57 lines
1.5 KiB
Matlab
57 lines
1.5 KiB
Matlab
function submit()
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addpath('./lib');
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conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
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conf.itemName = 'Multi-class Classification and Neural Networks';
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conf.partArrays = { ...
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{ ...
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'1', ...
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{ 'lrCostFunction.m' }, ...
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'Regularized Logistic Regression', ...
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}, ...
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{ ...
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'2', ...
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{ 'oneVsAll.m' }, ...
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'One-vs-All Classifier Training', ...
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}, ...
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{ ...
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'3', ...
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{ 'predictOneVsAll.m' }, ...
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'One-vs-All Classifier Prediction', ...
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}, ...
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{ ...
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'4', ...
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{ 'predict.m' }, ...
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'Neural Network Prediction Function' ...
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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, auxdata)
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% Random Test Cases
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X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
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y = sin(X(:,1) + X(:,2)) > 0;
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Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
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1 1 ; 1 2 ; 2 1 ; 2 2 ; ...
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-1 1 ; -1 2 ; -2 1 ; -2 2 ; ...
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1 -1 ; 1 -2 ; -2 -1 ; -2 -2 ];
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ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 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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if partId == '1'
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[J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
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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 == '2'
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out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
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elseif partId == '3'
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out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
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elseif partId == '4'
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out = sprintf('%0.5f ', predict(t1, t2, Xm));
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end
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end
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