cytnx::Tensor Class Reference

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
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)
	initialize a Tensor
	Tensor (const std::vector< cytnx_uint64 > &shape, const unsigned int &dtype=Type.Double, const int &device=-1)
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
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	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)
	[C++ only] 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 an 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	equiv (const Tensor &rhs)
Tensor	real ()
Tensor	imag ()
template<class T >
Tensor &	operator+= (const T &rc)
template<class T >
Tensor &	operator-= (const T &rc)
template<class T >
Tensor &	operator*= (const T &rc)
template<class T >
Tensor &	operator/= (const T &rc)
template<class T >
Tensor	Add (const T &rhs)
template<class T >
Tensor &	Add_ (const T &rhs)
template<class T >
Tensor	Sub (const T &rhs)
template<class T >
Tensor &	Sub_ (const T &rhs)
template<class T >
Tensor	Mul (const T &rhs)
template<class T >
Tensor &	Mul_ (const T &rhs)
template<class T >
Tensor	Div (const T &rhs)
template<class T >
Tensor &	Div_ (const T &rhs)
template<class T >
Tensor	Cpr (const T &rhs)
template<class T >
Tensor	Mod (const T &rhs)
Tensor	operator- ()
Tensor	flatten () const
void	flatten_ ()
void	append (const Tensor &rhs)
void	append (const Storage &srhs)
template<class T >
void	append (const T &rhs)
bool	same_data (const Tensor &rhs) const
std::vector< Tensor >	Svd (const bool &is_U=true, const bool &is_vT=true) const
std::vector< Tensor >	Eigh (const bool &is_V=true, const bool &row_v=false) const
Tensor &	InvM_ ()
Tensor	InvM () const
Tensor &	Inv_ (const double &clip)
Tensor	Inv (const double &clip) const
Tensor &	Conj_ ()
Tensor	Conj () const
Tensor &	Exp_ ()
Tensor	Exp () const
Tensor	Norm () const
Tensor	Pow (const cytnx_double &p) const
Tensor &	Pow_ (const cytnx_double &p)
Tensor	Trace (const cytnx_uint64 &a, const cytnx_uint64 &b) const
Tensor	Abs () const
Tensor &	Abs_ ()
Tensor	Max () const
Tensor	Min () const
template<>
Tensor &	operator+= (const Tensor &rc)
template<>
Tensor &	operator+= (const cytnx_complex128 &rc)
template<>
Tensor &	operator+= (const cytnx_complex64 &rc)
template<>
Tensor &	operator+= (const cytnx_double &rc)
template<>
Tensor &	operator+= (const cytnx_float &rc)
template<>
Tensor &	operator+= (const cytnx_int64 &rc)
template<>
Tensor &	operator+= (const cytnx_uint64 &rc)
template<>
Tensor &	operator+= (const cytnx_int32 &rc)
template<>
Tensor &	operator+= (const cytnx_uint32 &rc)
template<>
Tensor &	operator+= (const cytnx_int16 &rc)
template<>
Tensor &	operator+= (const cytnx_uint16 &rc)
template<>
Tensor &	operator+= (const cytnx_bool &rc)
template<>
Tensor &	operator+= (const Scalar &rc)
template<>
Tensor &	operator-= (const Tensor &rc)
template<>
Tensor &	operator-= (const cytnx_complex128 &rc)
template<>
Tensor &	operator-= (const cytnx_complex64 &rc)
template<>
Tensor &	operator-= (const cytnx_double &rc)
template<>
Tensor &	operator-= (const cytnx_float &rc)
template<>
Tensor &	operator-= (const cytnx_int64 &rc)
template<>
Tensor &	operator-= (const cytnx_uint64 &rc)
template<>
Tensor &	operator-= (const cytnx_int32 &rc)
template<>
Tensor &	operator-= (const cytnx_uint32 &rc)
template<>
Tensor &	operator-= (const cytnx_int16 &rc)
template<>
Tensor &	operator-= (const cytnx_uint16 &rc)
template<>
Tensor &	operator-= (const cytnx_bool &rc)
template<>
Tensor &	operator-= (const Scalar &rc)
template<>
Tensor &	operator*= (const Tensor &rc)
template<>
Tensor &	operator*= (const cytnx_complex128 &rc)
template<>
Tensor &	operator*= (const cytnx_complex64 &rc)
template<>
Tensor &	operator*= (const cytnx_double &rc)
template<>
Tensor &	operator*= (const cytnx_float &rc)
template<>
Tensor &	operator*= (const cytnx_int64 &rc)
template<>
Tensor &	operator*= (const cytnx_uint64 &rc)
template<>
Tensor &	operator*= (const cytnx_int32 &rc)
template<>
Tensor &	operator*= (const cytnx_uint32 &rc)
template<>
Tensor &	operator*= (const cytnx_int16 &rc)
template<>
Tensor &	operator*= (const cytnx_uint16 &rc)
template<>
Tensor &	operator*= (const cytnx_bool &rc)
template<>
Tensor &	operator*= (const Scalar &rc)
template<>
Tensor &	operator/= (const Tensor &rc)
template<>
Tensor &	operator/= (const cytnx_complex128 &rc)
template<>
Tensor &	operator/= (const cytnx_complex64 &rc)
template<>
Tensor &	operator/= (const cytnx_double &rc)
template<>
Tensor &	operator/= (const cytnx_float &rc)
template<>
Tensor &	operator/= (const cytnx_int64 &rc)
template<>
Tensor &	operator/= (const cytnx_uint64 &rc)
template<>
Tensor &	operator/= (const cytnx_int32 &rc)
template<>
Tensor &	operator/= (const cytnx_uint32 &rc)
template<>
Tensor &	operator/= (const cytnx_int16 &rc)
template<>
Tensor &	operator/= (const cytnx_uint16 &rc)
template<>
Tensor &	operator/= (const cytnx_bool &rc)
template<>
Tensor &	operator/= (const Scalar &rc)

