How to use LinearRegression & SVR with scikit-learn.

#!/usr/bin/python                                                       
# -*- encoding: utf-8 -*-                                               
                                                                        
import matplotlib.pyplot as plt                                         
import numpy as np                                                      
from sklearn import linear_model                                        
from sklearn import svm                                                 
                                                                        
sigma = 0.1                                                             
x = np.arange(0,np.pi * 2,0.1)                                          
y = np.sin(x) + sigma * np.random.randn(x.size)                         
#print x.reshape(x.size,1)                                              
                                                                        
X = x.reshape(x.size,1)                                                 
Y = y.reshape(y.size)                                                   
                                                                        
clf = linear_model.LinearRegression()                                   
clf.fit(X,Y)                                                            
                                                                        
reg = svm.SVR(kernel='rbf', C=10000, gamma=0.01).fit(X,Y)               
                                                                        
plt.plot(x,y, 'ro', x, np.sin(x),'-', x, x*clf.coef_[0] + clf.intercept_, 'g-', x, reg.predict(X), 'y^')
plt.show() 
print 'score:' , reg.score(X,Y)