线性回归——最小二乘法拟合

import numpy as np
from numpy.linalg import inv


x=[0.015348106072082663,0.021715879765738008,0.0316253067336889,
   0.03212431406271639,0.03828189026158509,0.03965578961513808,
   0.05502733389871053,0.06116957740576664,0.06170785924013203,
   0.07206404835977503]
y=[22, 0, 22, 11, 9, 31, 20, 31, 2, 20]


def plotLinearRegression1():
    X = np.array(x).reshape(len(x),1)
    reg=linear_model.LinearRegression(fit_intercept=True,normalize=False)
    reg.fit(X,y)
    k=reg.coef_#获取斜率w1,w2,w3,...,wn
    b=reg.intercept_#获取截距w0
    #x0=np.arange(0,10,0.2)
    x0 = np.array(x).reshape(len(x),)
    y0=k*x0+b
    plt.scatter(x,y)
    plt.plot(x0, y0)
    print(‘k‘,k);
    print(‘b‘,b)
  
      
‘‘‘
用least squares
‘‘‘
def plotLinearRegression2():
    X = np.vstack([np.ones((1,len(x))),x]).T
    print(‘X:\n‘,X)
    Xt = X.T
    Z = Xt.dot(X)
    invZ = inv(Z)
    print(‘invZ:\n‘,invZ)
    
    W = (invZ.dot(Xt)).dot(y)
    print(‘W:\n‘,W)
    
    #plot
    plt.scatter(x,y)
    x0 = np.array(x).reshape(len(x),)
   # x0=np.arange(0,10,0.2)
    plt.plot(x0, W[1]*x0+W[0])
    
    
def plotLinearRegression3():
    A = np.vstack([x, np.ones(len(x))]).T
    print(‘A:\n‘,A)
    m, c = np.linalg.lstsq(A, y, rcond=None)[0]
    print(‘m: ‘,m,‘  c:‘,c)
   
    x0 = np.array(x).reshape(len(x),)
    plt.plot(x, y, ‘o‘, label=‘Original data‘, markersize=10)
    plt.plot(x0, m*x0 + c, ‘r‘, label=‘Fitted line‘)
    plt.legend()
    plt.show()  
    
        
if __name__ == ‘__main__‘:
    plotLinearRegression2()