Static Public Member Functions
static Tensor	Load (const std::string &fname)
	Load current Tensor to file.
static Tensor	Load (const char *fname)
static Tensor	Fromfile (const std::string &fname, const unsigned int &dtype, const cytnx_int64 &count=-1)
static Tensor	Fromfile (const char *fname, const unsigned int &dtype, const cytnx_int64 &count=-1)
static Tensor	from_storage (const Storage &in)

Detailed Description

an tensor (multi-dimensional array)

Constructor & Destructor Documentation

Tensor()

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

inline

Member Function Documentation

Abs()

Tensor cytnx::Tensor::Abs

(

)

const

Abs_()

Tensor & cytnx::Tensor::Abs_

(

)

Add()

template<class T >

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

inline

Add_()

template<class T >

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

inline

append() [1/3]

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

inline

append() [2/3]

template<class T >

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

inline

append() [3/3]

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

inline

astype()

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

inline

return a new Tensor that cast to different dtype.

Parameters

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.

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A = zeros({3,4,5},Type.Double);
    cout << A;
 
    Tensor B = A.astype(Type.Uint64);
    cout << B;
 
    // the new type is the same as current dtype, return self.
    Tensor C = A.astype(Type.Double);
    cout << is(C,A) << endl; // this should be true.
 
    return 0;
}
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]]


Total elem: 60
type  : Uint64
cytnx device: CPU
Shape : (3,4,5)
[[[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]]

1

python API:

from cytnx import *
 
A = zeros([3,4,5],dtype=Type.Double)
print(A)
 
B = A.astype(Type.Uint64)
print(B)
 
C = A.astype(Type.Double)
print(C is A)
 
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]]




Total elem: 60
type  : Uint64
cytnx device: CPU
Shape : (3,4,5)
[[[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]]



True

at() [1/2]

template<class T >

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

inline

[C++ only] get an element at specific location.

Parameters

locator

the location of the element

Returns

[ref]

Note:

1. This is for C++ API only!
2. 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.
3. For python API, use [] directly to get element.

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A = arange(30,Type.Float).reshape(2,3,5);
    cout << A << endl;
 
    // note that type resolver should be consist with the dtype 
    cout << A.at<cytnx_float>(0,0,2) << endl;
 
 
    // the return is a ref., can be modify directly.
    A.at<cytnx_float>(0,0,2) = 999;
 
    
    cout << A.at<cytnx_float>(0,0,2) << endl;
 
 
    // [Note] there are two way to give argument:
    // Method 1: more like 'c++' way:
    //           (alternatively, you can also simply give a std::vector)
    A.at<cytnx_float>({0,0,2}); // note the braket{}
 
    // Method 2: more like 'python' way:
    A.at<cytnx_float>(0,0,2); 
 
 
 
    return 0;
}
 

output>

Total elem: 30
type  : Float32
cytnx device: CPU
Shape : (2,3,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]]
 [[1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]
  [2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]]]


2
999

at() [2/2]

template<class T >

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

inline

clone()

Tensor cytnx::Tensor::clone ( ) const

inline

return a clone of the current Tensor.

