min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_...
n_clusters_per_class=1, weights=[0.05], random_state=42)df = pd.concat([pd.DataFrame(X), pd.Series(y)], axis=1)df.columns = ['x1', 'x2', 'y']plot(df=df, x1='x1', x2='x2', y='y', title='Dataset with 2 classes - Class imbalance (y = 0)')下图就是对应...
print("Pick 2 Random element from list:", random.sample(city_list, 2)) random.choices() random.choices(population, weights=None, *, cum_weights=None, k=1) 1. 2. 3. 4. 5. 如果要从序列中随机选择多个元素,请使用此方法。在Python 3.6版中引入的Choices方法可以重复元素。这是带有替换的随机...
剩余字段全为特征列 y = rawdata['isrun'] #划分出训练集和测试集 from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3,random_state=0)#30%为测试集,则70%为训练集 x_train.shape#查看训练集数据量 x_test.shape#查看...
sampling_strategy=0.1,random_state=100)x_train_smote,y_train_smote=smote.fit_sample(x_train,y...
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.random.choices 从序列中按照权重分布采样也可以采用numpy.random.choice实现。其函数原型如下: ...
python 中好用的函数,random.sample等,持续更新 random.sample random.sample的函数原型为:random.sample(sequence, k),从指定序列中随机获取指定长度的片断。sample函数不会修改原有序列 1 2 3 4 5 6 7 importrandom list=[1,2,3,4,5,6,7,8,9,10] ...
当with_std=False时,等于无。 n_samples_seen_ int or ndarray of shape (n_features,). 估计器为每个特征处理的样本数。如果没有丢失的样本,则n_samples_seen将是一个整数,否则它将是一个dtype int数组。如果使用sample_weights,则它将是一个float(如果没有丢失数据)或一个dtype float数组,该数组对迄今...
smote_tomek = SMOTETomek(random_state=0) X_res, y_res = smote_tomek.fit_sample(X_train, y) X_res.groupby(['label']).size() # label # 0 36260 # 1 36260 2.4 模型集成 这里的模型集成主要体现在数据上,即用众多平衡的数据集(majortiry的样本进行欠采...
from skimage.filters.rank import median from skimage.morphology import disk noisy_image = (rgb2gray(imread('../images/lena.jpg'))*255).astype(np.uint8) noise = np.random.random(noisy_image.shape) noisy_image[noise > 0.9] = 255 noisy_image[noise < 0.1] = 0 fig, axes = pylab.subplots...