acousticModelTraining.py 文件源码

def bicGMMModelSelection(X):
    '''
    bic model selection
    :param X: features - observation * dimension
    :return:
    '''
    lowest_bic = np.infty
    bic = []
    n_components_range  = [10,15,20,25,30,35,40,45,50,55,60,65,70]
    best_n_components   = n_components_range[0]
    for n_components in n_components_range:
        # Fit a Gaussian mixture with EM
        print 'Fitting GMM with n_components =',str(n_components)
        gmm = mixture.GaussianMixture(n_components=n_components,
                                      covariance_type='diag')
        gmm.fit(X)
        bic.append(gmm.bic(X))
        if bic[-1] < lowest_bic:
            lowest_bic = bic[-1]
            best_n_components = n_components
            best_gmm          = gmm

    return best_n_components,gmm