from sklearn import datasets,preprocessing
import pandas as pd
import numpy as np
from numpy.linalg import inv, norm
import matplotlib.pyplot as plt
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import GridSearchCV
import os
print(os.getcwd()) # 打印当前工作目录
os.chdir(r'F://file/helinghuigui') # 将当前工作目录改变
#min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))#数据限制到（-1,1）
#X=min_max_scaler.fit_transform(X)
from sklearn.cross_validation import train_test_split
if __name__=='__main__':
dataframe=pd.read_excel('xunlianceshi.xlsx', sheetname='bianhuan', header=0)#XLabel:(46*13),XUnlabel:(420,13),YLabel:(46,)
DataArray=dataframe._values
X=DataArray[:,0:4]
y = DataArray[:, 4]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.25)
from sklearn.preprocessing import StandardScaler
ss_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.svm import SVR
linear_svr = SVR(kernel = 'linear')
linear_svr.fit(X_train, y_train)
linear_svr_y_predict = linear_svr.predict(X_test)
poly_svr = SVR(kernel = 'poly',degree=3)
poly_svr.fit(X_train, y_train)
poly_svr_y_predict = poly_svr.predict(X_test)
rbf_svr = SVR(kernel = 'rbf',epsilon=1,gamma='auto')
rbf_svr.fit(X_train, y_train)
rbf_svr_y_predict = rbf_svr.predict(X_test)
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
print 'R-squared value of linear SVR is: ', linear_svr.score(X_test, y_test)
print 'The mean squared error of linear SVR is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(linear_svr_y_predict))
print 'The mean absolute error of lin SVR is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(linear_svr_y_predict))
print 'R-squared of ploy SVR is: ', poly_svr.score(X_test, y_test)
print 'the value of mean squared error of poly SVR is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(poly_svr_y_predict))
print 'the value of mean ssbsolute error of poly SVR is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(poly_svr_y_predict))
print 'R-squared of rbf SVR is: ', rbf_svr.score(X_test, y_test)
print 'the value of mean squared error of rbf SVR is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rbf_svr_y_predict))
print 'the value of mean ssbsolute error of rbf SVR is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(rbf_svr_y_predict))
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