Returns

[Tensor]

description:
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:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A({3,4,5});
 
    Tensor B = A; // B shares same object with A
    Tensor C = A.clone(); // C is a copy of A
    
    // use is() to check if two variable shares same object
    cout << is(B,A) << endl;
    cout << is(C,A) << endl;
 
    return 0;
}
 

output>

1
0

python API:

from cytnx import *
 
A = Tensor([3,4,5])
 
B = A
C = A.clone()
 
print(B is A)
print(C is A)
 

output>

True
False

Conj()

Tensor cytnx::Tensor::Conj

(

)

const

Conj_()

Tensor & cytnx::Tensor::Conj_

(

)

contiguous()

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:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A({3,4,5});
    cout << A.shape() << endl;
 
    Tensor B = A.permute({0,2,1});
    cout << B.shape() << endl;
 
    //[Note] permute will not actually move the internal memory (storage) layout.
    //       this is called non-contiguous status. 
    //       the memory layout will only move when Tensor.contiguous() is called.
    Tensor C = B.contiguous(); //actual moving the memory
    cout << B.is_contiguous() << endl; // false.
    cout << C.is_contiguous() << endl; // true.
    cout << C.shape() << endl;
 
    cout << C.same_data(B) << endl; //false
    cout << B.same_data(A) << endl; //true
    
 
    return 0;
}
 

output>

Vector Print:
Total Elements:3
[3, 4, 5]

Vector Print:
Total Elements:3
[3, 5, 4]

0
1
Vector Print:
Total Elements:3
[3, 5, 4]

0
1

python API:

from cytnx import *
 
A = Tensor([3,4,5])
print(A.shape())
 
B = A.permute(0,2,1)
print(B.shape())
 
C = B.contiguous()
print(B.is_contiguous()) #false
print(C.is_contiguous()) #true
print(C.shape())
 

output>

[3, 4, 5]
[3, 5, 4]
False
True
[3, 5, 4]

contiguous_()

Tensor cytnx::Tensor::contiguous_ ( )

inline

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

See also Tensor::contiguous()

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A({3,4,5});
    cout << A.shape() << endl;
 
    Tensor B = A.permute({0,2,1});
    cout << B.shape() << endl;
 
    //[Note] permute will not actually move the internal memory (storage) layout.
    //       this is called non-contiguous status. 
    //       the memory layout will only move when Tensor.contiguous() is called.
    cout << B.is_contiguous() << endl; // false.
    B.contiguous_(); //actual moving the memory
    cout << B.is_contiguous() << endl; // true.
    
 
    return 0;
}
 

output>

Vector Print:
Total Elements:3
[3, 4, 5]

Vector Print:
Total Elements:3
[3, 5, 4]

0
1

python API:

from cytnx import *
 
A = Tensor([3,4,5])
print(A.shape())
 
B = A.permute(0,2,1)
print(B.shape())
 
print(B.is_contiguous()) #false
B.contiguous_()
print(B.is_contiguous()) #true
    
 

output>

[3, 4, 5]
[3, 5, 4]
False
True

Cpr()

template<class T >

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

inline

device()

int cytnx::Tensor::device ( ) const

inline

the device-id of the Tensor

Returns

[cytnx_int64] the device_id of the Tensor

device_str()

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

inline

the device (in string) of the Tensor

Returns

[std::string] the device of the Tensor

Div()

template<class T >

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

inline

Div_()

template<class T >

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

inline

dtype()

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

inline

the dtype-id of the Tensor

Returns

[cytnx_uint64] the dtype_id of the Tensor

dtype_str()

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

inline

the dtype (in string) of the Tensor

Returns

[std::string] the dtype of the Tensor

Eigh()

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

equiv()

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

inline

Exp()

Tensor cytnx::Tensor::Exp

(

)

const

Exp_()

Tensor & cytnx::Tensor::Exp_

(

)

fill()

template<class T >

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

inline

fill all the element of current Tensor with the value.

Parameters

val	the assigned value

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){
 
 
    Tensor A = arange(60).reshape({3,4,5});
    cout << A << endl;
 
    A.fill(999);
    cout << A << endl;
 
    return 0;
}

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]]
 [[9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]]
 [[9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]]]

python API

from cytnx import *
 
A = arange(60).reshape(3,4,5)
print(A)
 
A.fill(999)
print(A)

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]]
 [[9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]]
 [[9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 9.99000e+02 ]]]

flatten()

Tensor cytnx::Tensor::flatten ( ) const

inline

flatten_()

void cytnx::Tensor::flatten_ ( )

inline

from_storage()

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

inlinestatic

Fromfile() [1/2]

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

static

Fromfile() [2/2]

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

static

get()

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

inline

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

Returns

[Tensor]

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

Note:

1. 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:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){
 
    typedef Accessor ac;
 
    /*
        In C++ API, generally, there are two ways you can access Tensor.
        1. Using operator():
            This is more of 'python' way.
 
