-zero data and indices.
CooSemiringTensor<T>	CooSemiringTensor.makeLikeTensor(Shape shape, List<T> data, List<int[]> indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooSemiringTensor<T>	CooSemiringTensor.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooSemiringTensor<T>	CooSemiringTensor.makeLikeTensor(Shape shape, T[] data, int[][] indices)	Constructs a tensor of the same type as this tensor with the specified shape and non-zero data.
CooSemiringVector<T>	CooSemiringVector.makeLikeTensor(Shape shape, List<T> entries, List<Integer> indices)	Constructs a COO vector with the specified shape, non-zero data, and non-zero indices.
CooSemiringVector<T>	CooSemiringVector.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CooSemiringVector<T>	CooSemiringVector.makeLikeTensor(Shape shape, T[] entries, int[] indices)	Constructs a sparse COO vector of the same type as this vector with the specified non-zero data and indices.
CooTensor	CooTensor.makeLikeTensor(Shape shape, double[] entries)	Constructs a sparse tensor of the same type as this tensor with the same indices as this sparse tensor and with the provided the shape and data.
CooTensor	CooTensor.makeLikeTensor(Shape shape, double[] entries, int[][] indices)	Constructs a sparse tensor of the same type as this tensor with the given the shape, non-zero data, and non-zero indices.
CooTensor	CooTensor.makeLikeTensor(Shape shape, List<Double> entries, List<int[]> indices)	Constructs a sparse tensor of the same type as this tensor with the given the shape, non-zero data, and non-zero indices.
CooVector	CooVector.makeLikeTensor(Shape shape, double[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CsrCMatrix	CsrCMatrix.makeLikeTensor(Shape shape, List<Complex128> entries, List<Integer> rowPointers, List<Integer> colIndices)	Constructs a CSR matrix with the specified shape, non-zero data, and non-zero indices.
CsrCMatrix	CsrCMatrix.makeLikeTensor(Shape shape, Complex128[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CsrCMatrix	CsrCMatrix.makeLikeTensor(Shape shape, Complex128[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR tensor of the same type as this tensor with the specified non-zero data and indices.
CsrFieldMatrix<T>	CsrFieldMatrix.makeLikeTensor(Shape shape, List<T> entries, List<Integer> rowPointers, List<Integer> colIndices)	Constructs a CSR matrix with the specified shape, non-zero data, and non-zero indices.
CsrFieldMatrix<T>	CsrFieldMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CsrFieldMatrix<T>	CsrFieldMatrix.makeLikeTensor(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR tensor of the same type as this tensor with the specified non-zero data and indices.
CsrMatrix	CsrMatrix.makeLikeTensor(Shape shape, double[] entries)	Constructs a CSR matrix of the same type as this matrix with the given the shape and data and the same row pointers and column indices as this matrix.
CsrRingMatrix<T>	CsrRingMatrix.makeLikeTensor(Shape shape, List<T> entries, List<Integer> rowPointers, List<Integer> colIndices)	Constructs a CSR matrix with the specified shape, non-zero data, and non-zero indices.
CsrRingMatrix<T>	CsrRingMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CsrRingMatrix<T>	CsrRingMatrix.makeLikeTensor(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR tensor of the same type as this tensor with the specified non-zero data and indices.
CsrSemiringMatrix<T>	CsrSemiringMatrix.makeLikeTensor(Shape shape, List<T> entries, List<Integer> rowPointers, List<Integer> colIndices)	Constructs a CSR matrix with the specified shape, non-zero data, and non-zero indices.
CsrSemiringMatrix<T>	CsrSemiringMatrix.makeLikeTensor(Shape shape, T[] entries)	Constructs a tensor of the same type as this tensor with the given the shape and data.
CsrSemiringMatrix<T>	CsrSemiringMatrix.makeLikeTensor(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Constructs a sparse CSR tensor of the same type as this tensor with the specified non-zero data and indices.
CooCVector	CooCMatrix.makeLikeVector(Shape shape, Complex128[] entries, int[] indices)	Constructs a sparse COO vector of a similar type to this COO matrix.
CooFieldVector<T>	CooFieldMatrix.makeLikeVector(Shape shape, T[] entries, int[] indices)	Constructs a sparse COO vector of a similar type to this COO matrix.
CooRingVector<T>	CooRingMatrix.makeLikeVector(Shape shape, T[] entries, int[] indices)	Constructs a sparse COO vector of a similar type to this COO matrix.
CooSemiringVector<T>	CooSemiringMatrix.makeLikeVector(Shape shape, T[] entries, int[] indices)	Constructs a sparse COO vector of a similar type to this COO matrix.
CooMatrix	CooMatrix.reshape(Shape newShape)	Copies and reshapes this tensor.
CooTensor	CooTensor.reshape(Shape newShape)	Copies and reshapes this tensor.
CooVector	CooVector.reshape(Shape newShape)	Copies and reshapes this tensor.
CsrMatrix	CsrMatrix.reshape(Shape newShape)	Copies and reshapes this tensor.
CooCMatrix	CooCTensor.toMatrix(Shape newShape)	Converts this tensor to a matrix with specified shape.
CooFieldMatrix<T>	CooFieldTensor.toMatrix(Shape matShape)	Converts this tensor to a matrix with the specified shape.
CooRingMatrix<T>	CooRingTensor.toMatrix(Shape matShape)	Converts this tensor to a matrix with the specified shape.
CooSemiringMatrix<T>	CooSemiringTensor.toMatrix(Shape matShape)	Converts this tensor to a matrix with the specified shape.
CooCTensor	CooCMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified shape.
CooCTensor	CooCVector.toTensor(Shape newShape)	Converts this vector to an equivalent tensor with the specified shape.
CooFieldTensor<T>	CooFieldMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified shape.
CooFieldTensor<T>	CooFieldVector.toTensor(Shape newShape)	Converts this vector to an equivalent tensor with the specified shape.
CooRingTensor<T>	CooRingMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified shape.
CooRingTensor<T>	CooRingVector.toTensor(Shape newShape)	Converts this vector to an equivalent tensor with the specified shape.
CooSemiringTensor<T>	CooSemiringMatrix.toTensor(Shape newShape)	Converts this matrix to an equivalent tensor with the specified shape.
CooSemiringTensor<T>	CooSemiringVector.toTensor(Shape newShape)	Converts this vector to an equivalent tensor with the specified shape.
CooCTensor	CsrCMatrix.toTensor(Shape shape)	Converts this CSR matrix to an equivalent COO tensor with the specified shape.
CooFieldTensor<T>	CsrFieldMatrix.toTensor(Shape shape)	Converts this CSR matrix to an equivalent COO tensor with the specified shape.
CooRingTensor<T>	CsrRingMatrix.toTensor(Shape shape)	Converts this CSR matrix to an equivalent COO tensor with the specified shape.
CooSemiringTensor<T>	CsrSemiringMatrix.toTensor(Shape shape)	Converts this CSR matrix to an equivalent COO tensor with the specified shape.

