python - 为什么我的线性回归梯度下降算法会发散

Question

即使我减小 alpha 的值甚至删除它，线性回归的梯度下降也会发散！

def gradientDescent(sqft_living, price, theta, alpha, num_iters):
m = price.size

# make a copy of theta, to avoid changing the original array, since numpy arrays
# are passed by reference to functions
theta = theta.copy()

J_history = [] # Use a python list to save cost in every iteration
theta_history = []
for z in range(num_iters):
    term = 0
    term0 = 0        
    for i in range(m):
        h = np.dot(theta, sqft_living[i])
        term += (h - price[i]) 
        term0 += term * sqft_living[i][0]
    temp0 = theta[0] - alpha * (1/m) * term0


    term = 0
    term1 = 0
    for i in range(m):
        h = np.dot(theta, sqft_living[i])
        term += (h - price[i])
        term1 += term * sqft_living[i][1]
    temp1 = theta[1] - alpha * (1/m) * term1

    theta[0] = temp0
    theta[1] = temp1


    cost = computeCost(sqft_living, price, theta)
    print(cost)
    J_history.append(cost)

    theta_history.append(theta)
 
return theta, J_history

这是算法的使用

# initialize fitting parameters
theta = np.zeros(2)

# some gradient descent settings
iterations = 100
alpha = 0.00001

theta, J_history = gradientDescent(sqft_living, price, theta, alpha, iterations)
print(J_history)

这是输出的一部分：

成本 = 3.8144815615142405e+22

成本 = 8.337226954930875e+33

成本 = 1.8222489338606256e+45

成本 = 3.982848487760674e+56

成本 = 8.705222311669148e+67

成本 = 1.9026808508648173e+79

成本 = 4.158646718757122e+90

成本 = 9.089460549082665e+101

成本 = 1.9866629377457605e+113

成本 = 4.342204476162054e+124

成本 = 9.49065860875058e+135

成本 = 2.074351894810886e+147

成本 = 4.533864256311113e+158

标签： pythonmachine-learninglinear-regressiongradient-descent