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- 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()
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