import numpy as np # 定义两个示例向量 vector1 = np.array([1, 2, 3, 4, 5]) vector2 = np.array([6, 7, 8, 9, 10]) # 设定采样数量 sample_size = 3 # 有放回随机采样 sampled_with_replacement = (np.random.choice(vector1, sample_size, replace=True), np.random.choice(vector2,...
self.rng = random.Random(rng_seed)ifself.sample_with_replacement: self.sampled_clients = \ self.rng.choices( population=self.clients, weights=self.clients_weights, k=self.n_clients_per_round, )else: self.sampled_clients = self.rng.sample(self.clients, k=self.n_clients_per_round) 5.numpy...
from imblearn.over_sampling import RandomOverSampler ros = RandomOverSampler(sampling_strategy={0: 700,1:200,2:150 },random_state=0) X_resampled, y_resampled = ros.fit_sample(X, y) print(Counter(y_resampled)) 1. 2. 3. 4. 2、SMOTE 原理:在少数类样本之间进行插值来产生额外的样本。对于...
在上述代码中,random_sampling_without_replacement()函数接受两个参数:data表示要从中抽样的序列,k表示要抽取的样本数量。函数内部使用random.sample(data, k)来进行抽样,并返回一个包含k个随机元素的列表。 示例 下面是一个具体的示例,演示了如何使用random_sampling_without_replacement()函数进行不放回抽样: data=...
random.Random instanceChoose a random element from a non-empty sequence.No. 3 :Help on method choices in module random:choices(population, weights=None, *, cum_weights=None, k=1) method of random.Random instanceReturn a k sized list of population elements chosen with replacement.If the ...
defsample(n,r):"Generate r randomly chosen, sorted integers from [0,n)"rand=random.randompop=nforsampinxrange(r,0,-1):cumprob=1.0x=rand()whilex<cumprob:cumprob-=cumprob*samp/poppop-=1yieldn-pop-1# Example call to select three samples in range(0,10)>>>list(sample(10,3))[2...
此行为在sample()函数中提供,该函数从列表中随机选择一个样本而无需替换。该函数将列表和子集的大小作为参数进行选择。请注意,项目实际上并未从原始列表中删除,只是选择到列表的副本中。 下面的示例演示从20个整数列表中选择五个项目的子集。 # select a random sample without replacementfromrandomimportseedfromrando...
python: random.sample random.sample(population,k) Return aklength list of unique elements chosen from the population sequence. Used for random sampling without replacement.
>>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 Name: num_legs, dtype: int64 A random 50% sample of the ``DataFrame`` with replacement: >>> df.sample(frac=0.5, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 ...
len(x))):# pick an elementinx[:i+1]withwhich to exchange x[i]j=int(random()*(i+1))x...