original_docstring: r"""
  
          Calculates the n-th moment of the Gamma distribution with
          given parameters.
  
          .. math::
              E = \frac{\Gamma \left ( n + \alpha \right )}{\beta^{n}\Gamma
              \left ( \alpha \right )}
  
          Parameters
          ----------
  
          n : integer or numpy array of integers
              The ordinal of the moment to calculate
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          mean : scalar or numpy array
              The moment(s) of the Gamma distribution
  
          Examples
          --------
          >>> from surpyval import Gamma
          >>> Gamma.moment(3, 3, 4)
          0.9375
          
docstring: r"""
  Calculates the n-th moment of the Gamma distribution with
  given parameters.
  
  
  
  Parameters
  
  n : integer or numpy array of integers
  The ordinal of the moment to calculate
  alpha : numpy array or scalar
  The shape parameter for the Gamma distribution
  beta : numpy array or scalar
  The scale parameter for the Gamma distribution
  
  Returns
  
  mean : scalar or numpy array
  The moment(s) of the Gamma distribution
  
  Examples
  
  
docstring_tokens: r " " " Calculates the n - th moment of the Gamma distribution with given parameters . Parameters n : integer or numpy array of integers The ordinal of the moment to calculate alpha : numpy array or scalar The shape parameter for the Gamma distribution beta : numpy array or scalar The scale parameter for the Gamma distribution Returns mean : scalar or numpy array The moment ( s ) of the Gamma distribution Examples
code: def moment(self, n, alpha, beta):
          r"""
  
          Calculates the n-th moment of the Gamma distribution with
          given parameters.
  
          .. math::
              E = \frac{\Gamma \left ( n + \alpha \right )}{\beta^{n}\Gamma
              \left ( \alpha \right )}
  
          Parameters
          ----------
  
          n : integer or numpy array of integers
              The ordinal of the moment to calculate
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          mean : scalar or numpy array
              The moment(s) of the Gamma distribution
  
          Examples
          --------
          >>> from surpyval import Gamma
          >>> Gamma.moment(3, 3, 4)
          0.9375
          """
          return agamma(n + alpha) / (beta**n * agamma(alpha))
code_tokens: def moment ( self , n , alpha , beta ) : return agamma ( n + alpha ) / ( beta ** n * agamma ( alpha ) )
short_docstring: r"""
  Calculates the n-th moment of the Gamma distribution with
  given parameters.
short_docstring_tokens: r " " " Calculates the n - th moment of the Gamma distribution with given parameters .
comment: r"""
  
          Calculates the n-th moment of the Gamma distribution with
          given parameters.
  
          .. math::
              E = \frac{\Gamma \left ( n + \alpha \right )}{\beta^{n}\Gamma
              \left ( \alpha \right )}
  
          Parameters
          ----------
  
          n : integer or numpy array of integers
              The ordinal of the moment to calculate
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          mean : scalar or numpy array
              The moment(s) of the Gamma distribution
  
          Examples
          --------
          >>> from surpyval import Gamma
          >>> Gamma.moment(3, 3, 4)
          0.9375
          """