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Paddle/python/paddle/complex/tensor/math.py

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.common_ops_import import *
from ..helper import is_complex, is_real, complex_variable_exists
from ...fluid.framework import ComplexVariable
from ...fluid import layers
from ...tensor import math
__all__ = [
'elementwise_add',
'elementwise_sub',
'elementwise_mul',
'elementwise_div',
'kron',
'trace',
'sum',
]
def elementwise_add(x, y, axis=-1, name=None):
"""
The element-wise addition layer for complex number inputs. At least one of
inputs :attr:`x` and :attr:`y` must be a ComplexVariable. See the detailed
description for the function and other arguments
in :ref:`api_fluid_layers_elementwise_add` .
Args:
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
with dg.guard():
x = dg.to_variable(a)
y = dg.to_variable(b)
out = paddle.complex.elementwise_add(x, y)
print(out.numpy())
# [[ 6.+3.j 8.+3.j]
# [10.+3.j 12.+3.j]]
"""
complex_variable_exists([x, y], "elementwise_add")
(x_real, x_imag) = (x.real, x.imag) if is_complex(x) else (x, None)
(y_real, y_imag) = (y.real, y.imag) if is_complex(y) else (y, None)
real = layers.elementwise_add(x_real, y_real, axis=axis, name=name)
if is_real(x_imag) and is_real(y_imag):
imag = layers.elementwise_add(x_imag, y_imag, axis=axis, name=name)
elif is_real(x_imag):
imag = layers.assign(x_imag)
else:
imag = layers.elementwise_add(
layers.zeros_like(x_real), y_imag, axis=axis, name=name)
return ComplexVariable(real, imag)
def elementwise_sub(x, y, axis=-1, name=None):
"""
The element-wise subtraction layer for complex number inputs. At least one of
inputs :attr:`x` and :attr:`y` must be a ComplexVariable. See the detailed
description for the function and other arguments
in :ref:`api_fluid_layers_elementwise_sub` .
Args:
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
with dg.guard():
x = dg.to_variable(a)
y = dg.to_variable(b)
out = paddle.complex.elementwise_sub(x, y)
print(out.numpy())
# [[-4.-1.j -4.-1.j]
# [-4.-1.j -4.-1.j]]
"""
complex_variable_exists([x, y], "elementwise_sub")
(x_real, x_imag) = (x.real, x.imag) if is_complex(x) else (x, None)
(y_real, y_imag) = (y.real, y.imag) if is_complex(y) else (y, None)
real = layers.elementwise_sub(x_real, y_real, axis=axis, name=name)
if is_real(x_imag) and is_real(y_imag):
imag = layers.elementwise_sub(x_imag, y_imag, axis=axis, name=name)
elif is_real(x_imag):
imag = layers.assign(x_imag)
else:
imag = layers.elementwise_sub(
layers.zeros_like(x_real), y_imag, axis=axis, name=name)
return ComplexVariable(real, imag)
def elementwise_mul(x, y, axis=-1, name=None):
"""
The element-wise multiplication layer for complex number inputs. At least
one of inputs :attr:`x` and :attr:`y` must be a ComplexVariable. See the
detailed description for the function and other arguments
in :ref:`api_fluid_layers_elementwise_mul` .
Args:
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
with dg.guard():
x = dg.to_variable(a)
y = dg.to_variable(b)
out = paddle.complex.elementwise_mul(x, y)
print(out.numpy())
# [[ 3. +7.j 10.+10.j]
# [19.+13.j 30.+16.j]]
"""
complex_variable_exists([x, y], "elementwise_mul")
# (a + bi)(c + di) = (ac - bd) + (bc + ad)i
(a, b) = (x.real, x.imag) if is_complex(x) else (x, None)
(c, d) = (y.real, y.imag) if is_complex(y) else (y, None)
ac = layers.elementwise_mul(a, c, axis=axis, name=name)
bd = layers.elementwise_mul(
b, d, axis=axis, name=name) if is_real(b) and is_real(d) else None
bc = layers.elementwise_mul(
b, c, axis=axis, name=name) if is_real(b) else None
ad = layers.elementwise_mul(
a, d, axis=axis, name=name) if is_real(d) else None
real = ac - bd if is_real(bd) else ac
imag = bc + ad if is_real(bc) and is_real(ad) else bc if is_real(bc) else ad
return ComplexVariable(real, imag)
def elementwise_div(x, y, axis=-1, name=None):
"""
The element-wise division layer for complex number inputs. At least one of
inputs :attr:`x` and :attr:`y` must be a ComplexVariable. See the detailed
description for the function and other arguments
in :ref:`api_fluid_layers_elementwise_div` .
Args:
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
with dg.guard():
x = dg.to_variable(a)
y = dg.to_variable(b)
out = paddle.complex.elementwise_div(x, y)
print(out.numpy())
# [[0.24137931+0.10344828j 0.35 +0.05j ]
# [0.43396226+0.01886792j 0.5 +0.j ]]
"""
complex_variable_exists([x, y], "elementwise_div")
# (a + bi)/(c + di) = (a + bi)(c - di)/(c^2 + d^2)
(c, d) = (y.real, y.imag) if is_complex(y) else (y, None)
y_conj = ComplexVariable(c, -d) if is_real(d) else c
e = 1 / (layers.pow(c, 2.0) + layers.pow(d, 2.0)
) if is_real(d) else 1 / layers.pow(c, 2.0)
return elementwise_mul(
elementwise_mul(
x, y_conj, axis=axis, name=name),
e,
axis=axis,
name=name)
def trace(input, offset=0, dim1=0, dim2=1, name=None):
"""
The layer to compute the trace for a complex number tensor. input :attr:`input` must be a ComplexVariable.
