fix struct

src/tmp/ImplementingUsefulAlgorithms/MachineLearning/RandomForest.h

@@ -0,0 +1,48 @@
+#ifndef IGMDK_RANDOM_FOREST_H
+#define IGMDK_RANDOM_FOREST_H
+#include "ClassificationCommon.h"
+#include "DecisionTree.h"
+#include "../Utils/Vector.h"
+#include "../RandomNumberGeneration/Random.h"
+#include <cmath>
+namespace igmdk{
+
+class RandomForest
+{
+    Vector<DecisionTree> forest;
+    int nClasses;
+public:
+    template<typename DATA> RandomForest(DATA const& data, int nTrees = 300):
+        nClasses(findNClasses(data))
+    {
+        assert(data.getSize() > 1);
+        for(int i = 0; i < nTrees; ++i)
+        {
+            PermutedData<DATA> resample(data);
+            for(int j = 0; j < data.getSize(); ++j)
+                resample.addIndex(GlobalRNG().mod(data.getSize()));
+            forest.append(DecisionTree(resample, 0, true));
+        }
+    }
+    template <typename ENSEMBLE> static int classifyWork(NUMERIC_X const& x,
+        ENSEMBLE const& e, int nClasses)
+    {
+        Vector<int> counts(nClasses, 0);
+        for(int i = 0; i < e.getSize(); ++i) ++counts[e[i].predict(x)];
+        return argMax(counts.getArray(), counts.getSize());
+    }
+    int predict(NUMERIC_X const& x)const
+        {return classifyWork(x, forest, nClasses);}
+    Vector<double> classifyProbs(NUMERIC_X const& x)const
+    {
+        Vector<double> counts(nClasses, 0);
+        for(int i = 0; i < forest.getSize(); ++i)
+            ++counts[forest[i].predict(x)];
+        normalizeProbs(counts);
+        return counts;
+    }
+};
+
+}//end namespace
+#endif
+