an tensor (multi-dimensional array) More...

#include <Tensor.hpp>

Public Member Functions
void	Save (const std::string &fname) const
	Save current Tensor to file.

void	Save (const char *fname) const

void	Tofile (const std::string &fname) const
	Save current Tensor to the binary file.

void	Tofile (const char *fname) const

void	Tofile (std::fstream &f) const

void	Init (const std::vector< cytnx_uint64 > &shape, const unsigned int &dtype=Type.Double, const int &device=-1, const bool &init_zero=true)
	initialize a Tensor

	Tensor (const std::vector< cytnx_uint64 > &shape, const unsigned int &dtype=Type.Double, const int &device=-1, const bool &init_zero=1)
	Construct a new Tensor object.

unsigned int	dtype () const
	the dtype-id of the Tensor

int	device () const
	the device-id of the Tensor

std::string	dtype_str () const
	the dtype (in string) of the Tensor

std::string	device_str () const
	the device (in string) of the Tensor

const std::vector< cytnx_uint64 > &	shape () const
	the shape of the Tensor

cytnx_uint64	rank () const
	the rank of the Tensor

Tensor	clone () const
	return a clone of the current Tensor.

Tensor	to (const int &device) const
	copy a tensor to new device

void	to_ (const int &device)
	move the current Tensor to the device.

const bool &	is_contiguous () const
	return whether the Tensor is contiguous or not.

Tensor	permute_ (const std::vector< cytnx_uint64 > &rnks)

Tensor	permute (const std::vector< cytnx_uint64 > &rnks) const
	perform tensor permute on the cytnx::Tensor and return a new instance.

Tensor	contiguous () const
	Make the Tensor contiguous by coalescing the memory (storage).

Tensor	contiguous_ ()
	Make the Tensor contiguous by coalescing the memory (storage), inplacely.

void	reshape_ (const std::vector< cytnx_int64 > &new_shape)
	reshape the Tensor, inplacely

Tensor	reshape (const std::vector< cytnx_int64 > &new_shape) const
	return a new Tensor that is reshaped.

Tensor	reshape (const std::vector< cytnx_uint64 > &new_shape) const

Tensor	reshape (const std::initializer_list< cytnx_int64 > &new_shape) const

Tensor	astype (const int &new_type) const
	return a new Tensor that cast to different dtype.

template<class T >
T &	at (const std::vector< cytnx_uint64 > &locator)
	Get an element at specific location.

template<class T >
const T &	at (const std::vector< cytnx_uint64 > &locator) const

template<class T >
T &	item ()
	get the element from a rank-0 Tensor.

Tensor	get (const std::vector< cytnx::Accessor > &accessors) const
	get elements using Accessor (C++ API) / slices (python API)

void	set (const std::vector< cytnx::Accessor > &accessors, const Tensor &rhs)
	set elements with the input Tensor using Accessor (C++ API) / slices (python API)

template<class T >
void	set (const std::vector< cytnx::Accessor > &accessors, const T &rc)
	set elements with the input constant using Accessor (C++ API) / slices (python API)

Storage &	storage () const
	return the storage of current Tensor.

template<class T >
void	fill (const T &val)
	fill all the element of current Tensor with the value.

bool	equivshape (const Tensor &rhs)
	compare the shape of two tensors.

Tensor	real ()
	return the real part of the tensor.

Tensor	imag ()
	return the imaginary part of the tensor.

template<class T >
Tensor &	operator+= (const T &rc)
	addition assignment operator with a Tensor or a scalar.

template<class T >
Tensor &	operator-= (const T &rc)
	subtraction assignment operator with a Tensor or a scalar.

template<class T >
Tensor &	operator*= (const T &rc)
	multiplication assignment operator with a Tensor or a scalar.

template<class T >
Tensor &	operator/= (const T &rc)
	division assignment operator with a Tensor or a scalar.

template<class T >
Tensor	Add (const T &rhs)
	Addition function with a Tensor or a scalar. Same as cytnx::operator+(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Add_ (const T &rhs)
	Addition function with a Tensor or a scalar, inplacely. Same as operator+=(const T &rhs).

template<class T >
Tensor	Sub (const T &rhs)
	Subtraction function with a Tensor or a scalar. Same as cytnx::operator-(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Sub_ (const T &rhs)
	Subtraction function with a Tensor or a scalar, inplacely. Same as operator-=(const T &rhs).

template<class T >
Tensor	Mul (const T &rhs)
	Multiplication function with a Tensor or a scalar. Same as cytnx::operator*(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Mul_ (const T &rhs)
	Multiplication function with a Tensor or a scalar, inplacely. Same as operator*=(const T &rhs).

template<class T >
Tensor	Div (const T &rhs)
	Division function with a Tensor or a scalar. Same as cytnx::operator/(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Div_ (const T &rhs)
	Division function with a Tensor or a scalar, inplacely. Same as operator/=(const T &rhs).

template<class T >
Tensor	Cpr (const T &rhs)
	The comparison function.

template<class T >
Tensor	Mod (const T &rhs)

Tensor	operator- ()
	The negation function.

Tensor	flatten () const
	The flatten function.

void	flatten_ ()
	The flatten function, inplacely.

void	append (const Tensor &rhs)
	the append function.

void	append (const Storage &srhs)
	the append function of the Storage.

template<class T >
void	append (const T &rhs)
	the append function of the scalar.

bool	same_data (const Tensor &rhs) const
	Check whether two tensors share the same internal memory.

std::vector< Tensor >	Svd (const bool &is_UvT=true) const
	the SVD member function. Same as cytnx::linalg::Svd(const Tensor &Tin, const bool &is_UvT) , where `Tin` is the current Tensor.

std::vector< Tensor >	Eigh (const bool &is_V=true, const bool &row_v=false) const
	the Eigh member function. Same as cytnx::linalg::Eigh(const Tensor &Tin, const bool &is_V, const bool &row_v) , where `Tin` is the current Tensor.

Tensor &	InvM_ ()
	the InvM_ member function. Same as cytnx::linalg::InvM_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	InvM () const
	the InvM member function. Same as cytnx::linalg::InvM(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor &	Inv_ (const double &clip)
	the Inv_ member function. Same as cytnx::linalg::Inv_(Tensor &Tin, const double &clip)

Tensor	Inv (const double &clip) const
	the Inv member function. Same as cytnx::linalg::Inv(const Tensor &Tin, const double &clip)

Tensor &	Conj_ ()
	the Conj_ member function. Same as cytnx::linalg::Conj_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Conj () const
	the Conj member function. Same as cytnx::linalg::Conj(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor &	Exp_ ()
	the Exp_ member function. Same as linalg::Exp_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Exp () const
	the Exp member function. Same as linalg::Exp(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Norm () const
	the Norm member function. Same as linalg::Norm(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Pow (const cytnx_double &p) const
	the Pow member function. Same as linalg::Pow(const Tensor &Tin, const cytnx_double &p), where `Tin` is the current Tensor.

Tensor &	Pow_ (const cytnx_double &p)
	the Pow_ member function. Same as linalg::Pow_(Tensor &Tin, const cytnx_double &p), where `Tin` is the current Tensor.

Tensor	Trace (const cytnx_uint64 &a=0, const cytnx_uint64 &b=1) const
	the Trace member function. Same as linalg::Trace(const Tensor &Tin, const cytnx_uint64 &a, const cytnx_uint64 &b), where `Tin` is the current Tensor.

Tensor	Abs () const
	the Abs member function. Same as linalg::Abs(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor &	Abs_ ()
	the Abs_ member function. Same as linalg::Abs_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Max () const
	the Max member function. Same as linalg::Max(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Min () const
	the Min member function. Same as linalg::Min(const Tensor &Tin), where `Tin` is the current Tensor.

void	Save (const std::string &fname) const
	Save current Tensor to file.

void	Save (const char *fname) const

void	Tofile (const std::string &fname) const
	Save current Tensor to the binary file.

void	Tofile (const char *fname) const

void	Tofile (std::fstream &f) const

void	Init (const std::vector< cytnx_uint64 > &shape, const unsigned int &dtype=Type.Double, const int &device=-1, const bool &init_zero=true)
	initialize a Tensor

	Tensor (const std::vector< cytnx_uint64 > &shape, const unsigned int &dtype=Type.Double, const int &device=-1, const bool &init_zero=1)
	Construct a new Tensor object.

unsigned int	dtype () const
	the dtype-id of the Tensor

int	device () const
	the device-id of the Tensor

std::string	dtype_str () const
	the dtype (in string) of the Tensor

std::string	device_str () const
	the device (in string) of the Tensor

const std::vector< cytnx_uint64 > &	shape () const
	the shape of the Tensor

cytnx_uint64	rank () const
	the rank of the Tensor

Tensor	clone () const
	return a clone of the current Tensor.

