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Revert "CI: test_jacobi_svd"
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‎tests/test_jacobi_svd.py‎

Lines changed: 123 additions & 99 deletions
Original file line numberDiff line numberDiff line change
@@ -2,21 +2,20 @@
22
import numpy as np
33
import pytest
44

5-
full_u = nanoeigenpy.DecompositionOptions.ComputeFullU.value
6-
full_v = nanoeigenpy.DecompositionOptions.ComputeFullV.value
7-
thin_u = nanoeigenpy.DecompositionOptions.ComputeThinU.value
8-
thin_v = nanoeigenpy.DecompositionOptions.ComputeThinV.value
9-
105
_options = [
116
0,
12-
thin_u,
13-
thin_v,
14-
full_u,
15-
full_v,
16-
thin_u | thin_v,
17-
full_u | full_v,
18-
thin_u | full_v,
19-
full_u | thin_v,
7+
nanoeigenpy.DecompositionOptions.ComputeThinU.value,
8+
nanoeigenpy.DecompositionOptions.ComputeThinV.value,
9+
nanoeigenpy.DecompositionOptions.ComputeFullU.value,
10+
nanoeigenpy.DecompositionOptions.ComputeFullV.value,
11+
nanoeigenpy.DecompositionOptions.ComputeThinU.value
12+
| nanoeigenpy.DecompositionOptions.ComputeThinV.value,
13+
nanoeigenpy.DecompositionOptions.ComputeFullU.value
14+
| nanoeigenpy.DecompositionOptions.ComputeFullV.value,
15+
nanoeigenpy.DecompositionOptions.ComputeThinU.value
16+
| nanoeigenpy.DecompositionOptions.ComputeFullV.value,
17+
nanoeigenpy.DecompositionOptions.ComputeFullU.value
18+
| nanoeigenpy.DecompositionOptions.ComputeThinV.value,
2019
]
2120

2221
_classes = [
@@ -35,94 +34,119 @@ def test_jacobi(cls, options):
3534
A = rng.random((dim, dim))
3635
A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
3736

38-
skip_case = (
39-
cls == nanoeigenpy.FullPivHhJacobiSVD
40-
and options != 0
41-
and not (options & (full_u | full_v))
37+
if cls == nanoeigenpy.FullPivHhJacobiSVD:
38+
if options != 0 and not (
39+
options
40+
& (
41+
nanoeigenpy.DecompositionOptions.ComputeFullU.value
42+
| nanoeigenpy.DecompositionOptions.ComputeFullV.value
43+
)
44+
):
45+
return
46+
47+
jacobisvd = cls(A, options)
48+
assert jacobisvd.info() == nanoeigenpy.ComputationInfo.Success
49+
50+
if options & (
51+
nanoeigenpy.DecompositionOptions.ComputeThinU.value
52+
| nanoeigenpy.DecompositionOptions.ComputeFullU.value
53+
) and options & (
54+
nanoeigenpy.DecompositionOptions.ComputeThinV.value
55+
| nanoeigenpy.DecompositionOptions.ComputeFullV.value
56+
):
57+
X = rng.random((dim, 20))
58+
B = A @ X
59+
X_est = jacobisvd.solve(B)
60+
assert nanoeigenpy.is_approx(X, X_est)
61+
assert nanoeigenpy.is_approx(A @ X_est, B)
62+
63+
x = rng.random(dim)
64+
b = A @ x
65+
x_est = jacobisvd.solve(b)
66+
assert nanoeigenpy.is_approx(x, x_est)
67+
assert nanoeigenpy.is_approx(A @ x_est, b)
68+
69+
rows = jacobisvd.rows()
70+
cols = jacobisvd.cols()
71+
assert cols == dim
72+
assert rows == dim
73+
74+
_jacobisvd_compute = jacobisvd.compute(A)
75+
_jacobisvd_compute_options = jacobisvd.compute(A, options)
76+
77+
rank = jacobisvd.rank()
78+
singularvalues = jacobisvd.singularValues()
79+
nonzerosingularvalues = jacobisvd.nonzeroSingularValues()
80+
assert rank == nonzerosingularvalues
81+
assert len(singularvalues) == dim
82+
assert all(
83+
singularvalues[i] >= singularvalues[i + 1]
84+
for i in range(len(singularvalues) - 1)
4285
)
4386