        2. Using low-level API or operator[]:   
            This is more of 'c++' way.       
    */
 
    Tensor A = arange(60).reshape({3,4,5});
    cout << A << endl;
 
    //Method 1, Using operator():
    //-----------------------------------------
    Tensor B = A(2,":","2:5:1"); 
    cout << B << endl;
 
    /* [Note] 
        This is equivalent as:
        > Tensor B = A(2,ac::all(),ac::range(2,5,1));
        
        See also cytnx::Accessor.
    */
 
    //Method 2, Using operator[] or low-level API get():
    //----------------------------------------
    Tensor B2 = A[{ac(2),ac::all(),ac::range(2,5,1)}]; // remember the {}braket
    cout << B2 << endl;
 
    /* [Note] 
        You can also use the low-level API get() as
        > Tensor B2 = A.get({ac(2),ac::all(),ac::range(2,5,1)});
    */
    return 0;
}

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]



Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[4.20000e+01 4.30000e+01 4.40000e+01 ]
 [4.70000e+01 4.80000e+01 4.90000e+01 ]
 [5.20000e+01 5.30000e+01 5.40000e+01 ]
 [5.70000e+01 5.80000e+01 5.90000e+01 ]]



Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[4.20000e+01 4.30000e+01 4.40000e+01 ]
 [4.70000e+01 4.80000e+01 4.90000e+01 ]
 [5.20000e+01 5.30000e+01 5.40000e+01 ]
 [5.70000e+01 5.80000e+01 5.90000e+01 ]]

python API:

from cytnx import *
 
A = arange(60).reshape(3,4,5)
print(A)
 
B = A[2,:,2:5:1]
print(B)
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]




Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[4.20000e+01 4.30000e+01 4.40000e+01 ]
 [4.70000e+01 4.80000e+01 4.90000e+01 ]
 [5.20000e+01 5.30000e+01 5.40000e+01 ]
 [5.70000e+01 5.80000e+01 5.90000e+01 ]]

imag()

Tensor cytnx::Tensor::imag

(

)

Init()

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

inline

initialize a Tensor

Parameters

shape	the shape of tensor.
dtype	the dtype of tensor. This can be any of type defined in cytnx::Type
device	the device that tensor to be created. This can be cytnx::Device.cpu or cytnx::Device.cuda+<gpuid>

[Note]

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

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    /*
        1. Create a Tensor with 
        shape (3,4,5),
        dtype =Type.Double [default],
        device=Device.cpu [default]
    */
    Tensor A({3,4,5});
    cout << A << endl;
 
    /*
        2. Create a Tensor with 
        shape (3,4,5),
        dtype =Type.Uint64,
        device=Device.cpu [default],
        [Note] the dtype can be any one of the supported type.
    */
    Tensor B({3,4,5},Type.Uint64);
    cout << B << endl;
 
    /*
        3. Initialize a Tensor with 
        shape (3,4,5),
        dtype =Type.Double,
        device=Device.cuda+0, (on gpu with gpu-id=0)
        [Note] the gpu device can be set with Device.cuda+<gpu-id>
    */
    Tensor C({3,4,5},Type.Double,Device.cuda+0);
    cout << C << endl;
 
    //4. Create an empty Tensor, and init later
    Tensor D;
    D.Init({3,4,5},Type.Double,Device.cpu);
 
    return 0;
}
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]]



Total elem: 60
type  : Uint64
cytnx device: CPU
Shape : (3,4,5)
[[[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CUDA/GPU-id:0
Shape : (3,4,5)
[[[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]]

python API:

from cytnx import *
 
 
#1. Create a Tensor with 
#  shape (3,4,5),
#  dtype =Type.Double [default],
#  device=Device.cpu [default]
A = Tensor([3,4,5])
print(A)
 
#2. Create a Tensor with 
#  shape (3,4,5),
#  dtype =Type.Uint64,
#  device=Device.cpu [default],
#  [Note] the dtype can be any one of the supported type.
B = Tensor([3,4,5],dtype=Type.Uint64)
print(B)
 
