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
70
71
72
73
74
75
76
77
78
79
80
|
"""benchmarks for the scipy.sparse.linalg._expm_multiply module"""
from __future__ import division, print_function, absolute_import
import time
import numpy as np
from numpy.testing import (Tester, TestCase, assert_allclose, run_module_suite)
import scipy.linalg
from scipy.sparse.linalg import expm_multiply
def random_sparse(m, n, nnz_per_row):
# Copied from the scipy.sparse benchmark.
rows = np.arange(m).repeat(nnz_per_row)
cols = np.random.random_integers(low=0, high=n-1, size=nnz_per_row*m)
vals = np.random.random_sample(m*nnz_per_row)
M = scipy.sparse.coo_matrix((vals,(rows,cols)), (m,n), dtype=float)
return M.tocsr()
class BenchmarkExpmMultiply(TestCase):
def _help_bench_expm_multiply(self, A, i, j):
n = A.shape[0]
print('converting the sparse matrix to a dense array...')
tm_start = time.clock()
A_dense = A.toarray()
tm_end = time.clock()
print(tm_end - tm_start, ' seconds')
print()
print('computing full expm of the dense array...')
tm_start = time.clock()
A_expm = scipy.linalg.expm(A_dense)
full_expm_entry = A_expm[i, j]
tm_end = time.clock()
print('expm(A)[%d, %d]:' % (i, j), full_expm_entry)
print(tm_end - tm_start, ' seconds')
print()
print('computing only column', j, 'of expm of the sparse matrix...')
tm_start = time.clock()
v = np.zeros(n, dtype=float)
v[j] = 1
A_expm_col_j = expm_multiply(A, v)
expm_col_entry = A_expm_col_j[i]
tm_end = time.clock()
print('expm(A)[%d, %d]:' % (i, j), expm_col_entry)
print(tm_end - tm_start, ' seconds')
print()
if np.allclose(full_expm_entry, expm_col_entry):
print('The two methods give the same answer.')
else:
print('!!! The two methods give different answers. !!!')
print()
def bench_expm_multiply(self):
np.random.seed(1234)
n = 2000
i = 100
j = 200
shape = (n, n)
nnz_per_row = 25
print()
print('expm multiply benchmarking')
print('--------------------------')
print()
print('sampling a random sparse matrix...')
print('shape:', shape)
print('nnz per row:', nnz_per_row)
tm_start = time.clock()
A = random_sparse(n, n, nnz_per_row)
tm_end = time.clock()
print(tm_end - tm_start, ' seconds')
print()
self._help_bench_expm_multiply(A, i, j)
print()
if __name__ == '__main__':
Tester().bench()
|
