rftreenode.hpp

   1 //
   2 //  rftreenode.hpp
   3 //  rrf-fs-prototype
   4 //
   5 //  Created by Abu Zaher Faridee on 5/29/12.
   6 //  Copyright (c) 2012 Schloss Lab. All rights reserved.
   7 //
   8
   9 #ifndef RF_RFTREENODE_HPP
  10 #define RF_RFTREENODE_HPP
  11
  12 #include "mothurout.h"
  13 #include "macros.h"
  14
  15 class RFTreeNode{
  16
  17 public:
  18
  19     RFTreeNode(vector< vector<int> > bootstrappedTrainingSamples,
  20                vector<int> globalDiscardedFeatureIndices,
  21                int numFeatures,
  22                int numSamples,
  23                int numOutputClasses,
  24                int generation,
  25                int nodeId,
  26                float featureStandardDeviationThreshold = 0.0);
  27
  28     virtual ~RFTreeNode(){}
  29
  30     // getters
  31     // we need to return const reference so that we have the actual value and not a copy,
  32     // plus we do not modify the value as well
  33     const int getSplitFeatureIndex() { return splitFeatureIndex; }
  34     // TODO: check if this works properly or returs a shallow copy of the data
  35     const vector< vector<int> >& getBootstrappedTrainingSamples() { return bootstrappedTrainingSamples; }
  36     const int getSplitFeatureValue() { return splitFeatureValue; }
  37     const int getGeneration() { return generation; }
  38     const bool checkIsLeaf() { return isLeaf; }
  39     // TODO: fix this const pointer dillema
  40     // we do not want to modify the data pointer by getLeftChildNode
  41     RFTreeNode* getLeftChildNode() { return leftChildNode; }
  42     RFTreeNode* getRightChildNode() { return rightChildNode; }
  43     const int getOutputClass() { return outputClass; }
  44     const int getNumSamples() { return numSamples; }
  45     const int getNumFeatures() { return numFeatures; }
  46     const vector<int>& getLocalDiscardedFeatureIndices() { return localDiscardedFeatureIndices; }
  47     const vector< vector<int> >& getBootstrappedFeatureVectors() { return bootstrappedFeatureVectors; }
  48     const vector<int>& getBootstrappedOutputVector() { return bootstrappedOutputVector; }
  49     const vector<int>& getFeatureSubsetIndices() { return featureSubsetIndices; }
  50     const double getOwnEntropy() { return ownEntropy; }
  51     const int getTestSampleMisclassificationCount() { return testSampleMisclassificationCount; }
  52
  53     // setters
  54     void setIsLeaf(bool isLeaf) { this->isLeaf = isLeaf; }
  55     void setOutputClass(int outputClass) { this->outputClass = outputClass; }
  56     void setFeatureSubsetIndices(vector<int> featureSubsetIndices) { this->featureSubsetIndices = featureSubsetIndices; }
  57     void setLeftChildNode(RFTreeNode* leftChildNode) { this->leftChildNode = leftChildNode; }
  58     void setRightChildNode(RFTreeNode* rightChildNode) { this->rightChildNode = rightChildNode; }
  59     void setParentNode(RFTreeNode* parentNode) { this->parentNode = parentNode; }
  60     void setSplitFeatureIndex(int splitFeatureIndex) { this->splitFeatureIndex = splitFeatureIndex; }
  61     void setSplitFeatureValue(int splitFeatureValue) { this->splitFeatureValue = splitFeatureValue; }
  62     void setSplitFeatureEntropy(double splitFeatureEntropy) { this->splitFeatureEntropy = splitFeatureEntropy; }
  63
  64     // TODO: need to remove this mechanism of friend class
  65     //NOTE: friend classes can be useful for testing purposes, but I would avoid using them otherwise.
  66     friend class DecisionTree;
  67     friend class AbstractDecisionTree;
  68
  69 private:
  70     vector<vector<int> > bootstrappedTrainingSamples;
  71     vector<int> globalDiscardedFeatureIndices;
  72     vector<int> localDiscardedFeatureIndices;
  73     vector<vector<int> > bootstrappedFeatureVectors;
  74     vector<int> bootstrappedOutputVector;
  75     vector<int> featureSubsetIndices;
  76
  77     int numFeatures;
  78     int numSamples;
  79     int numOutputClasses;
  80     int generation;
  81     bool isLeaf;
  82     int outputClass;
  83     int splitFeatureIndex;
  84     int splitFeatureValue;
  85     double splitFeatureEntropy;
  86     double ownEntropy;
  87
  88     int nodeId;
  89     float featureStandardDeviationThreshold;
  90     int testSampleMisclassificationCount;
  91
  92     RFTreeNode* leftChildNode;
  93     RFTreeNode* rightChildNode;
  94     RFTreeNode* parentNode;
  95
  96     MothurOut* m;
  97
  98     int createLocalDiscardedFeatureList();
  99     int updateNodeEntropy();
 100
 101 };
 102
 103 #endif