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hackassin
GitHub Repository: hackassin/Coursera-Machine-Learning
 Path: blob/master/Week 5/Programming Assignment - 4/machine-learning-ex4/ex4/submit.m
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function submit()

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  addpath('./lib');

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  conf.assignmentSlug = 'neural-network-learning';

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  conf.itemName = 'Neural Networks Learning';

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  conf.partArrays = { ...

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    { ...

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      '1', ...

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      { 'nnCostFunction.m' }, ...

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      'Feedforward and Cost Function', ...

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    }, ...

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    { ...

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      '2', ...

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      { 'nnCostFunction.m' }, ...

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      'Regularized Cost Function', ...

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    }, ...

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    { ...

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      '3', ...

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      { 'sigmoidGradient.m' }, ...

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      'Sigmoid Gradient', ...

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    }, ...

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    { ...

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      '4', ...

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      { 'nnCostFunction.m' }, ...

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      'Neural Network Gradient (Backpropagation)', ...

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    }, ...

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    { ...

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      '5', ...

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      { 'nnCostFunction.m' }, ...

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      'Regularized Gradient', ...

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    }, ...

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  };

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  conf.output = @output;

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  submitWithConfiguration(conf);

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end

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function out = output(partId, auxstring)

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  % Random Test Cases

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  X = reshape(3 * sin(1:1:30), 3, 10);

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  Xm = reshape(sin(1:32), 16, 2) / 5;

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  ym = 1 + mod(1:16,4)';

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  t1 = sin(reshape(1:2:24, 4, 3));

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  t2 = cos(reshape(1:2:40, 4, 5));

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  t  = [t1(:) ; t2(:)];

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  if partId == '1'

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    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);

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    out = sprintf('%0.5f ', J);

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  elseif partId == '2'

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    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);

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    out = sprintf('%0.5f ', J);

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  elseif partId == '3'

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    out = sprintf('%0.5f ', sigmoidGradient(X));

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  elseif partId == '4'

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    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);

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    out = sprintf('%0.5f ', J);

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    out = [out sprintf('%0.5f ', grad)];

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  elseif partId == '5'

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    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);

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    out = sprintf('%0.5f ', J);

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    out = [out sprintf('%0.5f ', grad)];

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  end 

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end

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