def test_distributions_independent_random_sample(): d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10), ExponentialDistribution(7), LogNormalDistribution(0, 0.4)]) x = numpy.array([[5.882455, 2.219932, 0.03586 , 1.193024], [4.33826 , 8.707323, 0.292267, 0.876036], [9.861542, 2.067192, 0.033083, 2.644041]]) assert_array_almost_equal(d.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_distributions_independent_random_sample(): d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10), ExponentialDistribution(7), LogNormalDistribution(0, 0.4)]) x = numpy.array([[5.882455, 2.219932, 0.03586 , 1.193024], [4.33826 , 8.707323, 0.292267, 0.876036], [9.861542, 2.067192, 0.033083, 2.644041]]) assert_array_almost_equal(d.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_independent_splitting():
independent_distribution = IndependentComponentsDistribution(
[NormalDistribution( 4, 10 ),
NormalDistribution( 2, 10 )]
)
id_one, id_two = independent_distribution.split()
assert abs( id_one.distributions[0].parameters[0] - 2 ) < 0.01
assert abs( id_one.distributions[1].parameters[0] - 1 ) < 0.01
assert abs( id_one.distributions[0].parameters[1] - 17 ) < 0.01
assert abs( id_one.distributions[1].parameters[1] - 17 ) < 0.01
assert abs( id_two.distributions[0].parameters[0] - 2 ) < 0.01
assert abs( id_two.distributions[1].parameters[0] - 1 ) < 0.01
assert abs( id_two.distributions[0].parameters[1] - 3 ) < 0.01
assert abs( id_two.distributions[1].parameters[1] - 3 ) < 0.01
def test_multivariate_log_probability(): X = numpy.random.randn(100, 5) d = MultivariateGaussianDistribution.from_samples(X) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i]) d = IndependentComponentsDistribution.from_samples(X, distributions=NormalDistribution) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i])
def test_multivariate_log_probability(): X = numpy.random.randn(100, 5) d = MultivariateGaussianDistribution.from_samples(X) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i]) d = IndependentComponentsDistribution.from_samples(X, distributions=NormalDistribution) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i])
def test_independent():
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
ExponentialDistribution(2)])
assert_equal(round(d.log_probability((4, 1)), 4), -3.0439)
assert_equal(round(d.log_probability((100, 0.001)), 4), -1129.0459)
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
ExponentialDistribution(2)],
weights=[18., 1.])
assert_equal(round(d.log_probability((4, 1)), 4), -32.5744)
assert_equal(round(d.log_probability((100, 0.001)), 4), -20334.5764)
d.fit([(5, 1), (5.2, 1.7), (4.7, 1.9), (4.9, 2.4), (4.5, 1.2)])
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.86)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 0.2417)
assert_equal(round(d.parameters[0][1].parameters[0], 4), 0.6098)
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
UniformDistribution(0, 10)])
d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0),
(2, 20)],
inertia=0.5)
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0),
(2, 20)],
inertia=0.75)
assert_not_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_not_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_not_equal(d.parameters[0][1].parameters[0], -2.5)
assert_not_equal(d.parameters[0][1].parameters[1], 15)
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
UniformDistribution(0, 10)])
d.summarize([(0, 0), (5, 0), (3, 0)])
d.summarize([(5, -5), (7, 0)])
d.summarize([(3, 0), (4, 0), (5, 0), (2, 20)])
d.from_summaries(inertia=0.5)
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
d.freeze()
d.fit([(1, 7), (7, 2), (2, 4), (2, 4), (1, 4)])
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "IndependentComponentsDistribution")
assert_equal(round(e.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(e.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(e.parameters[0][1].parameters[0], -2.5)
assert_equal(e.parameters[0][1].parameters[1], 15)
f = pickle.loads(pickle.dumps(e))
assert_equal(e.name, "IndependentComponentsDistribution")
assert_equal(round(f.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(f.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(f.parameters[0][1].parameters[0], -2.5)
assert_equal(f.parameters[0][1].parameters[1], 15)
X = numpy.array([[0.5, 0.2, 0.7], [0.3, 0.1, 0.9], [0.4, 0.3, 0.8],
[0.3, 0.3, 0.9], [0.3, 0.2, 0.6], [0.5, 0.2, 0.8]])
d = IndependentComponentsDistribution.from_samples(
X, distributions=NormalDistribution)
assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4)
assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4)
assert_almost_equal(d.parameters[0][1].parameters[0], 0.21666, 4)
assert_almost_equal(d.parameters[0][1].parameters[1], 0.06872, 4)
assert_almost_equal(d.parameters[0][2].parameters[0], 0.78333, 4)
assert_almost_equal(d.parameters[0][2].parameters[1], 0.10672, 4)
d = IndependentComponentsDistribution.from_samples(
X, distributions=ExponentialDistribution)
assert_almost_equal(d.parameters[0][0].parameters[0], 2.6087, 4)
