1importnumpy as np23'''4第一个参数值5对应的a,即传入的数据组5第二个参数3就是数组的size,传入单值时,数据维度是一维的6此处将生成一个一维数据包含3个小于5的整数的数组7'''8x = np.random.choice(5, 3)9print(x)10#[4 0 4]1112#给数组中每个数据出现的概率赋值13x = np.random.choice(5, 3...
fit(X_train, y_train) # Create and train the decision tree regressor model tree_model = DecisionTreeRegressor(random_state=42) tree_model.fit(X_train, y_train) # Create and train the random forest regressor model forest_model = RandomForestRegressor(n_estimators=100, random_state=42) fores...
from sklearn.isotonic import IsotonicRegression from sklearn.utils import check_random_state ``` 人工生成一些数据,y = log(1,x),但是这里我们掺杂了噪声 ```python n = 100 x = np.arange(n) rs = check_random_state(0) y = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.ar...
安全专家已经确定 Python 是一种用于开发信息安全工具包的语言,例如 w3af。模块化设计、易读的代码和完全开发的库套件使 Python 适合安全研究人员和专家编写脚本并构建安全测试工具。 基于Python 的工具包括各种类型的模糊测试工具、代理甚至偶尔的漏洞利用。Python 是当前几种开源渗透测试工具的主要语言,从用于内存分析的 ...
# test regression dataset from sklearn.datasets import make_regression # define dataset X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, random_state=1) # summarize the dataset print(X.shape, y.shape) 运行示例，创建数据集，并确保所需的样本和特征数量。(1000...
from sklearn.utils import check_random_state 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 4.2 下载鸢尾花数据集并输出每个样本特征属性 AI检测代码解析 #然后以字典的形式加载鸢尾花数据集,使用y表示数据集中的标签,使用x表示数据集中的属性数据。
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(['Class'],axis=1).values X_test=X_test.drop(['Class'],axis=1).values # 提取所有正样本,作为测试集的一部分 ...
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...
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...
We are going to use nested for loops to get to each individual rule and then check to see if it is an “allow” or a “deny.” We do this by checking the allowance variable, and if it is false we add the path to our paths list. Once we've gone through all the rule lines, ...