Tensor	to (const int &device) const
	copy a tensor to new device

void	to_ (const int &device)
	move the current Tensor to the device.

const bool &	is_contiguous () const
	return whether the Tensor is contiguous or not.

Tensor	permute_ (const std::vector< cytnx_uint64 > &rnks)

Tensor	permute (const std::vector< cytnx_uint64 > &rnks) const
	perform tensor permute on the cytnx::Tensor and return a new instance.

Tensor	contiguous () const
	Make the Tensor contiguous by coalescing the memory (storage).

Tensor	contiguous_ ()
	Make the Tensor contiguous by coalescing the memory (storage), inplacely.

void	reshape_ (const std::vector< cytnx_int64 > &new_shape)
	reshape the Tensor, inplacely

Tensor	reshape (const std::vector< cytnx_int64 > &new_shape) const
	return a new Tensor that is reshaped.

Tensor	reshape (const std::vector< cytnx_uint64 > &new_shape) const

Tensor	reshape (const std::initializer_list< cytnx_int64 > &new_shape) const

Tensor	astype (const int &new_type) const
	return a new Tensor that cast to different dtype.

template<class T >
T &	at (const std::vector< cytnx_uint64 > &locator)
	Get an element at specific location.

template<class T >
const T &	at (const std::vector< cytnx_uint64 > &locator) const

template<class T >
T &	item ()
	get the element from a rank-0 Tensor.

Tensor	get (const std::vector< cytnx::Accessor > &accessors) const
	get elements using Accessor (C++ API) / slices (python API)

void	set (const std::vector< cytnx::Accessor > &accessors, const Tensor &rhs)
	set elements with the input Tensor using Accessor (C++ API) / slices (python API)

template<class T >
void	set (const std::vector< cytnx::Accessor > &accessors, const T &rc)
	set elements with the input constant using Accessor (C++ API) / slices (python API)

Storage &	storage () const
	return the storage of current Tensor.

template<class T >
void	fill (const T &val)
	fill all the element of current Tensor with the value.

bool	equivshape (const Tensor &rhs)
	compare the shape of two tensors.

Tensor	real ()
	return the real part of the tensor.

Tensor	imag ()
	return the imaginary part of the tensor.

template<class T >
Tensor &	operator+= (const T &rc)
	addition assignment operator with a Tensor or a scalar.

template<class T >
Tensor &	operator-= (const T &rc)
	subtraction assignment operator with a Tensor or a scalar.

template<class T >
Tensor &	operator*= (const T &rc)
	multiplication assignment operator with a Tensor or a scalar.

template<class T >
Tensor &	operator/= (const T &rc)
	division assignment operator with a Tensor or a scalar.

template<class T >
Tensor	Add (const T &rhs)
	Addition function with a Tensor or a scalar. Same as cytnx::operator+(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Add_ (const T &rhs)
	Addition function with a Tensor or a scalar, inplacely. Same as operator+=(const T &rhs).

template<class T >
Tensor	Sub (const T &rhs)
	Subtraction function with a Tensor or a scalar. Same as cytnx::operator-(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Sub_ (const T &rhs)
	Subtraction function with a Tensor or a scalar, inplacely. Same as operator-=(const T &rhs).

template<class T >
Tensor	Mul (const T &rhs)
	Multiplication function with a Tensor or a scalar. Same as cytnx::operator*(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Mul_ (const T &rhs)
	Multiplication function with a Tensor or a scalar, inplacely. Same as operator*=(const T &rhs).

template<class T >
Tensor	Div (const T &rhs)
	Division function with a Tensor or a scalar. Same as cytnx::operator/(const Tensor &self, const T &rhs).

template<class T >
Tensor &	Div_ (const T &rhs)
	Division function with a Tensor or a scalar, inplacely. Same as operator/=(const T &rhs).

template<class T >
Tensor	Cpr (const T &rhs)
	The comparison function.

template<class T >
Tensor	Mod (const T &rhs)

Tensor	operator- ()
	The negation function.

Tensor	flatten () const
	The flatten function.

void	flatten_ ()
	The flatten function, inplacely.

void	append (const Tensor &rhs)
	the append function.

void	append (const Storage &srhs)
	the append function of the Storage.

template<class T >
void	append (const T &rhs)
	the append function of the scalar.

bool	same_data (const Tensor &rhs) const
	Check whether two tensors share the same internal memory.

std::vector< Tensor >	Svd (const bool &is_UvT=true) const
	the SVD member function. Same as cytnx::linalg::Svd(const Tensor &Tin, const bool &is_UvT) , where `Tin` is the current Tensor.

std::vector< Tensor >	Eigh (const bool &is_V=true, const bool &row_v=false) const
	the Eigh member function. Same as cytnx::linalg::Eigh(const Tensor &Tin, const bool &is_V, const bool &row_v) , where `Tin` is the current Tensor.

Tensor &	InvM_ ()
	the InvM_ member function. Same as cytnx::linalg::InvM_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	InvM () const
	the InvM member function. Same as cytnx::linalg::InvM(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor &	Inv_ (const double &clip)
	the Inv_ member function. Same as cytnx::linalg::Inv_(Tensor &Tin, const double &clip)

Tensor	Inv (const double &clip) const
	the Inv member function. Same as cytnx::linalg::Inv(const Tensor &Tin, const double &clip)

Tensor &	Conj_ ()
	the Conj_ member function. Same as cytnx::linalg::Conj_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Conj () const
	the Conj member function. Same as cytnx::linalg::Conj(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor &	Exp_ ()
	the Exp_ member function. Same as linalg::Exp_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Exp () const
	the Exp member function. Same as linalg::Exp(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Norm () const
	the Norm member function. Same as linalg::Norm(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Pow (const cytnx_double &p) const
	the Pow member function. Same as linalg::Pow(const Tensor &Tin, const cytnx_double &p), where `Tin` is the current Tensor.

Tensor &	Pow_ (const cytnx_double &p)
	the Pow_ member function. Same as linalg::Pow_(Tensor &Tin, const cytnx_double &p), where `Tin` is the current Tensor.

Tensor	Trace (const cytnx_uint64 &a=0, const cytnx_uint64 &b=1) const
	the Trace member function. Same as linalg::Trace(const Tensor &Tin, const cytnx_uint64 &a, const cytnx_uint64 &b), where `Tin` is the current Tensor.

Tensor	Abs () const
	the Abs member function. Same as linalg::Abs(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor &	Abs_ ()
	the Abs_ member function. Same as linalg::Abs_(Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Max () const
	the Max member function. Same as linalg::Max(const Tensor &Tin), where `Tin` is the current Tensor.

Tensor	Min () const
	the Min member function. Same as linalg::Min(const Tensor &Tin), where `Tin` is the current Tensor.

Static Public Member Functions
static Tensor	Load (const std::string &fname)
	Load current Tensor from file.

static Tensor	Load (const char *fname)

static Tensor	Fromfile (const std::string &fname, const unsigned int &dtype, const cytnx_int64 &count=-1)
	Load current Tensor from the binary file.

static Tensor	Fromfile (const char *fname, const unsigned int &dtype, const cytnx_int64 &count=-1)

static Tensor	from_storage (const Storage &in)
	Convert a Storage to Tensor.

static Tensor	Load (const std::string &fname)
	Load current Tensor from file.

static Tensor	Load (const char *fname)

static Tensor	Fromfile (const std::string &fname, const unsigned int &dtype, const cytnx_int64 &count=-1)
	Load current Tensor from the binary file.

static Tensor	Fromfile (const char *fname, const unsigned int &dtype, const cytnx_int64 &count=-1)

static Tensor	from_storage (const Storage &in)
	Convert a Storage to Tensor.

Detailed Description

an tensor (multi-dimensional array)

Constructor & Destructor Documentation

◆ Tensor() [1/2]

cytnx::Tensor::Tensor	(	const std::vector< cytnx_uint64 > &	shape,
		const unsigned int &	dtype = `Type.Double`,
		const int &	device = `-1`,
		const bool &	init_zero = `1`
	)

inline

Construct a new Tensor object.

This is the constructor of Tensor. It will call cytnx::Tensor::Init() to initialize the Tensor.

Parameters

[in]	shape	the shape of tensor
[in]	dtype	the dtype of tensor. This can be any of type defined in cytnx::Type.
[in]	device	the device that tensor to be created. This can be cytnx::Device.cpu or cytnx::Device.cuda+<gpuid>, see cytnx::Device for more detail.
[in]	init_zero	if true, the content of Tensor will be initialized to zero. If false, the content of Tensor will be un-initialized.

See also: cytnx::Tensor::Init

◆ Tensor() [2/2]

cytnx::Tensor::Tensor	(	const std::vector< cytnx_uint64 > &	shape,
		const unsigned int &	dtype = `Type.Double`,
		const int &	device = `-1`,
		const bool &	init_zero = `1`
	)

inline

Construct a new Tensor object.

