coursera_machine_learning_ex/ex4/submit.m

function submit()
  addpath('./lib');

  conf.assignmentSlug = 'neural-network-learning';
  conf.itemName = 'Neural Networks Learning';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'nnCostFunction.m' }, ...
      'Feedforward and Cost Function', ...
    }, ...
    { ...
      '2', ...
      { 'nnCostFunction.m' }, ...
      'Regularized Cost Function', ...
    }, ...
    { ...
      '3', ...
      { 'sigmoidGradient.m' }, ...
      'Sigmoid Gradient', ...
    }, ...
    { ...
      '4', ...
      { 'nnCostFunction.m' }, ...
      'Neural Network Gradient (Backpropagation)', ...
    }, ...
    { ...
      '5', ...
      { 'nnCostFunction.m' }, ...
      'Regularized Gradient', ...
    }, ...
  };
  conf.output = @output;

  submitWithConfiguration(conf);
end

function out = output(partId, auxstring)
  % Random Test Cases
  X = reshape(3 * sin(1:1:30), 3, 10);
  Xm = reshape(sin(1:32), 16, 2) / 5;
  ym = 1 + mod(1:16,4)';
  t1 = sin(reshape(1:2:24, 4, 3));
  t2 = cos(reshape(1:2:40, 4, 5));
  t  = [t1(:) ; t2(:)];
  if partId == '1'
    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
    out = sprintf('%0.5f ', J);
  elseif partId == '2'
    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
    out = sprintf('%0.5f ', J);
  elseif partId == '3'
    out = sprintf('%0.5f ', sigmoidGradient(X));
  elseif partId == '4'
    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
    out = sprintf('%0.5f ', J);
    out = [out sprintf('%0.5f ', grad)];
  elseif partId == '5'
    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
    out = sprintf('%0.5f ', J);
    out = [out sprintf('%0.5f ', grad)];
  end 
end