class_docstr: Class for estimating regularized inverse covariance with
  nodewise regression
  
  Parameters
  ----------
  exog : array_like
      A weighted design matrix for covariance
  
  Attributes
  ----------
  exog : array_like
      A weighted design matrix for covariance
  alpha : scalar
      Regularizing constant
docstr: estimates the regularized inverse covariance using nodewise
  regression
  
  Parameters
  ----------
  alpha : scalar
      Regularizing constant
docstr_tok: ["estimates","the","regularized","inverse","covariance","using","nodewise","regression","Parameters","--","--","--","--","--","alpha",":","scalar","Regularizing","constant"]
code: def fit(self, alpha=0):
      """estimates the regularized inverse covariance using nodewise
      regression
  
      Parameters
      ----------
      alpha : scalar
          Regularizing constant
      """
  
      n, p = self.exog.shape
  
      nodewise_row_l = []
      nodewise_weight_l = []
  
      for idx in range(p):
          nodewise_row = _calc_nodewise_row(self.exog, idx, alpha)
          nodewise_row_l.append(nodewise_row)
  
          nodewise_weight = _calc_nodewise_weight(
              self.exog, nodewise_row, idx, alpha
          )
          nodewise_weight_l.append(nodewise_weight)
  
      nodewise_row_l = np.array(nodewise_row_l)
      nodewise_weight_l = np.array(nodewise_weight_l)
  
      approx_inv_cov = _calc_approx_inv_cov(
          nodewise_row_l, nodewise_weight_l
      )
  
      self._approx_inv_cov = approx_inv_cov
  
file_path: statsmodels/statsmodels/stats/regularized_covariance.py
imports: from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 2 for calling the RegularizedInvCovariance obj's underlying member method code with example usage: obj.fit(alpha) without return types