def test_distributions_uniform_kernel_random_sample(): d = BernoulliDistribution(0.2) x = numpy.array([0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]) assert_array_equal(d.sample(20, random_state=5), x) assert_raises(AssertionError, assert_array_equal, d.sample(20), x)
def test_distributions_uniform_kernel_random_sample(): d = BernoulliDistribution(0.2) x = numpy.array([0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]) assert_array_equal(d.sample(20, random_state=5), x) assert_raises(AssertionError, assert_array_equal, d.sample(20), x)
def test_bernoulli():
d = BernoulliDistribution(0.6)
assert_equal(d.probability(0), 0.4)
assert_equal(d.probability(1), 0.6)
assert_equal(d.parameters[0], 1 - d.probability(0))
assert_equal(d.parameters[0], d.probability(1))
d.fit([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
assert_not_equal(d.probability(1), 1.0)
assert_equal(d.probability(0), 1.0)
a = [0.0, 0.0, 0.0]
b = [1.0, 1.0, 1.0]
c = [1.0, 1.0, 1.0]
d.summarize(a)
d.from_summaries()
assert_equal(d.probability(0), 1)
assert_equal(d.probability(1), 0)
d.summarize(a)
d.summarize(b)
d.from_summaries()
assert_equal(d.probability(0), 0.5)
assert_equal(d.probability(1), 0.5)
assert_equal(d.parameters[0], d.probability(0))
assert_equal(d.parameters[0], d.probability(1))
d.summarize(a)
d.summarize(b)
d.summarize(c)
d.from_summaries()
assert_equal(round(d.probability(0), 4), 0.3333)
assert_equal(round(d.probability(1), 4), 0.6667)
assert_equal(d.parameters[0], d.probability(1))
d = BernoulliDistribution.from_samples([0.0, 0.0, 0.0, 0.0, 0.0, 1.0])
assert_equal(round(d.probability(0), 4), 0.8333)
assert_equal(round(d.probability(1), 4), 0.1667)
assert_almost_equal(d.parameters[0], d.probability(1))
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "BernoulliDistribution")
assert_equal(round(e.parameters[0], 4), 0.1667)
f = pickle.loads(pickle.dumps(e))
assert_equal(f.name, "BernoulliDistribution")
assert_equal(round(f.parameters[0], 4), 0.1667)
def test_bernoulli(): d = BernoulliDistribution(0.6) assert_equal(d.probability(0), 0.4) assert_equal(d.probability(1), 0.6) assert_equal(d.parameters[0], 1-d.probability(0)) assert_equal(d.parameters[0], d.probability(1)) d.fit([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert_not_equal(d.probability(1), 1.0) assert_equal(d.probability(0), 1.0) a = [0.0, 0.0, 0.0] b = [1.0, 1.0, 1.0] c = [1.0, 1.0, 1.0] d.summarize(a) d.from_summaries() assert_equal(d.probability(0), 1) assert_equal(d.probability(1), 0) d.summarize(a) d.summarize(b) d.from_summaries() assert_equal(d.probability(0), 0.5) assert_equal(d.probability(1), 0.5) assert_equal(d.parameters[0], d.probability(0)) assert_equal(d.parameters[0], d.probability(1)) d.summarize(a) d.summarize(b) d.summarize(c) d.from_summaries() assert_equal(round(d.probability(0), 4), 0.3333) assert_equal(round(d.probability(1), 4), 0.6667) assert_equal(d.parameters[0], d.probability(1)) d = BernoulliDistribution.from_samples([0.0, 0.0, 0.0, 0.0, 0.0, 1.0]) assert_equal(round(d.probability(0), 4), 0.8333) assert_equal(round(d.probability(1), 4), 0.1667) assert_almost_equal(d.parameters[0], d.probability(1)) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "BernoulliDistribution") assert_equal(round(e.parameters[0], 4), 0.1667) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "BernoulliDistribution") assert_equal(round(f.parameters[0], 4), 0.1667)
from pomegranate import (
NaiveBayes,
NormalDistribution,
UniformDistribution,
ExponentialDistribution,
GeneralMixtureModel,
MultivariateGaussianDistribution,
BernoulliDistribution,
)
import pandas as pd
import numpy as np
X = pd.DataFrame({"A": [1, 0, 1, 0, 1], "B": [1, 1, 1, 1, 0]})
x = BernoulliDistribution(0.4)
vals = []
[vals.append(x.sample()) for i in range(1000)]
model = NaiveBayes([
NormalDistribution(5, 2),
UniformDistribution(0, 10),
ExponentialDistribution(1.0)
])
model.predict(np.array([[10]]))
model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution,
n_components=3,
X=X)