import numpy as np
from scipy.stats import multivariate_normal
from sklearn.mixture import GaussianMixture
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()
def differential_entropies(X, labels):
n_samples, n_features = X.shape
labels = np.array(labels)
names = sorted(set(labels))
entropies = []
for name in names:
name_idx = np.where(labels == name)[0]
gm = GaussianMixture().fit(X[name_idx, :])
mn = multivariate_normal(
mean=gm.means_.flatten(),
cov=gm.covariances_.reshape(n_features, n_features)
)
entropies.append(mn.entropy())
probs = softmax(entropies)
for name, entropy, prob in zip(names, entropies, probs):
#print('{}\t{}\t{}'.format(name, entropy, prob))
print('{}\t{}'.format(name, entropy))
