def test_mixture_rvs_random(self):
# Test only medium small sample at 1 decimal
np.random.seed(0)
mix = MixtureDistribution()
res = mix.rvs([.75,.25], 1000, dist=[stats.norm, stats.norm], kwargs =
(dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
npt.assert_almost_equal(
np.array([res.std(),res.mean(),res.var()]),
np.array([1,-0.5,1]),
decimal=1)
def test_mixture_rvs_random(self):
# Test only medium small sample at 1 decimal
np.random.seed(0)
mix = MixtureDistribution()
res = mix.rvs([.75,.25], 1000, dist=[stats.norm, stats.norm], kwargs =
(dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
npt.assert_almost_equal(
np.array([res.std(),res.mean(),res.var()]),
np.array([1,-0.5,1]),
decimal=1)
def test_mixture_cdf(self):
mix = MixtureDistribution()
grid = np.linspace(-4,4, 10)
res = mix.cdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=
(dict(loc=-1,scale=.25),dict(loc=1,scale=.75)))
npt.assert_almost_equal(
res,
np.array([ 8.72261646e-12, 1.40592960e-08, 5.95819161e-06,
3.10250226e-02, 3.46993159e-01, 4.86283549e-01,
7.81092904e-01, 9.65606734e-01, 9.98373155e-01,
9.99978886e-01]))
def test_mixture_pdf(self):
mix = MixtureDistribution()
grid = np.linspace(-4,4, 10)
res = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=
(dict(loc=-1,scale=.25),dict(loc=1,scale=.75)))
npt.assert_almost_equal(
res,
np.array([ 7.92080017e-11, 1.05977272e-07, 3.82368500e-05,
2.21485447e-01, 1.00534607e-01, 2.69531536e-01,
3.21265627e-01, 9.39899015e-02, 6.74932493e-03,
1.18960201e-04]))
def test_mixture_cdf(self):
mix = MixtureDistribution()
grid = np.linspace(-4,4, 10)
res = mix.cdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=
(dict(loc=-1,scale=.25),dict(loc=1,scale=.75)))
npt.assert_almost_equal(
res,
np.array([ 8.72261646e-12, 1.40592960e-08, 5.95819161e-06,
3.10250226e-02, 3.46993159e-01, 4.86283549e-01,
7.81092904e-01, 9.65606734e-01, 9.98373155e-01,
9.99978886e-01]))
def test_mixture_pdf(self):
mix = MixtureDistribution()
grid = np.linspace(-4,4, 10)
res = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=
(dict(loc=-1,scale=.25),dict(loc=1,scale=.75)))
npt.assert_almost_equal(
res,
np.array([ 7.92080017e-11, 1.05977272e-07, 3.82368500e-05,
2.21485447e-01, 1.00534607e-01, 2.69531536e-01,
3.21265627e-01, 9.39899015e-02, 6.74932493e-03,
1.18960201e-04]))
def test_mixture_rvs_fixed(self):
mix = MixtureDistribution()
np.random.seed(1234)
res = mix.rvs([.15,.85], 50, dist=[stats.norm, stats.norm], kwargs =
(dict(loc=1,scale=.5),dict(loc=-1,scale=.5)))
npt.assert_almost_equal(
res,
np.array([-0.5794956 , -1.72290504, -1.70098664, -1.0504591 ,
-1.27412122,-1.07230975, -0.82298983, -1.01775651,
-0.71713085,-0.2271706 ,-1.48711817, -1.03517244,
-0.84601557, -1.10424938, -0.48309963,-2.20022682,
0.01530181, 1.1238961 , -1.57131564, -0.89405831,
-0.64763969, -1.39271761, 0.55142161, -0.76897013,
-0.64788589,-0.73824602, -1.46312716, 0.00392148,
-0.88651873, -1.57632955,-0.68401028, -0.98024366,
-0.76780384, 0.93160258,-2.78175833,-0.33944719,
-0.92368472, -0.91773523, -1.21504785, -0.61631563,
1.0091446 , -0.50754008, 1.37770699, -0.86458208,
