its a statistical package for estimating and working with certain types of matrices. offering this type of syntax helps people migrate from matlab environments. if overloading power and multiplication can be implemented easily and is performant maybe just a well placed documentation would help people along
import numpy as np
class Matrix(np.ndarray):
def __new__(cls, input_array):
return np.asarray(input_array).view(cls)
def __array_finalize__(self, obj) -> None:
if obj is None: return
# This attribute should be maintained!
default_attributes = {"attr": 1}
self.__dict__.update(default_attributes) # another way to set attributes
def __mul__(self, B):
return self.__class__(self.__array__() @ B.__array__())
The last entry,
__mul__ describes how the class should handle the * operator with another Matrix B
Then I created two normal numpy arrays that we would like to matrix multiply, and turned them into Matrix objects
A = np.arange(9).reshape((3,3))
B = A + 10
AM = Matrix(A)
BM = Matrix(B)
They look like:
>>> AM
Matrix([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> BM
Matrix([[10, 11, 12],
[13, 14, 15],
[16, 17, 18]])
And then we can just multiply them to do matrix multiplication:
>>> AM * BM
Matrix([[ 45, 48, 51],
[162, 174, 186],
[279, 300, 321]])
And we can check that they do the same thing as numpy matrix multiplication:
>>> A @ B
array([[ 45, 48, 51],
[162, 174, 186],
[279, 300, 321]])
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
np.xxx(a, 3) to generate
np.array (a[3] - a[0], a[4] - a[1], a[5] - a[2], a[6] - a[3], a[7] -a[4], a[8] - a[5])
np.xxx(a, 4) to generate
np.array (a[4] - a[0], a[5] - a[1], a[6] - a[2], a[7] - a[3], a[8] -a[4])
I've read about uninstalling and reinstalling but I'm not too inclined to do that because I have so many libraries that depend on numpy right now and it would take a whole day to set up my environment again.
Is there a known easy fix to this? Thanks a lot in advance!
Are you on the latest PyCharm?
PyCharm has had bugs where the conda environment isn't properly activated
One way to fix that is to activate the environment in the terminal then run PyCharm from the terminal with the activated environment
The error message was "failed to load / import dll.. "
I'm sorry I'm not in front of my laptop right now but I saw that numerous others have got this problem as well, so I thought that it's something common and I didn't need to describe too much
I am on the latest PyCharm. But I will surely try launching from the environment itself to see how that works.
Hey — is anyone testing on Apple M1? I'm getting segfaults when running the tests, specifically on test_crackfortran.py.
Hi, this might be a stupid question but
I've got a with shape (313596, 3)
a = array([[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
...,
[1, 1, 0],
[1, 1, 0],
[1, 1, 0]])and then b with shape (313596, 25, 25, 3)
b = array([[[[0.98823529, 0.98823529, 0.98823529],
[0.98431373, 0.98431373, 0.98431373],
[0.99215686, 0.99215686, 0.99215686],
...,I want to do [a[i] * b[i] for i in range(313596)]but with numpy so that the end result is (313596, 25, 25, 3)
I'm not sure how I can do this, I tried using dot and multiply but they didn't work, I somehow need to do do something like a dot product treating each of these sub (25,25,3) matrices as elements and then each multiplication in the dot product is a hadamard
np.dot(np.transpose(a), b) #-> shapes (3,313596) and (313596,25,25,3) not aligned: 313596 (dim 1) != 25 (dim 2)
np.matmulPlease can somebody help me out here, I've seen tensordot and einsum, not sure if they're my answer... I'm getting a bit frustrated trying to figure this out, maybe somebody could point me in the right direction?
In [28]: a = np.random.randn(313, 3)
In [29]: b = np.random.randn(313, 25, 25, 3)
In [30]: expected = np.stack([a[i] * b[i] for i in range(313)])
In [31]: expected.shape
Out[31]: (313, 25, 25, 3)
In [32]: actual = a[:, None, None, :] * b
In [33]: np.all(actual == expected)
Out[33]: True
In [34]: %timeit expected = np.stack([a[i] * b[i] for i in range(313)])
10.1 ms ± 325 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [35]: %timeit actual = a[:, None, None, :] * b
4.44 ms ± 182 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Hello! Could someone confirm if this is a bug? I'm trying to edit in place (no copy, "by reference") a sliced array that was passed as a function argument. It works for slice indexing, but not for boolean indexing.
import numpy as np
def test__without_fun_call__bool_indexing():
a = np.array([[1., 2.], [3., 4.]])
lines_to_zero = np.array([True, False])
a[lines_to_zero, :] = 0.
assert np.allclose(a, [[0., 0.], [3., 4.]]) # OK
def set_to_zero(x):
x[...] = 0.
def test__with_fun_call__bool_indexing():
a = np.array([[1., 2.], [3., 4.]])
lines_to_zero = np.array([True, False])
set_to_zero(a[lines_to_zero, :])
assert np.allclose(a, [[0., 0.], [3., 4.]]) # FAIL
def test__with_fun_call__slice_indexing():
a = np.array([[1., 2.], [3., 4.]])
set_to_zero(a[:1, :])
assert np.allclose(a, [[0., 0.], [3., 4.]]) # OK
1 reply
Where I can find the code for
np.dot .... the far I can go is here https://github.com/numpy/numpy/blob/872373b030eb6c181ab7f29c0928ccd8b0f74528/numpy/core/multiarray.py#L732 ... from here how can I find the c source file that correspond? ....either pointing out the file that correspond or explain how @array_function_from_c_func_and_dispatcher(_multiarray_umath.dot) mechanism work would be appreciated...
np.dot -> np.dot.__wrapped__ -> <builtin function dot> -> _multiarray_umath array_module_methods -> array_matrix_product -> PyArray_MatrixProduct2. From there it depends on the shape of the arguments.
also note
@ is np.matmul, not np.dot. They have different semantics for ndim>2