2、reverse是布尔类型的参数，默认为False，表示顺序排列，如果是True，则表示反序排列。Python中函数的参数（参数的使用和作用、形参和实参）正则表达式是一个特殊的字符序列，能够帮助我们方便的检查一个字符串中是否与某种模式匹配。比如说，我们接口中的token，cookie信息，还有爬虫爬取到数据之后进行匹配等等。都
random: The optional argument random is a function returning a random float number between 0.1 to 1.0. This function decides how to shuffle a sequence. If not specified, by default Python uses therandom.random()function. Note: thisparameter deprecatedsince version 3.9, will be removed in version...
即如下：#!/usr/bin/env pythonimportrandom # thefunctiondefshuffle(ll,include_list=None):ifnot i...
AI代码助手复制代码 Modify a sequence in-place by shuffling its contents. 举例 python>>> arr = np.arange(10)>>> np.random.shuffle(arr)>>> arr[1 7 5 2 9 4 3 6 0 8] AI代码助手复制代码 This function only shuffles the array along the first index of a multi-dimensional array(多维矩...
defshuffle(self,x,random=None):"""Shuffle list x in place, and return None. Optional argument random is a 0-argument function returning a random float in [0.0, 1.0); if it is the default None, the standard random.random will be used. ...
Optional arg random is a 0-argument function returning a random float in [0.0, 1.0); by default, the standard random.random."""if random is None:random = self.random #random=random.random #转成numpy if torch.is_tensor(x)==True:if self.use_cuda==True:x=x.cpu().numpy()else:x=...
>>> help(random.shuffle) Help on method shuffle in module random: shuffle(x, random=None) method of random.Random instance Shuffle list x in place, and return None. Optional argument random is a 0-argument function returning a random float in [0.0, 1.0); if it is the default None, th...
1. numpy.random.shuffle(x) Modify a sequence in-place by shuffling its contents. This function only shuffles the array along the first axis of a multi-dimensional array. The order of sub-arrays is changed but their contents remains the same.Parameters:x : array_like. The array or list ...
# shuffle function defshuffle(): newdeck.clear() foriinrange(0,26): newdeck.append(olddeck[i]) newdeck.append(olddeck[i+26]) olddeck.clear() decks_equal=False whiledecks_equal==False: cyclecount+=1 shuffle() ifdeck==newdeck: ...
with torch.autograd.profiler.record_function(self._profile_name): if self._sampler_iter is None: self._reset() data = self._next_data() self._num_yielded += 1 return data 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.