批量梯度下降法python具体实现,
np.random.seed(666)
x = 2 * np.random.random(size = 100)
y = x * 3.0 + 4.0 + np.random.normal(size = 100)
X = x.reshape(-1, 1)
#损失函数
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta))**2)/len(X_b)
except:
return float('inf')
#损失函数的梯度
def dJ(theta, X_b, y):
res = np.empty(len(theta))
res[0] = np.sum(X_b.dot(theta) - y)
for i in range(1, len(theta)):
res[i] = (X_b.dot(theta) - y).dot(X_b[:,i])
return res *2 /len(X_b)
