ZeroDivisionError: division by zero error in gradient descent.py

Question

def computeCost(X,y,theta,lam):
    tobesummed = np.power(((X.dot(theta.T))-y),2)+lam*np.sum(np.power(theta,2))
    return np.sum(tobesummed)/(2 * len(X))

def denormalise_price(price):
    global mean
    global stddev
    ret = price * stddev + mean
    return ret
def gradientDescent(X,y,theta,iters,alpha, lam):
    lam_matrix = lam * np.ones(theta.shape)
    lam_matrix[0][0] = 0
    for i in range(iters):
        theta = theta*(1- lam_matrix / len(X)) - (alpha/len(X)) * np.sum(X * (X @ theta.T - y), axis=0)

    return theta

def computeError(predicted, actual):
    error = 0
    for i in range(len(predicted)):
        error += abs(actual[i] - predicted[i]) / actual[i]
    error /= len(actual)
    return error[0]

def plotGraph(x,y,labelX='X',labelY='Y',title='X vs Y'):
    fig, ax = plt.subplots()  
    ax.plot(x, y, 'r')
    ax.set_xlabel(labelX) 
    ax.set_ylabel(labelY)  
    ax.set_title(title)
theta = np.zeros([1,X.shape[1]])
alpha = .1 #learning rate
iters = 500 #epoch
error_matrix = []
lam_range = 600 #maximam lamda
for lam in range(lam_range):
    g = gradientDescent(X_train,Y_train,theta,iters,alpha, lam)
    Cost = computeCost(X_train,Y_train,g,lam)

    Y_pred = X_test.dot(g.T)
    error = computeError(denormalise_price(Y_pred),denormalise_price(Y_test))
    error_matrix.append(error*100)
optimal_lambda = 0
min_error = 9999
for i in range(len(error_matrix)):
    if error_matrix[i] < min_error:
        optimal_lambda = i
        min_error = error_matrix[i]

print("min Error  : ", (min_error),'%')
print("Optimal Lambda : ", optimal_lambda)
plotGraph(np.arange(lam_range),error_matrix,'lambda','error','lambda vs error')

我收到错误：

ZeroDivisionError                         Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_18012/2755139905.py in <module>
      9 
     10     Y_pred = X_test.dot(g.T)
---> 11     error = computeError(denormalise_price(Y_pred),denormalise_price(Y_test))
     12     error_matrix.append(error*100)
     13 optimal_lambda = 0

~\AppData\Local\Temp/ipykernel_18012/1683987217.py in computeError(predicted, actual)
     13     for i in range(len(predicted)):
     14         error += abs(actual[i] - predicted[i]) / actual[i]
---> 15     error /= len(actual)
     16     return error[0]
     17 

ZeroDivisionError: division by zero

Answer 1

您在 error /= len(actual) 处除以零。如果 actual 没有元素，那么 len(actual) 将为 return 0。您可能需要添加一个检查来处理这种情况：

if len(actual) == 0:
    return 0

ZeroDivisionError: division by zero error in gradient descent.py

ZeroDivisionError: division by zero error in gradient descent.py

python

machine-learning

divide-by-zero

python-3.x

gradient-descent