This is the constructor of Tensor. It will call cytnx::Tensor::Init() to initialize the Tensor.

Parameters

[in]	shape	the shape of tensor
[in]	dtype	the dtype of tensor. This can be any of type defined in cytnx::Type.
[in]	device	the device that tensor to be created. This can be cytnx::Device.cpu or cytnx::Device.cuda+<gpuid>, see cytnx::Device for more detail.
[in]	init_zero	if true, the content of Tensor will be initialized to zero. If false, the content of Tensor will be un-initialized.

See also: cytnx::Tensor::Init

Member Function Documentation

◆ Abs() [1/2]

Tensor cytnx::Tensor::Abs ( ) const

the Abs member function. Same as linalg::Abs(const Tensor &Tin), where Tin is the current Tensor.

◆ Abs() [2/2]

Tensor cytnx::Tensor::Abs ( ) const

the Abs member function. Same as linalg::Abs(const Tensor &Tin), where Tin is the current Tensor.

◆ Abs_() [1/2]

Tensor & cytnx::Tensor::Abs_ ( )

the Abs_ member function. Same as linalg::Abs_(Tensor &Tin), where Tin is the current Tensor.

◆ Abs_() [2/2]

Tensor & cytnx::Tensor::Abs_ ( )

the Abs_ member function. Same as linalg::Abs_(Tensor &Tin), where Tin is the current Tensor.

◆ Add() [1/2]

template<class T >

Tensor cytnx::Tensor::Add ( const T & rhs )

inline

Addition function with a Tensor or a scalar. Same as cytnx::operator+(const Tensor &self, const T &rhs).

Parameters

[in] rhs the added Tensor or scalar.

◆ Add() [2/2]

template<class T >

Tensor cytnx::Tensor::Add ( const T & rhs )

inline

Addition function with a Tensor or a scalar. Same as cytnx::operator+(const Tensor &self, const T &rhs).

Parameters

[in] rhs the added Tensor or scalar.

◆ Add_() [1/2]

template<class T >

Tensor & cytnx::Tensor::Add_ ( const T & rhs )

inline

Addition function with a Tensor or a scalar, inplacely. Same as operator+=(const T &rhs).

Parameters

[in] rhs the added Tensor or scalar.

◆ Add_() [2/2]

template<class T >

Tensor & cytnx::Tensor::Add_ ( const T & rhs )

inline

Addition function with a Tensor or a scalar, inplacely. Same as operator+=(const T &rhs).

Parameters

[in] rhs the added Tensor or scalar.

◆ append() [1/6]

void cytnx::Tensor::append ( const Storage & srhs )

inline

the append function of the Storage.

This function is the append function of the Storage. It will append the srhs Storage to the current tensor. The current tensor must be rank-2 and the srhs Storage must have the same size as the second dimension of the current tensor. For example, if the current tensor is \(A\) with size \(M \times N\) and the srhs Storage is \(B\) with size \(N\), then the output tensor is \(C\) with size \(M \times (N+1)\) where

\[ C(i,j) = \begin{cases} A(i,j) & \text{if } j \neq N \\ B(i) & \text{if } j = N \end{cases} \]

Here indices \(i\) and \(j\) start from 0.

Parameters

[in] srhs the appended Storage.

Returns: The appended tensor.

Precondition

The srhs Storage and the current tensor cannot be empty.
The current tensor must be rank-2.
The srhs Storage must have the same size as the second dimension of the current tensor. Namely, srhs.size() == this->shape()[1].

Note: If the dtype of the srhs is different from the current tensor, the srhs will be casted to the dtype of the current tensor.

See also: append(const Tensor &rhs)

◆ append() [2/6]

void cytnx::Tensor::append ( const Storage & srhs )

inline

the append function of the Storage.

This function is the append function of the Storage. It will append the srhs Storage to the current tensor. The current tensor must be rank-2 and the srhs Storage must have the same size as the second dimension of the current tensor. For example, if the current tensor is \(A\) with size \(M \times N\) and the srhs Storage is \(B\) with size \(N\), then the output tensor is \(C\) with size \(M \times (N+1)\) where

\[ C(i,j) = \begin{cases} A(i,j) & \text{if } j \neq N \\ B(i) & \text{if } j = N \end{cases} \]

Here indices \(i\) and \(j\) start from 0.

Parameters

[in] srhs the appended Storage.

Returns: The appended tensor.

Precondition

The srhs Storage and the current tensor cannot be empty.
The current tensor must be rank-2.
The srhs Storage must have the same size as the second dimension of the current tensor. Namely, srhs.size() == this->shape()[1].

Note: If the dtype of the srhs is different from the current tensor, the srhs will be casted to the dtype of the current tensor.

See also: append(const Tensor &rhs)

◆ append() [3/6]

template<class T >

void cytnx::Tensor::append ( const T & rhs )

inline

the append function of the scalar.

This function is the append function of the scalar. It can only append scalar into rank-1 Tensor.

Parameters

[in] rhs the appended scalar.

Returns: The appended tensor.

Precondition

The current Tensor must be rank-1. (1D array)
The current Tensor must be contiguous.
rhs must be a scalar.

◆ append() [4/6]

template<class T >

void cytnx::Tensor::append ( const T & rhs )

inline

the append function of the scalar.

This function is the append function of the scalar. It can only append scalar into rank-1 Tensor.

Parameters

[in] rhs the appended scalar.

Returns: The appended tensor.

Precondition

The current Tensor must be rank-1. (1D array)
The current Tensor must be contiguous.
rhs must be a scalar.

◆ append() [5/6]

void cytnx::Tensor::append ( const Tensor & rhs )

inline

the append function.

This function is the append function. It will append the rhs tensor to the current tensor. The rhs tensor must have the same shape as the current tensor, except the first dimension. For example, if the current tensor is \(A(i,j,k)\) and the rhs tensor is \(B(j,k)\), then the output tensor is \(C(i,j,k)\) where

\[ C(i,j,k) = \begin{cases} A(i,j,k) & \text{if } i \neq N \\ B(j,k) & \text{if } i = N \end{cases} \]

where \(N\) is the number of the first dimension of the current tensor. Here indices \(i\), \(j\) and \(k\) start from 0.

Parameters

[in] rhs the appended tensor.

Returns: The appended tensor.

Precondition

The rhs tensor and the current tensor cannot be empty.
The rhs tensor must have the same shape as the current tensor, except the first dimension. Namely, rhs.shape()[i] == this->shape()[i+1] and rhs.shape().size() == this->shape().size()-1.

Note: If the dtype of the rhs is different from the current tensor, the rhs will be casted to the dtype of the current tensor.

See also: append(const Storage &rhs)

◆ append() [6/6]

void cytnx::Tensor::append ( const Tensor & rhs )

inline

the append function.

This function is the append function. It will append the rhs tensor to the current tensor. The rhs tensor must have the same shape as the current tensor, except the first dimension. For example, if the current tensor is \(A(i,j,k)\) and the rhs tensor is \(B(j,k)\), then the output tensor is \(C(i,j,k)\) where

\[ C(i,j,k) = \begin{cases} A(i,j,k) & \text{if } i \neq N \\ B(j,k) & \text{if } i = N \end{cases} \]

where \(N\) is the number of the first dimension of the current tensor. Here indices \(i\), \(j\) and \(k\) start from 0.

Parameters

[in] rhs the appended tensor.

Returns: The appended tensor.

Precondition

The rhs tensor and the current tensor cannot be empty.
The rhs tensor must have the same shape as the current tensor, except the first dimension. Namely, rhs.shape()[i] == this->shape()[i+1] and rhs.shape().size() == this->shape().size()-1.

Note: If the dtype of the rhs is different from the current tensor, the rhs will be casted to the dtype of the current tensor.

See also: append(const Storage &rhs)

◆ astype() [1/2]

Tensor cytnx::Tensor::astype ( const int & new_type ) const

inline

return a new Tensor that cast to different dtype.

Parameters

[in] new_type the new dtype. It can be any type defined in cytnx::Type

Returns: [Tensor]

Note: If the new_type is the same as dtype of the current Tensor, return self.

Attention: This function cannot convert complex type to real type, please use Tensor::real() or Tensor::imag() to get the real or imaginary part of the complex Tensor instead.

Example:

c++ API:

output>

python API:

output>

◆ astype() [2/2]

Tensor cytnx::Tensor::astype ( const int & new_type ) const

inline

return a new Tensor that cast to different dtype.

Parameters

[in] new_type the new dtype. It can be any type defined in cytnx::Type

Returns: [Tensor]

Note: If the new_type is the same as dtype of the current Tensor, return self.

Attention: This function cannot convert complex type to real type, please use Tensor::real() or Tensor::imag() to get the real or imaginary part of the complex Tensor instead.

Example:

c++ API:

output>

python API:

output>

◆ at() [1/4]

template<class T >

T & cytnx::Tensor::at ( const std::vector< cytnx_uint64 > & locator )

inline

Get an element at specific location.

