original_docstring: r"""
  
          Density function for the Gamma Distribution:
  
          .. math::
              f(x) = \frac{\beta^{\alpha }}{\Gamma \left ( \alpha \right )}
              x^{\alpha - 1}e^{-\beta x}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The values at which the function will be calculated
          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
          -------
  
          df : scalar or numpy array
              The density of the distribution at each x
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.df(x, 3, 2)
          array([0.54134113, 0.29305022, 0.08923508, 0.02146961, 0.00453999])
          
docstring: r"""
  Density function for the Gamma Distribution.
  
  
  
  Parameters
  
  x : numpy array or scalar
  The values at which the function will be calculated
  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
  
  df : scalar or numpy array
  The density of the distribution at each x
  
  Examples
  
  
docstring_tokens: r " " " Density function for the Gamma Distribution . Parameters x : numpy array or scalar The values at which the function will be calculated 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 df : scalar or numpy array The density of the distribution at each x Examples
code: def df(self, x, alpha, beta):
          r"""
  
          Density function for the Gamma Distribution:
  
          .. math::
              f(x) = \frac{\beta^{\alpha }}{\Gamma \left ( \alpha \right )}
              x^{\alpha - 1}e^{-\beta x}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The values at which the function will be calculated
          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
          -------
  
          df : scalar or numpy array
              The density of the distribution at each x
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.df(x, 3, 2)
          array([0.54134113, 0.29305022, 0.08923508, 0.02146961, 0.00453999])
          """
          return (((beta ** alpha) * x ** (alpha - 1)
                   * np.exp(-(x * beta)) / (agamma(alpha))))
code_tokens: def df ( self , x , alpha , beta ) : return ( ( ( beta ** alpha ) * x ** ( alpha - 1 ) * np . exp ( - ( x * beta ) ) / ( agamma ( alpha ) ) ) )
short_docstring: r"""
  Density function for the Gamma Distribution:
short_docstring_tokens: r " " " Density function for the Gamma Distribution :
comment: r"""
  
          Density function for the Gamma Distribution:
  
          .. math::
              f(x) = \frac{\beta^{\alpha }}{\Gamma \left ( \alpha \right )}
              x^{\alpha - 1}e^{-\beta x}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The values at which the function will be calculated
          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
          -------
  
          df : scalar or numpy array
              The density of the distribution at each x
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.df(x, 3, 2)
          array([0.54134113, 0.29305022, 0.08923508, 0.02146961, 0.00453999])
          """