Revision 0:ac93d9db8dbd, committed 2015-04-23

Comitter:

parkbanks

Date:

Thu Apr 23 20:16:51 2015 +0000

Commit message:

Initial commit, bot fully working.

Changed in this revision

diff -r 000000000000 -r ac93d9db8dbd Code/CGenAlg.cpp
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/CGenAlg.cpp	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,232 @@
+#include "CGenAlg.h"
+
+//const unsigned int numElite = 1;
+//const unsigned int numCopies = 2;
+
+// constructor
+// set up population with random floats
+CGenAlg::CGenAlg(int popsize, float MutRat, float CrossRat, int numweights) : m_iPopSize(popsize),
+                                                                                m_dMutationRate(MutRat),
+                                                                                m_dCrossoverRate(CrossRat),
+                                                                                m_iChromoLength(numweights),
+                                                                                m_dTotalFitness(0),
+                                                                                m_cGeneration(0),
+                                                                                m_iFittestGenome(0),
+                                                                                m_dBestFitness(0),
+                                                                                m_dWorstFitness(99999)
+{
+    //initialise population with randomly weighted chromosomes, set fitness to 0
+    for (int i=0; i<m_iPopSize; ++i)
+    {
+        m_vecPop.push_back(SGenome());
+
+        for (int j=0; j<m_iChromoLength; ++j)
+        {
+            m_vecPop[i].vecWeights.push_back(RandomClamped());
+        }
+    }
+}
+
+// Mutates a chromosome by perturbing its weights, m_dMutationRate given in main
+void CGenAlg::Mutate(vector<float> &chromo)
+{
+    //traverse the chromosome and mutate each weight dependent
+    //on the mutation rate
+    for (int i=0; i<chromo.size(); ++i)
+    {
+        //do we perturb this weight?
+        if (RandFloat() < m_dMutationRate)
+        {
+            //add or subtract a small value to the weight
+            chromo[i] += (RandomClamped() * 0.3);
+        }
+    }
+}
+
+// returns a chromo based on roulette wheel sampling
+SGenome CGenAlg::GetChromoRoulette()
+{
+    //generate a random number between 0 & total fitness count
+    float Slice = (float)(RandFloat() * m_dTotalFitness);
+
+    //this will be set to the chosen chromosome
+    SGenome TheChosenOne;
+    
+    //go through the chromosones adding up the fitness so far
+    float FitnessSoFar = 0;
+    
+    for (int i=0; i<m_iPopSize; ++i)
+    {
+        FitnessSoFar += m_vecPop[i].dFitness;
+        
+        //if the fitness so far > random number return the chromo at 
+        //this point
+        if (FitnessSoFar >= Slice)
+        {
+            TheChosenOne = m_vecPop[i];
+
+      break;
+        }
+        
+    }
+
+    return TheChosenOne;
+}
+
+// given parents and storage for the offspring this method performs
+// crossover according to the GAs crossover rate
+void CGenAlg::Crossover(const vector<float> &mum,
+                        const vector<float> &dad,
+                        vector<float>       &baby1,
+                        vector<float>       &baby2)
+{
+    //just return parents as offspring dependent on the rate
+    //or if parents are the same
+    if ( (RandFloat() > m_dCrossoverRate) || (mum == dad)) 
+    {
+        baby1 = mum;
+        baby2 = dad;
+
+        return;
+    }
+
+    //determine a crossover point
