added classify.shared command and random forest files. added count file to pcr.seqs...

[mothur.git] / abstractrandomforest.hpp

diff --git a/abstractrandomforest.hpp b/abstractrandomforest.hpp
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+//
+//  abstractrandomforest.hpp
+//  rrf-fs-prototype
+//
+//  Created by Abu Zaher Faridee on 7/20/12.
+//  Copyright (c) 2012 Schloss Lab. All rights reserved.
+//
+
+#ifndef rrf_fs_prototype_abstractrandomforest_hpp
+#define rrf_fs_prototype_abstractrandomforest_hpp
+
+#include "mothurout.h"
+#include "macros.h"
+#include "abstractdecisiontree.hpp"
+
+#define DEBUG_MODE
+
+/***********************************************************************/
+
+class AbstractRandomForest{
+public:
+    // intialization with vectors
+    AbstractRandomForest(const std::vector < std::vector<int> > dataSet, 
+                       const int numDecisionTrees, 
+                       const string);
+    virtual ~AbstractRandomForest(){ }
+    virtual int populateDecisionTrees() = 0;
+    virtual int calcForrestErrorRate() = 0;
+    virtual int calcForrestVariableImportance(string) = 0;
+ 
+/***********************************************************************/
+  
+protected:
+  
+    // TODO: create a better way of discarding feature
+    // currently we just set FEATURE_DISCARD_SD_THRESHOLD to 0 to solved this
+    // it can be tuned for better selection
+    // also, there might be other factors like Mean or other stuffs
+    // same would apply for createLocalDiscardedFeatureList in the TreeNode class
+  
+    // TODO: Another idea is getting an aggregated discarded feature indices after the run, from combining
+    // the local discarded feature indices
+    // this would penalize a feature, even if in global space the feature looks quite good
+    // the penalization would be averaged, so this woould unlikely to create a local optmina
+    
+    vector<int> getGlobalDiscardedFeatureIndices();
+    
+    int numDecisionTrees;
+    int numSamples;
+    int numFeatures;
+    vector< vector<int> > dataSet;
+    vector<int> globalDiscardedFeatureIndices;
+    vector<double> globalVariableImportanceList;
+    string treeSplitCriterion;
+    // This is a map of each feature to outcome count of each classes
+    // e.g. 1 => [2 7] means feature 1 has 2 outcome of 0 and 7 outcome of 1
+    map<int, vector<int> > globalOutOfBagEstimates;
+    
+    // TODO: fix this, do we use pointers?
+    vector<AbstractDecisionTree*> decisionTrees;
+    
+    MothurOut* m;
+  
+private:
+
+};
+#endif