assert_almost_equal(d.parameters[0][1].parameters[0], 4.6154, 4)
assert_almost_equal(d.parameters[0][2].parameters[0], 1.2766, 4)
d = IndependentComponentsDistribution.from_samples(X,
distributions=[
NormalDistribution,
NormalDistribution,
NormalDistribution
])
assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4)
assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4)
assert_almost_equal(d.parameters[0][1].parameters[0], 0.21666, 4)
assert_almost_equal(d.parameters[0][1].parameters[1], 0.06872, 4)
assert_almost_equal(d.parameters[0][2].parameters[0], 0.78333, 4)
assert_almost_equal(d.parameters[0][2].parameters[1], 0.10672, 4)
d = IndependentComponentsDistribution.from_samples(
X,
distributions=[
NormalDistribution, LogNormalDistribution, ExponentialDistribution
])
assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4)
assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4)
assert_almost_equal(d.parameters[0][1].parameters[0], -1.5898, 4)
assert_almost_equal(d.parameters[0][1].parameters[1], 0.36673, 4)
assert_almost_equal(d.parameters[0][2].parameters[0], 1.27660, 4)
def test_independent():
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
ExponentialDistribution(2)])
assert_equal(round(d.log_probability((4, 1)), 4), -3.0439)
assert_equal(round(d.log_probability((100, 0.001)), 4), -1129.0459)
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
ExponentialDistribution(2)],
weights=[18., 1.])
assert_equal(round(d.log_probability((4, 1)), 4), -0.1536)
assert_equal(round(d.log_probability((100, 0.001)), 4), -1126.1556)
d.fit([(5, 1), (5.2, 1.7), (4.7, 1.9), (4.9, 2.4), (4.5, 1.2)])
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.86)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 0.2417)
assert_equal(round(d.parameters[0][1].parameters[0], 4), 0.6098)
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
UniformDistribution(0, 10)])
d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0),
(2, 20)],
inertia=0.5)
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0),
(2, 20)],
inertia=0.75)
assert_not_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_not_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_not_equal(d.parameters[0][1].parameters[0], -2.5)
assert_not_equal(d.parameters[0][1].parameters[1], 15)
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2),
UniformDistribution(0, 10)])
d.summarize([(0, 0), (5, 0), (3, 0)])
d.summarize([(5, -5), (7, 0)])
d.summarize([(3, 0), (4, 0), (5, 0), (2, 20)])
d.from_summaries(inertia=0.5)
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
d.freeze()
d.fit([(1, 7), (7, 2), (2, 4), (2, 4), (1, 4)])
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "IndependentComponentsDistribution")
assert_equal(round(e.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(e.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(e.parameters[0][1].parameters[0], -2.5)
assert_equal(e.parameters[0][1].parameters[1], 15)
f = pickle.loads(pickle.dumps(e))
assert_equal(e.name, "IndependentComponentsDistribution")
assert_equal(round(f.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(f.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(f.parameters[0][1].parameters[0], -2.5)
assert_equal(f.parameters[0][1].parameters[1], 15)
def test_independent(): d = IndependentComponentsDistribution( [NormalDistribution(5, 2), ExponentialDistribution(2)]) assert_equal(round(d.log_probability((4, 1)), 4), -3.0439) assert_equal(round(d.log_probability((100, 0.001)), 4), -1129.0459) d = IndependentComponentsDistribution([NormalDistribution(5, 2), ExponentialDistribution(2)], weights=[18., 1.]) assert_equal(round(d.log_probability((4, 1)), 4), -32.5744) assert_equal(round(d.log_probability((100, 0.001)), 4), -20334.5764) d.fit([(5, 1), (5.2, 1.7), (4.7, 1.9), (4.9, 2.4), (4.5, 1.2)]) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.86) assert_equal(round(d.parameters[0][0].parameters[1], 4), 0.2417) assert_equal(round(d.parameters[0][1].parameters[0], 4), 0.6098) d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10)]) d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0), (2, 20)], inertia=0.5) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(d.parameters[0][1].parameters[0], -2.5) assert_equal(d.parameters[0][1].parameters[1], 15) d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0), (2, 20)], inertia=0.75) assert_not_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_not_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_not_equal(d.parameters[0][1].parameters[0], -2.5) assert_not_equal(d.parameters[0][1].parameters[1], 15) d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10)]) d.summarize([(0, 0), (5, 0), (3, 0)]) d.summarize([(5, -5), (7, 0)]) d.summarize([(3, 0), (4, 0), (5, 0), (2, 20)]) d.from_summaries(inertia=0.5) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(d.parameters[0][1].parameters[0], -2.5) assert_equal(d.parameters[0][1].parameters[1], 15) d.freeze() d.fit([(1, 7), (7, 2), (2, 4), (2, 4), (1, 4)]) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(d.parameters[0][1].parameters[0], -2.5) assert_equal(d.parameters[0][1].parameters[1], 15) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "IndependentComponentsDistribution") assert_equal(round(e.