Tests (trivial and nontrivial) for costFunction and gradient

ex2/test_costfunction_minimal.m

@@ -0,0 +1,17 @@
+function test_costfunction ()
+  % an extremely simple cost function calculation with
+  % one negative and one positive class
+  X = [1 1; 2 2];   % minimal 2D feature matrix
+  y = [0 1]';       % at least one negative and one positive
+  theta = [0 0]';   % simplify calculations by using 0 for theta
+
+  [J, grad] = costFunction(theta, X, y);
+
+  assert(J, 0.69315, 1e-5);
+  assert(grad, [-1/4 -1/4]');
+
+  % NOTE: I don't think it's important for the cost calculation to 
+  % set the first column to ones (FOR THIS TEST), but I may be incorrect 
+  % in this assumption.
+
+endfunction

ex2/test_costfunction_nontrivial.m

@@ -0,0 +1,21 @@
+function test_costfunction_nontrivial ()
+  % simple two-entry matrix with n+1 features including
+  % the first column as ones-vector
+  X = [ 1 2 3;         % non-trivial matrix
+        1 4 5 ];
+  y = [0 1]';          % one positive, one negative
+  theta = [.1 .2 .3]'; % non-trivial theta 
+
+  [J, grad] = costFunction(theta, X, y);
+
+  % non-trivial cost  
+  % ERROR if you get this result... you have something backwards
+  % assert(J, 1.3536, 1e-4 );
+
+  % the correct result   
+  assert(J, 0.85363, 1e-4 );
+
+  % non-grivial gradient
+  grad_exp = [ 0.35951 0.63584 0.99534 ]';
+  assert(grad, grad_exp, 1e-4);
+endfunction

ex2/test_ex2.m

@@ -2,3 +2,8 @@
 
 %!test test_sigmoid()
 
+%!test test_costfunction_minimal()
+
+%!test test_costfunction_nontrivial()
+
+%  !test test_gradient()

ex2/test_sigmoid.m

@@ -24,5 +24,12 @@ function test_sigmoid ()
           [0   0.5 1  ;
            0.5 1   0  ;
            1   0   0.5], 1e-6);  
+
+
+  %
+  % Apurva Dubey
+  %
+  assert(sigmoid([1 2 3]), [0.73106 0.88080 0.95257], 1e-4);
+  assert(sigmoid([4 -2 -3]), [0.98201 0.119203 0.047426], 1e-4);
 
 endfunction