Constructors in org.flag4j.arrays.sparse with parameters of type Shape

Modifier	Constructor	Description
	CooCMatrix(Shape shape)	Constructs a zero matrix of the specified shape.
	CooCMatrix(Shape shape, double[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooCMatrix(Shape shape, List<Complex128> entries, List<Integer> rowIndices, List<Integer> colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooCMatrix(Shape shape, Complex128[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooCTensor(Shape shape)	Creates a tensor with the specified data and shape.
	CooCTensor(Shape shape, double[] entries, int[][] indices)	Creates a tensor with the specified data and shape.
	CooCTensor(Shape shape, List<Complex128> entries, List<int[]> indices)	Creates a tensor with the specified data and shape.
	CooCTensor(Shape shape, Complex128[] entries, int[][] indices)	Creates a tensor with the specified data and shape.
	CooCTensor(Shape shape, Complex64[] entries, int[][] indices)	Creates a tensor with the specified data and shape.
	CooCVector(Shape shape, List<Complex128> entries, List<Integer> indices)	Constructs a complex COO vector with the specified size, non-zero data, and non-zero indices.
	CooCVector(Shape shape, Complex128[] entries, int[] indices)	Creates a tensor with the specified data and shape.
	CooFieldMatrix(Shape shape, List<T> entries, List<Integer> rowIndices, List<Integer> colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooFieldMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooFieldTensor(Shape shape, List<T> entries, List<int[]> indices)	Creates a tensor with the specified data and shape.
	CooFieldTensor(Shape shape, T[] entries, int[][] indices)	creates a tensor with the specified data and shape.
	CooFieldVector(Shape shape, T[] entries, int[] indices)	Creates sparse COO vector with the specified size, non-zero data, and non-zero indices.
	CooMatrix(Shape shape)	Constructs a zero matrix with the specified shape.
	CooMatrix(Shape shape, double[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooMatrix(Shape shape, int[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooMatrix(Shape shape, List<Double> entries, List<Integer> rowIndices, List<Integer> colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooRingMatrix(Shape shape, List<T> entries, List<Integer> rowIndices, List<Integer> colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooRingMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooRingTensor(Shape shape, List<T> data, List<int[]> indices)	Creates a tensor with the specified data and shape.
	CooRingTensor(Shape shape, T[] data, int[][] indices)	Creates a tensor with the specified data and shape.
	CooRingVector(Shape shape, List<T> entries, List<Integer> indices)	Creates sparse COO vector with the specified size, non-zero data, and non-zero indices.
	CooRingVector(Shape shape, T[] entries, int[] indices)	Creates a tensor with the specified data and shape.
	CooSemiringMatrix(Shape shape, List<T> entries, List<Integer> rowIndices, List<Integer> colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooSemiringMatrix(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Creates a sparse coo matrix with the specified non-zero data, non-zero indices, and shape.
	CooSemiringTensor(Shape shape, List<T> data, List<int[]> indices)	Creates a tensor with the specified data and shape.
	CooSemiringTensor(Shape shape, T[] data, int[][] indices)	Creates a tensor with the specified data and shape.
	CooSemiringVector(Shape shape, List<T> entries, List<Integer> indices)	Creates sparse COO vector with the specified size, non-zero data, and non-zero indices.
	CooSemiringVector(Shape shape, T[] entries, int[] indices)	Creates a tensor with the specified data and shape.
	CooTensor(Shape shape)	Creates a zero matrix with the specified shape.
	CooTensor(Shape shape, double[] entries, int[][] indices)	Creates a tensor with the specified data and shape.
	CooTensor(Shape shape, int[] entries, int[][] indices)	Creates a sparse COO matrix with the specified shape, non-zero data, and indices.
	CooTensor(Shape shape, List<Double> entries, List<int[]> indices)	Creates a tensor with the specified data and shape.
	CooVector(Shape shape, double[] entries, int[] indices)	Creates sparse COO vector with the specified size, non-zero data, and non-zero indices.
	CooVector(Shape shape, List<Double> data, List<Integer> indices)	Creates sparse COO vector with the specified size, non-zero data, and non-zero indices.
	CsrCMatrix(Shape shape)	Constructs a zero matrix of the specified shape.
	CsrCMatrix(Shape shape, List<Complex128> entries, List<Integer> rowPointers, List<Integer> colIndices)	Creates a complex sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrCMatrix(Shape shape, Complex128[] entries, int[] rowPointers, int[] colIndices)	Creates a complex sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrFieldMatrix(Shape shape, List<T> entries, List<Integer> rowPointers, List<Integer> colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrFieldMatrix(Shape shape, T fieldElement)	Constructs a sparse CSR matrix representing the zero matrix for the field which fieldElement belongs to.
	CsrFieldMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrMatrix(Shape shape)	Constructs zero matrix with the specified shape.
	CsrMatrix(Shape shape, double[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrRingMatrix(Shape shape, List<T> entries, List<Integer> rowPointers, List<Integer> colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrRingMatrix(Shape shape, T ringElement)	Constructs a sparse CSR matrix representing the zero matrix for the field which ringElement belongs to.
	CsrRingMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrSemiringMatrix(Shape shape, List<T> entries, List<Integer> rowPointers, List<Integer> colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	CsrSemiringMatrix(Shape shape, T semiringElement)	Constructs a sparse CSR matrix representing the zero matrix for the field which semiringElement belongs to.
	CsrSemiringMatrix(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Creates a sparse CSR matrix with the specified shape, non-zero data, row pointers, and non-zero column indices.
	PermutationMatrix(Shape shape)	Creates a permutation matrix which is equivalent to the identity matrix of the specified size.

Uses of Shape in org.flag4j.concurrency

Methods in org.flag4j.concurrency with parameters of type Shape

Modifier and Type	Method	Description
<T extends Semiring<T>> void	DenseSemiringTensorBinaryOperation.apply(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Applies the specified binary operation on the two dense tensors.

Uses of Shape in org.flag4j.io

Methods in org.flag4j.io with parameters of type Shape

Modifier and Type	Method	Description
static <T> String	PrettyPrint.matrixToString(Shape shape, double[] data)	Converts a matrix into a "pretty" string using parameters set in the PrintOptions class.
static <T> String	PrettyPrint.matrixToString(Shape shape, T[] data)	Converts a matrix into a "pretty" string using parameters set in the PrintOptions class.

Uses of Shape in org.flag4j.linalg.decompositions.svd

Methods in org.flag4j.linalg.decompositions.svd with parameters of type Shape

Modifier and Type	Method	Description
protected void	ComplexSVD.initUV(Shape src, int cols)	Initializes the unitary U and V matrices for the SVD.
protected void	RealSVD.initUV(Shape src, int cols)	Initializes the unitary U and V matrices for the SVD.
protected abstract void	SVD.initUV(Shape src, int cols)	Initializes the unitary U and V matrices for the SVD.

Uses of Shape in org.flag4j.linalg.ops

Fields in org.flag4j.linalg.ops declared as Shape

Modifier and Type	Field	Description
protected Shape	TensorDot.destShape
protected Shape	TensorDot.newShape1
protected Shape	TensorDot.newShape2
protected Shape	TensorDot.shape1
protected Shape	TensorDot.shape2

Methods in org.flag4j.linalg.ops that return Shape

Modifier and Type	Method	Description
Shape	TensorDot.getOutputShape()	Gets the shape of the tensor resulting from this tensor dot product as specified in the constructor.

Methods in org.flag4j.linalg.ops with parameters of type Shape

Modifier and Type	Method	Description
double[]	RealDenseTensorBinaryOperation.apply(double[] src1, Shape shape1, double[] src2, Shape shape2)	Applies the specified binary operation on the two tensors.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmComplex(Shape shape1, Shape shape2)	Dynamically chooses matrix multiply algorithm based on the shapes of the two matrices to multiply.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmComplexTranspose(Shape shape)	Dynamically chooses the matrix multiplication-transpose algorithm to used based on the shape of the first matrix.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmComplexVector(Shape shape)	Dynamically chooses matrix-vector multiply algorithm based on the shapes of the matrix to multiply.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmRealComplex(Shape shape1, Shape shape2)	Dynamically chooses matrix multiply algorithm based on the shapes of the two matrices to multiply.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmRealComplexTranspose(Shape shape)	Dynamically chooses the matrix multiplication-transpose algorithm to used based on the shape of the first matrix.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmRealComplexVector(Shape shape)	Dynamically chooses matrix-vector multiply algorithm based on the shapes of the matrix to multiply.
static org.flag4j.linalg.ops.MatrixMultiplyDispatcher.AlgorithmName	MatrixMultiplyDispatcher.chooseAlgorithmRealVector(Shape shape)	Dynamically chooses matrix-vector multiply algorithm based on the shapes of the matrix to multiply.
static <T extends Field<T>> T[]	MatrixMultiplyDispatcher.dispatch(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Dispatches a matrix multiplication problem to the appropriate algorithm based on the size.
static double[]	RealDenseMatrixMultiplyDispatcher.dispatch(double[] src1, Shape shape1, double[] src2, Shape shape2)	Dispatches a matrix multiply problem to the appropriate algorithm based on the size of the matrices.
static double[]	TransposeDispatcher.dispatch(double[] src, Shape shape)	Dispatches a matrix transpose problem to the appropriate algorithm based on its shape and size.
static Object[]	TransposeDispatcher.dispatch(Object[] src, Shape shape, Object[] dest)	Dispatches a matrix transpose problem to the appropriate algorithm based on its shape and size.
static <T extends Ring<T>> void	TransposeDispatcher.dispatchHermitian(T[] src, Shape shape, T[] dest)	Dispatches a matrix Hermitian transpose (i.e. conjugate transpose) problem to the appropriate algorithm based on its shape and size.
static double[]	TransposeDispatcher.dispatchTensor(double[] src, Shape shape, int[] axes)	Dispatches a tensor transpose problem to the appropriate algorithm based on its shape and size.
static Object[]	TransposeDispatcher.dispatchTensor(Object[] src, Shape shape, int[] axes, Object[] dest)	Dispatches a tensor transpose problem to the appropriate algorithm based on its shape and size.
static <T> T[]	TransposeDispatcher.dispatchTensor(T[] src, Shape srcShape, int axis1, int axis2, T[] dest)	Dispatches a tensor transpose problem to the appropriate algorithm based on its shape and size.
static <V extends Ring<V>> void	TransposeDispatcher.dispatchTensorHermitian(Shape shape, V[] src, int[] axes, V[] dest)	Dispatches a tensor Hermitian transpose problem to the appropriate algorithm based on its shape and size.
static <V extends Ring<V>> void	TransposeDispatcher.dispatchTensorHermitian(Shape shape, V[] src, int axis1, int axis2, V[] dest)	Dispatches a tensor Hermitian transpose problem to the appropriate algorithm based on its shape and size.

Constructors in org.flag4j.linalg.ops with parameters of type Shape

Modifier	Constructor	Description
protected	TensorDot(Shape shape1, T src1, Shape shape2, T src2, int[] src1Axes, int[] src2Axes)	Constructs a tensor dot product problem for computing the tensor contraction of two tensors over the specified set of axes.