This function is used to get an element at specific location. If the template type is not given, the return will be a Scalar.

Parameters

[in] locator the location of the element

Returns: [ref]

Note

This is for C++ API only!
need template instantiation to resolve the type, which should be consist with the dtype of the Tensor. An error will be issued if the template type is inconsist with the current dtype of Tensor.
For python API, use [] directly to get element.

Example:

c++ API:

output>

◆ at() [2/4]

template<class T >

T & cytnx::Tensor::at ( const std::vector< cytnx_uint64 > & locator )

inline

Get an element at specific location.

This function is used to get an element at specific location. If the template type is not given, the return will be a Scalar.

Parameters

[in] locator the location of the element

Returns: [ref]

Note

This is for C++ API only!
need template instantiation to resolve the type, which should be consist with the dtype of the Tensor. An error will be issued if the template type is inconsist with the current dtype of Tensor.
For python API, use [] directly to get element.

Example:

c++ API:

output>

◆ at() [3/4]

template<class T >

const T & cytnx::Tensor::at ( const std::vector< cytnx_uint64 > & locator ) const

inline

See also: at(const std::vector<cytnx_uint64> &locator)

◆ at() [4/4]

template<class T >

const T & cytnx::Tensor::at ( const std::vector< cytnx_uint64 > & locator ) const

inline

See also: at(const std::vector<cytnx_uint64> &locator)

◆ clone() [1/2]

Tensor cytnx::Tensor::clone ( ) const

inline

return a clone of the current Tensor.

Returns: [Tensor]

In C++ API, the behavior of assignment operator is designed to have same behavior as python,
to have a copy of the current tensor, we call clone to return a copy.

Example:

c++ API:

output>

python API:

output>

◆ clone() [2/2]

Tensor cytnx::Tensor::clone ( ) const

inline

return a clone of the current Tensor.

Returns: [Tensor]

In C++ API, the behavior of assignment operator is designed to have same behavior as python,
to have a copy of the current tensor, we call clone to return a copy.

Example:

c++ API:

output>

python API:

output>

◆ Conj() [1/2]

Tensor cytnx::Tensor::Conj ( ) const

the Conj member function. Same as cytnx::linalg::Conj(const Tensor &Tin), where Tin is the current Tensor.

◆ Conj() [2/2]

Tensor cytnx::Tensor::Conj ( ) const

the Conj member function. Same as cytnx::linalg::Conj(const Tensor &Tin), where Tin is the current Tensor.

◆ Conj_() [1/2]

Tensor & cytnx::Tensor::Conj_ ( )

the Conj_ member function. Same as cytnx::linalg::Conj_(Tensor &Tin), where Tin is the current Tensor.

◆ Conj_() [2/2]

Tensor & cytnx::Tensor::Conj_ ( )

the Conj_ member function. Same as cytnx::linalg::Conj_(Tensor &Tin), where Tin is the current Tensor.

◆ contiguous() [1/2]

Tensor cytnx::Tensor::contiguous ( ) const

inline

Make the Tensor contiguous by coalescing the memory (storage).

Returns: [Tensor] a new Tensor that is with contiguous memory (storage).

See also: Tensor::contiguous_()

Example:

c++ API:

output>

python API:

output>

◆ contiguous() [2/2]

Tensor cytnx::Tensor::contiguous ( ) const

inline

Make the Tensor contiguous by coalescing the memory (storage).

Returns: [Tensor] a new Tensor that is with contiguous memory (storage).

See also: Tensor::contiguous_()

Example:

c++ API:

output>

python API:

output>

◆ contiguous_() [1/2]

Tensor cytnx::Tensor::contiguous_ ( )

inline

Make the Tensor contiguous by coalescing the memory (storage), inplacely.

See also: Tensor::contiguous()

Example:

c++ API:

output>

python API:

output>

◆ contiguous_() [2/2]

Tensor cytnx::Tensor::contiguous_ ( )

inline

Make the Tensor contiguous by coalescing the memory (storage), inplacely.

See also: Tensor::contiguous()

Example:

c++ API:

output>

python API:

output>

◆ Cpr() [1/2]

template<class T >

Tensor cytnx::Tensor::Cpr ( const T & rhs )

inline

The comparison function.

This function is the comparison function. Same as cytnx::operator==(const Tensor &self, const T &rhs).

Parameters

[in] rhs the compared object.

◆ Cpr() [2/2]

template<class T >

Tensor cytnx::Tensor::Cpr ( const T & rhs )

inline

The comparison function.

This function is the comparison function. Same as cytnx::operator==(const Tensor &self, const T &rhs).

Parameters

[in] rhs the compared object.

◆ device() [1/2]

int cytnx::Tensor::device ( ) const

inline

the device-id of the Tensor

See also: cytnx::Device

Returns: [int] the device_id of the Tensor

◆ device() [2/2]

int cytnx::Tensor::device ( ) const

inline

the device-id of the Tensor

See also: cytnx::Device

Returns: [int] the device_id of the Tensor

◆ device_str() [1/2]

std::string cytnx::Tensor::device_str ( ) const

inline

the device (in string) of the Tensor

See also: cytnx::Device, device() const

Returns: [std::string] the device of the Tensor

◆ device_str() [2/2]

std::string cytnx::Tensor::device_str ( ) const

inline

the device (in string) of the Tensor

See also: cytnx::Device, device() const

Returns: [std::string] the device of the Tensor

◆ Div() [1/2]

template<class T >

Tensor cytnx::Tensor::Div ( const T & rhs )

inline

Division function with a Tensor or a scalar. Same as cytnx::operator/(const Tensor &self, const T &rhs).

Parameters

[in] rhs the divided Tensor or scalar. @attension rhs cannot be zero.

◆ Div() [2/2]

template<class T >

Tensor cytnx::Tensor::Div ( const T & rhs )

inline

Division function with a Tensor or a scalar. Same as cytnx::operator/(const Tensor &self, const T &rhs).

Parameters

[in] rhs the divided Tensor or scalar. @attension rhs cannot be zero.

◆ Div_() [1/2]

template<class T >

Tensor & cytnx::Tensor::Div_ ( const T & rhs )

inline

Division function with a Tensor or a scalar, inplacely. Same as operator/=(const T &rhs).

Parameters

[in] rhs the divided Tensor or scalar. @attension rhs cannot be zero.

◆ Div_() [2/2]

template<class T >

Tensor & cytnx::Tensor::Div_ ( const T & rhs )

inline

Division function with a Tensor or a scalar, inplacely. Same as operator/=(const T &rhs).

Parameters

[in] rhs the divided Tensor or scalar. @attension rhs cannot be zero.

◆ dtype() [1/2]

unsigned int cytnx::Tensor::dtype ( ) const

inline

the dtype-id of the Tensor

See also: cytnx::Type

Returns: [unsigned int] the dtype_id of the Tensor

◆ dtype() [2/2]

unsigned int cytnx::Tensor::dtype ( ) const

inline

the dtype-id of the Tensor

See also: cytnx::Type

Returns: [unsigned int] the dtype_id of the Tensor

◆ dtype_str() [1/2]

std::string cytnx::Tensor::dtype_str ( ) const

inline

the dtype (in string) of the Tensor

See also: cytnx::Type, dtype() const

Returns: [std::string] the dtype of the Tensor

◆ dtype_str() [2/2]

std::string cytnx::Tensor::dtype_str ( ) const

inline

the dtype (in string) of the Tensor

See also: cytnx::Type, dtype() const

Returns: [std::string] the dtype of the Tensor

◆ Eigh() [1/2]

std::vector< Tensor > cytnx::Tensor::Eigh	(	const bool &	is_V = `true`,
		const bool &	row_v = `false`
	)		const

the Eigh member function. Same as cytnx::linalg::Eigh(const Tensor &Tin, const bool &is_V, const bool &row_v) , where Tin is the current Tensor.

◆ Eigh() [2/2]

std::vector< Tensor > cytnx::Tensor::Eigh	(	const bool &	is_V = `true`,
		const bool &	row_v = `false`
	)		const

the Eigh member function. Same as cytnx::linalg::Eigh(const Tensor &Tin, const bool &is_V, const bool &row_v) , where Tin is the current Tensor.

◆ equivshape() [1/2]

bool cytnx::Tensor::equivshape ( const Tensor & rhs )

inline

compare the shape of two tensors.

Parameters

[in] rhs the tensor to be compared.

◆ equivshape() [2/2]

bool cytnx::Tensor::equivshape ( const Tensor & rhs )

inline

compare the shape of two tensors.

Parameters

[in] rhs the tensor to be compared.

◆ Exp() [1/2]

Tensor cytnx::Tensor::Exp ( ) const

the Exp member function. Same as linalg::Exp(const Tensor &Tin), where Tin is the current Tensor.

