4 examples of 'np.random.exponential' in Python
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344 def _exponential_internal(scale: float, 345 n: int, 346 antithetic: bool = False) -> ndarray: 347 u = rand(n) 348 u = minimum(u, 1.0 - np.finfo(np.float32).eps) 349 x: ndarray = log(1.0 - u) * (-scale) 350 return x
228 def exponential(x): 229 """Exponential's function. 230 231 It can be used with 'n' variables and has minimum at -1. 232 Also, it is expected to be within [-1, 1] bounds. 233 234 Args: 235 x (np.array): An n-dimensional input array. 236 237 Returns: 238 y = -exp(-0.5 * sum(x^2)) 239 240 """ 241 242 # Calculating Sphere's function 243 s = sphere(x) 244 245 return -np.exp(-0.5 * s)
8 def sampleFromExponential((lambdaParam, size)): 9 return numpy.random.exponential(lambdaParam, size)
1712 def random(self, array_or_shape): 1713 """ 1714 Generate a random sample with the same type as the layer. 1715 For an Exponential layer, draws from the exponential distribution 1716 with the rate determined by the params attribute. 1717 1718 Used for generating initial configurations for Monte Carlo runs. 1719 1720 Args: 1721 array_or_shape (array or shape tuple): 1722 If tuple, then this is taken to be the shape. 1723 If array, then its shape is used. 1724 1725 Returns: 1726 tensor: Random sample with desired shape. 1727 1728 """ 1729 try: 1730 shape = be.shape(array_or_shape) 1731 except Exception: 1732 shape = array_or_shape 1733 1734 r = self.rand(shape) 1735 return be.divide(self.params.loc, -be.log(r))