def test_distributions_uniform_freeze_thaw_fit(): d = UniformDistribution(0, 10) d.freeze() d.thaw() d.fit([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4]) assert_array_equal(d.parameters, [4, 6])
def test_distributions_uniform_freeze_thaw_fit(): d = UniformDistribution(0, 10) d.freeze() d.thaw() d.fit([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4]) assert_array_equal(d.parameters, [4, 6])
def test_uniform():
d = UniformDistribution(0, 10)
assert_almost_equal(d.log_probability(2.34), -2.3025850929940455, 8)
assert_equal(d.log_probability(2), d.log_probability(8))
assert_equal(d.log_probability(10), d.log_probability(3.4))
assert_equal(d.log_probability(1.7), d.log_probability(9.7))
assert_equal(d.log_probability(10.0001), float("-inf"))
assert_equal(d.log_probability(-0.0001), float("-inf"))
for i in range(10):
data = numpy.random.randn(100) * 100
d.fit(data)
assert_equal(d.parameters[0], data.min())
assert_equal(d.parameters[1], data.max())
minimum, maximum = data.min(), data.max()
for i in range(100):
sample = d.sample()
assert_less_equal(minimum, sample)
assert_less_equal(sample, maximum)
d = UniformDistribution(0, 10)
d.fit([-5, 20], inertia=0.5)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.fit([-100, 100], inertia=1.0)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.summarize([0, 50, 2, 24, 28])
d.summarize([-20, 7, 8, 4])
d.from_summaries(inertia=0.75)
assert_equal(d.parameters[0], -6.875)
assert_equal(d.parameters[1], 23.75)
d.summarize([0, 100])
d.summarize([100, 200])
d.from_summaries()
assert_equal(d.parameters[0], 0)
assert_equal(d.parameters[1], 200)
d.freeze()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 200])
d.thaw()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 8])
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "UniformDistribution")
assert_equal(e.parameters, [0, 8])
f = pickle.loads(pickle.dumps(e))
assert_equal(f.name, "UniformDistribution")
assert_equal(f.parameters, [0, 8])
def test_uniform():
d = UniformDistribution(0, 10)
assert_almost_equal(d.log_probability(2.34), -2.3025850929940455, 8)
assert_equal(d.log_probability(2), d.log_probability(8))
assert_equal(d.log_probability(10), d.log_probability(3.4))
assert_equal(d.log_probability(1.7), d.log_probability(9.7))
assert_equal(d.log_probability(10.0001), float("-inf"))
assert_equal(d.log_probability(-0.0001), float("-inf"))
for i in range(10):
data = numpy.random.randn(100) * 100
d.fit(data)
assert_equal(d.parameters[0], data.min())
assert_equal(d.parameters[1], data.max())
minimum, maximum = data.min(), data.max()
for i in range(100):
sample = d.sample()
assert_less_equal(minimum, sample)
assert_less_equal(sample, maximum)
d = UniformDistribution(0, 10)
d.fit([-5, 20], inertia=0.5)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.fit([-100, 100], inertia=1.0)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.summarize([0, 50, 2, 24, 28])
d.summarize([-20, 7, 8, 4])
d.from_summaries(inertia=0.75)
assert_equal(d.parameters[0], -6.875)
assert_equal(d.parameters[1], 23.75)
d.summarize([0, 100])
d.summarize([100, 200])
d.from_summaries()
assert_equal(d.parameters[0], 0)
assert_equal(d.parameters[1], 200)
d.freeze()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 200])
d.thaw()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 8])
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "UniformDistribution")
assert_equal(e.parameters, [0, 8])
f = pickle.loads(pickle.dumps(e))
assert_equal(f.name, "UniformDistribution")
assert_equal(f.parameters, [0, 8])