◆ Exp() [2/2]

Tensor cytnx::Tensor::Exp ( ) const

the Exp member function. Same as linalg::Exp(const Tensor &Tin), where Tin is the current Tensor.

◆ Exp_() [1/2]

Tensor & cytnx::Tensor::Exp_ ( )

the Exp_ member function. Same as linalg::Exp_(Tensor &Tin), where Tin is the current Tensor.

◆ Exp_() [2/2]

Tensor & cytnx::Tensor::Exp_ ( )

the Exp_ member function. Same as linalg::Exp_(Tensor &Tin), where Tin is the current Tensor.

◆ fill() [1/2]

template<class T >

void cytnx::Tensor::fill ( const T & val )

inline

fill all the element of current Tensor with the value.

Parameters

[in] val the assigned value

Example:

c++ API:

output>

python API

output>

◆ fill() [2/2]

template<class T >

void cytnx::Tensor::fill ( const T & val )

inline

fill all the element of current Tensor with the value.

Parameters

[in] val the assigned value

Example:

c++ API:

output>

python API

output>

◆ flatten() [1/2]

Tensor cytnx::Tensor::flatten ( ) const

inline

The flatten function.

This function is the flatten function. It will clone (deep copy) , contiguos the current tensor and reshape it to 1-rank Tensor.

Note: compare to the flatten_() function, this function will return a new tensor and the current tensor will not be changed.

◆ flatten() [2/2]

Tensor cytnx::Tensor::flatten ( ) const

inline

The flatten function.

This function is the flatten function. It will clone (deep copy) , contiguos the current tensor and reshape it to 1-rank Tensor.

Note: compare to the flatten_() function, this function will return a new tensor and the current tensor will not be changed.

◆ flatten_() [1/2]

void cytnx::Tensor::flatten_ ( )

inline

The flatten function, inplacely.

This function is the flatten function, inplacely. It will contiguos the current tensor and reshape it to 1-rank Tensor.

Note: compare to the flatten() function, this is an inplacely function, the current tensor will be changed.

◆ flatten_() [2/2]

void cytnx::Tensor::flatten_ ( )

inline

The flatten function, inplacely.

This function is the flatten function, inplacely. It will contiguos the current tensor and reshape it to 1-rank Tensor.

Note: compare to the flatten() function, this is an inplacely function, the current tensor will be changed.

◆ from_storage() [1/2]

static Tensor cytnx::Tensor::from_storage ( const Storage & in )

inlinestatic

Convert a Storage to Tensor.

Parameters

[in] in the Storage to be converted

Returns: [Tensor] a Tensor with the same dtype and device as the input Storage

◆ from_storage() [2/2]

static Tensor cytnx::Tensor::from_storage ( const Storage & in )

inlinestatic

Convert a Storage to Tensor.

Parameters

[in] in the Storage to be converted

Returns: [Tensor] a Tensor with the same dtype and device as the input Storage

◆ Fromfile() [1/4]

static Tensor cytnx::Tensor::Fromfile	(	const char *	fname,
		const unsigned int &	dtype,
		const cytnx_int64 &	count = `-1`
	)

static

◆ Fromfile() [2/4]

static Tensor cytnx::Tensor::Fromfile	(	const char *	fname,
		const unsigned int &	dtype,
		const cytnx_int64 &	count = `-1`
	)

static

◆ Fromfile() [3/4]

static Tensor cytnx::Tensor::Fromfile	(	const std::string &	fname,
		const unsigned int &	dtype,
		const cytnx_int64 &	count = `-1`
	)

static

Load current Tensor from the binary file.

This function will load the Tensor from the binary file which is saved by cytnx::Tensor::Tofile. Given the file name fname , data type dtype and number of elements count, this function will load the first count elements from the binary file fname with data type dtype.

Parameters

fname[in]	the file name of the binary file.
dtype[in]	the data type of the binary file. This can be any of the type defined in cytnx::Type.
count[in]	the number of elements to be loaded from the binary file. If set to -1, all elements in the binary file will be loaded.

Returns: Tensor

Precondition

The dtype cannot be Type.Void.
The dtype must be the same as the data type of the binary file.
The Nelem cannot be 0.
The Nelem cannot be larger than the number of elements in the binary file.
The file name fname must be valid.

See also: cytnx::Tensor::Tofile

◆ Fromfile() [4/4]

static Tensor cytnx::Tensor::Fromfile	(	const std::string &	fname,
		const unsigned int &	dtype,
		const cytnx_int64 &	count = `-1`
	)

static

Load current Tensor from the binary file.

This function will load the Tensor from the binary file which is saved by cytnx::Tensor::Tofile. Given the file name fname , data type dtype and number of elements count, this function will load the first count elements from the binary file fname with data type dtype.

Parameters

fname[in]	the file name of the binary file.
dtype[in]	the data type of the binary file. This can be any of the type defined in cytnx::Type.
count[in]	the number of elements to be loaded from the binary file. If set to -1, all elements in the binary file will be loaded.

Returns: Tensor

Precondition

The dtype cannot be Type.Void.
The dtype must be the same as the data type of the binary file.
The Nelem cannot be 0.
The Nelem cannot be larger than the number of elements in the binary file.
The file name fname must be valid.

See also: cytnx::Tensor::Tofile

◆ get() [1/2]

Tensor cytnx::Tensor::get ( const std::vector< cytnx::Accessor > & accessors ) const

inline

get elements using Accessor (C++ API) / slices (python API)

Parameters

[in] accessors the Accessor (C++ API) / slices (python API) to get the elements.

Returns: [Tensor]

See also: Accessor for cordinate with Accessor in C++ API.

Note

the return will be a new Tensor instance, which not share memory with the current Tensor.

Equivalently:

One can also using more intruisive way to get the slice using [] operator.

Example:

c++ API:

output>

python API:

output>

◆ get() [2/2]

Tensor cytnx::Tensor::get ( const std::vector< cytnx::Accessor > & accessors ) const

inline

get elements using Accessor (C++ API) / slices (python API)

Parameters

[in] accessors the Accessor (C++ API) / slices (python API) to get the elements.

Returns: [Tensor]

See also: Accessor for cordinate with Accessor in C++ API.

Note

the return will be a new Tensor instance, which not share memory with the current Tensor.

Equivalently:

One can also using more intruisive way to get the slice using [] operator.

Example:

c++ API:

output>

python API:

output>

◆ imag() [1/2]

Tensor cytnx::Tensor::imag ( )

return the imaginary part of the tensor.

Returns: [Tensor] the imaginary part of the tensor.

Precondition: the tensor must be complex type (Type.ComplexDouble or Type.ComplexFloat).

See also: cytnx::Type

◆ imag() [2/2]

Tensor cytnx::Tensor::imag ( )

return the imaginary part of the tensor.

Returns: [Tensor] the imaginary part of the tensor.

Precondition: the tensor must be complex type (Type.ComplexDouble or Type.ComplexFloat).

See also: cytnx::Type

◆ Init() [1/2]

void cytnx::Tensor::Init	(	const std::vector< cytnx_uint64 > &	shape,
		const unsigned int &	dtype = `Type.Double`,
		const int &	device = `-1`,
		const bool &	init_zero = `true`
	)

inline

initialize a Tensor

Parameters

[in]	shape	the shape of tensor.
[in]	dtype	the dtype of tensor. This can be any of type defined in cytnx::Type
[in]	device	the device that tensor to be created. This can be cytnx::Device.cpu or
[in]	init_zero	if true, the content of Tensor will be initialized to zero. if false, the content of Tensor will be un-initialize. cytnx::Device.cuda+<gpuid>, see cytnx::Device for more detail.

Note: The content of Tensor created will be un-initialize! See zeros(), ones() or arange() for generating an Tensor.

Example:

c++ API:

output>

python API:

output>

◆ Init() [2/2]

void cytnx::Tensor::Init	(	const std::vector< cytnx_uint64 > &	shape,
		const unsigned int &	dtype = `Type.Double`,
		const int &	device = `-1`,
		const bool &	init_zero = `true`
	)

inline

initialize a Tensor

Parameters

[in]	shape	the shape of tensor.
[in]	dtype	the dtype of tensor. This can be any of type defined in cytnx::Type
[in]	device	the device that tensor to be created. This can be cytnx::Device.cpu or
[in]	init_zero	if true, the content of Tensor will be initialized to zero. if false, the content of Tensor will be un-initialize. cytnx::Device.cuda+<gpuid>, see cytnx::Device for more detail.

Note: The content of Tensor created will be un-initialize! See zeros(), ones() or arange() for generating an Tensor.

