def mvnpdf(data, means, covs):
'''
Compute multivariate normal log pdf
Parameters
----------
Returns
-------
'''
logdets = [np.log(np.linalg.det(c)) for c in covs]
ichol_sigmas = [inv(chol(c)) for c in covs]
packed_params = util.pack_params(means, ichol_sigmas, logdets)
packed_data = util.pad_data(data)
return testmod.mvn_call(packed_data, packed_params, data.shape[1])
def mvnpdf(data, means, covs):
'''
Compute multivariate normal log pdf
Parameters
----------
Returns
-------
'''
logdets = [np.log(np.linalg.det(c)) for c in covs]
ichol_sigmas = [inv(chol(c)) for c in covs]
packed_params = util.pack_params(means, ichol_sigmas, logdets)
packed_data = util.pad_data(data)
return testmod.mvn_call(packed_data, packed_params,
data.shape[1])
n = 1e3
k = 16
data = randn(n, k).astype(np.float32)
mean = randn(k)
cov = np.array(util.random_cov(k), dtype=np.float32)
j = 32
padded_data = util.pad_data(data)
chol_sigma = chol(cov)
ichol_sigma = L.inv(chol_sigma)
logdet = np.log(np.linalg.det(cov))
means = (mean, ) * j
covs = (ichol_sigma, ) * j
logdets = (logdet, ) * j
packed_params = util.pack_params(means, covs, logdets)
cpu_func = lambda: testmod.cpu_mvnpdf(padded_data, packed_params, k
).squeeze()
gpu_func = lambda: testmod._mvnpdf(padded_data, packed_params, k).squeeze()
print cpu_func()
print gpu_func()
# bench(cpu_func, gpu_func, gruns=50)
testmod.set_device(0)
n = 1e3
k = 16
data = randn(n, k).astype(np.float32)
mean = randn(k)
cov = np.array(util.random_cov(k), dtype=np.float32)
j = 32
padded_data = util.pad_data(data)
chol_sigma = chol(cov)
ichol_sigma = L.inv(chol_sigma)
logdet = np.log(np.linalg.det(cov))
means = (mean,) * j
covs = (ichol_sigma,) * j
logdets = (logdet,) * j
packed_params = util.pack_params(means, covs, logdets)
cpu_func = lambda: testmod.cpu_mvnpdf(padded_data, packed_params, k).squeeze()
gpu_func = lambda: testmod._mvnpdf(padded_data, packed_params, k).squeeze()
print cpu_func()
print gpu_func()
# bench(cpu_func, gpu_func, gruns=50)