Uses of Shape in org.flag4j.linalg.ops.dense

Methods in org.flag4j.linalg.ops.dense with parameters of type Shape

Modifier and Type	Method	Description
static double[]	DenseTranspose.standard(double[] src, Shape shape, int[] axes, double[] dest)	Computes the transpose of a tensor.
static <T> Object[]	DenseTranspose.standard(Object[] src, Shape shape, int[] axes, Object[] dest)	Computes the transpose of a tensor.
static Object[]	DenseTranspose.standard(Object[] src, Shape shape, int axis1, int axis2, Object[] dest)	Transposes tensor along specified axes using a standard transpose algorithm.
static <T> Object[]	DenseTranspose.standardConcurrent(Object[] src, Shape shape, int[] axes, Object[] dest)	Computes the transpose of a tensor using a concurrent implementation.
static <T> Object[]	DenseTranspose.standardConcurrent(Object[] src, Shape shape, int axis1, int axis2, Object[] dest)	Transposes tensor along specified axes using a standard concurrent transpose algorithm.
static <T> void	DenseOps.swapCols(Shape shape, T[] data, int colIdx1, int colIdx2)	Swaps specified columns in the matrix.
static <T> void	DenseOps.swapColsUnsafe(Shape shape, T[] data, int colIdx1, int colIdx2, int start, int stop)	Swaps two columns, over a specified range of rows, within a matrix.
static <T> void	DenseOps.swapRows(Shape shape, T[] data, int rowIdx1, int rowIdx2)	Swaps specified rows in the matrix.
static <T> void	DenseOps.swapRowsUnsafe(Shape shape, T[] data, int rowIdx1, int rowIdx2, int start, int stop)	Swaps two rows, over a specified range of columns, within a matrix.
static <T> boolean	DenseEquals.tensorEquals(T[] src1, Shape shape1, T[] src2, Shape shape2)	Checks if two dense tensors are equal.

Constructors in org.flag4j.linalg.ops.dense with parameters of type Shape

Modifier	Constructor	Description
	DenseSemiringTensorDot(Shape shape1, T[] src1, Shape shape2, T[] src2, int[] src1Axes, int[] src2Axes)	Constructs a tensor dot product problem for computing the tensor contraction of two tensors over the specified set of axes.

Uses of Shape in org.flag4j.linalg.ops.dense_sparse.coo.field_ops

Methods in org.flag4j.linalg.ops.dense_sparse.coo.field_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Field<T>> void	DenseCooFieldMatMult.blockedVector(T[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.concurrentBlockedVector(T[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.concurrentStandard(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real sparse matrix and a real dense matrix using a concurrent standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.concurrentStandard(T[] src1, Shape shape1, T[] src2, int[] rowIndices, int[] colIndices, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix and a real sparse matrix using a concurrent standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.concurrentStandardVector(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the sparse matrix dense vector multiplication using a concurrent standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.concurrentStandardVector(T[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a concurrent standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatrixOps.elemMult(Shape shape1, T[] data1, Shape shape2, T[] data2, int[] rowIndices2, int[] colIndices2, T[] dest)	Computes the element-wise multiplication between a real dense matrix and a real sparse matrix.
static <T extends Field<T>> void	DenseCooFieldMatMult.standard(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a sparse COO matrix and a dense matrix using a standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.standard(T[] src1, Shape shape1, T[] src2, int[] rowIndices, int[] colIndices, Shape shape2, T[] dest)	Computes the matrix multiplication between a dense matrix and a sparse COO matrix using a standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.standardVector(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the sparse matrix dense vector multiplication using a standard algorithm.
static <T extends Field<T>> void	DenseCooFieldMatMult.standardVector(T[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a standard algorithm.

Uses of Shape in org.flag4j.linalg.ops.dense_sparse.coo.real

Methods in org.flag4j.linalg.ops.dense_sparse.coo.real with parameters of type Shape

Modifier and Type	Method	Description
static double[]	RealDenseSparseMatMult.blockedVector(double[] src1, Shape shape1, double[] src2, int[] indices)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static double[]	RealDenseSparseMatMult.concurrentBlockedVector(double[] src1, Shape shape1, double[] src2, int[] indices)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static double[]	RealDenseSparseMatMult.concurrentStandard(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication between a real sparse matrix and a real dense matrix using a concurrent standard algorithm.
static double[]	RealDenseSparseMatMult.concurrentStandard(double[] src1, Shape shape1, double[] src2, int[] rowIndices, int[] colIndices, Shape shape2)	Computes the matrix multiplication between a real dense matrix and a real sparse matrix using a concurrent standard algorithm.
static double[]	RealDenseSparseMatMult.concurrentStandardVector(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2)	Computes the sparse matrix dense vector multiplication using a concurrent standard algorithm.
static double[]	RealDenseSparseMatMult.concurrentStandardVector(double[] src1, Shape shape1, double[] src2, int[] indices)	Computes the dense matrix sparse vector multiplication using a concurrent standard algorithm.
static double[]	RealDenseSparseMatrixMultTranspose.multTranspose(double[] dSrc, Shape dShape, double[] spSrc, int[] rowIndices, int[] colIndices, Shape spShape)	Multiplies a real dense matrix to the transpose of a real sparse matrix.
static double[]	RealDenseSparseMatMult.standard(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication between a real sparse matrix and a real dense matrix using a standard algorithm.
static double[]	RealDenseSparseMatMult.standard(double[] src1, Shape shape1, double[] src2, int[] rowIndices, int[] colIndices, Shape shape2)	Computes the matrix multiplication between a real dense matrix and a real sparse matrix using a standard algorithm.
static double[]	RealDenseSparseMatMult.standardVector(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2)	Computes the sparse matrix dense vector multiplication using a standard algorithm.
static double[]	RealDenseSparseMatMult.standardVector(double[] src1, Shape shape1, double[] src2, int[] indices)	Computes the dense matrix sparse vector multiplication using a standard algorithm.

Uses of Shape in org.flag4j.linalg.ops.dense_sparse.coo.real_complex

Methods in org.flag4j.linalg.ops.dense_sparse.coo.real_complex with parameters of type Shape

Modifier and Type	Method	Description
static void	RealComplexDenseCooOps.add(Shape shape1, double[] src1, Shape shape2, Complex128[] src2, int[][] indices, Complex128[] dest)	Computes element-wise sum between a real dense tensor to a sparse COO complex tensor.

Uses of Shape in org.flag4j.linalg.ops.dense_sparse.coo.real_field_ops

Methods in org.flag4j.linalg.ops.dense_sparse.coo.real_field_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Field<T>> void	RealFieldDenseCooMatMult.blockedVector(double[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.blockedVector(T[] src1, Shape shape1, double[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentBlockedVector(double[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentBlockedVector(T[] src1, Shape shape1, double[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandard(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real sparse matrix and a dense field matrix using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandard(double[] src1, Shape shape1, T[] src2, int[] rowIndices, int[] colIndices, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix and a sparse field matrix using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandard(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real sparse matrix and a dense field matrix using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandard(T[] src1, Shape shape1, double[] src2, int[] rowIndices, int[] colIndices, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix and a sparse field matrix using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandardVector(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the sparse matrix dense vector multiplication using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandardVector(double[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandardVector(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the sparse matrix dense vector multiplication using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.concurrentStandardVector(T[] src1, Shape shape1, double[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a concurrent standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMultTranspose.multTranspose(double[] deSrc, Shape deShape, T[] spSrc, int[] rowIndices, int[] colIndices, Shape spShape, T[] dest)	Multiplies a real dense matrix to the transpose of a sparse field matrix.
static <T extends Field<T>> void	RealFieldDenseCooMatMultTranspose.multTranspose(T[] dSrc, Shape dShape, double[] spSrc, int[] rowIndices, int[] colIndices, Shape spShape, T[] dest)	Multiplies a dense field matrix to the transpose of a real sparse matrix.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standard(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real sparse matrix and a dense field matrix using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standard(double[] src1, Shape shape1, T[] src2, int[] rowIndices, int[] colIndices, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix and a sparse field matrix using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standard(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real sparse matrix and a dense field matrix using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standard(T[] src1, Shape shape1, double[] src2, int[] rowIndices, int[] colIndices, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix and a sparse field matrix using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standardVector(double[] src1, int[] rowIndices, int[] colIndices, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the sparse matrix dense vector multiplication using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standardVector(double[] src1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standardVector(T[] src1, int[] rowIndices, int[] colIndices, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the sparse matrix dense vector multiplication using a standard algorithm.
static <T extends Field<T>> void	RealFieldDenseCooMatMult.standardVector(T[] src1, Shape shape1, double[] src2, int[] indices, T[] dest)	Computes the dense matrix sparse vector multiplication using a standard algorithm.

Uses of Shape in org.flag4j.linalg.ops.dense.field_ops

Methods in org.flag4j.linalg.ops.dense.field_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Field<T>> void	DenseFieldElemDiv.dispatch(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Dynamically chooses and applies element-wise division algorithm to use based on the number of data in the tensors.
static <T extends Field<T>> void	DenseFieldOps.div(Shape shape1, T[] src1, Shape shape2, T[] src2, T[] dest)	Computes the element-wise division between two tensors.
static <T extends Field<T>> void	DenseFieldElemDiv.elemDiv(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise division of two tensors.
static <T extends Field<T>> void	DenseFieldElemDiv.elemDivConcurrent(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise division of two tensors using a concurrent algorithm.

