from sklearn.mixture import GaussianMixture
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import operator
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import numpy as np
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import math
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class GMMSet:
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def __init__(self, gmm_order = 32):
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self.gmms = []
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self.gmm_order = gmm_order
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self.y = []
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def fit_new(self, x, label):
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self.y.append(label)
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gmm = GaussianMixture(self.gmm_order)
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gmm.fit(x)
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self.gmms.append(gmm)
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def gmm_score(self, gmm, x):
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return np.sum(gmm.score(x))
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@staticmethod
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def softmax(scores):
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scores_sum = sum([math.exp(i) for i in scores])
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score_max = math.exp(max(scores))
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return round(score_max / scores_sum, 3)
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def predict_one(self, x):
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scores = [self.gmm_score(gmm, x) / len(x) for gmm in self.gmms]
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p = sorted(enumerate(scores), key=operator.itemgetter(1), reverse=True)
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p = [(str(self.y[i]), y, p[0][1] - y) for i, y in p]
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result = [(self.y[index], value) for (index, value) in enumerate(scores)]
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p = max(result, key=operator.itemgetter(1))
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softmax_score = self.softmax(scores)
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return p[0], softmax_score
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def before_pickle(self):
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pass
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def after_pickle(self):
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pass
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