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sampling better the KDEy-HD approach

This commit is contained in:
Alejandro Moreo Fernandez 2023-11-23 11:30:45 +01:00
parent dcfd61ae5b
commit 01ef81bf25
2 changed files with 2 additions and 2 deletions
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2
distribution_matching/commons.py
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@ -8,7 +8,7 @@ from distribution_matching.method_dirichlety import DIRy
from sklearn.linear_model import LogisticRegression
from method_kdey_closed_efficient import KDEyclosed_efficient
METHODS = ['ACC', 'PACC', 'HDy-OvA', 'DM-T', 'DM-HD', 'KDEy-DMhd3', 'KDEy-DMhd4', 'DM-CS', 'KDEy-closed++', 'DIR', 'EMQ', 'KDEy-ML'] #['ACC', 'PACC', 'HDy-OvA', 'DIR', 'DM', 'KDEy-DMhd3', 'KDEy-closed++', 'EMQ', 'KDEy-ML'] #, 'KDEy-DMhd2'] #, 'KDEy-DMhd2', 'DM-HD'] 'KDEy-DMjs', 'KDEy-DM', 'KDEy-ML+', 'KDEy-DMhd3+', 'EMQ-C',
METHODS = ['ACC', 'PACC', 'HDy-OvA', 'DM-T', 'DM-HD', 'KDEy-DMhd4', 'DM-CS', 'KDEy-closed++', 'DIR', 'EMQ', 'KDEy-ML'] #['ACC', 'PACC', 'HDy-OvA', 'DIR', 'DM', 'KDEy-DMhd3', 'KDEy-closed++', 'EMQ', 'KDEy-ML'] #, 'KDEy-DMhd2'] #, 'KDEy-DMhd2', 'DM-HD'] 'KDEy-DMjs', 'KDEy-DM', 'KDEy-ML+', 'KDEy-DMhd3+', 'EMQ-C',
BIN_METHODS = [x.replace('-OvA', '') for x in METHODS]

2
distribution_matching/method_kdey.py
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@ -130,7 +130,7 @@ class KDEy(AggregativeProbabilisticQuantifier):
N = self.montecarlo_trials
rs = self.random_state
self.reference_samples = np.vstack([kde_i.sample(N, random_state=rs) for kde_i in self.val_densities])
self.reference_classwise_densities = np.asarray([self.pdf(kde_j, samples_i) for kde_j in self.val_densities])
self.reference_classwise_densities = np.asarray([self.pdf(kde_j, self.reference_samples) for kde_j in self.val_densities])
self.reference_density = np.mean(self.reference_classwise_densities, axis=0) # equiv. to (uniform @ self.reference_classwise_densities)
elif self.target == 'min_divergence_deprecated': # the version of the first draft, with n*N presampled, then alpha*N chosen for class
self.class_samples = [kde_i.sample(self.montecarlo_trials, random_state=self.random_state) for kde_i in self.val_densities]