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我遇到了sklearn.mixture.dpgmm问题。主要问题是它没有为合成数据（2个分离的2D高斯）返回正确的协方差，它真的不应该有问题。特别是，当我做dpgmm._get_covars（）时，无论输入数据分布如何，协方差矩阵的对角线元素总是恰好为1.0太大。这看起来像一个错误，因为gmm完美地工作（当限制到已知确切数量的组时）Sklearn.mixture.dpgmm无法正常工作
另一个问题是dpgmm.weights_没有意义，它们总合为一，但数值看起来毫无意义。
有没有人有解决这个问题，或看到明显错误与我的例子？
这里是我运行的确切脚本：
import itertools
import numpy as np
from scipy import linalg
import matplotlib.pyplot as plt
import matplotlib as mpl
import pdb
from sklearn import mixture
# Generate 2D random sample, two gaussians each with 10000 points
rsamp1 = np.random.multivariate_normal(np.array([5.0,5.0]),np.array([[1.0,-0.2],[-0.2,1.0]]),10000)
rsamp2 = np.random.multivariate_normal(np.array([0.0,0.0]),np.array([[0.2,-0.0],[-0.0,3.0]]),10000)
X = np.concatenate((rsamp1,rsamp2),axis=0)
# Fit a mixture of Gaussians with EM using 2
gmm = mixture.GMM(n_components=2, covariance_type='full',n_iter=10000)
gmm.fit(X)
# Fit a Dirichlet process mixture of Gaussians using 10 components
dpgmm = mixture.DPGMM(n_components=10, covariance_type='full',min_covar=0.5,tol=0.00001,n_iter = 1000000)
dpgmm.fit(X)
print("Groups With data in them")
print(np.unique(dpgmm.predict(X)))
##print the input and output covars as example, should be very similar
correct_c0 = np.array([[1.0,-0.2],[-0.2,1.0]])
print "Input covar"
print correct_c0
covars = dpgmm._get_covars()
c0 = np.round(covars[0],decimals=1)
print "Output Covar"
print c0
print("Output Variances Too Big by 1.0")