def _estimate_gaussian_covariance_tied(resp, X, nk, means, reg_covar):
    avg_X2 = np.dot(X.T, X)
    avg_means2 = np.dot(nk * means.T, means)
    covariances = avg_X2 - avg_means2
    covariances /= X.shape[0]
    covariances.flat[::len(covariances) + 1] += reg_covar
    return covariances
The subnets which join to make the current subnet.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/mixture/gaussian_mixture.py/_estimate_gaussian_covariance_tied--crossover with--dataset/ETHPy150Open anandology/pyjamas/pyjs/src/pyjs/lib/ipaddr.py/_BaseNet.iter_subnets