44-
if not skip_case:
45-
jacobisvd = cls(A, options)
46-
assert jacobisvd.info() == nanoeigenpy.ComputationInfo.Success
47-
48-
if options & (thin_u | full_u) and options & (thin_v | full_v):
49-
X = rng.random((dim, 20))
50-
B = A @ X
51-
X_est = jacobisvd.solve(B)
52-
assert nanoeigenpy.is_approx(X, X_est)
53-
assert nanoeigenpy.is_approx(A @ X_est, B)
54-
55-
x = rng.random(dim)
56-
b = A @ x
57-
x_est = jacobisvd.solve(b)
58-
assert nanoeigenpy.is_approx(x, x_est)
59-
assert nanoeigenpy.is_approx(A @ x_est, b)
60-
61-
rows = jacobisvd.rows()
62-
cols = jacobisvd.cols()
63-
assert cols == dim
64-
assert rows == dim
65-
66-
_jacobisvd_compute = jacobisvd.compute(A)
67-
_jacobisvd_compute_options = jacobisvd.compute(A, options)
68-
69-
rank = jacobisvd.rank()
70-
singularvalues = jacobisvd.singularValues()
71-
nonzerosingularvalues = jacobisvd.nonzeroSingularValues()
72-
assert rank == nonzerosingularvalues
73-
assert len(singularvalues) == dim
74-
75-
compute_u = jacobisvd.computeU()
76-
compute_v = jacobisvd.computeV()
77-
expected_compute_u = bool(options & (thin_u | full_u))
78-
expected_compute_v = bool(options & (thin_v | full_v))
79-
assert compute_u == expected_compute_u
80-
assert compute_v == expected_compute_v
81-
82-
if compute_u:
83-
matrixU = jacobisvd.matrixU()
84-
if options & full_u:
85-
assert matrixU.shape == (dim, dim)
86-
elif options & thin_u:
87-
assert matrixU.shape == (dim, dim)
88-
assert nanoeigenpy.is_approx(matrixU.T @ matrixU, np.eye(matrixU.shape[1]))
89-
90-
if compute_v:
91-
matrixV = jacobisvd.matrixV()
92-
if options & full_v:
93-
assert matrixV.shape == (dim, dim)
94-
elif options & thin_v:
95-
assert matrixV.shape == (dim, dim)
96-
assert nanoeigenpy.is_approx(matrixV.T @ matrixV, np.eye(matrixV.shape[1]))
97-
98-
if compute_u and compute_v:
99-
U = jacobisvd.matrixU()
100-
V = jacobisvd.matrixV()
101-
S = jacobisvd.singularValues()
102-
S_matrix = np.diag(S)
103-
A_reconstructed = U @ S_matrix @ V.T
104-
assert nanoeigenpy.is_approx(A, A_reconstructed)
105-
106-
jacobisvd.setThreshold()
107-
_default_threshold = jacobisvd.threshold()
108-
jacobisvd.setThreshold(1e-8)
109-
assert jacobisvd.threshold() == 1e-8
110-
111-
decomp1 = cls()
112-
decomp2 = cls()
113-
id1 = decomp1.id()
114-
id2 = decomp2.id()
115-
assert id1 != id2
116-
assert id1 == decomp1.id()
117-
assert id2 == decomp2.id()
118-
119-
decomp3 = cls(dim, dim, options)
120-
decomp4 = cls(dim, dim, options)
121-
id3 = decomp3.id()
122-
id4 = decomp4.id()
123-
assert id3 != id4
124-
assert id3 == decomp3.id()
125-
assert id4 == decomp4.id()
87+
compute_u = jacobisvd.computeU()
88+
compute_v = jacobisvd.computeV()
89+
expected_compute_u = bool(
90+
options
91+
& (
92+
nanoeigenpy.DecompositionOptions.ComputeThinU.value
93+
| nanoeigenpy.DecompositionOptions.ComputeFullU.value
94+
)
95+
)
96+
expected_compute_v = bool(
97+
options
98+
& (
99+
nanoeigenpy.DecompositionOptions.ComputeThinV.value
100+
| nanoeigenpy.DecompositionOptions.ComputeFullV.value
101+
)
102+
)
103+
assert compute_u == expected_compute_u
104+
assert compute_v == expected_compute_v
105+
106+
if compute_u:
107+
matrixU = jacobisvd.matrixU()
108+
if options & nanoeigenpy.DecompositionOptions.ComputeFullU.value:
109+
assert matrixU.shape == (dim, dim)
110+
elif options & nanoeigenpy.DecompositionOptions.ComputeThinU.value:
111+
assert matrixU.shape == (dim, dim)
112+
assert nanoeigenpy.is_approx(matrixU.T @ matrixU, np.eye(matrixU.shape[1]))
113+
114+
if compute_v:
115+
matrixV = jacobisvd.matrixV()
116+
if options & nanoeigenpy.DecompositionOptions.ComputeFullV.value:
117+
assert matrixV.shape == (dim, dim)
118+
elif options & nanoeigenpy.DecompositionOptions.ComputeThinV.value:
119+
assert matrixV.shape == (dim, dim)
120+
assert nanoeigenpy.is_approx(matrixV.T @ matrixV, np.eye(matrixV.shape[1]))
121+
122+
if compute_u and compute_v:
123+
U = jacobisvd.matrixU()
124+
V = jacobisvd.matrixV()
125+
S = jacobisvd.singularValues()
126+
S_matrix = np.diag(S)
127+
A_reconstructed = U @ S_matrix @ V.T
128+
assert nanoeigenpy.is_approx(A, A_reconstructed)
129+
130+
jacobisvd.setThreshold()
131+
_default_threshold = jacobisvd.threshold()
132+
jacobisvd.setThreshold(1e-8)
133+
assert jacobisvd.threshold() == 1e-8
134+
135+
decomp1 = cls()
136+
decomp2 = cls()
137+
id1 = decomp1.id()
138+
id2 = decomp2.id()
139+
assert id1 != id2
140+
assert id1 == decomp1.id()
141+
assert id2 == decomp2.id()
142+
143+
decomp3 = cls(dim, dim, options)
144+
decomp4 = cls(dim, dim, options)
145+
id3 = decomp3.id()
146+
id4 = decomp4.id()
147+
assert id3 != id4
148+
assert id3 == decomp3.id()
149+
assert id4 == decomp4.id()
126150

127151

128152
if __name__ == "__main__":

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