Merge pull request #6 from ratiopharm88/master

Changes from friday

handin3/Code/classify.m

@@ -1,7 +1,7 @@
 function [ classvector ] = classify( data, model )
 %% Trains the SVM on the given data using the given parameters.
 %% Returns libsvm model data which can then be used with svmpredict.
-    dummylabels = ones(1, length(data));
+    dummylabels = ones(1, size(data, 1));
 
     classvector = svmpredict(dummylabels, data, model, '');
 

handin3/Code/dividedataset.m

@@ -0,0 +1,15 @@
+function [class1 class2] = dividedataset (A)
+%% Divides a given set of two dimensional classification data into one set for each class.
+%% The classes must be encoded by being 1 for the first class and different from one for the second.
+%% 
+%% A - the input data set
+    class1=ones(0,2);
+    class2=ones(0,2);
+    for i=1:length(A)
+        if A(i,3)==1
+            class1=[class1;A(i,1),A(i,2)];
+        else
+            class2=[class2;A(i,1),A(i,2)];
+        end
+    end
+

handin3/Code/dividesupportvectors.m

@@ -0,0 +1,17 @@
+function [free bounded] = dividesupportvectors (c, SVs, SVcoeffs)
+%% Takes a matrix containing all support vectors of a trained C-SVM
+%% and divides it into free and bounded support vectors.
+
+    free=ones(0, size(SVs, 2));
+    bounded=ones(0, size(SVs, 2));
+
+    for i=1:length(SVcoeffs)
+        current= SVs(i,:);
+        if c==abs(SVcoeffs(i))
+           bounded=[bounded; current];
+        else
+           free=[free; current]; 
+        end
+    end
+
+end