Exercise 3

[JingxinLee]

`
def oneVsAll(X, y, num_labels, lambda_):
# Some useful variables
m, n = X.shape

# You need to return the following variables correctly 
all_theta = np.zeros((num_labels, n + 1))

# Add ones to the X data matrix
X = np.concatenate([np.ones((m, 1)), X], axis=1)

# ====================== YOUR CODE HERE ======================
initial_theta = np.zeros((n+1,1)) 
options = {'maxiter': 50}
for c in np.arange(1, num_labels+1):
    res = optimize.minimize(lrCostFunction, 
                            initial_theta, 
                            (X, (y == c), lambda_), 
                            jac=True, 
                            method='TNC',
                            options=options) 
    all_theta[c-1] = res.x


# ============================================================
return all_theta

`

Hi,Dib,
Could you please tell me what's wrong with my code? I am confused.
Thanks.

[nirmalhk7]

I'm facing problems in the same block. Here's my code:
`
# Some useful variables
m, n = X.shape

# You need to return the following variables correctly 
ll_theta = np.zeros((num_labels, n + 1))

# Add ones to the X data matrix
X = np.concatenate([np.ones((m, 1)), X], axis=1)

# ====================== YOUR CODE HERE ======================
for c in np.arange(num_labels):
    initial_theta=np.zeros(n+1)
    options={'maxiter':50}
    res=optimize.minimize(lrCostFunction,initial_theta,(X,(y==c),lambda_),jac=True,method='TNC',options=options)
    all_theta[c]=res.x
# ============================================================
return all_theta`

I'm able to call the function without errors, from the next block
lambda_ = 0.1 all_theta = oneVsAll(X, y, num_labels, lambda_)

Then, when I try to submit the work, no error is thrown, but following is the result:

The scores aren't shown on Coursera either. Please help.

Thanks!

[Julescorbara]

#6 Fixes this issue, the author used the wrong minimization method