Uses of Shape in org.flag4j.linalg.ops.dense.real

Methods in org.flag4j.linalg.ops.dense.real with parameters of type Shape

Modifier and Type	Method	Description
static void	RealDenseOps.addEq(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes element-wise addition between tensors and stores the result in the first tensor.
static double[]	RealDenseMatMult.blocked(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication of two real dense matrices using a blocked algorithm.
static double[]	RealDenseMatMult.blockedReordered(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication of two real dense matrices using a blocked algorithm with the j-k loops swapped.
static double[]	RealDenseMatMult.blockedVector(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the multiplication of a real dense matrix with a real dense vector using a blocked algorithm.
static double[]	RealDenseMatMult.concurrentBlocked(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication of two real dense matrices using a concurrent implementation of a blocked algorithm.
static double[]	RealDenseMatMult.concurrentBlockedReordered(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication of two real dense matrices using a concurrent implementation of a blocked algorithm with the j-k loops swapped.
static double[]	RealDenseMatMult.concurrentBlockedVector(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the multiplication of a real dense matrix with a real dense vector using a concurrent implementation of a blocked algorithm.
static double[]	RealDenseMatMult.concurrentReordered(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication of two real dense matrices using a concurrent implementation of the standard matrix multiplication algorithm with j-k loops swapped.
static double[]	RealDenseMatMult.concurrentStandard(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication of two real dense matrices using a concurrent implementation of the standard matrix multiplication algorithm.
static double[]	RealDenseMatMult.concurrentStandardVector(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the multiplication of a real dense matrix with a real dense vector using a concurrent implementation of the standard matrix multiplication algorithm.
static boolean	RealDenseProperties.isAntiSymmetric(double[] src, Shape shape)	Checks if a real dense matrix is anti-symmetric.
static boolean	RealDenseProperties.isSymmetric(double[] src, Shape shape)	Checks if a real dense matrix is symmetric.
static double[]	RealDenseMatMultTranspose.multTranspose(double[] src1, Shape shape1, double[] src2, Shape shape2)	Multiplies a matrix to the transpose of a second matrix.
static double[]	RealDenseMatMultTranspose.multTransposeBlocked(double[] src1, Shape shape1, double[] src2, Shape shape2)	Multiplies a matrix to the transpose of a second matrix using a blocked algorithm.
static double[]	RealDenseMatMultTranspose.multTransposeBlockedConcurrent(double[] src1, Shape shape1, double[] src2, Shape shape2)	Multiplies a matrix to the transpose of a second matrix using a concurrent implementation of a blocked algorithm.
static double[]	RealDenseMatMultTranspose.multTransposeConcurrent(double[] src1, Shape shape1, double[] src2, Shape shape2)	Multiplies a matrix to the transpose of a second matrix using a concurrent algorithm.
static double[]	RealDenseMatMult.reordered(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication between two real dense matrices using the standard algorithm with j-k loops swapped.
static void	RealDenseSetOps.set(double[] src, Shape shape, double value, int... indices)	Sets an element of a tensor to the specified value.
static double[]	RealDenseMatMult.standard(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the matrix multiplication between two real dense matrices using the standard algorithm.
static double[]	RealDenseTranspose.standard(double[] src, Shape shape, int[] axes)	Computes the transpose of a tensor.
static double[]	RealDenseTranspose.standard(double[] src, Shape shape, int axis1, int axis2)	Transposes tensor along specified axes using a standard transpose algorithm.
static double[]	RealDenseTranspose.standardConcurrent(double[] src, Shape shape, int[] axes)	Computes the transpose of a tensor using a concurrent implementation.
static double[]	RealDenseTranspose.standardConcurrent(double[] src, Shape shape, int axis1, int axis2)	Transposes tensor along specified axes using a standard concurrent transpose algorithm.
static double[]	RealDenseMatMult.standardVector(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes the multiplication of a real dense matrix with a real dense vector using the standard algorithm.
static void	RealDenseOps.subEq(double[] src1, Shape shape1, double[] src2, Shape shape2)	Computes element-wise subtraction between tensors and stores the result in the first tensor.
static void	RealDenseOps.swapColsUnsafe(Shape shape, double[] data, int colIdx1, int colIdx2, int start, int stop)	Swaps two columns, over a specified range of rows, within a matrix.
static void	RealDenseOps.swapRowsUnsafe(Shape shape, double[] data, int rowIdx1, int rowIdx2, int start, int stop)	Swaps two rows, over a specified range of columns, within a matrix.
static boolean	RealDenseEquals.tensorEquals(double[] src1, Shape shape1, double[] src2, Shape shape2)	Checks if two dense tensors are equal.
static void	RealDenseOps.tensorTr(Shape shape, double[] src, int axis1, int axis2, Shape destShape, double[] dest)	Computes the generalized trace of this tensor along the specified axes.

Constructors in org.flag4j.linalg.ops.dense.real with parameters of type Shape

Modifier	Constructor	Description
	RealDenseTensorDot(Shape shape1, double[] src1, Shape shape2, double[] src2, int[] src1Axes, int[] src2Axes)	Constructs a tensor dot product problem for computing the tensor contraction of two tensors over the specified set of axes.

Uses of Shape in org.flag4j.linalg.ops.dense.real_field_ops

Methods in org.flag4j.linalg.ops.dense.real_field_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Field<T>> void	RealFieldDenseOps.add(Shape shape1, T[] src1, Shape shape2, double[] src2, T[] dest)	Computes the element-wise addition of two tensors.
static <T extends Field<T>> void	RealFieldDenseMatMult.blocked(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.blocked(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.blockedReordered(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a blocked algorithm with the j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.blockedReordered(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a blocked algorithm with the j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.blockedVector(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.blockedVector(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentBlocked(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentBlocked(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentBlockedReordered(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of a blocked algorithm with the j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentBlockedReordered(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of a blocked algorithm with the j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentBlockedVector(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using a concurrent implementation of a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentBlockedVector(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using a concurrent implementation of a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentReordered(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of the standard matrix multiplication algorithm with j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentReordered(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of the standard matrix multiplication algorithm with j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentStandard(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of the standard matrix multiplication algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentStandard(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of a real dense matrix with a dense field matrix using a concurrent implementation of the standard matrix multiplication algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentStandardVector(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using a concurrent implementation of the standard matrix multiplication algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.concurrentStandardVector(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using a concurrent implementation of the standard matrix multiplication algorithm.
static <T extends Field<T>> void	RealFieldDenseElemDiv.dispatch(Shape shape1, double[] src1, Shape shape2, T[] src2, T[] dest)	Dynamically chooses and applies the appropriate algorithm for element-wise tensor multiplication.
static <T extends Field<T>> void	RealFieldDenseElemDiv.dispatch(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Dynamically chooses and applies the appropriate algorithm for element-wise tensor multiplication.
static <T extends Field<T>> void	RealFieldDenseElemMult.dispatch(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Dynamically chooses and applies element-wise multiplication algorithm to use based on the number of data in the tensors.
static <T extends Field<T>> void	RealFieldDenseElemDiv.elemDiv(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise division of two tensors.
static <T extends Field<T>> void	RealFieldDenseElemDiv.elemDiv(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the element-wise division of two tensors.
static <T extends Field<T>> void	RealFieldDenseOps.elemDiv(Shape shape1, double[] src1, Shape shape2, T[] src2, T[] dest)	Computes the element-wise division between two dense tensors.
static <T extends Field<T>> void	RealFieldDenseOps.elemDiv(Shape shape1, T[] src1, Shape shape2, double[] src2, T[] dest)	Computes the element-wise division between two dense tensors.
static <T extends Field<T>> void	RealFieldDenseElemDiv.elemDivConcurrent(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise division of two tensors using a concurrent algorithm.
static <T extends Field<T>> void	RealFieldDenseElemDiv.elemDivConcurrent(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the element-wise division of two tensors using a concurrent algorithm.
static <T extends Field<T>> void	RealFieldDenseElemMult.elemMult(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the element-wise multiplication of two tensors.
static <T extends Field<T>> void	RealFieldDenseOps.elemMult(Shape shape1, T[] src1, Shape shape2, double[] src2, T[] dest)	Computes the element-wise multiplication between two dense tensors.
static <T extends Field<T>> void	RealFieldDenseElemMult.elemMultConcurrent(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the element-wise multiplication of two tensors using a concurrent algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTranspose(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTranspose(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTransposeBlocked(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTransposeBlocked(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTransposeBlockedConcurrent(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a concurrent implementation of a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTransposeBlockedConcurrent(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a concurrent implementation of a blocked algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTransposeConcurrent(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a concurrent algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMultTranspose.multTransposeConcurrent(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a concurrent algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.reordered(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix with a dense field matrix using the standard algorithm with j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.reordered(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix with a dense field matrix using the standard algorithm with j-k loops swapped.
static <T extends Field<T>> void	RealFieldDenseMatMult.standard(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix with a dense field matrix using the standard algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.standard(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between a real dense matrix with a dense field matrix using the standard algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.standardVector(double[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using the standard algorithm.
static <T extends Field<T>> void	RealFieldDenseMatMult.standardVector(T[] src1, Shape shape1, double[] src2, Shape shape2, T[] dest)	Computes the multiplication of a real dense matrix with a dense field vector using the standard algorithm.
static <T extends Field<T>> void	RealFieldDenseOps.sub(Shape shape1, double[] src1, Shape shape2, T[] src2, T[] dest)	Computes the element-wise subtraction of two tensors.
static <T extends Field<T>> void	RealFieldDenseOps.sub(Shape shape1, T[] src1, Shape shape2, double[] src2, T[] dest)	Computes the element-wise subtraction of two tensors.
static <T extends Field<T>> boolean	RealFieldDenseEquals.tensorEquals(double[] src1, Shape shape1, T[] src2, Shape shape2)	Checks if two dense tensors are equal.

