import operator
import numpy as np
from sklearn.mixture import GMM
class GMMSet(object):
def __init__(self, gmm_order = 32):
self.gmms = []
self.gmm_order = gmm_order
self.y = []
def fit_new(self, x, label):
self.y.append(label)
gmm = GMM(self.gmm_order)
gmm.fit(x)
self.gmms.append(gmm)
def gmm_score(self, gmm, x):
return np.sum(gmm.score(x))
def predict_one(self, x):
scores = [self.gmm_score(gmm, x) / len(x) for gmm in self.gmms]
p = sorted(enumerate(scores), key=operator.itemgetter(1), reverse=True)
result = [(self.y[index], value) for (index, value) in enumerate(scores)]
p = max(result, key=operator.itemgetter(1))
return p
