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)