线性回归-意外结果（Python）

Question

I am getting unexpected results for the implementation of linear regression I coded. Sometimes I get out of memory error, squaring errors, multiplication errors, basically that I've run out of size.

The code seems pretty okay to me, and I'm unable to identify why it fails to work.

X = np.array([ 6.1101,  5.5277,  8.5186,  7.0032,  5.8598,  8.3829, 7.4764,
        8.5781,  6.4862,  5.0546,  5.7107, 14.164 ,  5.734 ,  8.4084,
        5.6407,  5.3794,  6.3654,  5.1301,  6.4296,  7.0708,  6.1891,
       20.27  ,  5.4901,  6.3261,  5.5649, 18.945 , 12.828 , 10.957 ,
       13.176 , 22.203 ,  5.2524,  6.5894,  9.2482,  5.8918,  8.2111,
        7.9334,  8.0959,  5.6063, 12.836 ,  6.3534,  5.4069,  6.8825,
       11.708 ,  5.7737,  7.8247,  7.0931,  5.0702,  5.8014, 11.7   ,
        5.5416,  7.5402,  5.3077,  7.4239,  7.6031,  6.3328,  6.3589,
        6.2742,  5.6397,  9.3102,  9.4536,  8.8254,  5.1793, 21.279 ,
       14.908 , 18.959 ,  7.2182,  8.2951, 10.236 ,  5.4994, 20.341 ,
       10.136 ,  7.3345,  6.0062,  7.2259,  5.0269,  6.5479,  7.5386,
        5.0365, 10.274 ,  5.1077,  5.7292,  5.1884,  6.3557,  9.7687,
        6.5159,  8.5172,  9.1802,  6.002 ,  5.5204,  5.0594,  5.7077,
        7.6366,  5.8707,  5.3054,  8.2934, 13.394 ,  5.4369])
y = np.array([17.592  ,  9.1302 , 13.662  , 11.854  ,  6.8233 , 11.886  ,
        4.3483 , 12.     ,  6.5987 ,  3.8166 ,  3.2522 , 15.505  ,
        3.1551 ,  7.2258 ,  0.71618,  3.5129 ,  5.3048 ,  0.56077,
        3.6518 ,  5.3893 ,  3.1386 , 21.767  ,  4.263  ,  5.1875 ,
        3.0825 , 22.638  , 13.501  ,  7.0467 , 14.692  , 24.147  ,
       -1.22   ,  5.9966 , 12.134  ,  1.8495 ,  6.5426 ,  4.5623 ,
        4.1164 ,  3.3928 , 10.117  ,  5.4974 ,  0.55657,  3.9115 ,
        5.3854 ,  2.4406 ,  6.7318 ,  1.0463 ,  5.1337 ,  1.844  ,
        8.0043 ,  1.0179 ,  6.7504 ,  1.8396 ,  4.2885 ,  4.9981 ,
        1.4233 , -1.4211 ,  2.4756 ,  4.6042 ,  3.9624 ,  5.4141 ,
        5.1694 , -0.74279, 17.929  , 12.054  , 17.054  ,  4.8852 ,
        5.7442 ,  7.7754 ,  1.0173 , 20.992  ,  6.6799 ,  4.0259 ,
        1.2784 ,  3.3411 , -2.6807 ,  0.29678,  3.8845 ,  5.7014 ,
        6.7526 ,  2.0576 ,  0.47953,  0.20421,  0.67861,  7.5435 ,
        5.3436 ,  4.2415 ,  6.7981 ,  0.92695,  0.152  ,  2.8214 ,
        1.8451 ,  4.2959 ,  7.2029 ,  1.9869 ,  0.14454,  9.0551 ,
        0.61705])
theta = np.array([0,0]) #Initial values of theta_0 and theta_1

#Calculates cost function for given theta
def costFunction(X,y,theta):
    m = y.size
    hypothesis = (X * theta[1]) + theta[0]
    return (1/m) * sum((hypothesis - y) ** 2)

#Calculates the partial derivatives of theta_0 and theta_1
def slope(X,y,theta):
    m=y.size
    hypothesis = (X * theta[1]) + theta[0]
    theta_0 = 2/(m) * sum(hypothesis - y) 
    theta_1 = 2/(m) * sum((hypothesis - y) * X)
    return np.array([theta_0,theta_1])

#running the gradient descent with 200 iters with learning rate 0.1
for i in range(200):
    theta = theta - 0.1*slope(X,y,theta)

costFunction(X,y,theta) # Prints inf

Answer 1

Your learning rate is too large and GD does not converge. Try changing it to 0.01 and run it for more epochs, it worked for me.