Uses of Shape in org.flag4j.linalg.ops.dense.ring_ops

Methods in org.flag4j.linalg.ops.dense.ring_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Ring<T>> boolean	DenseRingTensorOps.isCloseToIdentity(Shape shape, T[] src)	Checks if a matrix is the identity matrix approximately.
static <T extends Ring<T>> boolean	DenseRingTensorOps.isHermitian(Shape shape, T[] src)	Checks if a complex dense matrix is Hermitian.
static <T extends Ring<T>> void	DenseRingHermitianTranspose.standardConcurrentHerm(T[] src, Shape shape, int[] axes, T[] dest)	Computes the conjugate transpose of a tensor using a concurrent implementation.
static <T extends Ring<T>> void	DenseRingHermitianTranspose.standardConcurrentHerm(T[] src, Shape shape, int axis1, int axis2, T[] dest)	Computes complex conjugate transpose of a tensor along specified axes using a standard concurrent transpose algorithm.
static <T extends Ring<T>> void	DenseRingHermitianTranspose.standardHerm(T[] src, Shape shape, int[] axes, T[] dest)	Computes the conjugate transpose of a tensor using a standard implementation.
static <T extends Ring<T>> void	DenseRingHermitianTranspose.standardHerm(T[] src, Shape shape, int axis1, int axis2, T[] dest)	Computes complex conjugate transpose of a tensor along specified axes using a standard transpose algorithm.
static <T extends Ring<T>> void	DenseRingTensorOps.sub(Shape shape1, T[] src1, Shape shape2, T[] src2, T[] dest)	Computes the element-wise difference between two dense tensors.

Uses of Shape in org.flag4j.linalg.ops.dense.semiring_ops

Methods in org.flag4j.linalg.ops.dense.semiring_ops that return Shape

Modifier and Type	Method	Description
static Shape	DenseSemiringOps.getTrShape(Shape shape, int axis1, int axis2)	Computes the shape of the tensor resulting from the generalized tensor trace along the specified axes for a tensor with the specified shape.

Methods in org.flag4j.linalg.ops.dense.semiring_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Semiring<T>> T[]	DenseSemiringOps.add(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise addition of two tensors.
static <T extends Semiring<T>> void	DenseSemiringMatMult.blocked(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of two dense matrices using a blocked algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.blockedReordered(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of two dense matrices using a blocked algorithm with the j-k loops swapped.
static <T extends Semiring<T>> void	DenseSemiringMatMult.blockedVector(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a dense matrix with a dense vector using a blocked algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.concurrentBlocked(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of two dense matrices using a concurrent implementation of a blocked algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.concurrentBlockedReordered(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of two dense matrices using a concurrent implementation of a blocked algorithm with the j-k loops swapped.
static <T extends Semiring<T>> void	DenseSemiringMatMult.concurrentBlockedVector(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a dense matrix with a dense vector using a concurrent implementation of a blocked algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.concurrentReordered(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of two dense matrices using a concurrent implementation of the standard matrix multiplication algorithm with j-k loops swapped.
static <T extends Semiring<T>> void	DenseSemiringMatMult.concurrentStandard(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication of two dense matrices using a concurrent implementation of the standard matrix multiplication algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.concurrentStandardVector(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a dense matrix with a dense vector using a concurrent implementation of the standard matrix multiplication algorithm.
static <T extends Semiring<T>> void	DenseSemiringElemMult.dispatch(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Dynamically chooses and applies an element-wise multiplication algorithm to use based on the number of data in the tensors.
static <T extends Semiring<T>> void	DenseSemiringMatMultDispatcher.dispatch(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Dispatches a matrix multiply problem to the appropriate algorithm based on the size of the matrices.
static <T extends Semiring<T>> void	DenseSemiringMatMultDispatcher.dispatchTranspose(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Dispatches a matrix multiply-transpose problem equivalent to src1.mult(src2.T()) to the appropriate algorithm based on the size of the matrices.
static <T extends Semiring<T>> void	DenseSemiringMatMultDispatcher.dispatchVector(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Dispatches a matrix-vector multiplication problem to the appropriate algorithm based on the size of the matrix and vector.
static <T extends Semiring<T>> void	DenseSemiringElemMult.elemMult(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise multiplication of two tensors.
static <T extends Semiring<T>> T[]	DenseSemiringOps.elemMult(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise product of two tensors.
static <T extends Semiring<T>> void	DenseSemiringElemMult.elemMultConcurrent(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the element-wise multiplication of two tensors using a concurrent algorithm.
static Shape	DenseSemiringOps.getTrShape(Shape shape, int axis1, int axis2)	Computes the shape of the tensor resulting from the generalized tensor trace along the specified axes for a tensor with the specified shape.
static <T extends Semiring<T>> void	DenseSemiringMatMultTranspose.multTranspose(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix.
static <T extends Semiring<T>> void	DenseSemiringMatMultTranspose.multTransposeBlocked(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a blocked algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMultTranspose.multTransposeBlockedConcurrent(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a concurrent implementation of a blocked algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMultTranspose.multTransposeConcurrent(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Multiplies a matrix to the transpose of a second matrix using a concurrent algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.reordered(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between two dense matrices using the standard algorithm with j-k loops swapped.
static DenseSemiringMatMultDispatcher.AlgorithmNames	DenseSemiringMatMultDispatcher.selectAlgorithmVector(Shape shape)	Dynamically chooses matrix-vector multiply algorithm based on the shapes of the matrix to multiply.
static <T extends Semiring<T>> void	DenseSemiringMatMult.standard(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the matrix multiplication between two dense matrices using the standard algorithm.
static <T extends Semiring<T>> void	DenseSemiringMatMult.standardVector(T[] src1, Shape shape1, T[] src2, Shape shape2, T[] dest)	Computes the multiplication of a dense matrix with a dense vector using the standard algorithm.
static <T extends Semiring<T>> void	DenseSemiringOps.tensorTr(Shape shape, T[] src, int axis1, int axis2, Shape destShape, T[] dest)	Computes the generalized trace of this tensor along the specified axes.
static <T extends Semiring<T>> SparseMatrixData<T>	DenseSemiringConversions.toCoo(Shape shape, T[] entries)	Converts a dense matrix to an equivalent sparse COO matrix.
static <T extends Semiring<T>> SparseMatrixData<T>	DenseSemiringConversions.toCoo(Shape shape, T[] entries, double estimatedSparsity)	Converts a dense matrix to an equivalent sparse COO matrix.
static <T extends Semiring<T>> SparseTensorData<T>	DenseSemiringConversions.toCooTensor(Shape shape, T[] entries)	Converts a dense tensor to an equivalent sparse COO tensor.
static <T extends Semiring<T>> SparseTensorData<T>	DenseSemiringConversions.toCooTensor(Shape shape, T[] entries, double estimatedSparsity)	Converts a dense tensor to an equivalent sparse COO tensor.

