1importnumpy as np23x = np.random.randint(3)4print(x)5#167x = np.random.randint(1,5)8print(x)9#31011x = np.random.randint(3, size=5)12print(x)13#[0 1 2 2 0]1415x = np.random.randint(3, size=[2, 2])16print(x)17''
import numpy as np import matplotlib.pyplot as plt from matplotlib.collections import LineCollection from sklearn.linear_model import LinearRegression from sklearn.isotonic import IsotonicRegression from sklearn.utils import check_random_state n = 100 x = np.arange(n) rs = check_random_state(0)...
rng = check_random_state(0) # Training samples X_train = rng.uniform(-1, 1, 100).reshape(50, 2) y_train = X_train[:, 0]**2 - X_train[:, 1]**2 + X_train[:, 1] - 1 # Testing samples X_test = rng.uniform(-1, 1, 100).reshape(50, 2) y_test = X_test[:, 0]...
data=d.drop(['Time'],axis=1)data['Amount']=StandardScaler().fit_transform(data[['Amount']])# 为无监督新颖点检测方法,只提取负样本,并且按照8:2切成训练集和测试集 mask=(data['Class']==0)X_train,X_test=train_test_split(data[mask],test_size=0.2,random_state=0)X_train=X_train.drop(...
random.getstate()# 返回一个当前生成器的内部状态的对象 random.setstate(state)# 传入一个先前利用getstate方法获得的状态对象,使得生成器恢复到这个状态。 random.getrandbits(k)# 返回range(0,2**k)之间的一个整数,相当于randrange(0,2**k)
CheckData:检查选择的数据是否重复。 OutputData:输出选择的数据。 [*]:终止状态,表示整个过程结束。 总结 本文介绍了两种方法来实现随机选择批量数据并保证不重复。第一种方法是使用Python内置的random模块的sample函数,第二种方法是使用numpy库中的random模块的choice函数。两种方法都可以很方便地实现随机选择批量数据的...
ca=prince.CA(n_components=3,n_iter=3,copy=True,check_input=True,engine='sklearn',random_state=42)ca=ca.fit(dataset) 数据样例可见: 其中: prince.CA一定需要指定engine='sklearn'; n_components是降维维度数量,如果你只有两列,只能降低到1维 ...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) 5. 实现决策树 # 创建决策树模型dt_model = DecisionTreeClassifier(random_state=42)# 训练模型dt_model.fit(X_train, y_train)# 预测dt_predictions = dt_model.predict(X_test)# 计算准确率dt_ac...
>>> @check_args ... def test(*args): ... print args 还原成容易理解的⽅方式: >>> test = check_args(test) 类似的做法,我们在使⽤用 staticmethod,classmethod 时就已⻅见过. >>> def check_args(func): ... def wrap(*args): ... args = filter(bool, args) ... func(*args) ...
clf = DecisionTreeClassifier(random_state=42) lpo = LeavePOut(p=2) scores = cross_val_score(clf, X, y, cv = lpo) print("Cross Validation Scores: ", scores) print("Average CV Score: ", scores.mean()) print("Number of CV Scores used in Average: ",len(scores)) ...