Parker Banks / mbeddedNets

Generates neural net instances through genetic algorithm for use in task learning by m3pi. Reward determined by detection of black areas on arena floor, or by amount of area explored in limited time. Due to memory size, limited # of nets.

Dependencies:   Ping m3pimaze mbed

Diff: Code/CGenAlg.h

Revision:
0:ac93d9db8dbd
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/CGenAlg.h	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,67 @@
+#ifndef CGENALG_H
+#define CGENALG_H
+
+#include <vector>
+#include <algorithm>
+#include <iostream>
+#include <fstream>
+#include "utils.h"
+
+using namespace std;
+
+// structure to hold genomes
+struct SGenome
+{
+    vector <float> vecWeights;
+    float dFitness;
+    SGenome():dFitness(0){}
+    SGenome( vector <float> w, float f): vecWeights(w), dFitness(f){}
+    //overload '<' for sorting
+    friend bool operator<(const SGenome& lhs, const SGenome& rhs)
+    {
+        return (lhs.dFitness < rhs.dFitness);
+    }
+};
+
+// GA class
+class CGenAlg
+{
+private:   
+    vector <SGenome> m_vecPop;//vector of chromosome population
+    int m_iPopSize;//population size
+    int m_iChromoLength;//# weights per chromosome
+    float m_dTotalFitness;//total fitness of population
+    float m_dBestFitness;//best fitness
+    float m_dWorstFitness;//worst fitness
+    int m_iFittestGenome;//keeps track of the best genome
+    float m_dMutationRate;//probability of mutation, 0.05-0.3 ideal
+    float m_dCrossoverRate;//probability of chromosone cross-over, .7 ideal
+    int   m_cGeneration;//generation counter
+
+    void Crossover(const vector<float> &mum,
+                    const vector<float> &dad,
+                    vector<float> &baby1,
+                    vector<float> &baby2);
+    
+    void Mutate(vector<float> &chromo);
+    
+    SGenome GetChromoRoulette();
+
+    //introduce elitism
+    void GrabNBest(int NBest, const int NumCopies, vector<SGenome> &vecPop);
+    void CalculateBestWorstAvTot();
+    void Reset();
+
+public:       
+    CGenAlg(int popsize, float MutRat, float CrossRat, int numweights);
+
+    //runs GA for one generation.
+    vector<SGenome> Epoch(vector<SGenome> &old_pop);
+
+    //accessor functions
+    vector<SGenome> GetChromos()const{return m_vecPop;}
+    float AverageFitness()const{return m_dTotalFitness / m_iPopSize;}
+    float BestFitness()const{return m_dBestFitness;}
+};
+
+#endif
\ No newline at end of file