parameters: (self, low=0.0, high=1.0, shape=None)
return_statement: return self.random_sample(shape).astype(np.float64) * (high - low) + low
docstring: Draw samples from a uniform distribution.
  
          Samples are uniformly distributed over the half-open interval
          ``[low, high)`` (includes low, but excludes high).  In other words,
          any value within the given interval is equally likely to be drawn
          by `uniform`.
  
          Parameters
          ----------
          low : float, optional
              Lower boundary of the output interval.  All values generated will be
              greater than or equal to low.  The default value is 0.
          high : float
              Upper boundary of the output interval.  All values generated will be
              less than high.  The default value is 1.0.
          shape : int or tuple of ints, optional
              Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
              ``m * n * k`` samples are drawn.  Default is None, in which case a
              single value is returned.
  
          Returns
          -------
          out : BhArray
              Drawn samples, with shape `shape`.
  
          See Also
          --------
          randint : Discrete uniform distribution, yielding integers.
          random_integers : Discrete uniform distribution over the closed
                            interval ``[low, high]``.
          random_sample : Floats uniformly distributed over ``[0, 1)``.
          random : Alias for `random_sample`.
          rand : Convenience function that accepts dimensions as input, e.g.,
                 ``rand(2,2)`` would generate a 2-by-2 array of floats,
                 uniformly distributed over ``[0, 1)``.
  
          Notes
          -----
          The probability density function of the uniform distribution is
  
          .. math:: p(x) = \\frac{1}{b - a}
  
          anywhere within the interval ``[a, b)``, and zero elsewhere.
  
          same as:
          random_sample(size) * (high - low) + low
  
          Examples
          --------
          Draw samples from the distribution:
  
          >>> s = np.random.uniform(-1,0,1000)
  
          All values are within the given interval:
  
          >>> np.all(s >= -1)
          True
          >>> np.all(s < 0)
          True
  
          Display the histogram of the samples, along with the
          probability density function:
  
          >>> import matplotlib.pyplot as plt
          >>> count, bins, ignored = plt.hist(s, 15, normed=True)
          >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
          >>> plt.show()
docstring_summary: Draw samples from a uniform distribution.
docstring_tokens: Draw samples from a uniform distribution .
function: def uniform(self, low=0.0, high=1.0, shape=None):
          """Draw samples from a uniform distribution.
  
          Samples are uniformly distributed over the half-open interval
          ``[low, high)`` (includes low, but excludes high).  In other words,
          any value within the given interval is equally likely to be drawn
          by `uniform`.
  
          Parameters
          ----------
          low : float, optional
              Lower boundary of the output interval.  All values generated will be
              greater than or equal to low.  The default value is 0.
          high : float
              Upper boundary of the output interval.  All values generated will be
              less than high.  The default value is 1.0.
          shape : int or tuple of ints, optional
              Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
              ``m * n * k`` samples are drawn.  Default is None, in which case a
              single value is returned.
  
          Returns
          -------
          out : BhArray
              Drawn samples, with shape `shape`.
  
          See Also
          --------
          randint : Discrete uniform distribution, yielding integers.
          random_integers : Discrete uniform distribution over the closed
                            interval ``[low, high]``.
          random_sample : Floats uniformly distributed over ``[0, 1)``.
          random : Alias for `random_sample`.
          rand : Convenience function that accepts dimensions as input, e.g.,
                 ``rand(2,2)`` would generate a 2-by-2 array of floats,
                 uniformly distributed over ``[0, 1)``.
  
          Notes
          -----
          The probability density function of the uniform distribution is
  
          .. math:: p(x) = \\frac{1}{b - a}
  
          anywhere within the interval ``[a, b)``, and zero elsewhere.
  
          same as:
          random_sample(size) * (high - low) + low
  
          Examples
          --------
          Draw samples from the distribution:
  
          >>> s = np.random.uniform(-1,0,1000)
  
          All values are within the given interval:
  
          >>> np.all(s >= -1)
          True
          >>> np.all(s < 0)
          True
  
          Display the histogram of the samples, along with the
          probability density function:
  
          >>> import matplotlib.pyplot as plt
          >>> count, bins, ignored = plt.hist(s, 15, normed=True)
          >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
          >>> plt.show()
  
          """
          return self.random_sample(shape).astype(np.float64) * (high - low) + low
function_tokens: def uniform ( self , low = 0.0 , high = 1.0 , shape = None ) : return self . random_sample ( shape ) . astype ( np . float64 ) * ( high - low ) + low