def test_to_dict_sample_size(self):
model = GaussianKDE(sample_size=10)
model.fit(self.constant)
params = model.to_dict()
assert params['type'] == 'copulas.univariate.gaussian_kde.GaussianKDE'
assert len(params['dataset']) == 10
def test_to_dict_constant(self):
model = GaussianKDE()
model.fit(self.constant)
params = model.to_dict()
assert params == {
'type': 'copulas.univariate.gaussian_kde.GaussianKDE',
'dataset': [5] * 100
}
def test_to_dict_from_dict(self):
model = GaussianKDE()
model.fit(self.data)
sampled_data = model.sample(50)
params = model.to_dict()
model2 = GaussianKDE.from_dict(params)
pdf = model.probability_density(sampled_data)
pdf2 = model2.probability_density(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
cdf = model.cumulative_distribution(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(cdf, cdf2, atol=0.01))