1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
https://github.com/scipy/scipy/issues/18759
https://github.com/scipy/scipy/pull/18763
https://github.com/scipy/scipy/commit/61d892c9faa543ad80bd5e2d0bf69821188487e0
From 61d892c9faa543ad80bd5e2d0bf69821188487e0 Mon Sep 17 00:00:00 2001
From: Ilhan Polat <ilhanpolat@gmail.com>
Date: Tue, 27 Jun 2023 12:00:38 +0200
Subject: [PATCH] MAINT:linalg.det:Return scalars for singleton inputs (#18763)
--- a/scipy/linalg/_basic.py
+++ b/scipy/linalg/_basic.py
@@ -1001,7 +1001,8 @@ def det(a, overwrite_a=False, check_finite=True):
det : (...) float or complex
Determinant of `a`. For stacked arrays, a scalar is returned for each
(m, m) slice in the last two dimensions of the input. For example, an
- input of shape (p, q, m, m) will produce a result of shape (p, q).
+ input of shape (p, q, m, m) will produce a result of shape (p, q). If
+ all dimensions are 1 a scalar is returned regardless of ndim.
Notes
-----
@@ -1066,11 +1067,17 @@ def det(a, overwrite_a=False, check_finite=True):
# Scalar case
if a1.shape[-2:] == (1, 1):
- if a1.dtype.char in 'dD':
- return np.squeeze(a1)
+ # Either ndarray with spurious singletons or a single element
+ if max(*a1.shape) > 1:
+ temp = np.squeeze(a1)
+ if a1.dtype.char in 'dD':
+ return temp
+ else:
+ return (temp.astype('d') if a1.dtype.char == 'f' else
+ temp.astype('D'))
else:
- return (np.squeeze(a1).astype('d') if a1.dtype.char == 'f' else
- np.squeeze(a1).astype('D'))
+ return (np.float64(a1.item()) if a1.dtype.char in 'fd' else
+ np.complex128(a1.item()))
# Then check overwrite permission
if not _datacopied(a1, a): # "a" still alive through "a1"
--- a/scipy/linalg/tests/test_basic.py
+++ b/scipy/linalg/tests/test_basic.py
@@ -930,6 +930,23 @@ class TestDet:
def setup_method(self):
self.rng = np.random.default_rng(1680305949878959)
+ def test_1x1_all_singleton_dims(self):
+ a = np.array([[1]])
+ deta = det(a)
+ assert deta.dtype.char == 'd'
+ assert np.isscalar(deta)
+ assert deta == 1.
+ a = np.array([[[[1]]]], dtype='f')
+ deta = det(a)
+ assert deta.dtype.char == 'd'
+ assert np.isscalar(deta)
+ assert deta == 1.
+ a = np.array([[[1 + 3.j]]], dtype=np.complex64)
+ deta = det(a)
+ assert deta.dtype.char == 'D'
+ assert np.isscalar(deta)
+ assert deta == 1.+3.j
+
def test_1by1_stacked_input_output(self):
a = self.rng.random([4, 5, 1, 1], dtype=np.float32)
deta = det(a)
|
