def test_seeded_one_N_one_p_with_out():
K = 5
N = 10
p_K = np.ones(K) / K
n_iter = 10000
z_K = np.zeros(K)
for _ in xrange(n_iter):
n_K = np.zeros(K, dtype=np.float)
seeded_multinomial(N, p_K, out=n_K)
assert n_K.sum() == N
z_K += n_K
assert np.allclose(z_K / z_K.sum(), p_K, atol=1e-2)
def test_seeded_one_N_one_p_with_out():
K = 5
N = 10
p_K = np.ones(K) / K
n_iter = 10000
z_K = np.zeros(K)
for _ in xrange(n_iter):
n_K = np.zeros(K, dtype=np.float)
seeded_multinomial(N, p_K, out=n_K)
assert n_K.sum() == N
z_K += n_K
assert np.allclose(z_K / z_K.sum(), p_K, atol=1e-2)
def test_seeded_one_N_multi_p_with_out():
K = 5
I = 3
N = 10
p_IK = np.ones((I, K)) / K
p_IK[0, :] = [0.5, 0.25, 0.05, 0.1, 0.1] # make one non-uniform
n_iter = 10000
z_IK = np.zeros((I, K))
for _ in xrange(n_iter):
n_IK = np.zeros((I, K))
seeded_multinomial(N, p_IK, out=n_IK)
assert (n_IK.sum(axis=1) == N).all()
z_IK += n_IK
norm_z_IK = z_IK.astype(float) / np.sum(z_IK, axis=1, keepdims=True)
assert np.allclose(norm_z_IK, p_IK, atol=1e-2)
def test_seeded_multi_N_one_p_with_out():
K = 5
I = 10
N_I = np.ones(I) * 10
p_K = np.ones(K) / K
n_iter = 10000
z_IK = np.zeros((I, K))
for _ in xrange(n_iter):
n_IK = np.zeros((I, K))
seeded_multinomial(N_I, p_K, out=n_IK)
assert np.allclose(n_IK.sum(axis=1), N_I)
z_IK += n_IK
norm_z_IK = z_IK.astype(float) / np.sum(z_IK, axis=1, keepdims=True)
p_IK = np.ones((I, K)) * p_K
assert np.allclose(norm_z_IK, p_IK, atol=1e-2)
def test_seeded_one_N_multi_p_with_out():
K = 5
I = 3
N = 10
p_IK = np.ones((I, K)) / K
p_IK[0, :] = [0.5, 0.25, 0.05, 0.1, 0.1] # make one non-uniform
n_iter = 10000
z_IK = np.zeros((I, K))
for _ in xrange(n_iter):
n_IK = np.zeros((I, K))
seeded_multinomial(N, p_IK, out=n_IK)
assert (n_IK.sum(axis=1) == N).all()
z_IK += n_IK
norm_z_IK = z_IK.astype(float) / np.sum(z_IK, axis=1, keepdims=True)
assert np.allclose(norm_z_IK, p_IK, atol=1e-2)
def test_seeded_multi_N_one_p_with_out():
K = 5
I = 10
N_I = np.ones(I) * 10
p_K = np.ones(K) / K
n_iter = 10000
z_IK = np.zeros((I, K))
for _ in xrange(n_iter):
n_IK = np.zeros((I, K))
seeded_multinomial(N_I, p_K, out=n_IK)
assert np.allclose(n_IK.sum(axis=1), N_I)
z_IK += n_IK
norm_z_IK = z_IK.astype(float) / np.sum(z_IK, axis=1, keepdims=True)
p_IK = np.ones((I, K)) * p_K
assert np.allclose(norm_z_IK, p_IK, atol=1e-2)
def test_seeded_multi_N_multi_p_with_out():
K = 5
A = 3
B = 2
N_AB = (np.arange(1, A*B+1) * 10).reshape((A, B))
p_ABK = np.ones((A, B, K)) / K
p_ABK[0, 1, :] = [0.5, 0.25, 0.05, 0.1, 0.1] # make one non-uniform
p_ABK[1, 0, :] = [0.9, 0.05, 0.03, 0.01, 0.01] # make one really non-uniform
n_iter = 10000
z_ABK = np.zeros((A, B, K))
for _ in xrange(n_iter):
n_ABK = np.zeros((A, B, K), dtype=np.uint32)
seeded_multinomial(N_AB, p_ABK, out=n_ABK)
