gradient descent seems to fail

Question

I implemented a gradient descent algorithm to minimize a cost function in order to gain a hypothesis for determining whether an image has a good quality. I did that in Octav

Answer 1

Using only vectors here is the compact implementation of LR with Gradient Descent in Mathematica:

Theta = {0, 0}
alpha = 0.0001;
iteration = 1500;
Jhist = Table[0, {i, iteration}];
Table[  
  Theta = Theta - 
  alpha * Dot[Transpose[X], (Dot[X, Theta] - Y)]/m; 
  Jhist[[k]] = 
  Total[ (Dot[X, Theta] - Y[[All]])^2]/(2*m); Theta, {k, iteration}]

Note: Of course one assumes that X is a n * 2 matrix, with X[[,1]] containing only 1s'