python机器学习之分类器
简单分类器
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1 import numpy as np 2 import matplotlib.pyplot as mp 3 x = np.array([ 4 [3, 1], 5 [2, 5], 6 [1, 8], 7 [6, 4], 8 [5, 2], 9 [3, 5], 10 [4, 7], 11 [4, -1]]) 12 y = np.array([0, 1, 1, 0, 0, 1, 1, 0]) 13 14 # X[:,0]是numpy中数组的一种写法,表示对一个二维数组,取该二维数组第一维中的所有数据,第二维中取第0个数据,直观来说,X[:,0]就是取所有行的第0个数据, 15 # X[:,1] 就是取所有行的第1个数据。 16 l, r, h = x[:, 0].min() - 1, x[:, 0].max() + 1, 0.005 17 b, t, v = x[:, 1].min() - 1, x[:, 1].max() + 1, 0.005 18 # meshgrid()根据传入的两个一维数组参数生成两个数组元素的列表 19 grid_x = np.meshgrid(np.arange(l, r, h), np.arange(b, t, v)) 20 # 做列拼接 21 # np.c_是按行连接两个矩阵,就是把两矩阵左右相加,要求行数相等,类似于pandas中的merge() 22 flat_x = np.c_[grid_x[0].ravel(), grid_x[1].ravel()] 23 flat_y = np.zeros(len(flat_x), dtype=int) 24 25 flat_y[flat_x[:, 0] < flat_x[:, 1]] = 1 26 grid_y = flat_y.reshape(grid_x[0].shape) 27 28 29 mp.figure('Simple Classfication', facecolor='lightgray') 30 mp.title('Simple Classfication', fontsize=20) 31 mp.xlabel('x', fontsize=14) 32 mp.ylabel('y', fontsize=14) 33 mp.tick_params(labelsize=10) 34 # 用颜色绘制网格 35 mp.pcolormesh(grid_x[0], grid_x[1], grid_y, cmap='gray') 36 mp.scatter(x[:, 0], x[:, 1], c=y, cmap='brg', s=60) 37 mp.show()
逻辑分类
J(k1,k2,b) = sigma(-ylog(y')-(1-y)log(1-y'))/m +m正则函数(||k1,k2,b||)x正则强度
接口:
sklearn.linear_model.LogisticRegression(solver='liblinear', C=正则强度)
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
1 import numpy as np 2 import sklearn.linear_model as lm 3 import matplotlib.pyplot as mp 4 x = np.array([ 5 [4, 7], 6 [3.5, 8], 7 [3.1, 6.2], 8 [0.5, 1], 9 [1, 2], 10 [1.2, 1.9], 11 [6, 2], 12 [5.7, 1.5], 13 [5.4, 2.2]]) 14 y = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]) 15 # 创建逻辑分类器 16 model = lm.LogisticRegression(solver='liblinear', 17 C=1000) 18 model.fit(x, y) 19 l, r, h = x[:, 0].min() - 1, x[:, 0].max() + 1, 0.005 20 b, t, v = x[:, 1].min() - 1, x[:, 1].max() + 1, 0.005 21 grid_x = np.meshgrid(np.arange(l, r, h), 22 np.arange(b, t, v)) 23 flat_x = np.c_[grid_x[0].ravel(), grid_x[1].ravel()] 24 flat_y = model.predict(flat_x) 25 grid_y = flat_y.reshape(grid_x[0].shape) 26 mp.figure('Logistic Classification', 27 facecolor='lightgray') 28 mp.title('Logistic Classification', fontsize=20) 29 mp.xlabel('x', fontsize=14) 30 mp.ylabel('y', fontsize=14) 31 mp.tick_params(labelsize=10) 32 mp.pcolormesh(grid_x[0], grid_x[1], grid_y, cmap='gray') 33 mp.scatter(x[:, 0], x[:, 1], c=y, cmap='brg', s=60) 34 mp.show()