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208 def _uniform(val_range): 209 """ 210 Uniformly sample from the given range. 211 212 Args 213 val_range: A pair of lower and upper bound. 214 """ 215 return np.random.uniform(val_range[0], val_range[1])
309 def uniform(self, a, b): 310 """Get a random number in the range [a, b) or [a, b] depending on rounding.""" 311 return a + (b - a) * self.random()
98 def _uniform(self, np_array): 99 np_array[:] = np.random.uniform(self.low, self.high, len(np_array))
32 def get_uniform(self): 33 return np.random.uniform(self.value[0], self.value[1])
47 def uniform(self, low, high, axes, dtype=None): 48 """ 49 Returns a tensor initialized with a uniform distribution from low to high with axes. 50 51 Arguments: 52 low: The lower limit of the distribution. 53 high: The upper limit of the distribution. 54 axes: The axes of the tensor. 55 dtype: If supplied, the type of the values. 56 57 Returns: 58 The initialized tensor. 59 60 """ 61 if dtype is None: 62 dtype = self.dtype 63 64 return np.array( 65 self.rng.uniform( 66 low, 67 high, 68 ng.make_axes(axes).lengths), 69 dtype=dtype)
178 def rand(*args, **kargs): 179 low, high = kargs["low"], kargs["high"] 180 if low == high: 181 return np.full(kargs["size"], low) 182 else: 183 return rs.rand(*kargs["size"])*(high - low) + low
32 def _random(self, size=None): 33 uu = np.random.uniform(size=size) 34 return np.exp(uu * self._fac + np.log(self.a))
19 def uniform(low, up, size=None): 20 try: 21 return [np.random.uniform(a, b) for a, b in zip(low, up)] 22 except TypeError: 23 return [np.random.uniform(a, b) for a, b in zip([low] * size, [up] * size)]
360 def rng_uniform(rng): 361 """Get the unform/randint from the rng.""" 362 # prefer Generator.integers, fall back to RandomState.randint 363 return getattr(rng, 'integers', getattr(rng, 'randint', None))
379 def _gen_random(self, X): 380 return np.random.random(size = X.shape[0])