AI课程笔记（四）

编写线性回归训练/预测程序

import matplotlib.pyplot as plt
    import numpy as np
    from sklearn import datasets, linear_model
    from sklearn.metrics import mean_squared_error, r2_score

    experiences = np.array([0,1,2,3,4,5,6,7,8,9,10])
    salaries = np.array([103100, 104900, 106800, 108700, 110400, 112300, 114200, 116100, 117800, 119700, 121600])

    # 将特征数据集分为训练集和测试集，除了最后 4 个作为测试用例，其他都用于训练
    X_train = experiences[:7]
    X_train = X_train.reshape(-1,1)
    X_test = experiences[7:]
    X_test = X_test.reshape(-1,1)

    # 把目标数据（特征对应的真实值）也分为训练集和测试集
    y_train = salaries[:7]
    y_test = salaries[7:]

    # 创建线性回归模型
    regr = linear_model.LinearRegression()

    # 用训练集训练模型——看就这么简单，一行搞定训练过程
    regr.fit(X_train, y_train)

    # 用训练得出的模型进行预测
    diabetes_y_pred = regr.predict(X_test)

    # 将测试结果以图标的方式显示出来
    plt.scatter(X_test, y_test,  color='black')
    plt.plot(X_test, diabetes_y_pred, color='blue', linewidth=3)

    plt.xticks(())
    plt.yticks(())

    plt.show()