#3. Initialize a Tensor with 
#  shape (3,4,5),
#  dtype =Type.Double,
#  device=Device.cuda+0, (on gpu with gpu-id=0)
#  [Note] the gpu device can be set with Device.cuda+<gpu-id>
C = Tensor([3,4,5],dtype=Type.Double,device=Device.cuda+0);
print(C)
 
#4. Create an empty Tensor, and init later
D = Tensor()
D.Init([3,4,5],dtype=Type.Double,device=Device.cpu);
 
 
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]]




Total elem: 60
type  : Uint64
cytnx device: CPU
Shape : (3,4,5)
[[[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]
 [[                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]
  [                  0                   0                   0                   0                   0 ]]]




Total elem: 60
type  : Double (Float64)
cytnx device: CUDA/GPU-id:0
Shape : (3,4,5)
[[[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]
 [[0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 ]]]

Inv()

Tensor cytnx::Tensor::Inv

(

const double &

clip

)

const

Inv_()

Tensor & cytnx::Tensor::Inv_

(

const double &

clip

)

InvM()

Tensor cytnx::Tensor::InvM

(

)

const

InvM_()

Tensor & cytnx::Tensor::InvM_

(

)

is_contiguous()

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

inline

item()

template<class T >

T & cytnx::Tensor::item ( )

inline

get an from a rank-0 Tensor

Returns

[T]

Note:

1. 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.
2. Although the return is by reference in C++ part, the return in python is not.
3. 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:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A = ones(1,Type.Uint64);
    cout << A << endl;
 
    // note that type resolver should be consist with the dtype 
    cout << A.item<cytnx_uint64>() << endl;
    
 
    return 0;
}
 

output>

Total elem: 1
type  : Uint64
cytnx device: CPU
Shape : (1)
[                  1 ]


1

python API:

from cytnx import *
 
A = ones(1,Type.Uint64)
print(A)
 
print(A.item())
 
 

output>

Total elem: 1
type  : Uint64
cytnx device: CPU
Shape : (1)
[                  1 ]



1

Load() [1/2]

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

static

Load() [2/2]

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

static

Load current Tensor to file.

Parameters

fname

file name

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

Max()

Tensor cytnx::Tensor::Max

(

)

const

Min()

Tensor cytnx::Tensor::Min

(

)

const

Mod()

template<class T >

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

inline

Mul()

template<class T >

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

inline

Mul_()

template<class T >

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

inline

Norm()

Tensor cytnx::Tensor::Norm

(

)

const

operator*=() [1/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_bool &

rc

)

operator*=() [2/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_complex128 &

rc

)

operator*=() [3/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_complex64 &

rc

)

operator*=() [4/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_double &

rc

)

operator*=() [5/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_float &

rc

)

operator*=() [6/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_int16 &

rc

)

operator*=() [7/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_int32 &

rc

)

operator*=() [8/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_int64 &

rc

)

operator*=() [9/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_uint16 &

rc

)

operator*=() [10/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_uint32 &

rc

)

operator*=() [11/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const cytnx_uint64 &

rc

)

operator*=() [12/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const Scalar &

rc

)

operator*=() [13/14]

template<class T >

Tensor & cytnx::Tensor::operator*=

(

const T &

rc

)

operator*=() [14/14]

template<>

Tensor & cytnx::Tensor::operator*=

(

const Tensor &

rc

)

operator+=() [1/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_bool &

rc

)

operator+=() [2/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_complex128 &

rc

)

operator+=() [3/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_complex64 &

rc

)

operator+=() [4/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_double &

rc

)

operator+=() [5/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_float &

rc

)

operator+=() [6/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_int16 &

rc

)

operator+=() [7/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_int32 &

rc

)

operator+=() [8/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_int64 &

rc

)

operator+=() [9/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_uint16 &

rc

)

operator+=() [10/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_uint32 &

rc

)

operator+=() [11/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const cytnx_uint64 &

rc

)

operator+=() [12/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const Scalar &

rc

)

operator+=() [13/14]

template<class T >

Tensor & cytnx::Tensor::operator+=

(

const T &

rc

)

operator+=() [14/14]

template<>

Tensor & cytnx::Tensor::operator+=

(

const Tensor &

rc

)

operator-()

