from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt


def main():
    # 加载数据集,只加载两个特征
    iris = load_iris()
    x = iris.data[:, :2]
    y = iris.target

    h = .02

    # 逻辑回归训练并预测
    lr = LogisticRegression(C=1e5)
    lr.fit(x, y)
    # 返回坐标矩阵
    x_min, x_max = x[:, 0].min() - .5, x[:, 0].max() + .5
    y_min, y_max = x[:, 1].min() - .5, x[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = lr.predict(np.c_[xx.ravel(), yy.ravel()])

    # 结果可视化
    Z = Z.reshape(xx.shape)
    plt.figure(1, figsize=(4, 3))
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
    plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())
    plt.show()


if __name__ == "__main__":
    main()