copulas/univariate/gaussian_kde.py
def cumulative_distribution(self, X):
        """Computes the integral of a 1-D pdf between two bounds

        Args:
            X(numpy.array): Shaped (1, n), containing the datapoints.

        Returns:
            numpy.array: estimated cumulative distribution.
        """
        self.check_fit()

        low_bounds = self.model.dataset.mean() - (5 * self.model.dataset.std())

        result = []
        for value in X:
            result.append(self.model.integrate_box_1d(low_bounds, value))

        return np.array(result)
Computes the integral of a 1-D pdf between two bounds

        Args:
            X(numpy.array): Shaped (1, n), containing the datapoints.

        Returns:
            numpy.array: estimated cumulative distribution.
        
Computes the integral of a 1-D pdf between two bounds
Computes the integral of a 1 - D pdf between two bounds
def cumulative_distribution(self, X):
        self.check_fit()
        low_bounds = self.model.dataset.mean() - (5 * self.model.dataset.std())
        result = []
        for value in X:
            result.append(self.model.integrate_box_1d(low_bounds, value))
        return np.array(result)
def cumulative_distribution ( self , X ) : self . check_fit ( ) low_bounds = self . model . dataset . mean ( ) - ( 5 * self . model . dataset . std ( ) ) result = [ ] for value in X : result . append ( self . model . integrate_box_1d ( low_bounds , value ) ) return np . array ( result )
Computes the integral of a 1-D pdf between two bounds
Computes the integral of a 1 - D pdf between two bounds
"""Computes the integral of a 1-D pdf between two bounds

        Args:
            X(numpy.array): Shaped (1, n), containing the datapoints.

        Returns:
            numpy.array: estimated cumulative distribution.
        """
estimated cumulative distribution.
estimated cumulative distribution .
numpy.array
self
X
Shaped (1, n), containing the datapoints.
Shaped ( 1 n ) containing the datapoints .