def cal_cost(theta,X,y):
m = len(y)
λ = 0.01
predictions = X.dot(theta)
cost = (1/2*m) * (np.sum(np.abs(predictions-y)) + np.sum(λ * np.square(theta))) #regularized absolute cost error
return cost
def minibatch_gradient_descent(X,y,batch_size):
m = len(y)
iterations = 450
cost_history = np.zeros(iterations)
time_history = np.zeros(iterations)
learning_rate =0.3
theta = np.zeros((X_train.shape[1], 1))
gd_update = np.zeros(iterations)
t2_start = time.time()
for it in range(iterations):
cost =0.0
indices = np.random.permutation(m)
X = X[indices]
y = y[indices]
for i in range(0,m,batch_size):
n_batches = int(m/batch_size)
X_i = X[i:i+batch_size]
y_i = y[i:i+batch_size]
prediction = np.dot(X_i,theta)
theta = theta -(1/m)*learning_rate* ((prediction - y_i)/abs(prediction - y_i))
cost += cal_cost(theta,X_i,y_i)
cost_history[it] = cost
return theta, cost_history
batch_size = [1,16,32,64,128,256,404]
minibatch_gradient_descent(X_train, y_train,batch_size[i])
当我运行上述代码时，出现上述错误。 X_train.shape为（404,13）和y_train.shape为（404,1） 请帮助我纠正我错的代码。