olpy/classifiers/sop.py
def _setup(self, X: np.ndarray):
        """Initializes the values for the model' parameters.

        Based on the data in argument, this method initializes 
        the covariance matrix `sigma`.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                m columns

        Returns:
            None
        """
        self._sigma = self._a * np.identity(X.shape[1])
Initializes the values for the model' parameters.

        Based on the data in argument, this method initializes 
        the covariance matrix `sigma`.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                m columns

        Returns:
            None
        
Initializes the values for the model' parameters.
Based on the data in argument, this method initializes
the covariance matrix `sigma`.
Initializes the values for the model ' parameters . Based on the data in argument this method initializes the covariance matrix ` sigma ` .
def _setup(self, X: np.ndarray):
        self._sigma = self._a * np.identity(X.shape[1])
def _setup ( self , X : np . ndarray ) : self . _sigma = self . _a * np . identity ( X . shape [ 1 ] )
Initializes the values for the model' parameters.
Initializes the values for the model ' parameters .
"""Initializes the values for the model' parameters.

        Based on the data in argument, this method initializes 
        the covariance matrix `sigma`.

        Args:
            X (:obj:`numpy.ndarray`): Input data with n rows and
                m columns

        Returns:
            None
        """
None
self
X
np.ndarray
X (
`numpy.ndarray`): Input data with n rows and
m columns
` numpy . ndarray ` ) : Input data with n rows and m columns