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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
|
from __future__ import division, print_function, absolute_import
import sys
from numpy.testing import measure, rand, assert_, TestCase, run_module_suite
import numpy.linalg as nl
import scipy.linalg as sl
def random(size):
return rand(*size)
class TestSolve(TestCase):
def bench_random(self):
numpy_solve = nl.solve
scipy_solve = sl.solve
print()
print(' Solving system of linear equations')
print(' ==================================')
print(' | contiguous | non-contiguous ')
print('----------------------------------------------')
print(' size | scipy | numpy | scipy | numpy ')
for size,repeat in [(20,1000),(100,150),(500,2),(1000,1)][:-1]:
repeat *= 2
print('%5s' % size, end=' ')
sys.stdout.flush()
a = random([size,size])
# larger diagonal ensures non-singularity:
for i in range(size):
a[i,i] = 10*(.1+a[i,i])
b = random([size])
print('| %6.2f ' % measure('scipy_solve(a,b)',repeat), end=' ')
sys.stdout.flush()
print('| %6.2f ' % measure('numpy_solve(a,b)',repeat), end=' ')
sys.stdout.flush()
a = a[-1::-1,-1::-1] # turn into a non-contiguous array
assert_(not a.flags['CONTIGUOUS'])
print('| %6.2f ' % measure('scipy_solve(a,b)',repeat), end=' ')
sys.stdout.flush()
print('| %6.2f ' % measure('numpy_solve(a,b)',repeat), end=' ')
sys.stdout.flush()
print(' (secs for %s calls)' % (repeat))
class TestInv(TestCase):
def bench_random(self):
numpy_inv = nl.inv
scipy_inv = sl.inv
print()
print(' Finding matrix inverse')
print(' ==================================')
print(' | contiguous | non-contiguous ')
print('----------------------------------------------')
print(' size | scipy | numpy | scipy | numpy')
for size,repeat in [(20,1000),(100,150),(500,2),(1000,1)][:-1]:
repeat *= 2
print('%5s' % size, end=' ')
sys.stdout.flush()
a = random([size,size])
# large diagonal ensures non-singularity:
for i in range(size):
a[i,i] = 10*(.1+a[i,i])
print('| %6.2f ' % measure('scipy_inv(a)',repeat), end=' ')
sys.stdout.flush()
print('| %6.2f ' % measure('numpy_inv(a)',repeat), end=' ')
sys.stdout.flush()
a = a[-1::-1,-1::-1] # turn into a non-contiguous array
assert_(not a.flags['CONTIGUOUS'])
print('| %6.2f ' % measure('scipy_inv(a)',repeat), end=' ')
sys.stdout.flush()
print('| %6.2f ' % measure('numpy_inv(a)',repeat), end=' ')
sys.stdout.flush()
print(' (secs for %s calls)' % (repeat))
class TestDet(TestCase):
def bench_random(self):
numpy_det = nl.det
scipy_det = sl.det
print()
print(' Finding matrix determinant')
print(' ==================================')
print(' | contiguous | non-contiguous ')
print('----------------------------------------------')
print(' size | scipy | numpy | scipy | numpy ')
for size,repeat in [(20,1000),(100,150),(500,2),(1000,1)][:-1]:
repeat *= 2
print('%5s' % size, end=' ')
sys.stdout.flush()
a = random([size,size])
print('| %6.2f ' % measure('scipy_det(a)',repeat), end=' ')
sys.stdout.flush()
print('| %6.2f ' % measure('numpy_det(a)',repeat), end=' ')
sys.stdout.flush()
a = a[-1::-1,-1::-1] # turn into a non-contiguous array
assert_(not a.flags['CONTIGUOUS'])
print('| %6.2f ' % measure('scipy_det(a)',repeat), end=' ')
sys.stdout.flush()
print('| %6.2f ' % measure('numpy_det(a)',repeat), end=' ')
sys.stdout.flush()
print(' (secs for %s calls)' % (repeat))
if __name__ == "__main__":
run_module_suite()
|
