106 lines
4.9 KiB
Python
106 lines
4.9 KiB
Python
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# Copyright (c) 2013 Ana Martinez Pardo <anamartinezpardo@gmail.com>
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# License: BSD-3 [see LICENSE.txt]
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import numpy as np
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import numpy.testing as npt
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from statsmodels.distributions.mixture_rvs import (mv_mixture_rvs,
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MixtureDistribution)
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import statsmodels.sandbox.distributions.mv_normal as mvd
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from scipy import stats
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class TestMixtureDistributions:
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def test_mixture_rvs_random(self):
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# Test only medium small sample at 1 decimal
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np.random.seed(0)
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mix = MixtureDistribution()
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res = mix.rvs([.75,.25], 1000, dist=[stats.norm, stats.norm], kwargs =
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(dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
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npt.assert_almost_equal(
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np.array([res.std(),res.mean(),res.var()]),
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np.array([1,-0.5,1]),
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decimal=1)
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def test_mv_mixture_rvs_random(self):
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cov3 = np.array([[ 1. , 0.5 , 0.75],
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[ 0.5 , 1.5 , 0.6 ],
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[ 0.75, 0.6 , 2. ]])
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mu = np.array([-1, 0.0, 2.0])
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mu2 = np.array([4, 2.0, 2.0])
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mvn3 = mvd.MVNormal(mu, cov3)
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mvn32 = mvd.MVNormal(mu2, cov3/2.)
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np.random.seed(0)
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res = mv_mixture_rvs([0.4, 0.6], 5000, [mvn3, mvn32], 3)
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npt.assert_almost_equal(
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np.array([res.std(),res.mean(),res.var()]),
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np.array([1.874,1.733,3.512]),
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decimal=1)
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def test_mixture_pdf(self):
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mix = MixtureDistribution()
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grid = np.linspace(-4,4, 10)
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res = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=
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(dict(loc=-1,scale=.25),dict(loc=1,scale=.75)))
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npt.assert_almost_equal(
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res,
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np.array([ 7.92080017e-11, 1.05977272e-07, 3.82368500e-05,
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2.21485447e-01, 1.00534607e-01, 2.69531536e-01,
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3.21265627e-01, 9.39899015e-02, 6.74932493e-03,
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1.18960201e-04]))
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def test_mixture_cdf(self):
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mix = MixtureDistribution()
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grid = np.linspace(-4,4, 10)
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res = mix.cdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=
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(dict(loc=-1,scale=.25),dict(loc=1,scale=.75)))
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npt.assert_almost_equal(
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res,
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np.array([ 8.72261646e-12, 1.40592960e-08, 5.95819161e-06,
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3.10250226e-02, 3.46993159e-01, 4.86283549e-01,
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7.81092904e-01, 9.65606734e-01, 9.98373155e-01,
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9.99978886e-01]))
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def test_mixture_rvs_fixed(self):
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mix = MixtureDistribution()
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np.random.seed(1234)
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res = mix.rvs([.15,.85], 50, dist=[stats.norm, stats.norm], kwargs =
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(dict(loc=1,scale=.5),dict(loc=-1,scale=.5)))
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npt.assert_almost_equal(
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res,
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np.array([-0.5794956 , -1.72290504, -1.70098664, -1.0504591 ,
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-1.27412122,-1.07230975, -0.82298983, -1.01775651,
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-0.71713085,-0.2271706 ,-1.48711817, -1.03517244,
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-0.84601557, -1.10424938, -0.48309963,-2.20022682,
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0.01530181, 1.1238961 , -1.57131564, -0.89405831,
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-0.64763969, -1.39271761, 0.55142161, -0.76897013,
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-0.64788589,-0.73824602, -1.46312716, 0.00392148,
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-0.88651873, -1.57632955,-0.68401028, -0.98024366,
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-0.76780384, 0.93160258,-2.78175833,-0.33944719,
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-0.92368472, -0.91773523, -1.21504785, -0.61631563,
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1.0091446 , -0.50754008, 1.37770699, -0.86458208,
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-0.3040069 ,-0.96007884, 1.10763429, -1.19998229,
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-1.51392528, -1.29235911]))
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def test_mv_mixture_rvs_fixed(self):
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np.random.seed(1234)
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cov3 = np.array([[ 1. , 0.5 , 0.75],
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[ 0.5 , 1.5 , 0.6 ],
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[ 0.75, 0.6 , 2. ]])
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mu = np.array([-1, 0.0, 2.0])
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mu2 = np.array([4, 2.0, 2.0])
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mvn3 = mvd.MVNormal(mu, cov3)
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mvn32 = mvd.MVNormal(mu2, cov3/2)
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res = mv_mixture_rvs([0.2, 0.8], 10, [mvn3, mvn32], 3)
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npt.assert_almost_equal(
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res,
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np.array([[-0.23955497, 1.73426482, 0.36100243],
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[ 2.52063189, 1.0832677 , 1.89947131],
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[ 4.36755379, 2.14480498, 2.22003966],
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[ 3.1141545 , 1.21250505, 2.58511199],
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[ 4.1980202 , 2.50017561, 1.87324933],
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[ 3.48717503, 0.91847424, 2.14004598],
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[ 3.55904133, 2.74367622, 0.68619582],
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[ 3.60521933, 1.57316531, 0.82784584],
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[ 3.86102275, 0.6211812 , 1.33016426],
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[ 3.91074761, 2.037155 , 2.22247051]]))
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