def matrix_power(input, count):
"""
Raise a square matrix to the (integer) power `count`.
Parameters
----------
input : sequence of array_like
Returns
-------
output : array
Returns the dot product of the supplied arrays.
See Also
--------
:obj:`numpy.linalg.matrix_power`
"""
if not use_origin_backend() and count > 0:
result = input
for id in range(count - 1):
result = dpnp.matmul(result, input)
return result
return call_origin(numpy.linalg.matrix_power, input, count)
def test_matmul3(self):
array_data1 = numpy.full((513, 513), 5)
array_data2 = numpy.full((513, 513), 2)
out = numpy.empty((513, 513), dtype=numpy.float64)
# DPNP
array1 = inp.array(array_data1, dtype=inp.float64)
array2 = inp.array(array_data2, dtype=inp.float64)
out1 = inp.array(out, dtype=inp.float64)
result = inp.matmul(array1, array2, out=out1)
# original
array_1 = numpy.array(array_data1, dtype=numpy.float64)
array_2 = numpy.array(array_data2, dtype=numpy.float64)
expected = numpy.matmul(array_1, array_2, out=out)
numpy.testing.assert_array_equal(expected, result)
def test_matmul2(self):
array_data1 = [1., 2., 3., 4., 5., 6.]
array_data2 = [1., 2., 3., 4., 5., 6., 7., 8.]
# DPNP
array1 = inp.reshape(inp.array(array_data1, dtype=inp.float64), (3, 2))
array2 = inp.reshape(inp.array(array_data2, dtype=inp.float64), (2, 4))
result = inp.matmul(array1, array2)
# print(result)
# original
array_1 = numpy.array(array_data1, dtype=numpy.float64).reshape((3, 2))
array_2 = numpy.array(array_data2, dtype=numpy.float64).reshape((2, 4))
expected = numpy.matmul(array_1, array_2)
# print(expected)
numpy.testing.assert_array_equal(expected, result)
def test_matmul(self):
array_data = [1., 2., 3., 4.]
size = 2
# DPNP
array1 = inp.array(array_data, dtype=inp.float64).reshape((size, size))
array2 = inp.array(array_data, dtype=inp.float64).reshape((size, size))
result = inp.matmul(array1, array2)
# print(result)
# original
array_1 = numpy.array(array_data, dtype=numpy.float64).reshape((size, size))
array_2 = numpy.array(array_data, dtype=numpy.float64).reshape((size, size))
expected = numpy.matmul(array_1, array_2)
# print(expected)
# passed
numpy.testing.assert_array_equal(expected, result)
def matrix_power(input, count):
"""
Raise a square matrix to the (integer) power `count`.
Parameters
----------
input : sequence of array_like
Returns
-------
output : dparray
Returns the dot product of the supplied arrays.
See Also
--------
:obj:`numpy.linalg.matrix_power`
"""
is_input_dparray = isinstance(input, dparray)
if not use_origin_backend(input) and is_input_dparray and count > 0:
result = input
for id in range(count - 1):
result = dpnp.matmul(result, input)
return result
input1 = dpnp.asnumpy(input) if is_input_dparray else input
# TODO need to put dparray memory into NumPy call
result_numpy = numpy.linalg.matrix_power(input1, count)
result = result_numpy
if isinstance(result, numpy.ndarray):
result = dparray(result_numpy.shape, dtype=result_numpy.dtype)
for i in range(result.size):
result._setitem_scalar(i, result_numpy.item(i))
return result
def __rmatmul__(self, other):
return dpnp.matmul(other, self)
def __matmul__(self, other):
return dpnp.matmul(self, other)
