Python TriangleKernelDensity Examples

Example #1

File: test_distributions.py Project: sephib/pomegranate

def test_distributions_triangle_kernel_random_sample():
	d = TriangleKernelDensity([0, 4, 3, 5, 7, 4, 2])

	x = numpy.array([4.118801, 2.31576 , 4.018591, 1.770455, 4.612734])

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

Example #2

File: test_distributions.py Project: jmschrei/pomegranate

def test_distributions_triangle_kernel_random_sample():
	d = TriangleKernelDensity([0, 4, 3, 5, 7, 4, 2])

	x = numpy.array([4.118801, 2.31576 , 4.018591, 1.770455, 4.612734])

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

Example #3

File: test_distributions.py Project: jannesgg/parallel-programming

def test_triangular_kernel():
    d = TriangleKernelDensity([1, 6, 3, 4, 5, 2])
    assert_equal(round(d.log_probability(6.5), 4), -2.4849)

    d = TriangleKernelDensity([1, 8, 100])
    assert_not_equal(round(d.log_probability(6.5), 4), -2.4849)

    d.summarize([1, 6])
    d.summarize([3, 4, 5])
    d.summarize([2])
    d.from_summaries()
    assert_equal(round(d.log_probability(6.5), 4), -2.4849)

    d.freeze()
    d.fit([1, 4, 6, 7, 3, 5, 7, 8, 3, 3, 4])
    assert_equal(round(d.log_probability(6.5), 4), -2.4849)

    e = Distribution.from_json(d.to_json())
    assert_equal(e.name, "TriangleKernelDensity")
    assert_equal(round(e.log_probability(6.5), 4), -2.4849)

    f = pickle.loads(pickle.dumps(e))
    assert_equal(f.name, "TriangleKernelDensity")
    assert_equal(round(f.log_probability(6.5), 4), -2.4849)

Example #4

File: test_distributions.py Project: pixelou/pomegranate

def test_triangular_kernel():
	d = TriangleKernelDensity([1, 6, 3, 4, 5, 2])
	assert_equal(round(d.log_probability(6.5), 4), -2.4849)

	d = TriangleKernelDensity([1, 8, 100])
	assert_not_equal(round(d.log_probability(6.5), 4), -2.4849)

	d.summarize([1, 6])
	d.summarize([3, 4, 5])
	d.summarize([2])
	d.from_summaries()
	assert_equal(round(d.log_probability(6.5), 4), -2.4849)

	d.freeze()
	d.fit([1, 4, 6, 7, 3, 5, 7, 8, 3, 3, 4])
	assert_equal(round(d.log_probability(6.5), 4), -2.4849)

	e = Distribution.from_json(d.to_json())
	assert_equal(e.name, "TriangleKernelDensity")
	assert_equal(round(e.log_probability(6.5), 4), -2.4849)

	f = pickle.loads(pickle.dumps(e))
	assert_equal(f.name, "TriangleKernelDensity")
	assert_equal(round(f.log_probability(6.5), 4), -2.4849)