python - Scipy optimize function: matrixes not aligned -

edit: data set mnist data set homework of week 4 of andrew ng's machine learning course

i've checked question on scipy optimize still couldn't figure out wrong code. trying optimize theta onevsall question on andrew ng coursera course.

here relevant code

def sigmoid(x): = [] item in x:     a.append(1/(1+math.exp(-item))) return  def hypothesis(x, theta):     return np.array(sigmoid(np.dot(x, theta)))  def costfunction(theta, x, y, lamba_):     m = x.shape[0]      part1 = np.dot(y.t, np.log(hypothesis(x, theta)).reshape(m,1))     part2 = np.dot((np.ones((m,1)) - y).t, np.log( 1 - hypothesis(x, theta)).reshape(m,1))      summ = (part1 + part2)      return -summ[0]/m  def gradientvect(theta, x, y, lambda_):     n = x.shape[1]     m = x.shape[0]     gradient = []      theta = theta.reshape(n,1)      beta = hypothesis(x, theta) - y      reg = theta[1:] * lambda_/m      grad = np.dot(x.t, beta) * 1./m      grad[1:] = grad[1:] * reg      return grad.flatten()   scipy import optimize  def optimizetheta(x, y, nlabels, lambda_):      in np.arange(0, nlabels):         theta = np.zeros((n,1))         res = optimize.minimize(costfunction, theta, args=(x, (y == i)*1, lambda_), method=none,                        jac=gradientvect, options={'maxiter':50})         print(res)     return result 

but running

optimizetheta(x, y, 10, 0) # x shape = 401, 500 

gives me following error:

--------------------------------------------------------------------------- valueerror                                traceback (most recent call last) <ipython-input-247-e0e6e4c1eddd> in <module>()       3 n = x.shape[1]       4  ----> 5 optimizetheta(x, y, 10, 0)  <ipython-input-246-0a15e9f4769a> in optimizetheta(x, y, nlabels, lambda_)      54         theta = np.zeros((n,1))      55         res = optimize.minimize(costfunction, x0 = theta, args=(x, (y == i)*1, lambda_), method=none, ---> 56                        jac=gradientvect, options={'maxiter':50})      57         print(res)      58     return result  //anaconda/lib/python3.5/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)     439         return _minimize_cg(fun, x0, args, jac, callback, **options)     440     elif meth == 'bfgs': --> 441         return _minimize_bfgs(fun, x0, args, jac, callback, **options)     442     elif meth == 'newton-cg':     443         return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,  //anaconda/lib/python3.5/site-packages/scipy/optimize/optimize.py in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)     859     gnorm = vecnorm(gfk, ord=norm)     860     while (gnorm > gtol) , (k < maxiter): --> 861         pk = -numpy.dot(hk, gfk)     862         try:     863             alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \  valueerror: shapes (401,401) , (2005000,) not aligned: 401 (dim 1) != 2005000 (dim 0) 

and can't figure out why shapes not aligned.

thanks!

so realized wrong question. problem sigmoid function returning list , not integer , therefore messed matrixes multiplications afterwards. new sigmoid function is

def sigmoid(z): return(1 / (1 + np.exp(-z)))