"""
Copyright 2018 Johns Hopkins University (Author: Jesus Villalba)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
import numpy as np
import h5py
from .score_norm import ScoreNorm
from .t_norm import TNorm
from .z_norm import ZNorm
[docs]class SNorm(ScoreNorm):
"""Class for S-Norm, symmetric score normalization."""
[docs] def __init__(self, **kwargs):
super(SNorm, self).__init__(*kwargs)
self.t_norm = TNorm(**kwargs)
self.z_norm = ZNorm(**kwargs)
[docs] def predict(
self,
scores,
scores_coh_test,
scores_enr_coh,
mask_coh_test=None,
mask_enr_coh=None,
):
scores_z_norm = self.z_norm.predict(scores, scores_enr_coh, mask_enr_coh)
scores_t_norm = self.t_norm.predict(scores, scores_coh_test, mask_coh_test)
return (scores_z_norm + scores_t_norm) / np.sqrt(2)