copulas/multivariate/gaussian.py
def probability_density(self, X):
        """Compute probability density function for given copula family.

        Args:
            X: `numpy.ndarray` or `pandas.DataFrame`

        Returns:
            np.array: Probability density for the input values.
        """
        self.check_fit()

        # make cov positive semi-definite
        covariance = self.covariance * np.identity(self.covariance.shape[0])
        return stats.multivariate_normal.pdf(X, cov=covariance)
Compute probability density function for given copula family.

        Args:
            X: `numpy.ndarray` or `pandas.DataFrame`

        Returns:
            np.array: Probability density for the input values.
        
Compute probability density function for given copula family.
Compute probability density function for given copula family .
def probability_density(self, X):
        self.check_fit()
        covariance = self.covariance * np.identity(self.covariance.shape[0])
        return stats.multivariate_normal.pdf(X, cov=covariance)
def probability_density ( self , X ) : self . check_fit ( ) covariance = self . covariance * np . identity ( self . covariance . shape [ 0 ] ) return stats . multivariate_normal . pdf ( X , cov = covariance )
Compute probability density function for given copula family.
Compute probability density function for given copula family .
"""Compute probability density function for given copula family.

        Args:
            X: `numpy.ndarray` or `pandas.DataFrame`

        Returns:
            np.array: Probability density for the input values.
        """ # make cov positive semi-definite
Probability density for the input values.
Probability density for the input values .
np.array
self
X
`numpy.ndarray` or `pandas.DataFrame`
` numpy . ndarray ` or ` pandas . DataFrame `