ProbabilisticModel.py 文件源码

def fit(self, data):
        if self.n is None:
            means = []
            stds = []
            weights = []
            score = []
            for n in range(self.n_min, self.n_max):
                gmm = GMM(n_components=n, covariance_type='full')
                gmm.fit(data)
                means.append(gmm.means_)
                stds.append(gmm.covars_)
                weights.append(gmm.weights_)
                if self.n_estimator == 'BIC':
                    score.append(gmm.bic(data))

            i_best = self._chosebestformetric(self.n_estimator, score)

            self.means = means[i_best]
            self.stds = stds[i_best]
            self.weights = weights[i_best]

        else:
            gmm = GMM(n_components=self.n, covariance_type='full')
            gmm.fit(data)
            self.means = gmm.means_
            self.stds = gmm.covars_
            self.weights = gmm.weights_