python实现K近邻回归，采用等权重和不等权重

from sklearn.datasets import load_bostonboston = load_boston()from sklearn.cross_validation import train_test_splitimport numpy as np;X = boston.datay = boston.targetX_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.25)print 'The max target value is: ', np.max(boston.target)print 'The min target value is: ', np.min(boston.target)print 'The average terget value is: ', np.mean(boston.target)from sklearn.preprocessing import StandardScalerss_X = StandardScaler()ss_y = StandardScaler()X_train = ss_X.fit_transform(X_train)X_test = ss_X.transform(X_test)y_train = ss_y.fit_transform(y_train)y_test = ss_y.transform(y_test)from sklearn.neighbors import KNeighborsRegressoruni_knr = KNeighborsRegressor(weights = 'uniform')uni_knr.fit(X_train, y_train)uni_knr_y_predict = uni_knr.predict(X_test)dis_knr = KNeighborsRegressor(weights = 'distance')dis_knr.fit(X_train, y_train)dis_knr_y_predict = dis_knr.predict(X_test)from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_errorprint 'R-squared value of uniform weights KNeighorRegressor is: ', uni_knr.score(X_test, y_test)print 'The mean squared error of uniform weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))print 'The mean absolute error of uniform weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))print 'R-squared of distance weights KNeighorRegressor is: ', dis_knr.score(X_test, y_test)print 'the value of mean squared error of distance weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))print 'the value of mean ssbsolute error of distance weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))