Tensor cytnx::Tensor::operator- ( )

inline

operator-=() [1/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_bool &

rc

)

operator-=() [2/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_complex128 &

rc

)

operator-=() [3/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_complex64 &

rc

)

operator-=() [4/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_double &

rc

)

operator-=() [5/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_float &

rc

)

operator-=() [6/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_int16 &

rc

)

operator-=() [7/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_int32 &

rc

)

operator-=() [8/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_int64 &

rc

)

operator-=() [9/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_uint16 &

rc

)

operator-=() [10/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_uint32 &

rc

)

operator-=() [11/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const cytnx_uint64 &

rc

)

operator-=() [12/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const Scalar &

rc

)

operator-=() [13/14]

template<class T >

Tensor & cytnx::Tensor::operator-=

(

const T &

rc

)

operator-=() [14/14]

template<>

Tensor & cytnx::Tensor::operator-=

(

const Tensor &

rc

)

operator/=() [1/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_bool &

rc

)

operator/=() [2/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_complex128 &

rc

)

operator/=() [3/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_complex64 &

rc

)

operator/=() [4/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_double &

rc

)

operator/=() [5/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_float &

rc

)

operator/=() [6/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_int16 &

rc

)

operator/=() [7/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_int32 &

rc

)

operator/=() [8/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_int64 &

rc

)

operator/=() [9/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_uint16 &

rc

)

operator/=() [10/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_uint32 &

rc

)

operator/=() [11/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const cytnx_uint64 &

rc

)

operator/=() [12/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const Scalar &

rc

)

operator/=() [13/14]

template<class T >

Tensor & cytnx::Tensor::operator/=

(

const T &

rc

)

operator/=() [14/14]

template<>

Tensor & cytnx::Tensor::operator/=

(

const Tensor &

rc

)

permute()

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

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

Returns

[Tensor] a permuted new Tensor

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A({3,4,5});
    cout << A.shape() << endl;
 
    Tensor B = A.permute({0,2,1});
    cout << B.shape() << endl;
 
    cout << is(B,A)  << endl; // this should be false, different object.
    
    cout << B.same_data(A) << endl; // this should be true, since no new pointer/memory is created.
 
    return 0;
}
 

output>

Vector Print:
Total Elements:3
[3, 4, 5]

Vector Print:
Total Elements:3
[3, 5, 4]

0
1

python API:

from cytnx import *
 
A = Tensor([3,4,5])
print(A.shape())
 
B = A.permute(0,2,1)
print(B.shape())
 
print(B is A) #False
 
print(B.same_data(A)) #True
 
 

output>

[3, 4, 5]
[3, 5, 4]
False
True

permute_()

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

inline

Pow()

Tensor cytnx::Tensor::Pow

(

const cytnx_double &

p

)

const

Pow_()

Tensor & cytnx::Tensor::Pow_

(

const cytnx_double &

p

)

rank()

cytnx_uint64 cytnx::Tensor::rank ( ) const

inline

the rank of the Tensor

Returns

[cytnx_uint64] the rank of the Tensor

real()

Tensor cytnx::Tensor::real

(

)

reshape()

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

inline

return a new Tensor that is reshaped.

Parameters

new_shape

the new shape of the Tensor.

Returns

[Tensor]

See also Tensor::reshape_()

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A = arange(60);
 
    
    // there are two ways you can give argument to reshape:
 
    // Method 1: more like 'C++' way: 
    Tensor B = A.reshape({5,12}); // note the braket{}
    cout << A << endl;
    cout << B << endl;
 
    // Method 2: more like 'python' way:
    Tensor B2 = A.reshape(5,12);
 
 
    return 0;
}
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,12)
[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 ]
 [1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 ]
 [2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 ]
 [3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 ]
 [4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,12)
[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 ]
 [1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 ]
 [2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 ]
 [3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 ]
 [4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]

python API:

from cytnx import *
 
A = arange(60)
 
B = A.reshape(5,12)
print(A)
print(B)

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,12)
[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 ]
 [1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 ]
 [2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 ]
 [3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 ]
 [4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,12)
[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 ]
 [1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 ]
 [2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 ]
 [3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 ]
 [4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]

reshape_()

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

inline

reshape the Tensor, inplacely

Parameters

new_shape

the new shape of the Tensor.

