Python 为什么我的线性回归梯度下降算法发散
线性回归的梯度下降会发散，即使我减小alpha的值，甚至删除它Python 为什么我的线性回归梯度下降算法发散,python,machine-learning,linear-regression,gradient-descent,Python,Machine Learning,Linear Regression,Gradient Descent,线性回归的梯度下降会发散，即使我减小alpha的值，甚至删除它 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.co
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
# 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)