+    int cp = RandInt(0, m_iChromoLength - 1);
+
+    //create the offspring
+    for (int i=0; i<cp; ++i)
+    {
+        baby1.push_back(mum[i]);
+        baby2.push_back(dad[i]);
+    }
+
+    int i;
+    for (i=cp; i<mum.size(); ++i)
+    {
+        baby1.push_back(dad[i]);
+        baby2.push_back(mum[i]);
+    }
+    
+    
+    return;
+}
+
+
+// takes a population of chromosones and runs the algorithm through one cycle.
+//  Returns a new population of chromosones.
+vector<SGenome> CGenAlg::Epoch(vector<SGenome> &old_pop)
+{
+    //assign the given population to the classes population
+  m_vecPop = old_pop;
+
+  //reset the appropriate variables
+  Reset();
+
+  //sort the population (for scaling and elitism)
+  sort(m_vecPop.begin(), m_vecPop.end());
+
+  //calculate best, worst, average and total fitness
+    CalculateBestWorstAvTot();
+  
+  //create a temporary vector to store new chromosones
+    vector <SGenome> vecNewPop;
+
+    //Now to add a little elitism we shall add in some copies of the
+    //fittest genomes. Make sure we add an EVEN number or the roulette
+  //wheel sampling will crash
+
+    if (m_vecPop[1].dFitness > 130 && m_vecPop[2].dFitness > 130)//2 copies of 1 elite
+    {
+        GrabNBest(2, 2, vecNewPop);
+    }
+    else if (m_vecPop[1].dFitness > 130)//2 copies of 1 elite
+    {
+        GrabNBest(1, 4, vecNewPop);
+    }
+    
+
+    //now we enter the GA loop
+    
+    //repeat until a new population is generated
+    while (vecNewPop.size() < m_iPopSize)
+    {
+        //grab two chromosones
+        SGenome mum = GetChromoRoulette();
+        SGenome dad = GetChromoRoulette();
+
+        //create some offspring via crossover
+        vector<float>      baby1, baby2;
+
+        Crossover(mum.vecWeights, dad.vecWeights, baby1, baby2);
+
+        //now we mutate
+        Mutate(baby1);
+        Mutate(baby2);
+
+        //now copy into vecNewPop population
+        vecNewPop.push_back(SGenome(baby1, 0));
+        vecNewPop.push_back(SGenome(baby2, 0));
+    }
+
+    //finished so assign new pop back into m_vecPop
+    m_vecPop = vecNewPop;
+
+    return m_vecPop;
+}
+
+// Elitism, inserts NumCopies of NBest fittest genomes into a pop vector
+void CGenAlg::GrabNBest(int             NBest,
+                        const int         NumCopies,
+                        vector<SGenome> &Pop)
+{
+  //add the required amount of copies of the n most fittest 
+    //to the supplied vector
+    while(NBest--)
+    {
+        for (int i=0; i<NumCopies; ++i)
+        {
+            Pop.push_back(m_vecPop[(m_iPopSize - 1) - NBest]);
+      }
+    }
+}
+
+//  calculates fittest and weakest genomes
+void CGenAlg::CalculateBestWorstAvTot()
+{
+    m_dTotalFitness = 0;
+    
+    float HighestSoFar = 0;
+    float LowestSoFar  = 99999;
+    
+    for (int i=0; i<m_iPopSize; ++i)
+    {
+        //update fittest if necessary
+        if (m_vecPop[i].dFitness > HighestSoFar)
+        {
+            HighestSoFar     = m_vecPop[i].dFitness;
+            
+            m_iFittestGenome = i;
+
+            m_dBestFitness   = HighestSoFar;
+        }
+        
+        //update worst if necessary
+        if (m_vecPop[i].dFitness < LowestSoFar)
+        {
+            LowestSoFar = m_vecPop[i].dFitness;
+            
+            m_dWorstFitness = LowestSoFar;
+        }
+        
+        m_dTotalFitness += m_vecPop[i].dFitness;
+        
+        
+    }//next chromosome
+}
+
+void CGenAlg::Reset()
+{
+    m_dTotalFitness     = 0;
+    m_dBestFitness      = 0;
+    m_dWorstFitness     = 99999;
+}
\ No newline at end of file