Uses of Shape in org.flag4j.linalg.ops.sparse

Methods in org.flag4j.linalg.ops.sparse with parameters of type Shape

Modifier and Type	Method	Description
static SparseVectorData<Double>	SparseUtils.coalesce(BinaryOperator<Double> aggregator, Shape shape, double[] data, int[] indices)	Coalesces this sparse COO vector.
static SparseTensorData<Double>	SparseUtils.coalesce(BinaryOperator<Double> aggregator, Shape shape, double[] data, int[][] indices)	Coalesces this sparse COO tensor.
static SparseMatrixData<Double>	SparseUtils.coalesce(BinaryOperator<Double> aggregator, Shape shape, double[] data, int[] rowIndices, int[] colIndices)	Coalesces this sparse COO matrix.
static <T> SparseVectorData<T>	SparseUtils.coalesce(BinaryOperator<T> aggregator, Shape shape, T[] data, int[] indices)	Coalesces this sparse COO vector.
static <T> SparseTensorData<T>	SparseUtils.coalesce(BinaryOperator<T> aggregator, Shape shape, T[] data, int[][] indices)	Coalesces this sparse COO tensor.
static <T> SparseMatrixData<T>	SparseUtils.coalesce(BinaryOperator<T> aggregator, Shape shape, T[] data, int[] rowIndices, int[] colIndices)	Coalesces this sparse COO matrix.
static int[][]	SparseUtils.cooFlattenIndices(Shape shape, int[][] indices)	Flattens the non-zero indices of a sparse COO tensor.
static int[][]	SparseUtils.cooFlattenIndices(Shape shape, int[][] indices, int axis)	Flattens the non-zero indices of a sparse COO tensor along a specified axis.
static int[][]	SparseUtils.cooReshape(Shape oldShape, Shape newShape, int[][] indices)	Computes new indices for the reshaping of a sparse COO tensor.
static SparseVectorData<Double>	SparseUtils.dropZeros(Shape shape, double[] data, int[] indices)	Drops any explicit zeros in this sparse COO vector.
static SparseTensorData<Double>	SparseUtils.dropZeros(Shape shape, double[] data, int[][] indices)	Drops any explicit zeros in this sparse COO tensor.
static SparseMatrixData<Double>	SparseUtils.dropZeros(Shape shape, double[] data, int[] rowIndices, int[] colIndices)	Drops any explicit zeros in this sparse COO matrix.
static <T extends Semiring<T>> SparseVectorData<T>	SparseUtils.dropZeros(Shape shape, T[] data, int[] indices)	Drops any explicit zeros in this sparse COO vector.
static <T extends Semiring<T>> SparseTensorData<T>	SparseUtils.dropZeros(Shape shape, T[] data, int[][] indices)	Drops any explicit zeros in this sparse COO tensor.
static <T extends Semiring<T>> SparseMatrixData<T>	SparseUtils.dropZeros(Shape shape, T[] data, int[] rowIndices, int[] colIndices)	Drops any explicit zeros in this sparse COO matrix.
static <T extends Semiring<T>> SparseMatrixData<T>	SparseUtils.dropZerosCsr(Shape shape, T[] data, int[] rowPointers, int[] colIndices)	Drops all explicit zeros within a sparse CSR matrix.
static void	SparseUtils.validateCsrMatrix(Shape shape, int nnz, int[] rowPointers, int[] colIndices)	Validates that the provided arguments specify a valid CSR matrix.
static void	SparseUtils.validateSlice(Shape shape, int rowStart, int rowEnd, int colStart, int colEnd)	Validates that the specified slice is a valid slice of a matrix with the specified shape.

Uses of Shape in org.flag4j.linalg.ops.sparse.coo

Methods in org.flag4j.linalg.ops.sparse.coo that return Shape

Modifier and Type	Method	Description
static <T> Shape	CooConcat.repeat(T[] src, int[] srcIndices, int size, int n, int axis, T[] destEntries, int[] destRows, int[] destCols)	Repeats a sparse COO vector n times along a certain axis to create a matrix.

Methods in org.flag4j.linalg.ops.sparse.coo with parameters of type Shape

Modifier and Type	Method	Description
static <T> SparseVectorData<T>	CooGetSet.getCol(Shape shape, T[] entries, int[] rowIndices, int[] colIndices, int colIdx, int start, int end)	Gets a specified column of a COO matrix between start (inclusive) and end (exclusive).
static <T> SparseVectorData<T>	CooGetSet.getDiag(Shape shape, T[] entries, int[] rowIndices, int[] colIndices, int diagOffset)	Gets the elements of a COO matrix along the specified diagonal.
static <T> SparseVectorData<T>	CooGetSet.getRow(Shape shape, T[] entries, int[] rowIndices, int[] colIndices, int rowIdx, int start, int end)	Gets a specified row of a COO matrix between start (inclusive) and end (exclusive).
static <T> SparseMatrixData<T>	CooGetSet.getSlice(Shape shape, T[] entries, int[] rowIndices, int[] colIndices, int rowStart, int rowEnd, int colStart, int colEnd)	Extracts a specified slice from a sparse COO matrix.
static <T> SparseMatrixData<T>	CooGetSet.getTriL(int diagOffset, Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Gets the lower-triangular portion of a sparse COO matrix with a possible diagonal offset.
static <T> SparseMatrixData<T>	CooGetSet.getTriU(int diagOffset, Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Gets the upper-triangular portion of a sparse COO matrix with a possible diagonal offset.
static <T> boolean	CooProperties.isSymmetric(Shape shape, T[] data, int[] rowIndices, int[] colIndices, T zeroValue)	Checks if a sparse COO matrix is symmetric.
static <T> SparseMatrixData<T>	CooGetSet.setCol(Shape srcShape, T[] srcEntries, int[] rowIndices, int[] colIndices, int colIdx, int size, T[] col, int[] indices)	Sets a column of a sparse matrix to the values in a sparse tensor.
static <T> SparseMatrixData<T>	CooGetSet.setRow(Shape srcShape, T[] srcEntries, int[] rowIndices, int[] colIndices, int rowIdx, int size, T[] row, int[] indices)	Sets a specified row of a real sparse COO matrix to the values in a sparse COO vector.
static <T> SparseMatrixData<T>	CooGetSet.setSlice(Shape shape1, T[] src1Entries, int[] src1RowIndices, int[] src1ColIndices, Shape shape2, T[] src2Entries, int[] src2RowIndices, int[] src2ColIndices, int row, int col)	Copies a sparse matrix and sets a slice of the sparse COO matrix to the data of another sparse COO matrix.
static void	CooManipulations.swapCols(Shape shape, Object[] entries, int[] rowIndices, int[] colIndices, int colIdx1, int colIdx2)	Swaps two columns, in place, in a sparse COO matrix.
static void	CooManipulations.swapRows(Shape shape, Object[] entries, int[] rowIndices, int[] colIndices, int rowIdx1, int rowIdx2)	Swaps two rows, in place, in a sparse COO matrix.
static void	CooTranspose.tensorTranspose(Shape shape, Object[] srcEntries, int[][] srcIndices, int[] axes, Object[] destEntries, int[][] destIndices)	Computes the transpose of a sparse COO tensor.
static void	CooTranspose.tensorTranspose(Shape shape, Object[] srcEntries, int[][] srcIndices, int axis1, int axis2, Object[] destEntries, int[][] destIndices)	Computes the transpose of a sparse COO tensor by exchanging axis1 and axis2.
static <T> void	CooConversions.toCsr(Shape shape, T[] entries, int[] rowIndices, int[] colIndices, T[] destEntries, int[] destRowPointers, int[] destColIndices)	Converts a COO matrix to an equivalent CSR matrix.
static <T> void	CooConversions.toDense(Shape shape, T[] entries, int[][] indices, T[] dest)	Converts a sparse COO tensor to an equivalent dense tensor.

Constructors in org.flag4j.linalg.ops.sparse.coo with parameters of type Shape

Modifier	Constructor	Description
	CooTensorDot(Shape shape1, T[] src1, int[][] indices1, Shape shape2, T[] src2, int[][] indices2, int[] src1Axes, int[] src2Axes)	Constructs a tensor dot product problem for computing the tensor contraction of two tensors over the specified set of axes.

Uses of Shape in org.flag4j.linalg.ops.sparse.coo.real

Methods in org.flag4j.linalg.ops.sparse.coo.real with parameters of type Shape

Modifier and Type	Method	Description
static double[]	RealSparseMatMult.concurrentStandard(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2)	Computes the matrix multiplication between two sparse matrices using a concurrent implementation of the standard algorithm.
static double[]	RealSparseMatMult.concurrentStandardVector(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] indices)	Computes the multiplication between a sparse matrix and a sparse vector using a concurrent implementation of the standard algorithm.
static boolean	RealSparseMatrixProperties.isAntiSymmetric(Shape shape, double[] data, int[] rowIndices, int[] colIndices)	Checks if a sparse COO matrix is symmetric.
static boolean	RealSparseMatrixProperties.isSymmetric(Shape shape, double[] data, int[] rowIndices, int[] colIndices)	Checks if a sparse COO matrix is symmetric.
static double[]	RealSparseMatMult.standard(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2)	Computes the matrix multiplication between two sparse matrices using a standard algorithm.
static double[]	RealSparseMatMult.standardVector(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] indices)	Computes the multiplication between a sparse matrix and a sparse vector using a standard algorithm.

Uses of Shape in org.flag4j.linalg.ops.sparse.coo.real_complex

Methods in org.flag4j.linalg.ops.sparse.coo.real_complex with parameters of type Shape

Modifier and Type	Method	Description
static Complex128[]	RealComplexSparseMatrixMultiplication.concurrentStandard(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, Complex128[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2)	Computes the matrix multiplication between two sparse matrices using a concurrent implementation of the standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.concurrentStandard(Complex128[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2)	Computes the matrix multiplication between two sparse matrices using a concurrent implementation of the standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.concurrentStandardVector(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, Complex128[] src2, int[] indices, Shape shape2)	Computes the multiplication between a sparse matrix and a sparse vector using a concurrent implementation of the standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.concurrentStandardVector(Complex128[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] indices, Shape shape2)	Computes the multiplication between a sparse matrix and a sparse vector using a concurrent implementation of the standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.standard(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, Complex128[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2)	Computes the matrix multiplication between two sparse matrices using a standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.standard(Complex128[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2)	Computes the matrix multiplication between two sparse matrices using a standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.standardVector(double[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, Complex128[] src2, int[] indices, Shape shape2)	Computes the multiplication between a sparse matrix and a sparse vector using a standard algorithm.
static Complex128[]	RealComplexSparseMatrixMultiplication.standardVector(Complex128[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, double[] src2, int[] indices, Shape shape2)	Computes the multiplication between a sparse matrix and a sparse vector using a standard algorithm.

