def test_mv_mixture_rvs_random(self):
cov3 = np.array([[1., 0.5, 0.75], [0.5, 1.5, 0.6], [0.75, 0.6, 2.]])
mu = np.array([-1, 0.0, 2.0])
mu2 = np.array([4, 2.0, 2.0])
mvn3 = mvd.MVNormal(mu, cov3)
mvn32 = mvd.MVNormal(mu2, cov3 / 2.)
np.random.seed(0)
res = mv_mixture_rvs([0.4, 0.6], 5000, [mvn3, mvn32], 3)
npt.assert_almost_equal(np.array([res.std(),
res.mean(),
res.var()]),
np.array([1.874, 1.733, 3.512]),
decimal=1)
def test_mv_mixture_rvs_fixed(self):
np.random.seed(1234)
cov3 = np.array([[1., 0.5, 0.75], [0.5, 1.5, 0.6], [0.75, 0.6, 2.]])
mu = np.array([-1, 0.0, 2.0])
mu2 = np.array([4, 2.0, 2.0])
mvn3 = mvd.MVNormal(mu, cov3)
mvn32 = mvd.MVNormal(mu2, cov3 / 2)
res = mv_mixture_rvs([0.2, 0.8], 10, [mvn3, mvn32], 3)
npt.assert_almost_equal(
res,
np.array([[-0.23955497, 1.73426482, 0.36100243],
[2.52063189, 1.0832677, 1.89947131],
[4.36755379, 2.14480498, 2.22003966],
[3.1141545, 1.21250505, 2.58511199],
[4.1980202, 2.50017561, 1.87324933],
[3.48717503, 0.91847424, 2.14004598],
[3.55904133, 2.74367622, 0.68619582],
[3.60521933, 1.57316531, 0.82784584],
[3.86102275, 0.6211812, 1.33016426],
[3.91074761, 2.037155, 2.22247051]]))
for comparison I used R mvtnorm version 0.9-96 """ from __future__ import print_function import numpy as np import statsmodels.sandbox.distributions.mv_normal as mvd from numpy.testing import assert_array_almost_equal cov3 = np.array([[1., 0.5, 0.75], [0.5, 1.5, 0.6], [0.75, 0.6, 2.]]) mu = np.array([-1, 0.0, 2.0]) #************** multivariate normal distribution *************** mvn3 = mvd.MVNormal(mu, cov3) #compare with random sample x = mvn3.rvs(size=1000000) xli = [[2., 1., 1.5], [0., 2., 1.5], [1.5, 1., 2.5], [0., 1., 1.5]] xliarr = np.asarray(xli).T[None, :, :] #from R session #pmvnorm(lower=-Inf,upper=(x[0,.]-mu)/sqrt(diag(cov3)),mean=rep(0,3),corr3) r_cdf = [0.3222292, 0.3414643, 0.5450594, 0.3116296] r_cdf_errors = [1.715116e-05, 1.590284e-05, 5.356471e-05, 3.567548e-05] n_cdf = [mvn3.cdf(a) for a in xli] assert_array_almost_equal(r_cdf, n_cdf, decimal=4)