Python IndependentComponentsDistribution.IndependentComponentsDistribution Exemples

Exemple #1

Fichier : test_distributions.py Projet : sephib/pomegranate

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)

Exemple #2

Fichier : test_distributions.py Projet : jannesgg/parallel-programming

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)

Exemple #3

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)