copulas/multivariate/gaussian.py
def fit(self, X):
        """Compute the distribution for each variable and then its covariance matrix.

        Args:
            X(numpy.ndarray or pandas.DataFrame): Data to model.

        Returns:
            None
        """
        LOGGER.debug('Fitting Gaussian Copula')
        column_names = self.get_column_names(X)
        distribution_class = import_object(self.distribution)

        for column_name in column_names:
            self.distribs[column_name] = distribution_class()
            column = self.get_column(X, column_name)
            self.distribs[column_name].fit(column)

        self.covariance = self._get_covariance(X)
        self.fitted = True
Compute the distribution for each variable and then its covariance matrix.

        Args:
            X(numpy.ndarray or pandas.DataFrame): Data to model.

        Returns:
            None
        
Compute the distribution for each variable and then its covariance matrix.
Compute the distribution for each variable and then its covariance matrix .
def fit(self, X):
        LOGGER.debug('Fitting Gaussian Copula')
        column_names = self.get_column_names(X)
        distribution_class = import_object(self.distribution)
        for column_name in column_names:
            self.distribs[column_name] = distribution_class()
            column = self.get_column(X, column_name)
            self.distribs[column_name].fit(column)
        self.covariance = self._get_covariance(X)
        self.fitted = True
def fit ( self , X ) : LOGGER . debug ( 'Fitting Gaussian Copula' ) column_names = self . get_column_names ( X ) distribution_class = import_object ( self . distribution ) for column_name in column_names : self . distribs [ column_name ] = distribution_class ( ) column = self . get_column ( X , column_name ) self . distribs [ column_name ] . fit ( column ) self . covariance = self . _get_covariance ( X ) self . fitted = True
Compute the distribution for each variable and then its covariance matrix.
Compute the distribution for each variable and then its covariance matrix .
"""Compute the distribution for each variable and then its covariance matrix.

        Args:
            X(numpy.ndarray or pandas.DataFrame): Data to model.

        Returns:
            None
        """
None
self
X
Data to model.
Data to model .