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blas.py
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#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
BLAS functions for arrayfire.
"""
from .library import *
from .array import *
def matmul(lhs, rhs, lhs_opts=MATPROP.NONE, rhs_opts=MATPROP.NONE):
"""
Generalized matrix multiplication for two matrices.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
lhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `lhs`.
- af.MATPROP.TRANS - If `lhs` has to be transposed before multiplying.
- af.MATPROP.CTRANS - If `lhs` has to be hermitian transposed before multiplying.
rhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `rhs`.
- af.MATPROP.TRANS - If `rhs` has to be transposed before multiplying.
- af.MATPROP.CTRANS - If `rhs` has to be hermitian transposed before multiplying.
Returns
-------
out : af.Array
Output of the matrix multiplication on `lhs` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(ct.pointer(out.arr), lhs.arr, rhs.arr,
lhs_opts.value, rhs_opts.value))
return out
def matmulTN(lhs, rhs):
"""
Matrix multiplication after transposing the first matrix.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
Returns
-------
out : af.Array
Output of the matrix multiplication on `transpose(lhs)` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(ct.pointer(out.arr), lhs.arr, rhs.arr,
MATPROP.TRANS.value, MATPROP.NONE.value))
return out
def matmulNT(lhs, rhs):
"""
Matrix multiplication after transposing the second matrix.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
Returns
-------
out : af.Array
Output of the matrix multiplication on `lhs` and `transpose(rhs)`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(ct.pointer(out.arr), lhs.arr, rhs.arr,
MATPROP.NONE.value, MATPROP.TRANS.value))
return out
def matmulTT(lhs, rhs):
"""
Matrix multiplication after transposing both inputs.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
Returns
-------
out : af.Array
Output of the matrix multiplication on `transpose(lhs)` and `transpose(rhs)`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(ct.pointer(out.arr), lhs.arr, rhs.arr,
MATPROP.TRANS.value, MATPROP.TRANS.value))
return out
def dot(lhs, rhs, lhs_opts=MATPROP.NONE, rhs_opts=MATPROP.NONE):
"""
Dot product of two input vectors.
Parameters
----------
lhs : af.Array
A 1 dimensional, real or complex arrayfire array.
rhs : af.Array
A 1 dimensional, real or complex arrayfire array.
lhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `lhs`.
- No other options are currently supported.
rhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `rhs`.
- No other options are currently supported.
Returns
-------
out : af.Array
Output of dot product of `lhs` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_dot(ct.pointer(out.arr), lhs.arr, rhs.arr,
lhs_opts.value, rhs_opts.value))
return out