Example:

c++ API:

output>

python API:

output>

◆ Inv() [1/2]

Tensor cytnx::Tensor::Inv ( const double & clip ) const

the Inv member function. Same as cytnx::linalg::Inv(const Tensor &Tin, const double &clip)

◆ Inv() [2/2]

Tensor cytnx::Tensor::Inv ( const double & clip ) const

the Inv member function. Same as cytnx::linalg::Inv(const Tensor &Tin, const double &clip)

◆ Inv_() [1/2]

Tensor & cytnx::Tensor::Inv_ ( const double & clip )

the Inv_ member function. Same as cytnx::linalg::Inv_(Tensor &Tin, const double &clip)

◆ Inv_() [2/2]

Tensor & cytnx::Tensor::Inv_ ( const double & clip )

the Inv_ member function. Same as cytnx::linalg::Inv_(Tensor &Tin, const double &clip)

◆ InvM() [1/2]

Tensor cytnx::Tensor::InvM ( ) const

the InvM member function. Same as cytnx::linalg::InvM(const Tensor &Tin), where Tin is the current Tensor.

◆ InvM() [2/2]

Tensor cytnx::Tensor::InvM ( ) const

the InvM member function. Same as cytnx::linalg::InvM(const Tensor &Tin), where Tin is the current Tensor.

◆ InvM_() [1/2]

Tensor & cytnx::Tensor::InvM_ ( )

the InvM_ member function. Same as cytnx::linalg::InvM_(Tensor &Tin), where Tin is the current Tensor.

◆ InvM_() [2/2]

Tensor & cytnx::Tensor::InvM_ ( )

the InvM_ member function. Same as cytnx::linalg::InvM_(Tensor &Tin), where Tin is the current Tensor.

◆ is_contiguous() [1/2]

const bool & cytnx::Tensor::is_contiguous ( ) const

inline

return whether the Tensor is contiguous or not.

Returns: [bool] true if the Tensor is contiguous, false otherwise.

◆ is_contiguous() [2/2]

const bool & cytnx::Tensor::is_contiguous ( ) const

inline

return whether the Tensor is contiguous or not.

Returns: [bool] true if the Tensor is contiguous, false otherwise.

◆ item() [1/2]

template<class T >

T & cytnx::Tensor::item ( )

inline

get the element from a rank-0 Tensor.

This function is used to get the element from a rank-0 Tensor. If the template type is not given, the return will be a Scalar.

Returns: [T]

Note

This can only be called on a rank-0 Tensor (scalar). For C++ API, a template instantiation of type is needed to resolve the type, which should be connsist with the dtype of the Tensor. An error will be issued if the template type if inconsist with the current dtype of Tensor.
Although the return is by reference in C++ part, the return in python is not.
From 2., We recommend user to use at<T> (C++ API) and [] (python API) to modify the value of the element to have consistant syntax across two languages.

Example:

c++ API:

output>

python API:

output>

◆ item() [2/2]

template<class T >

T & cytnx::Tensor::item ( )

inline

get the element from a rank-0 Tensor.

This function is used to get the element from a rank-0 Tensor. If the template type is not given, the return will be a Scalar.

Returns: [T]

Note

This can only be called on a rank-0 Tensor (scalar). For C++ API, a template instantiation of type is needed to resolve the type, which should be connsist with the dtype of the Tensor. An error will be issued if the template type if inconsist with the current dtype of Tensor.
Although the return is by reference in C++ part, the return in python is not.
From 2., We recommend user to use at<T> (C++ API) and [] (python API) to modify the value of the element to have consistant syntax across two languages.

Example:

c++ API:

output>

python API:

output>

◆ Load() [1/4]

static Tensor cytnx::Tensor::Load ( const char * fname )

static

See also: Load(const std::string &fname)

◆ Load() [2/4]

static Tensor cytnx::Tensor::Load ( const char * fname )

static

See also: Load(const std::string &fname)

◆ Load() [3/4]

static Tensor cytnx::Tensor::Load ( const std::string & fname )

static

Load current Tensor from file.

Parameters

fname[in] file name

load the Storage from file with file path specify with input param 'fname'

Precondition: the file must be a Tensor object which is saved by cytnx::Tensor::Save.

◆ Load() [4/4]

static Tensor cytnx::Tensor::Load ( const std::string & fname )

static

Load current Tensor from file.

Parameters

fname[in] file name

load the Storage from file with file path specify with input param 'fname'

Precondition: the file must be a Tensor object which is saved by cytnx::Tensor::Save.

◆ Max() [1/2]

Tensor cytnx::Tensor::Max ( ) const

the Max member function. Same as linalg::Max(const Tensor &Tin), where Tin is the current Tensor.

◆ Max() [2/2]

Tensor cytnx::Tensor::Max ( ) const

the Max member function. Same as linalg::Max(const Tensor &Tin), where Tin is the current Tensor.

◆ Min() [1/2]

Tensor cytnx::Tensor::Min ( ) const

the Min member function. Same as linalg::Min(const Tensor &Tin), where Tin is the current Tensor.

◆ Min() [2/2]

Tensor cytnx::Tensor::Min ( ) const

the Min member function. Same as linalg::Min(const Tensor &Tin), where Tin is the current Tensor.

◆ Mod() [1/2]

template<class T >

Tensor cytnx::Tensor::Mod ( const T & rhs )

inline

◆ Mod() [2/2]

template<class T >

Tensor cytnx::Tensor::Mod ( const T & rhs )

inline

◆ Mul() [1/2]

template<class T >

Tensor cytnx::Tensor::Mul ( const T & rhs )

inline

Multiplication function with a Tensor or a scalar. Same as cytnx::operator*(const Tensor &self, const T &rhs).

Parameters

[in] rhs the multiplied Tensor or scalar.

◆ Mul() [2/2]

template<class T >

Tensor cytnx::Tensor::Mul ( const T & rhs )

inline

Multiplication function with a Tensor or a scalar. Same as cytnx::operator*(const Tensor &self, const T &rhs).

Parameters

[in] rhs the multiplied Tensor or scalar.

◆ Mul_() [1/2]

template<class T >

Tensor & cytnx::Tensor::Mul_ ( const T & rhs )

inline

Multiplication function with a Tensor or a scalar, inplacely. Same as operator*=(const T &rhs).

Parameters

[in] rhs the multiplied Tensor or scalar.

◆ Mul_() [2/2]

template<class T >

Tensor & cytnx::Tensor::Mul_ ( const T & rhs )

inline

Multiplication function with a Tensor or a scalar, inplacely. Same as operator*=(const T &rhs).

Parameters

[in] rhs the multiplied Tensor or scalar.

◆ Norm() [1/2]

Tensor cytnx::Tensor::Norm ( ) const

the Norm member function. Same as linalg::Norm(const Tensor &Tin), where Tin is the current Tensor.

◆ Norm() [2/2]

Tensor cytnx::Tensor::Norm ( ) const

the Norm member function. Same as linalg::Norm(const Tensor &Tin), where Tin is the current Tensor.

◆ operator*=() [1/2]

template<class T >

Tensor & cytnx::Tensor::operator*= ( const T & rc )

multiplication assignment operator with a Tensor or a scalar.

This function will multiply the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be multiplied to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the multiplied Tensor or scalar.

Precondition: If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.

◆ operator*=() [2/2]

template<class T >

Tensor & cytnx::Tensor::operator*= ( const T & rc )

multiplication assignment operator with a Tensor or a scalar.

This function will multiply the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be multiplied to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the multiplied Tensor or scalar.

Precondition: If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.

◆ operator+=() [1/2]

template<class T >

Tensor & cytnx::Tensor::operator+= ( const T & rc )

addition assignment operator with a Tensor or a scalar.

This function will add the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be added to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the added Tensor or scalar.

Precondition: If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.

◆ operator+=() [2/2]

template<class T >

Tensor & cytnx::Tensor::operator+= ( const T & rc )

addition assignment operator with a Tensor or a scalar.

This function will add the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be added to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the added Tensor or scalar.

Precondition: If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.

◆ operator-() [1/2]

Tensor cytnx::Tensor::operator- ( )

inline

The negation function.

This function is the negation function. Namely, if the current tensor is \(A\), then the output tensor is \(-A\).

Returns: The negation of the current tensor.

◆ operator-() [2/2]

Tensor cytnx::Tensor::operator- ( )

inline

The negation function.

This function is the negation function. Namely, if the current tensor is \(A\), then the output tensor is \(-A\).

Returns: The negation of the current tensor.

◆ operator-=() [1/2]

template<class T >

Tensor & cytnx::Tensor::operator-= ( const T & rc )

subtraction assignment operator with a Tensor or a scalar.

This function will subtract the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be subtracted to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the subtracted Tensor or scalar.

Precondition: If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.

◆ operator-=() [2/2]

template<class T >

Tensor & cytnx::Tensor::operator-= ( const T & rc )

subtraction assignment operator with a Tensor or a scalar.

This function will subtract the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be subtracted to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the subtracted Tensor or scalar.

Precondition: If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.

◆ operator/=() [1/2]

template<class T >

Tensor & cytnx::Tensor::operator/= ( const T & rc )

division assignment operator with a Tensor or a scalar.