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(e.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(e.parameters[0][1].parameters[0], -2.5) assert_equal(e.parameters[0][1].parameters[1], 15) f = pickle.loads(pickle.dumps(e)) assert_equal(e.name, "IndependentComponentsDistribution") assert_equal(round(f.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(f.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(f.parameters[0][1].parameters[0], -2.5) assert_equal(f.parameters[0][1].parameters[1], 15) X = numpy.array([[0.5, 0.2, 0.7], [0.3, 0.1, 0.9], [0.4, 0.3, 0.8], [0.3, 0.3, 0.9], [0.3, 0.2, 0.6], [0.5, 0.2, 0.8]]) d = IndependentComponentsDistribution.from_samples(X, distributions=NormalDistribution) assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4) assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4) assert_almost_equal(d.parameters[0][1].parameters[0], 0.21666, 4) assert_almost_equal(d.parameters[0][1].parameters[1], 0.06872, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 0.78333, 4) assert_almost_equal(d.parameters[0][2].parameters[1], 0.10672, 4) d = IndependentComponentsDistribution.from_samples(X, distributions=ExponentialDistribution) assert_almost_equal(d.parameters[0][0].parameters[0], 2.6087, 4) assert_almost_equal(d.parameters[0][1].parameters[0], 4.6154, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 1.2766, 4) d = IndependentComponentsDistribution.from_samples(X, distributions=[NormalDistribution, NormalDistribution, NormalDistribution]) assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4) assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4) assert_almost_equal(d.parameters[0][1].parameters[0], 0.21666, 4) assert_almost_equal(d.parameters[0][1].parameters[1], 0.06872, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 0.78333, 4) assert_almost_equal(d.parameters[0][2].parameters[1], 0.10672, 4) d = IndependentComponentsDistribution.from_samples(X, distributions=[NormalDistribution, LogNormalDistribution, ExponentialDistribution]) assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4) assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4) assert_almost_equal(d.parameters[0][1].parameters[0], -1.5898, 4) assert_almost_equal(d.parameters[0][1].parameters[1], 0.36673, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 1.27660, 4)
def test_independent():
d = IndependentComponentsDistribution(
[NormalDistribution(5, 2), ExponentialDistribution(2)])
assert_equal(round(d.log_probability((4, 1)), 4), -3.0439)
assert_equal(round(d.log_probability((100, 0.001)), 4), -1129.0459)
d = IndependentComponentsDistribution([NormalDistribution(5, 2),
ExponentialDistribution(2)],
weights=[18., 1.])
assert_equal(round(d.log_probability((4, 1)), 4), -0.1536)
assert_equal(round(d.log_probability((100, 0.001)), 4), -1126.1556)
d.fit([(5, 1), (5.2, 1.7), (4.7, 1.9), (4.9, 2.4), (4.5, 1.2)])
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.86)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 0.2417)
assert_equal(round(d.parameters[0][1].parameters[0], 4), 0.6098)
d = IndependentComponentsDistribution([NormalDistribution(5, 2),
UniformDistribution(0, 10)])
d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0),
(3, 0), (4, 0), (5, 0), (2, 20)], inertia=0.5)
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0),
(3, 0), (4, 0), (5, 0), (2, 20)], inertia=0.75)
assert_not_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_not_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_not_equal(d.parameters[0][1].parameters[0], -2.5)
assert_not_equal(d.parameters[0][1].parameters[1], 15)
d = IndependentComponentsDistribution([NormalDistribution(5, 2),
UniformDistribution(0, 10)])
d.summarize([(0, 0), (5, 0), (3, 0)])
d.summarize([(5, -5), (7, 0)])
d.summarize([(3, 0), (4, 0), (5, 0), (2, 20)])
d.from_summaries(inertia=0.5)
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
d.freeze()
d.fit([(1, 7), (7, 2), (2, 4), (2, 4), (1, 4)])
assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(d.parameters[0][1].parameters[0], -2.5)
assert_equal(d.parameters[0][1].parameters[1], 15)
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "IndependentComponentsDistribution")
assert_equal(round(e.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(e.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(e.parameters[0][1].parameters[0], -2.5)
assert_equal(e.parameters[0][1].parameters[1], 15)
f = pickle.loads(pickle.dumps(e))
assert_equal(e.name, "IndependentComponentsDistribution")
assert_equal(round(f.parameters[0][0].parameters[0], 4), 4.3889)
assert_equal(round(f.parameters[0][0].parameters[1], 4), 1.9655)
assert_equal(f.parameters[0][1].parameters[0], -2.5)
assert_equal(f.parameters[0][1].parameters[1], 15)
