def score(train_a_dist: FrequencyDistribution,
test_a_dist: FrequencyDistribution,
train_c_dist: FrequencyDistribution,
test_c_dist: FrequencyDistribution,
dbca_config: DBCASplitterConfig) -> float:
"""
Parameters
----------
train_a_dist : FrequencyDistribution
DESCRIPTION.
test_a_dist : FrequencyDistribution
DESCRIPTION.
train_c_dist : FrequencyDistribution
DESCRIPTION.
test_c_dist : FrequencyDistribution
DESCRIPTION.
Returns
-------
float
DESCRIPTION.
"""
atom_score = chernoff_divergence(train_a_dist, test_a_dist,
dbca_config.alpha_a)
if atom_score < dbca_config.D_A:
atom_score = 0
compound_score = np.abs(
chernoff_divergence(train_c_dist, test_c_dist, dbca_config.alpha_c) -
dbca_config.D_C)
return compound_score + atom_score
def calc_compound_divergence(self, dist_a: FrequencyDistribution,
dist_b: FrequencyDistribution) -> float:
return chernoff_divergence(dist_a, dist_b, alpha=self.config.alpha_c)