parameters: (X, n_support, remaining_iterations=30, initial_estimates=None,
             verbose=False, cov_computation_method=empirical_covariance,
             random_state=None)
return_statement: 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)
docstring: C_step procedure described in [Rouseeuw1984]_ aiming at computing MCD.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          Data set in which we look for the n_support observations whose
          scatter matrix has minimum determinant.
  
      n_support : int, > n_samples / 2
          Number of observations to compute the robust estimates of location
          and covariance from.
  
      remaining_iterations : int, optional
          Number of iterations to perform.
          According to [Rouseeuw1999]_, two iterations are sufficient to get
          close to the minimum, and we never need more than 30 to reach
          convergence.
  
      initial_estimates : 2-tuple, optional
          Initial estimates of location and shape from which to run the c_step
          procedure:
          - initial_estimates[0]: an initial location estimate
          - initial_estimates[1]: an initial covariance estimate
  
      verbose : boolean, optional
          Verbose mode.
  
      cov_computation_method : callable, default empirical_covariance
          The function which will be used to compute the covariance.
          Must return shape (n_features, n_features)
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      location : array-like, shape (n_features,)
          Robust location estimates.
  
      covariance : array-like, shape (n_features, n_features)
          Robust covariance estimates.
  
      support : array-like, shape (n_samples,)
          A mask for the `n_support` observations whose scatter matrix has
          minimum determinant.
  
      References
      ----------
      .. [Rouseeuw1999] A Fast Algorithm for the Minimum Covariance Determinant
          Estimator, 1999, American Statistical Association and the American
          Society for Quality, TECHNOMETRICS
docstring_summary: C_step procedure described in [Rouseeuw1984]_ aiming at computing MCD.
docstring_tokens: C_step procedure described in [ Rouseeuw1984 ] _ aiming at computing MCD .
function: def c_step(X, n_support, remaining_iterations=30, initial_estimates=None,
             verbose=False, cov_computation_method=empirical_covariance,
             random_state=None):
      """C_step procedure described in [Rouseeuw1984]_ aiming at computing MCD.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          Data set in which we look for the n_support observations whose
          scatter matrix has minimum determinant.
  
      n_support : int, > n_samples / 2
          Number of observations to compute the robust estimates of location
          and covariance from.
  
      remaining_iterations : int, optional
          Number of iterations to perform.
          According to [Rouseeuw1999]_, two iterations are sufficient to get
          close to the minimum, and we never need more than 30 to reach
          convergence.
  
      initial_estimates : 2-tuple, optional
          Initial estimates of location and shape from which to run the c_step
          procedure:
          - initial_estimates[0]: an initial location estimate
          - initial_estimates[1]: an initial covariance estimate
  
      verbose : boolean, optional
          Verbose mode.
  
      cov_computation_method : callable, default empirical_covariance
          The function which will be used to compute the covariance.
          Must return shape (n_features, n_features)
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      location : array-like, shape (n_features,)
          Robust location estimates.
  
      covariance : array-like, shape (n_features, n_features)
          Robust covariance estimates.
  
      support : array-like, shape (n_samples,)
          A mask for the `n_support` observations whose scatter matrix has
          minimum determinant.
  
      References
      ----------
      .. [Rouseeuw1999] A Fast Algorithm for the Minimum Covariance Determinant
          Estimator, 1999, American Statistical Association and the American
          Society for Quality, TECHNOMETRICS
  
      """
      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)
function_tokens: 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 )