Uses of Shape in org.flag4j.linalg.ops.sparse.coo.ring_ops

Methods in org.flag4j.linalg.ops.sparse.coo.ring_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Ring<T>> boolean	CooRingMatrixOps.isHermitian(Shape shape, T[] data, int[] rowIndices, int[] colIndices)	Checks if a sparse COO Ring matrix is Hermitian.
static <V extends Ring<V>> SparseMatrixData<V>	CooRingMatrixOps.sub(Shape shape1, V[] src1Entries, int[] src1RowIndices, int[] src1ColIndices, Shape shape2, V[] src2Entries, int[] src2RowIndices, int[] src2ColIndices)	Computes the element-wise difference of two sparse matrices.
static <V extends Ring<V>> SparseTensorData<V>	CooRingTensorOps.sub(Shape shape1, V[] src1Entries, int[][] src1Indices, Shape shape2, V[] src2Entries, int[][] src2Indices)	Sums two complex sparse COO tensors and stores result in a new COO tensor.
static <T extends Ring<T>> SparseVectorData<T>	CooRingVectorOps.sub(Shape shape1, T[] src1, int[] src1Indices, Shape shape2, T[] src2, int[] src2Indices)	Computes the element-wise vector subtraction between two real sparse vectors.
static <T extends Ring<T>> void	CooRingHermTranspose.tensorHermTranspose(Shape shape, T[] srcEntries, int[][] srcIndices, int[] axes, T[] destEntries, int[][] destIndices)	Computes the Hermitian transpose of a sparse COO tensor.
static <T extends Ring<T>> void	CooRingHermTranspose.tensorHermTranspose(Shape shape, T[] srcEntries, int[][] srcIndices, int axis1, int axis2, T[] destEntries, int[][] destIndices)	Computes the Hermitian transpose of a sparse COO tensor by exchanging axis1 and axis2.

Uses of Shape in org.flag4j.linalg.ops.sparse.coo.semiring_ops

Methods in org.flag4j.linalg.ops.sparse.coo.semiring_ops with parameters of type Shape

Modifier and Type	Method	Description
static <V extends Semiring<V>> SparseMatrixData<V>	CooSemiringMatrixOps.add(Shape shape1, V[] src1Entries, int[] src1RowIndices, int[] src1ColIndices, Shape shape2, V[] src2Entries, int[] src2RowIndices, int[] src2ColIndices)	Adds two sparse matrices.
static <V extends Semiring<V>> SparseTensorData<V>	CooSemiringTensorOps.add(Shape shape1, V[] src1Entries, int[][] src1Indices, Shape shape2, V[] src2Entries, int[][] src2Indices)	Sums two complex sparse COO tensors and stores result in a new COO tensor.
static <T extends Semiring<T>> SparseVectorData<T>	CooSemiringVectorOps.add(Shape shape1, T[] src1, int[] src1Indices, Shape shape2, T[] src2, int[] src2Indices)	Computes the element-wise vector addition between two real sparse vectors.
static <T extends Semiring<T>> void	CooSemiringMatMult.concurrentStandard(T[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, T[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2, T[] dest)	Computes the matrix multiplication between two sparse matrices using a concurrent implementation of the standard algorithm.
static <T extends Semiring<T>> void	CooSemiringMatMult.concurrentStandardVector(T[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the multiplication between a sparse matrix and a sparse vector using a concurrent implementation of the standard algorithm.
static <T extends Semiring<T>> T	CooSemiringVectorOps.dot(Shape shape1, T[] src1, int[] src1Indices, Shape shape2, T[] src2, int[] src2Indices)	Computes the dot product between two sparse COO vectors.
static <V extends Semiring<V>> SparseMatrixData<V>	CooSemiringMatrixOps.elemMult(Shape shape1, V[] src1Entries, int[] src1RowIndices, int[] src1ColIndices, Shape shape2, V[] src2Entries, int[] src2RowIndices, int[] src2ColIndices)	Multiplies two sparse matrices element-wise.
static <V extends Semiring<V>> SparseTensorData<V>	CooSemiringTensorOps.elemMult(Shape shape1, V[] src1Entries, int[][] src1Indices, Shape shape2, V[] src2Entries, int[][] src2Indices)	Computes the element-wise multiplication between two complex sparse COO tensors.
static <T extends Semiring<T>> SparseVectorData<T>	CooSemiringVectorOps.elemMult(Shape shape1, T[] src1, int[] src1Indices, Shape shape2, T[] src2, int[] src2Indices)	Computes the element-wise vector multiplication between two real sparse vectors.
static <T extends Semiring<T>> boolean	CooSemiringMatrixProperties.isIdentity(Shape shape, T[] entries, int[] rowIndices, int[] colIndices)	Checks if a complex sparse COO matrix is the identity matrix.
static <T extends Semiring<T>> void	CooSemiringMatMult.standard(T[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, T[] src2, int[] rowIndices2, int[] colIndices2, Shape shape2, T[] dest)	Computes the matrix multiplication between two sparse matrices using a standard algorithm.
static <T extends Semiring<T>> void	CooSemiringMatMult.standardVector(T[] src1, int[] rowIndices1, int[] colIndices1, Shape shape1, T[] src2, int[] indices, T[] dest)	Computes the multiplication between a sparse matrix and a sparse vector using a standard algorithm.
static <T extends Semiring<T>> SparseTensorData<T>	CooSemiringTensorOps.tensorTr(Shape shape, T[] entries, int[][] indices, int axis1, int axis2)	Computes the generalized trace of a tensor along the specified axes.

Uses of Shape in org.flag4j.linalg.ops.sparse.csr

Methods in org.flag4j.linalg.ops.sparse.csr that return Shape

Modifier and Type	Method	Description
static <T> Shape	CsrConversions.flatten(Shape shape, T[] entries, int[] rowPointers, int[] colIndices, int axis, int[] destRowPointers, int[] destColIndices)	Computes the new row pointers and column indices for a sparse CSR matrix flattened along some axis.

Methods in org.flag4j.linalg.ops.sparse.csr with parameters of type Shape

Modifier and Type	Method	Description
static <T> SparseMatrixData<T>	CsrOps.applyBinOpp(Shape shape1, T[] src1Entries, int[] src1RowPointers, int[] src1ColIndices, Shape shape2, T[] src2Entries, int[] src2RowPointers, int[] src2ColIndices, BinaryOperator<T> opp, UnaryOperator<T> uOpp)	Applies an element-wise binary operation to two csr matrices.
static <T> Shape	CsrConversions.flatten(Shape shape, T[] entries, int[] rowPointers, int[] colIndices, int axis, int[] destRowPointers, int[] destColIndices)	Computes the new row pointers and column indices for a sparse CSR matrix flattened along some axis.
static <T> SparseMatrixData<T>	CsrOps.getSlice(Shape shape, T[] entries, int[] rowPointers, int[] colIndices, int rowStart, int rowEnd, int colStart, int colEnd)	Gets a specified slice of a CSR matrix.
static <T> boolean	CsrProperties.isSymmetric(Shape shape, T[] values, int[] rowPointers, int[] colIndices, T zeroValue)	Checks if a sparse CSR matrix is symmetric.
static <T> void	CsrConversions.toCoo(Shape shape, T[] entries, int[] rowPointers, int[] colIndices, T[] destEntries, int[] destRowIndices, int[] destColIndices)	Converts a sparse CSR matrix to an equivalent sparse COO matrix.
static <T> void	CsrConversions.toDense(Shape shape, T[] entries, int[] rowPointers, int[] colIndices, T[] dest, T zero)	Converts a sparse CSR matrix to a dense matrix.

Uses of Shape in org.flag4j.linalg.ops.sparse.csr.ring_ops

Methods in org.flag4j.linalg.ops.sparse.csr.ring_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Ring<T>> boolean	CsrRingProperties.isHermitian(Shape shape, T[] values, int[] rowPointers, int[] colIndices)	Checks if a sparse CSR matrix is Hermitian.

Uses of Shape in org.flag4j.linalg.ops.sparse.csr.semiring_ops

Methods in org.flag4j.linalg.ops.sparse.csr.semiring_ops with parameters of type Shape

Modifier and Type	Method	Description
static <T extends Semiring<T>> boolean	SemiringCsrProperties.isIdentity(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Checks if the src matrix is the identity matrix.
static <T extends Semiring<T>> boolean	SemiringCsrProperties.isTriL(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Checks if a sparse CSR matrix is lower-triangular.
static <T extends Semiring<T>> boolean	SemiringCsrProperties.isTriU(Shape shape, T[] entries, int[] rowPointers, int[] colIndices)	Checks if a sparse CSR matrix is upper-triangular.
static <T extends Semiring<T>> void	SemiringCsrMatMult.standard(Shape shape1, T[] src1Entries, int[] src1RowPointers, int[] src1ColIndices, Shape shape2, T[] src2Entries, int[] src2RowPointers, int[] src2ColIndices, T[] destEntries, Semiring<T> zero)	Computes the matrix multiplication between two sparse CSR matrices.
static <T extends Semiring<T>> SparseMatrixData<T>	SemiringCsrMatMult.standardToSparse(Shape shape1, T[] src1Entries, int[] src1RowPointers, int[] src1ColIndices, Shape shape2, T[] src2Entries, int[] src2RowPointers, int[] src2ColIndices)	Computes the matrix multiplication between two sparse CSR matrices and returns the result as a sparse matrix.
static <T extends Semiring<T>> void	SemiringCsrMatMult.standardVector(Shape shape, T[] src1, int[] rowPointers, int[] colIndices, int size, T[] src2, int[] indices, T[] dest, T zero)	Computes the matrix-vector multiplication between a sparse CSR matrix and a sparse COO vector.

