test inverse of square matrices of arbitrary size

inverse.py

# Benchmark linalg.inv for square matrices.
# Copyright Matti Picus, 2015
# This is free and unencumbered software released into the public domain.

#Anyone is free to copy, modify, publish, use, compile, sell, or
#distribute this software, either in source code form or as a compiled
#binary, for any purpose, commercial or non-commercial, and by any
#means.

#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
#EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
#MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
#IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
#OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
#ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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import time, sys
import numpy as np

if len(sys.argv) < 2:
    N = [10]
else:
    N = [int(s) for s in sys.argv[1:]]

np.random.seed(10)
first = True
for n in N:
    a = np.random.random([n, n])

    if first:
        #warmup
        for i in xrange(5000):
            b = np.linalg.inv(a)
    first = False

    tic = time.clock()

    for i in xrange(100000000 / (n * n)):
        b = np.linalg.inv(a)

    toc = time.clock()
    delta = toc - tic

    print 'after %8d, for n=%3d, time=%.2f, %9.2f msec/1000 loops' % (i, n, delta, delta*1000*1000/i)