Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/scipy/linalg/_testutils.py
2023-09-20 19:46:58 +02:00

64 lines
1.7 KiB
Python

import numpy as np
class _FakeMatrix:
def __init__(self, data):
self._data = data
self.__array_interface__ = data.__array_interface__
class _FakeMatrix2:
def __init__(self, data):
self._data = data
def __array__(self):
return self._data
def _get_array(shape, dtype):
"""
Get a test array of given shape and data type.
Returned NxN matrices are posdef, and 2xN are banded-posdef.
"""
if len(shape) == 2 and shape[0] == 2:
# yield a banded positive definite one
x = np.zeros(shape, dtype=dtype)
x[0, 1:] = -1
x[1] = 2
return x
elif len(shape) == 2 and shape[0] == shape[1]:
# always yield a positive definite matrix
x = np.zeros(shape, dtype=dtype)
j = np.arange(shape[0])
x[j, j] = 2
x[j[:-1], j[:-1]+1] = -1
x[j[:-1]+1, j[:-1]] = -1
return x
else:
np.random.seed(1234)
return np.random.randn(*shape).astype(dtype)
def _id(x):
return x
def assert_no_overwrite(call, shapes, dtypes=None):
"""
Test that a call does not overwrite its input arguments
"""
if dtypes is None:
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
for dtype in dtypes:
for order in ["C", "F"]:
for faker in [_id, _FakeMatrix, _FakeMatrix2]:
orig_inputs = [_get_array(s, dtype) for s in shapes]
inputs = [faker(x.copy(order)) for x in orig_inputs]
call(*inputs)
msg = "call modified inputs [%r, %r]" % (dtype, faker)
for a, b in zip(inputs, orig_inputs):
np.testing.assert_equal(a, b, err_msg=msg)