copulas/multivariate/gaussian.py
def cumulative_distribution(self, X):
        """Computes the cumulative distribution function for the copula

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

        Returns:
            np.array: cumulative probability
        """
        self.check_fit()

        # Wrapper for pdf to accept vector as args
        def func(*args):
            return self.probability_density(list(args))

        # Lower bound for integral, to split significant part from tail
        lower_bound = self.get_lower_bound()

        ranges = [[lower_bound, val] for val in X]
        return integrate.nquad(func, ranges)[0]
Computes the cumulative distribution function for the copula

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

        Returns:
            np.array: cumulative probability
        
Computes the cumulative distribution function for the copula
Computes the cumulative distribution function for the copula
def cumulative_distribution(self, X):
        self.check_fit()
        def func(*args):
            return self.probability_density(list(args))
        lower_bound = self.get_lower_bound()
        ranges = [[lower_bound, val] for val in X]
        return integrate.nquad(func, ranges)[0]
def cumulative_distribution ( self , X ) : self . check_fit ( ) def func ( * args ) : return self . probability_density ( list ( args ) ) lower_bound = self . get_lower_bound ( ) ranges = [ [ lower_bound , val ] for val in X ] return integrate . nquad ( func , ranges ) [ 0 ]
Computes the cumulative distribution function for the copula
Computes the cumulative distribution function for the copula
"""Computes the cumulative distribution function for the copula

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

        Returns:
            np.array: cumulative probability
        """ # Wrapper for pdf to accept vector as args # Lower bound for integral, to split significant part from tail
None
np.array
self
X
`numpy.ndarray` or `pandas.DataFrame`
` numpy . ndarray ` or ` pandas . DataFrame `