See also Tensor::reshape()

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){ 
 
    Tensor A = arange(60);
    cout << A << endl;
 
 
    //there are two ways you can assign the argument:
 
    // Method 1: more like 'c++' way:
    A.reshape_({5,12}); //note the braket{}
    cout << A << endl;
 
    // Method 2: more like 'python' way:
    A.reshape_(5,4,3);
    cout << A << endl;
 
    return 0;
}
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (60)
[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,12)
[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 ]
 [1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 ]
 [2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 ]
 [3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 ]
 [4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,4,3)
[[[0.00000e+00 1.00000e+00 2.00000e+00 ]
  [3.00000e+00 4.00000e+00 5.00000e+00 ]
  [6.00000e+00 7.00000e+00 8.00000e+00 ]
  [9.00000e+00 1.00000e+01 1.10000e+01 ]]
 [[1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 ]
  [1.80000e+01 1.90000e+01 2.00000e+01 ]
  [2.10000e+01 2.20000e+01 2.30000e+01 ]]
 [[2.40000e+01 2.50000e+01 2.60000e+01 ]
  [2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 ]
  [3.30000e+01 3.40000e+01 3.50000e+01 ]]
 [[3.60000e+01 3.70000e+01 3.80000e+01 ]
  [3.90000e+01 4.00000e+01 4.10000e+01 ]
  [4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 ]]
 [[4.80000e+01 4.90000e+01 5.00000e+01 ]
  [5.10000e+01 5.20000e+01 5.30000e+01 ]
  [5.40000e+01 5.50000e+01 5.60000e+01 ]
  [5.70000e+01 5.80000e+01 5.90000e+01 ]]]

python API:

from cytnx import *
 
A = arange(60)
print(A)
A.reshape_(5,12)
print(A)

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (60)
[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (5,12)
[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 1.00000e+01 1.10000e+01 ]
 [1.20000e+01 1.30000e+01 1.40000e+01 1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 ]
 [2.40000e+01 2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 3.50000e+01 ]
 [3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 4.50000e+01 4.60000e+01 4.70000e+01 ]
 [4.80000e+01 4.90000e+01 5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]

same_data()

bool cytnx::Tensor::same_data

(

const Tensor &

rhs

)

const

Save() [1/2]

void cytnx::Tensor::Save

(

const char *

fname

)

const

Save() [2/2]

void cytnx::Tensor::Save

(

const std::string &

fname

)

const

Save current Tensor to file.

Parameters

fname

file name

description: save the Storage to file with file path specify with input param 'fname' with postfix ".cytn"

set() [1/2]

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

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

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

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){
 
    typedef Accessor ac;
 
 
    // Let's define two Tensor. 
    Tensor A = arange(60).reshape({3,4,5});
    cout << A << endl;
 
    Tensor B = zeros({4,3});
    cout << B << endl;
 
 
    // I. To set part of A with Tesnor B, or constant:
    // [Method 1] Using direct assignment
    //-------------------------------------
    A(2,":","2:5:1") = B; 
    cout << A << endl;
 
    A(2,":","2:5:1") = 999; 
    cout << A << endl;
 
    // note this is the same as 
    // A(ac(2),ac::all(),ac::range(2,5,1)) = B;
    // A[{ac(2),ac::all(),ac::range(2,5,1)}] = B; // note that braket{}
 
 
    // [Method 2] Using low-level API set():
    //--------------------------------------
    A.set({ac(2),ac::all(),ac::range(2,5,1)},B);
    cout << A << endl;
    
    A.set({ac(2),ac::all(),ac::range(0,2,1)},999);
    cout << A << endl;
     
 
 
 
    return 0;
}

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]



Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [4.50000e+01 4.60000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [5.00000e+01 5.10000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [5.50000e+01 5.60000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]]]

python API:

from cytnx import *
 
A = arange(60).reshape(3,4,5)
print(A)
 
B = zeros([4,3])
print(B)
 
A[2,:,2:5:1] = B
print(A)
 
A[0,:,0:2:1] = 999
print(A)
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]




Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[9.99000e+02 9.99000e+02 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [9.99000e+02 9.99000e+02 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [9.99000e+02 9.99000e+02 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [9.99000e+02 9.99000e+02 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]

set() [2/2]

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

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:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){
 
    typedef Accessor ac;
 
 
    // Let's define two Tensor. 
    Tensor A = arange(60).reshape({3,4,5});
    cout << A << endl;
 
    Tensor B = zeros({4,3});
    cout << B << endl;
 
 
    // I. To set part of A with Tesnor B, or constant:
    // [Method 1] Using direct assignment
    //-------------------------------------
    A(2,":","2:5:1") = B; 
    cout << A << endl;
 
    A(2,":","2:5:1") = 999; 
    cout << A << endl;
 
    // note this is the same as 
    // A(ac(2),ac::all(),ac::range(2,5,1)) = B;
    // A[{ac(2),ac::all(),ac::range(2,5,1)}] = B; // note that braket{}
 
 
    // [Method 2] Using low-level API set():
    //--------------------------------------
    A.set({ac(2),ac::all(),ac::range(2,5,1)},B);
    cout << A << endl;
    