This function will divide the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be divided to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the divided Tensor or scalar.

Precondition

If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.
rc cannot be zero.

◆ operator/=() [2/2]

template<class T >

Tensor & cytnx::Tensor::operator/= ( const T & rc )

division assignment operator with a Tensor or a scalar.

This function will divide the template type to the current tensor, inplacely. The template can be either a scalar or a tensor. If the template is a scalar, then the scalar will be divided to all the elements of the current tensor. If the template is a tensor, then the shape of the template tensor must be the same as the current tensor. The supported type of the template are Tensor, Scalar or any scalar type (see cytnx_complex128, cytnx_complex64, cytnx_double, cytnx_float, cytnx_int64, cytnx_int32, cytnx_int16, cytnx_uint64, cytnx_uint32, cytnx_uint16, cytnx_bool).

Parameters

[in] rc the divided Tensor or scalar.

Precondition

If the template type is Tensor, then the shape of the template tensor must be the same as the current tensor.
rc cannot be zero.

◆ permute() [1/2]

Tensor cytnx::Tensor::permute ( const std::vector< cytnx_uint64 > & rnks ) const

inline

perform tensor permute on the cytnx::Tensor and return a new instance.

Parameters

[in] rnks the permute indices, should have No. of elements equal to the rank of tensor.

Returns: [Tensor] a permuted new Tensor

Precondition

The size of input and output Tensor should be the same.
rnks cannot contain duplicated elements.

Example:

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python API:

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◆ permute() [2/2]

Tensor cytnx::Tensor::permute ( const std::vector< cytnx_uint64 > & rnks ) const

inline

perform tensor permute on the cytnx::Tensor and return a new instance.

Parameters

[in] rnks the permute indices, should have No. of elements equal to the rank of tensor.

Returns: [Tensor] a permuted new Tensor

Precondition

The size of input and output Tensor should be the same.
rnks cannot contain duplicated elements.

Example:

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python API:

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◆ permute_() [1/2]

Tensor cytnx::Tensor::permute_ ( const std::vector< cytnx_uint64 > & rnks )

inline

◆ permute_() [2/2]

Tensor cytnx::Tensor::permute_ ( const std::vector< cytnx_uint64 > & rnks )

inline

◆ Pow() [1/2]

Tensor cytnx::Tensor::Pow ( const cytnx_double & p ) const

the Pow member function. Same as linalg::Pow(const Tensor &Tin, const cytnx_double &p), where Tin is the current Tensor.

◆ Pow() [2/2]

Tensor cytnx::Tensor::Pow ( const cytnx_double & p ) const

the Pow member function. Same as linalg::Pow(const Tensor &Tin, const cytnx_double &p), where Tin is the current Tensor.

◆ Pow_() [1/2]

Tensor & cytnx::Tensor::Pow_ ( const cytnx_double & p )

the Pow_ member function. Same as linalg::Pow_(Tensor &Tin, const cytnx_double &p), where Tin is the current Tensor.

◆ Pow_() [2/2]

Tensor & cytnx::Tensor::Pow_ ( const cytnx_double & p )

the Pow_ member function. Same as linalg::Pow_(Tensor &Tin, const cytnx_double &p), where Tin is the current Tensor.

◆ rank() [1/2]

cytnx_uint64 cytnx::Tensor::rank ( ) const

inline

the rank of the Tensor

Returns: [cytnx_uint64] the rank of the Tensor

◆ rank() [2/2]

cytnx_uint64 cytnx::Tensor::rank ( ) const

inline

the rank of the Tensor

Returns: [cytnx_uint64] the rank of the Tensor

◆ real() [1/2]

Tensor cytnx::Tensor::real ( )

return the real part of the tensor.

Returns: [Tensor] the real part of the tensor.

Precondition: the tensor must be complex type (Type.ComplexDouble or Type.ComplexFloat).

See also: cytnx::Type

◆ real() [2/2]

Tensor cytnx::Tensor::real ( )

return the real part of the tensor.

Returns: [Tensor] the real part of the tensor.

Precondition: the tensor must be complex type (Type.ComplexDouble or Type.ComplexFloat).

See also: cytnx::Type

◆ reshape() [1/6]

Tensor cytnx::Tensor::reshape ( const std::initializer_list< cytnx_int64 > & new_shape ) const

inline

See also: reshape(const std::vector<cytnx_int64> &new_shape) const

◆ reshape() [2/6]

Tensor cytnx::Tensor::reshape ( const std::initializer_list< cytnx_int64 > & new_shape ) const

inline

See also: reshape(const std::vector<cytnx_int64> &new_shape) const

◆ reshape() [3/6]

Tensor cytnx::Tensor::reshape ( const std::vector< cytnx_int64 > & new_shape ) const

inline

return a new Tensor that is reshaped.

Parameters

[in] new_shape the new shape of the Tensor.

Returns: [Tensor]

Precondition

The size of input and output Tensor should be the same.
new_shape cannot be empty.

Note

This function will not change the original Tensor.
You can use Tensor::reshape_() to reshape the Tensor inplacely.
You can set new_shape to -1, which will be automatically determined by the size of the Tensor. The behavior is the same as numpy.reshape().

See also: Tensor::reshape_()

Example:

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◆ reshape() [4/6]

Tensor cytnx::Tensor::reshape ( const std::vector< cytnx_int64 > & new_shape ) const

inline

return a new Tensor that is reshaped.

Parameters

[in] new_shape the new shape of the Tensor.

Returns: [Tensor]

Precondition

The size of input and output Tensor should be the same.
new_shape cannot be empty.

Note

This function will not change the original Tensor.
You can use Tensor::reshape_() to reshape the Tensor inplacely.
You can set new_shape to -1, which will be automatically determined by the size of the Tensor. The behavior is the same as numpy.reshape().

See also: Tensor::reshape_()

Example:

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python API:

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◆ reshape() [5/6]

Tensor cytnx::Tensor::reshape ( const std::vector< cytnx_uint64 > & new_shape ) const

inline

See also: reshape(const std::vector<cytnx_int64> &new_shape) const

◆ reshape() [6/6]

Tensor cytnx::Tensor::reshape ( const std::vector< cytnx_uint64 > & new_shape ) const

inline

See also: reshape(const std::vector<cytnx_int64> &new_shape) const

◆ reshape_() [1/2]

void cytnx::Tensor::reshape_ ( const std::vector< cytnx_int64 > & new_shape )

inline

reshape the Tensor, inplacely

Parameters

[in] new_shape the new shape of the Tensor.

Precondition

The size of input and output Tensor should be the same.
new_shape cannot be empty.

See also: Tensor::reshape()

Note: Compare to reshape(), this function will not create a new Tensor, but reshape the current Tensor inplacely.

Example:

c++ API:

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python API:

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◆ reshape_() [2/2]

void cytnx::Tensor::reshape_ ( const std::vector< cytnx_int64 > & new_shape )

inline

reshape the Tensor, inplacely

Parameters

[in] new_shape the new shape of the Tensor.

Precondition

The size of input and output Tensor should be the same.
new_shape cannot be empty.

See also: Tensor::reshape()

Note: Compare to reshape(), this function will not create a new Tensor, but reshape the current Tensor inplacely.

Example:

c++ API:

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python API:

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◆ same_data() [1/2]

bool cytnx::Tensor::same_data ( const Tensor & rhs ) const

Check whether two tensors share the same internal memory.

This function will check whether two tensors share the same internal memory. If the two tensors share the same internal memory, then the function will return true. Otherwise, it will return false. See user guide for more details.

Parameters

[in] rhs the tensor to be compared.

◆ same_data() [2/2]

bool cytnx::Tensor::same_data ( const Tensor & rhs ) const

Check whether two tensors share the same internal memory.

This function will check whether two tensors share the same internal memory. If the two tensors share the same internal memory, then the function will return true. Otherwise, it will return false. See user guide for more details.

Parameters

[in] rhs the tensor to be compared.

◆ Save() [1/4]

void cytnx::Tensor::Save ( const char * fname ) const

See also: Save(const std::string &fname) const

◆ Save() [2/4]

void cytnx::Tensor::Save ( const char * fname ) const

See also: Save(const std::string &fname) const

◆ Save() [3/4]

void cytnx::Tensor::Save ( const std::string & fname ) const

Save current Tensor to file.

Parameters

[in] fname file name (without file extension)

save the Tensor to file with file path specify with input param fname with postfix ".cytn"

See also: Load(const std::string &fname)

◆ Save() [4/4]

void cytnx::Tensor::Save ( const std::string & fname ) const

Save current Tensor to file.

