methods.py 文件源码

def gmm(X,y,M,C,K=4,cov_type='diag',reps=10):


    # Fit Gaussian mixture model

    gmm = GMM(n_components=K, covariance_type=cov_type, n_init=reps, params='wmc').fit(X)

    cls = gmm.predict(X)    # extract cluster labels

    cds = gmm.means_        # extract cluster centroids (means of gaussians)

    covs = gmm.covars_      # extract cluster shapes (covariances of gaussians)



    if cov_type == 'diag':

        new_covs = np.zeros([K,M,M])

        count = 0

        for elem in covs:

            temp_m = np.zeros([M,M])

            for i in range(len(elem)):

                temp_m[i][i] = elem[i]

            new_covs[count] = temp_m

            count += 1

        covs = new_covs

    clusterPlot(X,cls,K,C,y,cds,covs)