使用np.random.choice()函数可以从数组中进行随机采样: importnumpyasnp# 从数组中随机选择5个元素,允许重复sample=np.random.choice(['a','b','c','d','e'],size=5,replace=True)print("Random sample with replacement from numpyarray.com:",sample)# 从数组中随机选择3个元素,不允许重复sample_no_re...
>>>torch.multinomial(weights,4)# ERROR! RuntimeError: invalid argument 2: invalid multinomial distribution (with replacement=False, not enough non-negative category to sample) at ../aten/src/TH/generic/THTensorRandom.cpp:320 >>>torch.multinomial(weights,4, replacement=True) tensor([ 2, 1, ...
python # 从数组中随机抽取3个元素(有放回抽样) sample_with_replacement = np.random.choice(arr, size=3, replace=True) print("有放回随机抽取的样本:", sample_with_replacement) 通过这种方式,你可以轻松地从 numpy.ndarray 中实现随机抽样。
replace : boolean, optional 替换:布尔型,可选 Whether the sample is with or without replacement 样本是否有重复值(False,没有;True,有;默认:True) p : 1-D array-like, optional1维数组,可选The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution ...
Generate a non-uniform random sample from np.arange(5) of size 3 without replacement: rng.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0]) array([3, 2, 0]) Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance: ...
replace: Whether to sample with replacement (True) or without replacement (False). p: The probabilities associated with each entry in the array. Example: python # Randomly pick 3 items from a list with replacementsample_with_replacement = np.random.choice([1,2,3,4,5], size=3, replace=Tr...
replace : boolean, optional Whether the sample is with or without replacement p : 1-D array-like, optional The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a....
Generate a non-uniform random sample from np.arange(5) of size 3 without replacement: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 rng.choice(5,3,replace=False,p=[0.1,0,0.3,0.6,0]) array([3, 2, 0]) Any of the above can be repeated with an arbitrary array-like instead of ...
with_replacement: unique = list(set(samples)) while len(samples) != len(unique): n_new = len(samples) - len(unique) samples = unique + self.sample(n_new).tolist() unique = list(set(samples)) return np.array(samples, dtype=int) # 定义一个名为 Dict 的字典子类 class Dict(dict):...
Whether the sample is with or without replacement. Default is True,meaning that a value ofacan be selected multiple times. 这不能理解为元素去重 上述代码生成[0,14]范围内序列,返回3行4列的矩阵(元素不重复) output array([[ 2, 5, 9, 14], ...