-0.3040069 ,-0.96007884, 1.10763429, -1.19998229,
-1.51392528, -1.29235911]))
def test_mixture_rvs_fixed(self):
mix = MixtureDistribution()
np.random.seed(1234)
res = mix.rvs([.15,.85], 50, dist=[stats.norm, stats.norm], kwargs =
(dict(loc=1,scale=.5),dict(loc=-1,scale=.5)))
npt.assert_almost_equal(
res,
np.array([-0.5794956 , -1.72290504, -1.70098664, -1.0504591 ,
-1.27412122,-1.07230975, -0.82298983, -1.01775651,
-0.71713085,-0.2271706 ,-1.48711817, -1.03517244,
-0.84601557, -1.10424938, -0.48309963,-2.20022682,
0.01530181, 1.1238961 , -1.57131564, -0.89405831,
-0.64763969, -1.39271761, 0.55142161, -0.76897013,
-0.64788589,-0.73824602, -1.46312716, 0.00392148,
-0.88651873, -1.57632955,-0.68401028, -0.98024366,
-0.76780384, 0.93160258,-2.78175833,-0.33944719,
-0.92368472, -0.91773523, -1.21504785, -0.61631563,
1.0091446 , -0.50754008, 1.37770699, -0.86458208,
-0.3040069 ,-0.96007884, 1.10763429, -1.19998229,
-1.51392528, -1.29235911]))
examples = ['chebyt', 'fourier', 'hermite']#[2]
nobs = 10000
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import (
mixture_rvs, MixtureDistribution)
#np.random.seed(12345)
## obs_dist = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm],
## kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.75)))
mix_kwds = (dict(loc=-0.5,scale=.5),dict(loc=1,scale=.2))
obs_dist = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm],
kwargs=mix_kwds)
mix = MixtureDistribution()
#obs_dist = np.random.randn(nobs)/4. #np.sqrt(2)
if "chebyt_" in examples: # needed for Cheby example below
#obs_dist = np.clip(obs_dist, -2, 2)/2.01
#chebyt [0,1]
obs_dist = obs_dist[(obs_dist>-2) & (obs_dist<2)]/2.0 #/4. + 2/4.0
#fourier [0,1]
#obs_dist = obs_dist[(obs_dist>-2) & (obs_dist<2)]/4. + 2/4.0
f_hat, grid, coeffs, polys = density_orthopoly(obs_dist, ChebyTPoly, order=20, xeval=None)
#f_hat /= f_hat.sum() * (grid.max() - grid.min())/len(grid)
f_hat0 = f_hat
fint = integrate.trapz(f_hat, grid)# dx=(grid.max() - grid.min())/len(grid))
#f_hat -= fint/2.
examples = ['chebyt', 'fourier', 'hermite']#[2]
nobs = 10000
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import (
mixture_rvs, MixtureDistribution)
#np.random.seed(12345)
## obs_dist = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm],
## kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.75)))
mix_kwds = (dict(loc=-0.5,scale=.5),dict(loc=1,scale=.2))
obs_dist = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm],
kwargs=mix_kwds)
mix = MixtureDistribution()
#obs_dist = np.random.randn(nobs)/4. #np.sqrt(2)
if "chebyt_" in examples: # needed for Cheby example below
#obs_dist = np.clip(obs_dist, -2, 2)/2.01
#chebyt [0,1]
obs_dist = obs_dist[(obs_dist>-2) & (obs_dist<2)]/2.0 #/4. + 2/4.0
#fourier [0,1]
#obs_dist = obs_dist[(obs_dist>-2) & (obs_dist<2)]/4. + 2/4.0
f_hat, grid, coeffs, polys = density_orthopoly(obs_dist, ChebyTPoly, order=20, xeval=None)
#f_hat /= f_hat.sum() * (grid.max() - grid.min())/len(grid)
f_hat0 = f_hat
from scipy import integrate
fint = integrate.trapz(f_hat, grid)# dx=(grid.max() - grid.min())/len(grid))
