def c_step(X, n_support, remaining_iterations=30, initial_estimates=None, verbose=False, cov_computation_method=empirical_covariance, random_state=None):
 X = np.asarray(X)
 random_state = check_random_state(random_state)
 return _c_step(X, n_support, remaining_iterations=remaining_iterations, initial_estimates=initial_estimates, verbose=verbose, cov_computation_method=cov_computation_method, random_state=random_state)

def c_step ( X , n_support , remaining_iterations = 30 , initial_estimates = None , verbose = False , cov_computation_method = empirical_covariance , random_state = None ) : X = np . asarray ( X ) random_state = check_random_state ( random_state ) return _c_step ( X , n_support , remaining_iterations = remaining_iterations , initial_estimates = initial_estimates , verbose = verbose , cov_computation_method = cov_computation_method , random_state = random_state )
c_step procedure described in [rouseeuw1984]_ aiming at computing mcd .