def test_distributions_uniform_kernel_random_sample(): d = UniformKernelDensity([0, 4, 3, 5, 7, 4, 2]) x = numpy.array([4.223488, 2.531816, 4.036836, 1.593601, 4.375442]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_distributions_uniform_kernel_random_sample(): d = UniformKernelDensity([0, 4, 3, 5, 7, 4, 2]) x = numpy.array([4.223488, 2.531816, 4.036836, 1.593601, 4.375442]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_uniform_kernel():
d = UniformKernelDensity([1, 3, 5, 6, 2, 2, 3, 2, 2])
assert_equal(round(d.log_probability(2.2), 4), -0.4055)
assert_equal(round(d.log_probability(6.2), 4), -2.1972)
assert_equal(d.log_probability(10), float('-inf'))
d = UniformKernelDensity([1, 100, 200])
assert_not_equal(round(d.log_probability(2.2), 4), -0.4055)
assert_not_equal(round(d.log_probability(6.2), 4), -2.1972)
d.summarize([1, 3, 5, 6, 2])
d.summarize([2, 3, 2, 2])
d.from_summaries()
assert_equal(round(d.log_probability(2.2), 4), -0.4055)
assert_equal(round(d.log_probability(6.2), 4), -2.1972)
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "UniformKernelDensity")
assert_equal(round(e.log_probability(2.2), 4), -0.4055)
assert_equal(round(e.log_probability(6.2), 4), -2.1972)
f = pickle.loads(pickle.dumps(e))
assert_equal(e.name, "UniformKernelDensity")
assert_equal(round(f.log_probability(2.2), 4), -0.4055)
assert_equal(round(f.log_probability(6.2), 4), -2.1972)
def test_uniform_kernel():
d = UniformKernelDensity([1, 3, 5, 6, 2, 2, 3, 2, 2])
assert_equal(round(d.log_probability(2.2), 4), -0.4055)
assert_equal(round(d.log_probability(6.2), 4), -2.1972)
assert_equal(d.log_probability(10), float('-inf'))
d = UniformKernelDensity([1, 100, 200])
assert_not_equal(round(d.log_probability(2.2), 4), -0.4055)
assert_not_equal(round(d.log_probability(6.2), 4), -2.1972)
d.summarize([1, 3, 5, 6, 2])
d.summarize([2, 3, 2, 2])
d.from_summaries()
assert_equal(round(d.log_probability(2.2), 4), -0.4055)
assert_equal(round(d.log_probability(6.2), 4), -2.1972)
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "UniformKernelDensity")
assert_equal(round(e.log_probability(2.2), 4), -0.4055)
assert_equal(round(e.log_probability(6.2), 4), -2.1972)
f = pickle.loads(pickle.dumps(e))
assert_equal(e.name, "UniformKernelDensity")
assert_equal(round(f.log_probability(2.2), 4), -0.4055)
assert_equal(round(f.log_probability(6.2), 4), -2.1972)