    A.set({ac(2),ac::all(),ac::range(0,2,1)},999);
    cout << A << endl;
     
 
 
 
    return 0;
}

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]



Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [4.50000e+01 4.60000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [5.00000e+01 5.10000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]
  [5.50000e+01 5.60000e+01 9.99000e+02 9.99000e+02 9.99000e+02 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]



Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [9.99000e+02 9.99000e+02 0.00000e+00 0.00000e+00 0.00000e+00 ]]]

python API:

from cytnx import *
 
A = arange(60).reshape(3,4,5)
print(A)
 
B = zeros([4,3])
print(B)
 
A[2,:,2:5:1] = B
print(A)
 
A[0,:,0:2:1] = 999
print(A)
 

output>

Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 4.20000e+01 4.30000e+01 4.40000e+01 ]
  [4.50000e+01 4.60000e+01 4.70000e+01 4.80000e+01 4.90000e+01 ]
  [5.00000e+01 5.10000e+01 5.20000e+01 5.30000e+01 5.40000e+01 ]
  [5.50000e+01 5.60000e+01 5.70000e+01 5.80000e+01 5.90000e+01 ]]]




Total elem: 12
type  : Double (Float64)
cytnx device: CPU
Shape : (4,3)
[[0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]
 [0.00000e+00 0.00000e+00 0.00000e+00 ]]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]




Total elem: 60
type  : Double (Float64)
cytnx device: CPU
Shape : (3,4,5)
[[[9.99000e+02 9.99000e+02 2.00000e+00 3.00000e+00 4.00000e+00 ]
  [9.99000e+02 9.99000e+02 7.00000e+00 8.00000e+00 9.00000e+00 ]
  [9.99000e+02 9.99000e+02 1.20000e+01 1.30000e+01 1.40000e+01 ]
  [9.99000e+02 9.99000e+02 1.70000e+01 1.80000e+01 1.90000e+01 ]]
 [[2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]
  [3.00000e+01 3.10000e+01 3.20000e+01 3.30000e+01 3.40000e+01 ]
  [3.50000e+01 3.60000e+01 3.70000e+01 3.80000e+01 3.90000e+01 ]]
 [[4.00000e+01 4.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [4.50000e+01 4.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.00000e+01 5.10000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]
  [5.50000e+01 5.60000e+01 0.00000e+00 0.00000e+00 0.00000e+00 ]]]

shape()

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()

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

inline

return the storage of current Tensor.

Returns

[Storage]

Note:

1. 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()

template<class T >

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

inline

Sub_()

template<class T >

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

inline

Svd()

std::vector< Tensor > cytnx::Tensor::Svd	(	const bool &	is_U = true,
		const bool &	is_vT = true
	)		const

to()

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

inline

copy a tensor to new device

Parameters

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:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){
 
    Tensor A({3,4,5});
 
    //move the tensor to different device by creating a clone object
    Tensor B = A.to(Device.cuda+0);
    cout << B.device_str() << endl;
    cout << A.device_str() << endl;    
 
 
    return 0;
}

output>

cytnx device: CUDA/GPU-id:0
cytnx device: CPU

python API:

from cytnx import *
 
A = Tensor([3,4,5])
B = A.to(Device.cuda+0);
print(B.device_str())
print(A.device_str())
 
 

output>

cytnx device: CUDA/GPU-id:0
cytnx device: CPU

to_()

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

inline

move the current Tensor to the device.

Parameters

device

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

description:
see also: Tensor.to

Example:

c++ API:

#include "cytnx.hpp"
#include <iostream>
 
 
using namespace cytnx;
using namespace std;
int main(){
 
    Tensor A({3,4,5});
 
    // move the instance tensor to different device
    A.to_(Device.cuda+0);
    cout << A.device_str() << endl;
 
    return 0;
}

output>

cytnx device: CUDA/GPU-id:0

python API:

from cytnx import *
 
A = Tensor([3,4,5])
 
A.to_(Device.cuda+0);
print(A.device_str())
 

output>

cytnx device: CUDA/GPU-id:0

Tofile() [1/3]

void cytnx::Tensor::Tofile

(

const char *

fname

)

const

Tofile() [2/3]

void cytnx::Tensor::Tofile

(

const std::string &

fname

)

const

Tofile() [3/3]

void cytnx::Tensor::Tofile

(

std::fstream &

f

)

const

Trace()

Tensor cytnx::Tensor::Trace	(	const cytnx_uint64 &	a,
		const cytnx_uint64 &	b
	)		const

---

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

• include/Tensor.hpp
• src/Tensor.cpp