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import numpy as N
from numpy.random import randn
from numpy.ctypeslib import load_library, ndpointer
from ctypes import cdll, c_uint, c_int, c_double, POINTER
lib = load_library("blop.so", "file")
arg1 = ndpointer(dtype=N.float64)
arg2 = c_uint
arg3 = c_uint
arg4 = ndpointer(dtype=N.float64)
arg5 = ndpointer(dtype=N.float64)
lib.compute.argtypes = [arg1, arg2, arg3, arg4, arg5]
lib.compute.restype = c_int
# Compare computing per component likelihood for frame per row vs frame per column
def component_likelihood(x, mu, va, log = False):
"""expect one frame to be one row (rank 2). mu and var are rank 1 array."""
x -= mu
x **= 2
return N.exp(N.dot(x, N.ones((mu.size, 1), x.dtype)))
def component_likelihood3(x, mu, va, log = False):
"""expect one frame to be one row (rank 2). mu and var are rank 1 array."""
y = N.empty(x.shape[0], x.dtype)
lib.compute(x, x.shape[0], x.shape[1], mu, y)
return y
def bench(func, mode = 'diag'):
d = 30
n = 1e5
niter = 10
print "Compute %d times densities, %d dimension, %d frames" % (niter, d, n)
mu = 0.1 * randn(d)
va = 0.1 * abs(randn(d))
X = 0.1 * randn(n, d)
for i in range(niter):
Y = func(X, mu, va)
def benchpy():
bench(component_likelihood)
def benchpy3():
bench(component_likelihood3)
def benchpy2():
bench2(component_likelihood2)
if __name__ == "__main__":
#import hotshot, hotshot.stats
#profile_file = 'gdenpy.prof'
#prof = hotshot.Profile(profile_file, lineevents=1)
#prof.runcall(benchpy)
#p = hotshot.stats.load(profile_file)
#print p.sort_stats('cumulative').print_stats(20)
#prof.close()
#profile_file = 'gdenc.prof'
#prof = hotshot.Profile(profile_file, lineevents=1)
#prof.runcall(benchpy3)
#p = hotshot.stats.load(profile_file)
#print p.sort_stats('cumulative').print_stats(20)
#prof.close()
#import cProfile as profile
#profile.run('benchpy()', 'fooprof')
benchpy()
benchpy3()
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