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import pandas as pdimport numpy as nptrain_full=pd.read_csv(‘../zip.train‘,sep=‘ ‘,engine=‘c‘,header=None).values[:,0:-1]test_full=pd.read_csv(‘../zip.test‘,sep=‘ ‘,engine=‘c‘,header=None).values train = np.vstack((train_full[train_full[:,0]==2],train_full[train_full[:,0]==3]))test = np.vstack((test_full[test_full[:,0]==2],test_full[test_full[:,0]==3])) train_x=train[:,1:]train_y=train[:,0]test_x=test[:,1:]test_y=test[:,0] dc = []from sklearn.linear_model import LinearRegressionlrcf = LinearRegression()lrcf.fit(train_x, train_y)dc.append((‘linear regression‘,lrcf)) from sklearn.neighbors import KNeighborsClassifierfor i in [1,3,5,7,15]: knn = KNeighborsClassifier(n_neighbors=i) knn.fit(train_x,train_y) dc.append((‘%d-nearest neighbor‘ %(i),knn)) def acc(clf,x,y): res = clf.predict(x) if type(clf)==LinearRegression: res[res>2.5]=3 res[res<2.5]=2 n = y.shape[0] r = res[(res==y)].shape[0] return r*1.0/n for i in dc: accr = acc(i[1],test_x,test_y) print ‘%s: %.5f‘ %(i[0],accr) |
linear regression: 0.95879
1-nearest neighbor: 0.97527
3-nearest neighbor: 0.96978
5-nearest neighbor: 0.96978
7-nearest neighbor: 0.96703
15-nearest neighbor: 0.96154
原文：http://www.cnblogs.com/porco/p/4724940.html