np.allclose(n_ABK.sum(axis=-1), N_AB)
z_ABK += n_ABK
norm_z_ABK = z_ABK.astype(float) / np.sum(z_ABK, axis=-1, keepdims=True)
assert np.allclose(norm_z_ABK, p_ABK, atol=1e-2)
def test_seeded_multi_N_multi_p_with_out():
K = 5
A = 3
B = 2
N_AB = (np.arange(1, A * B + 1) * 10).reshape((A, B))
p_ABK = np.ones((A, B, K)) / K
p_ABK[0, 1, :] = [0.5, 0.25, 0.05, 0.1, 0.1] # make one non-uniform
p_ABK[1, 0, :] = [0.9, 0.05, 0.03, 0.01,
0.01] # make one really non-uniform
n_iter = 10000
z_ABK = np.zeros((A, B, K))
for _ in xrange(n_iter):
n_ABK = np.zeros((A, B, K), dtype=np.uint32)
seeded_multinomial(N_AB, p_ABK, out=n_ABK)
np.allclose(n_ABK.sum(axis=-1), N_AB)
z_ABK += n_ABK
norm_z_ABK = z_ABK.astype(float) / np.sum(z_ABK, axis=-1, keepdims=True)
assert np.allclose(norm_z_ABK, p_ABK, atol=1e-2)
from gslrandom import PyRNG, multinomial, seeded_multinomial, get_omp_num_threads
import numpy as np
import numpy.random as rn
import time
I = 100000
K = 1000
N_I = rn.poisson(rn.gamma(2, 500, size=I)).astype(np.uint32)
P_IK = 1. / K * np.ones((I, K), dtype=np.float)
N_IK = np.ones((I, K), dtype=np.uint32)
s = time.time()
seeded_multinomial(N_I, P_IK, N_IK)
print '%fs: No PyRNG parallel version with %d cores' % (time.time() - s,
get_omp_num_threads())
assert (N_IK.sum(axis=1) == N_I).all()
rngs = [PyRNG(rn.randint(2**16)) for _ in xrange(get_omp_num_threads())]
N_IK = np.ones((I, K), dtype=np.uint32)
s = time.time()
multinomial(rngs, N_I, P_IK, N_IK)
print '%fs: PyRNG parallel version with %d cores' % (time.time() - s,
len(rngs))
assert (N_IK.sum(axis=1) == N_I).all()
rng = PyRNG(rn.randint(2**16))
N_IK = np.ones((I, K), dtype=np.uint32)
s = time.time()
multinomial(rng, N_I, P_IK, N_IK)
print '%fs: PyRNG version with 1 core' % (time.time() - s)
from gslrandom import PyRNG, multinomial, seeded_multinomial, get_omp_num_threads import numpy as np import numpy.random as rn import time I = 100000 K = 1000 N_I = rn.poisson(rn.gamma(2, 500, size=I)).astype(np.uint32) P_IK = 1./K * np.ones((I, K), dtype=np.float) N_IK = np.ones((I, K), dtype=np.uint32) s = time.time() seeded_multinomial(N_I, P_IK, N_IK) print '%fs: No PyRNG parallel version with %d cores' % (time.time() - s, get_omp_num_threads()) assert (N_IK.sum(axis=1) == N_I).all() rngs = [PyRNG(rn.randint(2**16)) for _ in xrange(get_omp_num_threads())] N_IK = np.ones((I, K), dtype=np.uint32) s = time.time() multinomial(rngs, N_I, P_IK, N_IK) print '%fs: PyRNG parallel version with %d cores' % (time.time() - s, len(rngs)) assert (N_IK.sum(axis=1) == N_I).all() rng = PyRNG(rn.randint(2**16)) N_IK = np.ones((I, K), dtype=np.uint32) s = time.time() multinomial(rng, N_I, P_IK, N_IK) print '%fs: PyRNG version with 1 core' % (time.time() - s) assert (N_IK.sum(axis=1) == N_I).all()