Python LogNormalDistribution示例

示例#1

文件： test_distributions.py 项目： sephib/pomegranate

def test_distributions_lognormal_random_sample():
	d = LogNormalDistribution(0, 1)

	x = numpy.array([1.55461432,  0.71829843, 11.36764528,  0.77717313,  
		1.11584263])

	assert_array_almost_equal(d.sample(5, random_state=5), x)
	assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)

示例#2

文件： test_distributions.py 项目： jmschrei/pomegranate

def test_distributions_lognormal_random_sample():
	d = LogNormalDistribution(0, 1)

	x = numpy.array([1.55461432,  0.71829843, 11.36764528,  0.77717313,  
		1.11584263])

	assert_array_almost_equal(d.sample(5, random_state=5), x)
	assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)

示例#3

文件： test_distributions.py 项目： 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)

示例#4

文件： test_distributions.py 项目： jannesgg/parallel-programming

def test_lognormal():
    d = LogNormalDistribution(5, 2)
    assert_equal(round(d.log_probability(5), 4), -4.6585)

    d.fit([5.1, 5.03, 4.98, 5.05, 4.91, 5.2, 5.1, 5., 4.8, 5.21])
    assert_equal(round(d.parameters[0], 4), 1.6167)
    assert_equal(round(d.parameters[1], 4), 0.0237)

    d.summarize([5.1, 5.03, 4.98, 5.05])
    d.summarize([4.91, 5.2, 5.1])
    d.summarize([5., 4.8, 5.21])
    d.from_summaries()

    assert_equal(round(d.parameters[0], 4), 1.6167)
    assert_equal(round(d.parameters[1], 4), 0.0237)

    e = Distribution.from_json(d.to_json())
    assert_equal(e.name, "LogNormalDistribution")
    assert_equal(round(e.parameters[0], 4), 1.6167)
    assert_equal(round(e.parameters[1], 4), 0.0237)

    f = pickle.loads(pickle.dumps(e))
    assert_equal(f.name, "LogNormalDistribution")
    assert_equal(round(f.parameters[0], 4), 1.6167)
    assert_equal(round(f.parameters[1], 4), 0.0237)

示例#5

文件： test_distributions.py 项目： pixelou/pomegranate

def test_lognormal():
	d = LogNormalDistribution(5, 2)
	assert_equal(round(d.log_probability(5), 4), -4.6585)

	d.fit([5.1, 5.03, 4.98, 5.05, 4.91, 5.2, 5.1, 5., 4.8, 5.21])
	assert_equal(round(d.parameters[0], 4), 1.6167)
	assert_equal(round(d.parameters[1], 4), 0.0237)

	d.summarize([5.1, 5.03, 4.98, 5.05])
	d.summarize([4.91, 5.2, 5.1])
	d.summarize([5., 4.8, 5.21])
	d.from_summaries()

	assert_equal(round(d.parameters[0], 4), 1.6167)
	assert_equal(round(d.parameters[1], 4), 0.0237)

	e = Distribution.from_json(d.to_json())
	assert_equal(e.name, "LogNormalDistribution")
	assert_equal(round(e.parameters[0], 4), 1.6167)
	assert_equal(round(e.parameters[1], 4), 0.0237)

	f = pickle.loads(pickle.dumps(e))
	assert_equal(f.name, "LogNormalDistribution")
	assert_equal(round(f.parameters[0], 4), 1.6167)
	assert_equal(round(f.parameters[1], 4), 0.0237)