Uses of Shape in org.flag4j.linalg.solvers.exact

Methods in org.flag4j.linalg.solvers.exact that return Shape

Modifier and Type	Method	Description
protected Shape	ExactTensorSolver.getOutputShape(T A, T B, int aRankOriginal)	Constructs the shape of the output.

Methods in org.flag4j.linalg.solvers.exact with parameters of type Shape

Modifier and Type	Method	Description
protected CTensor	ComplexExactTensorSolver.wrap(CVector x, Shape outputShape)	Wraps solution as a tensor and reshapes to the proper shape.
protected abstract T	ExactTensorSolver.wrap(V x, Shape outputShape)	Wraps solution as a tensor and reshapes to the proper shape.
protected Tensor	RealExactTensorSolver.wrap(Vector x, Shape outputShape)	Wraps solution as a tensor and reshapes to the proper shape.

Uses of Shape in org.flag4j.rng

Methods in org.flag4j.rng with parameters of type Shape

Modifier and Type	Method	Description
CMatrix	RandomDenseTensor.randnCMatrix(Shape shape)	Generates a matrix filled with pseudorandom values sampled from a bivariate standard Gaussian (normal) distribution with mean zero and standard deviation one along both the real and imaginary axes.
CMatrix	RandomDenseTensor.randnCMatrix(Shape shape, double mean, double std)	Generates a matrix filled with pseudorandom values sampled from a bivariate Gaussian (normal) distribution with specified mean and standard deviation along both the real and imaginary axes.
CMatrix	RandomDenseTensor.randnCMatrix(Shape shape, double meanRe, double stdRe, double meanIm, double stdIm, double corrCoeff)	Generates a matrix filled with pseudorandom values sampled from a bivariate Gaussian (normal) distribution.
CTensor	RandomDenseTensor.randnCTensor(Shape shape)	Generates a tensor filled with pseudorandom values sampled from a bivariate standard Gaussian (normal) distribution with mean zero and standard deviation one along both the real and imaginary axes.
CTensor	RandomDenseTensor.randnCTensor(Shape shape, double mean, double std)	Generates a tensor filled with pseudorandom values sampled from a bivariate Gaussian (normal) distribution with specified mean and standard deviation along both the real and imaginary axes.
CTensor	RandomDenseTensor.randnCTensor(Shape shape, double meanRe, double stdRe, double meanIm, double stdIm, double corrCoeff)	Generates a tensor filled with pseudorandom values sampled from a bivariate Gaussian (normal) distribution.
Matrix	RandomDenseTensor.randnMatrix(Shape shape)	Generates a matrix filled with pseudorandom values sampled from a normal distribution with mean of 0.0 and standard deviation of 1.0.
Matrix	RandomDenseTensor.randnMatrix(Shape shape, double mean, double std)	Generates a matrix filled with pseudorandom values sampled from a normal distribution with specified mean and standard deviation.
Tensor	RandomDenseTensor.randnTensor(Shape shape)	Generates a tensor filled with pseudorandom values sampled from a normal distribution with mean of 0.0 and standard deviation of 1.0.
Tensor	RandomDenseTensor.randnTensor(Shape shape, double mean, double std)	Generates a tensor filled with pseudorandom values sampled from a normal distribution with specified mean and standard deviation.
CMatrix	RandomDenseTensor.randomCMatrix(Shape shape)	Generates a matrix filled with pseudorandom complex values uniformly distributed in the unit disk centered at the origin of the complex plane.
CMatrix	RandomDenseTensor.randomCMatrix(Shape shape, double min, double max)	Generates a matrix filled with pseudorandom complex values uniformly distributed in an annulus (i.e. washer) centered at the origin of the complex plane.
CooMatrix	RandomSparseTensor.randomCooMatrix(Shape shape, double min, double max, double sparsity)	Generates a random sparse matrix with the specified sparsity.
CooMatrix	RandomSparseTensor.randomCooMatrix(Shape shape, double min, double max, int numNonZeroEntries)	Generates a random sparse matrix with the specified number of non-zero data.
CsrMatrix	RandomSparseTensor.randomCsrMatrix(Shape shape, double min, double max, double sparsity)	Generates a random sparse matrix with the specified sparsity.
CsrMatrix	RandomSparseTensor.randomCsrMatrix(Shape shape, double min, double max, int numNonZeroEntries)	Generates a random sparse matrix with the specified number of non-zero data.
CTensor	RandomDenseTensor.randomCTensor(Shape shape)	Generates a tensor filled with pseudorandom complex values uniformly distributed in the unit disk centered at the origin of the complex plane.
CTensor	RandomDenseTensor.randomCTensor(Shape shape, double min, double max)	Generates a tensor filled with pseudorandom complex values uniformly distributed in an annulus (i.e. washer) centered at the origin of the complex plane.
Matrix	RandomDenseTensor.randomMatrix(Shape shape)	Generates a matrix filled with pseudorandom values uniformly distributed in [0, 1).
Matrix	RandomDenseTensor.randomMatrix(Shape shape, double min, double max)	Generates a matrix filled with pseudorandom values uniformly distributed in [min, max).
CooCMatrix	RandomSparseTensor.randomSparseCMatrix(Shape shape, double min, double max, double sparsity)	Generates a random sparse matrix with the specified sparsity.
CooCMatrix	RandomSparseTensor.randomSparseCMatrix(Shape shape, double min, double max, int numNonZeroEntries)	Generates a random sparse matrix with the specified number of non-zero data.
Tensor	RandomDenseTensor.randomTensor(Shape shape)	Generates a tensor filled with pseudorandom values uniformly distributed in [0, 1).
Tensor	RandomDenseTensor.randomTensor(Shape shape, double min, double max)	Generates a tensor filled with pseudorandom values uniformly distributed in [min, max).

Uses of Shape in org.flag4j.util

Methods in org.flag4j.util that return Shape

Modifier and Type	Method	Description
static Shape	ArrayUtils.nDArrayShape(Object nDArray)	Infers the shape of a rectangular nD Java array.

Methods in org.flag4j.util with parameters of type Shape

Modifier and Type	Method	Description
static void	ValidateParameters.ensureEqualShape(Shape shape1, Shape shape2)	Checks if two Shape objects are equivalent.
static void	ValidateParameters.ensureMatMultShapes(Shape shape1, Shape shape2)	Checks if two Shape objects satisfy the requirements of matrix-matrix or matrix-vector multiplication.
static void	ValidateParameters.ensureRank(Shape shape, int expRank)	Checks that a shape has the specified rank.
static void	ValidateParameters.ensureSquare(Shape shape)	Checks if a shape represents a square tensor.
static void	ValidateParameters.ensureSquareMatrix(Shape shape)	Checks if a shape represents a square matrix.
static void	ValidateParameters.ensureTotalEntriesEqual(Shape shape1, Shape shape2)	Checks that two shapes have the same total number of data.
static void	ValidateParameters.ensureValidAxes(Shape shape, int... axes)	Checks if all provided axes are valid with respect to the rank of the given shape.
static String	ErrorMessages.equalShapeErrMsg(Shape shape1, Shape shape2)	Gets an error message for two tensors with mismatching shapes.
static String	ErrorMessages.getShapeTotalEntriesErr(Shape shape1, Shape shape2)	Gets an error message for two shapes which cannot be broadcast together.
static String	ErrorMessages.getSquareShapeErr(Shape shape)	Gets an error message for a shape which was expected to be square but was not
static String	ErrorMessages.matMultShapeErrMsg(Shape shape1, Shape shape2)	Gets an error message for two matrices with shapes not conducive with matrix multiplication.
static int	ArrayUtils.nDFlatten(Object nDArray, Shape shape, double[] flatArray, int offset)	Recursively validates the shape of the nD array and flattens it into the provided 1D array.
static <T> int	ArrayUtils.nDFlatten(Object nDArray, Shape shape, T[] flatArray, int offset)	Recursively validates the shape of the nD array and flattens it into the provided 1D array.
static String	ErrorMessages.shapeEntriesError(Shape shape, int numEntries)	Gets an error message for a shape which cannot hold a specified number of data.
static void	ValidateParameters.validateTensorIndex(Shape shape, int... index)	Checks if the provided nD index is contained in a tensor defined by the given shape.
static void	ValidateParameters.validateTensorIndices(Shape shape, int[]... indices)	Checks that a set of nD indices are valid indices for a tensor with the specified shape.