Question

# create data set
n = 1000
_mus = np.array([[0,4], [-2,0]])
_sigmas = np.array([[[3, 0], [0, 0.5]], [[1,0],[0,2]]])
_pis = np.array([0.6, 0.4])
X = np.concatenate([np.random.multivariate_normal(mu, sigma, int(pi*n))
                    for pi, mu, sigma in zip(_pis, _mus, _sigmas)])

kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(X)
dens = kde.score_samples(X)

plt.figure(figsize=(8,8))
plt.scatter(X[:, 0], X[:, 1], c=dens);