mixture = sklearn.mixture.GaussianMixture(n_components=1, covariance_type='full').fit(my_array)

covariances = mixture.covariances_ # shape = (1, 22, 22) where 1 is the 1 component I fit and 22x22 is the covariance matrix
means = mixture_component.means_ # shape = (1, 22), 22 means; one for each feautre
dependent_data = features[:, 0:2] #shape = (10000, 2)
conditional_data = features[:, 2:] #shape = (10000, 20)
mu_a = means[:, 0:2]  # Mu of the dependent variables
mu_b = means[:, 2:]  # Mu of the independent variables
cov_aa = covariances[0, 0:2, 0:2] # Cov of the dependent vars
cov_bb = covariances[0, 2:, 2:]  # Cov of independent vars
cov_ab = covariances[0, 0:2, 2:]
cov_ba = covariances[0, 2:, 0:2]
A = (conditional_data.transpose() - mu_b.transpose())
B = cov_ab.dot(np.linalg.inv(cov_bb))
conditional_mu = mu_a + B.dot(A).transpose()
conditional_cov = cov_aa - cov_ab.dot(np.linalg.inv(cov_bb)).dot(cov_ba)

conditional_mu.shape
(10000, 2)
conditional_cov.shape
(2,2)