Logistic回归，梯度计算误差 我目前正在攻读安得烈NG机器学习课程。在第二个练习中，进行逻辑回归。我遇到了一个大麻烦，到目前为止还无法解决。错误发生在成本函数中。（这是我正在使用的python版本）

看起来我已经完美地计算了梯度（我也复制了许多不同的计算梯度的方法），当我运行这个单元格时，这个错误会弹出。有人能帮我解决这个问题吗

initial_theta = np.zeros((n+1,1))
cost, grad = costFunction(initial_theta, X, y)

print('Cost at initial theta (zeros): {:.3f}'.format(cost))
print('Expected cost (approx): 0.693\n')
print('Gradient at initial theta (zeros):')
print('\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))
print('Expected gradients (approx):\n\t[-0.1000, -12.0092, -11.2628]\n')

# Compute and display cost and gradient with non-zero theta
test_theta = np.array([-24, 0.2, 0.2])
cost, grad = costFunction(test_theta, X, y)

print('Cost at test theta: {:.3f}'.format(cost))
print('Expected cost (approx): 0.218\n')

print('Gradient at test theta:')
print('\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))
print('Expected gradients (approx):\n\t[0.043, 2.566, 2.647]')

initial_theta = np.zeros((n+1,1))
cost, grad = costFunction(initial_theta, X, y)

print('Cost at initial theta (zeros): {:.3f}'.format(cost))
print('Expected cost (approx): 0.693\n')
print('Gradient at initial theta (zeros):')
print('\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))
print('Expected gradients (approx):\n\t[-0.1000, -12.0092, -11.2628]\n')

# Compute and display cost and gradient with non-zero theta
test_theta = np.array([-24, 0.2, 0.2])
cost, grad = costFunction(test_theta, X, y)

print('Cost at test theta: {:.3f}'.format(cost))
print('Expected cost (approx): 0.218\n')

print('Gradient at test theta:')
print('\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))
print('Expected gradients (approx):\n\t[0.043, 2.566, 2.647]')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-43-4242e4a47821> in <module>
      7 print('Expected cost (approx): 0.693\n')
      8 print('Gradient at initial theta (zeros):')
----> 9 print('\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))
     10 print('Expected gradients (approx):\n\t[-0.1000, -12.0092, -11.2628]\n')
     11 

TypeError: unsupported format string passed to numpy.ndarray.__format__

> [[  0.5          0.5          0.5         -0.5         -0.5
>     0.5         -0.5         -0.5         -0.5         -0.5
>     0.5          0.5         -0.5         -0.5          0.5    -0.5         -0.5          0.5         -0.5         -0.5
>     0.5         -0.5          0.5          0.5         -0.5    -0.5         -0.5          0.5          0.5          0.5    -0.5         -0.5          0.5         -0.5          0.5
>     0.5          0.5         -0.5          0.5          0.5    -0.5          0.5         -0.5          0.5          0.5
>     0.5         -0.5         -0.5         -0.5         -0.5    -0.5         -0.5         -0.5          0.5          0.5
>     0.5         -0.5          0.5         -0.5         -0.5    -0.5          0.5          0.5          0.5          0.5
>     0.5         -0.5          0.5         -0.5         -0.5
>     0.5         -0.5         -0.5         -0.5         -0.5    -0.5         -0.5         -0.5          0.5          0.5    -0.5         -0.5         -0.5         -0.5         -0.5    -0.5          0.5         -0.5         -0.5          0.5    -0.5         -0.5          0.5         -0.5         -0.5    -0.5         -0.5         -0.5         -0.5         -0.5       ]  [ 32.82213703  32.82213703  32.82213703 -32.82213703 -32.82213703
>    32.82213703 -32.82213703 -32.82213703 -32.82213703 -32.82213703
>    32.82213703  32.82213703 -32.82213703 -32.82213703  32.82213703   -32.82213703 -32.82213703  32.82213703 -32.82213703 -32.82213703
>    32.82213703 -32.82213703  32.82213703  32.82213703 -32.82213703   -32.82213703 -32.82213703  32.82213703  32.82213703  32.82213703   -32.82213703 -32.82213703  32.82213703 -32.82213703  32.82213703
>    32.82213703  32.82213703 -32.82213703  32.82213703  32.82213703   -32.82213703  32.82213703 -32.82213703  32.82213703  32.82213703
>    32.82213703 -32.82213703 -32.82213703 -32.82213703 -32.82213703   -32.82213703 -32.82213703 -32.82213703  32.82213703  32.82213703
>    32.82213703 -32.82213703  32.82213703 -32.82213703 -32.82213703   -32.82213703  32.82213703  32.82213703  32.82213703  32.82213703
>    32.82213703 -32.82213703  32.82213703 -32.82213703 -32.82213703
>    32.82213703 -32.82213703 -32.82213703 -32.82213703 -32.82213703   -32.82213703 -32.82213703 -32.82213703  32.82213703  32.82213703   -32.82213703 -32.82213703 -32.82213703 -32.82213703 -32.82213703   -32.82213703  32.82213703 -32.82213703 -32.82213703  32.82213703   -32.82213703 -32.82213703  32.82213703 -32.82213703 -32.82213703   -32.82213703 -32.82213703 -32.82213703 -32.82213703 -32.82213703]  [ 33.11099904  33.11099904  33.11099904 -33.11099904 -33.11099904
>    33.11099904 -33.11099904 -33.11099904 -33.11099904 -33.11099904
>    33.11099904  33.11099904 -33.11099904 -33.11099904  33.11099904   -33.11099904 -33.11099904  33.11099904 -33.11099904 -33.11099904
>    33.11099904 -33.11099904  33.11099904  33.11099904 -33.11099904   -33.11099904 -33.11099904  33.11099904  33.11099904  33.11099904   -33.11099904 -33.11099904  33.11099904 -33.11099904  33.11099904
>    33.11099904  33.11099904 -33.11099904  33.11099904  33.11099904   -33.11099904  33.11099904 -33.11099904  33.11099904  33.11099904
>    33.11099904 -33.11099904 -33.11099904 -33.11099904 -33.11099904   -33.11099904 -33.11099904 -33.11099904  33.11099904  33.11099904
>    33.11099904 -33.11099904  33.11099904 -33.11099904 -33.11099904   -33.11099904  33.11099904  33.11099904  33.11099904  33.11099904
>    33.11099904 -33.11099904  33.11099904 -33.11099904 -33.11099904
>    33.11099904 -33.11099904 -33.11099904 -33.11099904 -33.11099904   -33.11099904 -33.11099904 -33.11099904  33.11099904  33.11099904   -33.11099904 -33.11099904 -33.11099904 -33.11099904 -33.11099904   -33.11099904  33.11099904 -33.11099904 -33.11099904  33.11099904   -33.11099904 -33.11099904  33.11099904 -33.11099904 -33.11099904   -33.11099904 -33.11099904 -33.11099904 -33.11099904 -33.11099904]]