See the detailed description for the function and other arguments
in :ref:`api_tensor_math_trace` .
Args:
input(ComplexVariable): The input ComplexVariable. Must be at least 2-dimensional.
The supported data types include complex64 and complex128.
offset(int, optional): Which diagonals in input tensor will be taken. Default: 0 (main diagonals).
dim1(int, optional): The first dimension with respect to take diagonal. Default: 0.
dim2(int, optional): The second dimension with respect to take diagonal. Default: 1.
name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None.
Returns:
ComplexVariable: The trace result of input tensor, it's data type is the same as input data type.
Examples:
.. code-block:: python
import paddle
import paddle.fluid.dygraph as dg
import numpy as np
case1 = np.random.randn(3, 10, 10).astype('float64') + 1j * np.random.randn(3, 10, 10).astype('float64')
with dg.guard():
case1 = dg.to_variable(case1)
data1 = paddle.complex.trace(case1, offset=1, dim1=1, dim2=2) # data1.shape = [3]
"""
complex_variable_exists([input], "trace")
real = math.trace(input.real, offset, dim1, dim2, name)
imag = math.trace(input.imag, offset, dim1, dim2, name)
return ComplexVariable(real, imag)
def sum(input, dim=None, keep_dim=False, name=None):
"""
The layer to compute the sum for a complex number tensor elements over the given dimension. input :attr:`input` must be a ComplexVariable.
See the detailed description for the function and other arguments
in :ref:`api_tensor_math_sum` .
Args:
input(ComplexVariable): The input ComplexVariable with any number of dimensions.
The supported data types include complex64 and complex128.
dim (list|int, optional): The dimensions along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
the dimension to reduce is :math:`rank + dim[i]`.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
ComplexVariable: Results of summation operation on the specified dim of input tensor,
it's data type is the same as input.
Raises:
ValueError: the :attr:`dtype` must be float64 or int64.
Examples:
.. code-block:: python
import paddle.complex as cpx
import paddle.fluid.dygraph as dg
import numpy as np
with dg.guard():
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9],
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = np.array([[0.2, 0.3, 0.5, 0.9],[0.1, 0.2, 0.6, 0.7]]) + 1j * np.array([[0.3, 0.4, 0.5, 0.2],[0.3, 0.6, 0.8, 0.3]])
x = dg.to_variable(x)
out1 = cpx.sum(x) # [3.5+3.4j]
out2 = cpx.sum(x, dim=0) # [0.3+0.6j, 0.5+1.j, 1.1+1.3j, 1.6+0.5j]
out3 = cpx.sum(x, dim=-1) # [1.9+1.4j, 1.6+2.j]
out4 = cpx.sum(x, dim=1, keep_dim=True) # [[1.9+1.4j], [1.6+2.j]]
# y is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1, 2], [3, 4]],
# [[5, 6], [7, 8]]]
# Each example is followed by the corresponding output tensor.
y = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + 1j * np.array([[[4, 3], [2, 1]], [[8, 7], [6, 5]]])
y = dg.to_variable(y)
out5 = cpx.sum(y, dim=[1, 2]) # [10.+10.j, 26.+26.j]
out6 = cpx.sum(y, dim=[0, 1]) # [16.+20.j, 20.+16.j]
"""
complex_variable_exists([input], "sum")
real = math.sum(input.real, dim=dim, keep_dim=keep_dim, name=name)
imag = math.sum(input.imag, dim=dim, keep_dim=keep_dim, name=name)
return ComplexVariable(real, imag)
def kron(x, y, name=None):
"""
The kronecker product of two complex tensors. At least one of inputs :attr:`x`
and :attr:`y` must be a ComplexVariable. See the detailed description for
the function and other arguments in :ref:`api_paddle_tensor_kron` .
Let $x = a + ib$, and $y = c + id$, the euqation is
.. math::
kron(x, y) = kron(a, c) - kron(b, d) + i(kron(a, d) + kron(b, c))
Args:
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
ComplexVariable: The kronecker product, data type: complex64 or complex128, depending on the data type of x and y. If the data types of x and y are float32/complex64, the data type of the output is complex64, else if the data types of x and y are float64/complex128, the data type of the output is complex128.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
place = fluid.CPUPlace()
with dg.guard(place):
x = dg.to_variable(a)
y = dg.to_variable(b)
out = paddle.complex.kron(x, y)
print(out.numpy())
# [[ 3. +7.j 4. +8.j 8. +9.j 10.+10.j]
# [ 5. +9.j 6.+10.j 12.+11.j 14.+12.j]
# [13.+11.j 16.+12.j 18.+13.j 22.+14.j]
# [19.+13.j 22.+14.j 26.+15.j 30.+16.j]]
"""
complex_variable_exists([x, y], "kron")
# X = A + Bi, Y = C+Di
# kron(X, Y) = kron(A, C) - kron(B, D) + (kron(A, D) + kron(B, C))i
(a, b) = (x.real, x.imag) if is_complex(x) else (x, None)
(c, d) = (y.real, y.imag) if is_complex(y) else (y, None)
if is_real(b) and is_real(d):
real = math.kron(a, c) - math.kron(b, d)
imag = math.kron(a, d) + math.kron(b, c)
elif is_real(b):
real = math.kron(a, c)
imag = math.kron(b, c)
else:
# is_real(d)
real = math.kron(a, c)
imag = math.kron(a, d)
return ComplexVariable(real, imag)