Parameters

[in] fname file name (without file extension)

save the Tensor to file with file path specify with input param fname with postfix ".cytn"

See also: Load(const std::string &fname)

◆ set() [1/4]

template<class T >

void cytnx::Tensor::set	(	const std::vector< cytnx::Accessor > &	accessors,
		const T &	rc
	)

inline

set elements with the input constant using Accessor (C++ API) / slices (python API)

Parameters

[in]	accessors	the list(vector) of accessors.
	rc	[Const]

See also: Tensor::fill for filling all elements with assigned constant.

Example:

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◆ set() [2/4]

template<class T >

void cytnx::Tensor::set	(	const std::vector< cytnx::Accessor > &	accessors,
		const T &	rc
	)

inline

set elements with the input constant using Accessor (C++ API) / slices (python API)

Parameters

[in]	accessors	the list(vector) of accessors.
	rc	[Const]

See also: Tensor::fill for filling all elements with assigned constant.

Example:

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◆ set() [3/4]

void cytnx::Tensor::set	(	const std::vector< cytnx::Accessor > &	accessors,
		const Tensor &	rhs
	)

inline

set elements with the input Tensor using Accessor (C++ API) / slices (python API)

Parameters

[in]	accessors	the list(vector) of accessors.
	rhs	[Tensor]

Note: : the shape of the input Tensor should be the same as the shape that indicated using Accessor. The memory is not shared with the input Tensor.

Example:

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python API:

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◆ set() [4/4]

void cytnx::Tensor::set	(	const std::vector< cytnx::Accessor > &	accessors,
		const Tensor &	rhs
	)

inline

set elements with the input Tensor using Accessor (C++ API) / slices (python API)

Parameters

[in]	accessors	the list(vector) of accessors.
	rhs	[Tensor]

Note: : the shape of the input Tensor should be the same as the shape that indicated using Accessor. The memory is not shared with the input Tensor.

Example:

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python API:

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◆ shape() [1/2]

const std::vector< cytnx_uint64 > & cytnx::Tensor::shape ( ) const

inline

the shape of the Tensor

Returns: [std::vector<cytnx_uint64>] the shape of the Tensor

◆ shape() [2/2]

const std::vector< cytnx_uint64 > & cytnx::Tensor::shape ( ) const

inline

the shape of the Tensor

Returns: [std::vector<cytnx_uint64>] the shape of the Tensor

◆ storage() [1/2]

Storage & cytnx::Tensor::storage ( ) const

inline

return the storage of current Tensor.

Returns: [Storage]

Note: The return storage shares the same instance of the storage of current Tensor. Use Storage.clone() to create a new instance of the returned Storage.

◆ storage() [2/2]

Storage & cytnx::Tensor::storage ( ) const

inline

return the storage of current Tensor.

Returns: [Storage]

Note: The return storage shares the same instance of the storage of current Tensor. Use Storage.clone() to create a new instance of the returned Storage.

◆ Sub() [1/2]

template<class T >

Tensor cytnx::Tensor::Sub ( const T & rhs )

inline

Subtraction function with a Tensor or a scalar. Same as cytnx::operator-(const Tensor &self, const T &rhs).

Parameters

[in] rhs the subtracted Tensor or scalar.

◆ Sub() [2/2]

template<class T >

Tensor cytnx::Tensor::Sub ( const T & rhs )

inline

Subtraction function with a Tensor or a scalar. Same as cytnx::operator-(const Tensor &self, const T &rhs).

Parameters

[in] rhs the subtracted Tensor or scalar.

◆ Sub_() [1/2]

template<class T >

Tensor & cytnx::Tensor::Sub_ ( const T & rhs )

inline

Subtraction function with a Tensor or a scalar, inplacely. Same as operator-=(const T &rhs).

Parameters

[in] rhs the subtracted Tensor or scalar.

◆ Sub_() [2/2]

template<class T >

Tensor & cytnx::Tensor::Sub_ ( const T & rhs )

inline

Subtraction function with a Tensor or a scalar, inplacely. Same as operator-=(const T &rhs).

Parameters

[in] rhs the subtracted Tensor or scalar.

◆ Svd() [1/2]

std::vector< Tensor > cytnx::Tensor::Svd ( const bool & is_UvT = true ) const

the SVD member function. Same as cytnx::linalg::Svd(const Tensor &Tin, const bool &is_UvT) , where Tin is the current Tensor.

◆ Svd() [2/2]

std::vector< Tensor > cytnx::Tensor::Svd ( const bool & is_UvT = true ) const

the SVD member function. Same as cytnx::linalg::Svd(const Tensor &Tin, const bool &is_UvT) , where Tin is the current Tensor.

◆ to() [1/2]

Tensor cytnx::Tensor::to ( const int & device ) const

inline

copy a tensor to new device

Parameters

[in] device the device-id that is moving to. it can be any device defined in cytnx::Device

Returns: [Tensor]

description:
if the device-id is the same as current Tensor's device, then return self.
otherwise, return a copy of instance that located on the target device.
see also: Tensor.to_

Example:

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python API:

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◆ to() [2/2]

Tensor cytnx::Tensor::to ( const int & device ) const

inline

copy a tensor to new device

Parameters

[in] device the device-id that is moving to. it can be any device defined in cytnx::Device

Returns: [Tensor]

description:
if the device-id is the same as current Tensor's device, then return self.
otherwise, return a copy of instance that located on the target device.
see also: Tensor.to_

Example:

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◆ to_() [1/2]

void cytnx::Tensor::to_ ( const int & device )

inline

move the current Tensor to the device.

Parameters

[in] device the device-id that is moving to. it can be any device defined in cytnx::Device

description:
see also: Tensor.to

Example:

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◆ to_() [2/2]

void cytnx::Tensor::to_ ( const int & device )

inline

move the current Tensor to the device.

Parameters

[in] device the device-id that is moving to. it can be any device defined in cytnx::Device

description:
see also: Tensor.to

Example:

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python API:

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◆ Tofile() [1/6]

void cytnx::Tensor::Tofile ( const char * fname ) const

See also: Tofile(const std::string &fname) const

◆ Tofile() [2/6]

void cytnx::Tensor::Tofile ( const char * fname ) const

See also: Tofile(const std::string &fname) const

◆ Tofile() [3/6]

void cytnx::Tensor::Tofile ( const std::string & fname ) const

Save current Tensor to the binary file.

This function will save the Tensor to the binary file with file name fname .

Parameters

fname[in] the file name of the binary file.

Precondition: The file name fname must be valid.

See also: cytnx::Tensor::Fromfile

◆ Tofile() [4/6]

void cytnx::Tensor::Tofile ( const std::string & fname ) const

Save current Tensor to the binary file.

This function will save the Tensor to the binary file with file name fname .

Parameters

fname[in] the file name of the binary file.

Precondition: The file name fname must be valid.

See also: cytnx::Tensor::Fromfile

◆ Tofile() [5/6]

void cytnx::Tensor::Tofile ( std::fstream & f ) const

See also: Tofile(const std::string &fname) const

◆ Tofile() [6/6]

void cytnx::Tensor::Tofile ( std::fstream & f ) const

See also: Tofile(const std::string &fname) const

◆ Trace() [1/2]

Tensor cytnx::Tensor::Trace	(	const cytnx_uint64 &	a = `0`,
		const cytnx_uint64 &	b = `1`
	)		const

the Trace member function. Same as linalg::Trace(const Tensor &Tin, const cytnx_uint64 &a, const cytnx_uint64 &b), where Tin is the current Tensor.

◆ Trace() [2/2]

Tensor cytnx::Tensor::Trace	(	const cytnx_uint64 &	a = `0`,
		const cytnx_uint64 &	b = `1`
	)		const

the Trace member function. Same as linalg::Trace(const Tensor &Tin, const cytnx_uint64 &a, const cytnx_uint64 &b), where Tin is the current Tensor.

The documentation for this class was generated from the following files:

include/Tensor.hpp
include/Tensor.old.hpp

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ Tensor() [1/2]

◆ Tensor() [2/2]

Member Function Documentation

◆ Abs() [1/2]

◆ Abs() [2/2]

◆ Abs_() [1/2]

◆ Abs_() [2/2]

◆ Add() [1/2]

◆ Add() [2/2]

◆ Add_() [1/2]

◆ Add_() [2/2]

◆ append() [1/6]

◆ append() [2/6]

◆ append() [3/6]

◆ append() [4/6]

◆ append() [5/6]

◆ append() [6/6]

◆ astype() [1/2]

Example:

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◆ astype() [2/2]

Example:

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◆ at() [1/4]

Example:

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◆ at() [2/4]

Example:

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◆ at() [3/4]

◆ at() [4/4]

◆ clone() [1/2]

Example:

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◆ clone() [2/2]

Example:

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◆ Conj() [1/2]

◆ Conj() [2/2]

◆ Conj_() [1/2]

◆ Conj_() [2/2]

◆ contiguous() [1/2]

Example:

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◆ contiguous() [2/2]

Example:

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◆ contiguous_() [1/2]

Example:

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◆ contiguous_() [2/2]

Example:

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