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()