initial commit

ex3/submit.m

@@ -0,0 +1,56 @@
+function submit()
+  addpath('./lib');
+
+  conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
+  conf.itemName = 'Multi-class Classification and Neural Networks';
+  conf.partArrays = { ...
+    { ...
+      '1', ...
+      { 'lrCostFunction.m' }, ...
+      'Regularized Logistic Regression', ...
+    }, ...
+    { ...
+      '2', ...
+      { 'oneVsAll.m' }, ...
+      'One-vs-All Classifier Training', ...
+    }, ...
+    { ...
+      '3', ...
+      { 'predictOneVsAll.m' }, ...
+      'One-vs-All Classifier Prediction', ...
+    }, ...
+    { ...
+      '4', ...
+      { 'predict.m' }, ...
+      'Neural Network Prediction Function' ...
+    }, ...
+  };
+  conf.output = @output;
+
+  submitWithConfiguration(conf);
+end
+
+function out = output(partId, auxdata)
+  % Random Test Cases
+  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
+  y = sin(X(:,1) + X(:,2)) > 0;
+  Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
+          1 1 ;  1 2 ;  2 1 ; 2 2 ; ...
+         -1 1 ;  -1 2 ;  -2 1 ; -2 2 ; ...
+          1 -1 ; 1 -2 ;  -2 -1 ; -2 -2 ];
+  ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';
+  t1 = sin(reshape(1:2:24, 4, 3));
+  t2 = cos(reshape(1:2:40, 4, 5));
+
+  if partId == '1'
+    [J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
+    out = sprintf('%0.5f ', J);
+    out = [out sprintf('%0.5f ', grad)];
+  elseif partId == '2'
+    out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
+  elseif partId == '3'
+    out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
+  elseif partId == '4'
+    out = sprintf('%0.5f ', predict(t1, t2, Xm));
+  end 
+end