I am getting a stack over flow error when using the optimize function from Optim package

[SajeelBongale]

Hello, i am new to julia. 

I am trying to implement logistic regression as per some course on Machine learning. I am using the Atom editor for running Julia. 

However i am unable to find the right arguments for using the optimize function. 

I am sending my differentiable function which is the costFunction and initial_theta as the initial parameters. What other arguments are needed? 

Which optimizer function should be used? 

I am attaching my code here.

This is my cost function:

"This function calculates the cost and gradient at a given theta"

function costFunction( initial_theta,X, y)

  m = length(y);

  J = 0;

  grad = zeros(size(initial_theta));

  z = X * initial_theta;

  h_theta = sigmoid(z);

  J = (-1/m) * ( y .* log(h_theta) + (1-y) .* log(1 - h_theta) );

  grad = (1/m) * X' * ( h_theta - y );

  return J, grad;

end

This is the snippet from my main function:

l() = Optim.DifferentiableFunction(costFunction(initial_theta,X,y))

methodToUse = Optim.GradientDescent()

options = Optim.OptimizationOptions(iterations = 400)

optimize(l,initial_theta)                  #########          when i do this, i get a stack over flow error

optimize(l,initial_theta, methodToUse,options)            ########         with this i get a Method error : no method matching finite_difference

Let me know if any additional information is needed. 

Thanks!

[Páll Haraldsson]

On Friday, October 21, 2016 at 1:50:13 PM UTC, SajeelBongale wrote:

This is my cost function:

"This function calculates the cost and gradient at a given theta"

function costFunction( initial_theta,X, y)

I find it likely, this should go to julia-opt.

See: http://julialang.org/community/

I can't see anything obviously wring with the function, like recursively calling itself (I guess Optim, may do that to it..).