import numpy as np from sklearn.linear_model import LogisticRegression import matplotlib.pyplot as plt import matplotlib as mpl from sklearn import preprocessing import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline if __name__ == '__main__': path = 'iris.data' data = pd.read_csv('iris.data', header=None) iris_types = data[4].unique() for i, type in enumerate(iris_types): data.set_value(data[4] == type, 4, i) x, y = np.split(data.values, (4,), axis=1) x = x.astype(np.float) y = y.astype(np.int) print('x = \n', x) print('y = \n', y) # 仅使用前两列特征 x = x[:, :2] lr = Pipeline([('sc', StandardScaler()), ('clf', LogisticRegression())]) lr.fit(x, y.ravel()) y_hat = lr.predict(x) y_hat_prob = lr.predict_proba(x) np.set_printoptions(suppress=True) print('y_hat = \n', y_hat) print('y_hat_prob = \n', y_hat_prob) print('accuracy:%.2f%%' % (100*np.mean(y_hat == y.ravel()))) # 画图 # 横纵各采样多少个值 N, M = 1000, 1000 # 第0列的范围 x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第1列的范围 x2_min, x2_max = x[:, 1].min(), x[:, 1].max() t1 = np.linspace(x1_min, x1_max, N) t2 = np.linspace(x2_min, x2_max, M) # 生成网格采样点 x1, x2 = np.meshgrid(t1, t2) # 测试点 x_test = np.stack((x1.flat, x2.flat), axis=1) cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) # 预测值 y_hat = lr.predict(x_test) # 使之与输入的形状相同 y_hat = y_hat.reshape(x1.shape) plt.figure(facecolor='w') # 预测值的显示 plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 样本的显示 plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolors='k', s=50, cmap=cm_dark) plt.xlabel('calyx length', fontsize=14) plt.ylabel('calyx width', fontsize=14) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.grid() plt.title('iris flower data in Logistic classifier - standardscaler', fontsize=17) plt.show()
Iris数据跑回归模型
猜你喜欢
转载自blog.csdn.net/oliverkingli/article/details/80583236
今日推荐
周排行