diff -r 000000000000 -r ac93d9db8dbd Code/CGenAlg.h
--- /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

diff -r 000000000000 -r ac93d9db8dbd Code/CNeuralNet.cpp
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/CNeuralNet.cpp	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,144 @@
+#include "CNeuralNet.h"
+
+
+// Neuron Methods
+SNeuron::SNeuron(int NumInputs): m_NumInputs(NumInputs+1)                                           
+{
+    //we need an additional weight for the bias hence the +1
+    for (int i=0; i<NumInputs+1; ++i)
+    {
+        //set up the weights with an initial random value
+        m_vecWeight.push_back(RandomClamped());
+    }
+}
+
+
+// Neuron Layer Methods
+// Calls SNeuron constructor for each neuron in each layer
+SNeuronLayer::SNeuronLayer(int NumNeurons, int NumInputsPerNeuron): m_NumNeurons(NumNeurons)
+{
+    for (int i=0; i<NumNeurons; ++i)
+
+        m_vecNeurons.push_back(SNeuron(NumInputsPerNeuron));
+}
+
+
+// CNeuralNet Methods
+// creates a ANN based on given values
+// add function so values are taken from main
+CNeuralNet::CNeuralNet() 
+{
+    m_NumInputs = 2;
+    m_NumOutputs = 2;
+    m_NumHiddenLayers = 0;
+    m_NeuronsPerHiddenLyr = 0;
+    CreateNet();
+}
+
+// Builds ANN with random weights from -1 to 1.
+void CNeuralNet::CreateNet()
+{
+    //network layers
+    if (m_NumHiddenLayers > 0)
+    {
+        //first hidden layer
+        m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr, m_NumInputs));
+    
+        for (int i=0; i<m_NumHiddenLayers-1; ++i)
+        {
+            m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr, m_NeuronsPerHiddenLyr));
+        }
+        //output layer
+        m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NeuronsPerHiddenLyr));
+    }
+    else{
+        m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NumInputs));
+    }
+}
+
+//  returns vector of weights
+vector<float> CNeuralNet::GetWeights() const
+{
+    //this will hold the weights
+    vector<float> weights;
+    
+    for (int i=0; i<m_NumHiddenLayers + 1; ++i){
+        for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j){
+            for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k){
+                weights.push_back(m_vecLayers[i].m_vecNeurons[j].m_vecWeight[k]);
+            }
+        }
+    }
+    return weights;
+}
+
+// replaces weights with values given in float vector
+void CNeuralNet::PutWeights(vector<float> &weights)
+{
+    int cWeight = 0;
+    for (int i=0; i<m_NumHiddenLayers + 1; ++i){
+        for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j){
+            for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k){
+                m_vecLayers[i].m_vecNeurons[j].m_vecWeight[k] = weights[cWeight++];
+            }
+        }
+    }
+    return;
+}
+
+// returns # of weights needed for net
+int CNeuralNet::GetNumberOfWeights() const
+{
+    int weights = 0;
+    for (int i=0; i<m_NumHiddenLayers + 1; ++i){
+        for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j){
+            for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k){
+                weights++;
+            }   
+        }
+    }
+    return weights;
+}
+
+// Calculates output values from inputs
+vector<float> CNeuralNet::Update(vector<float> &inputs)
+{
+    //stores the resultant outputs from each layer
+    vector<float> outputs;
+    int cWeight = 0;
+    
+    //layer
+    for (int i=0; i<m_NumHiddenLayers + 1; ++i){       
+        if ( i > 0 ){
+            inputs = outputs;
+        }
+        outputs.clear();
+        cWeight = 0;
+
+        //for each neuron sum inputs*weights and feed to sigmoid for weight
+        for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j){
+            float netinput = 0;
+            int NumInputs = m_vecLayers[i].m_vecNeurons[j].m_NumInputs;
+            
+            //for each weight
+            for (int k=0; k<NumInputs - 1; ++k){
+                //sum weights*inputs
+                netinput += m_vecLayers[i].m_vecNeurons[j].m_vecWeight[k] * inputs[cWeight++];
+            }
+
+            //add in bias
+            netinput += m_vecLayers[i].m_vecNeurons[j].m_vecWeight[NumInputs-1] * -1; //bias is -1
+
+            //store outputs from each layer, feed combined activation through sigmoid 
+            outputs.push_back(Sigmoid(netinput, 1));//activation response of 1
+            cWeight = 0;
+        }
+    }
+    return outputs;
+}
+
+// Sigmoid function
+float CNeuralNet::Sigmoid(float netinput, float response){
+    return ( 1 / ( 1 + exp(-netinput / response)));
+}
+

diff -r 000000000000 -r ac93d9db8dbd Code/CNeuralNet.h
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/CNeuralNet.h	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,49 @@
+#ifndef CNEURALNET_H
+#define CNEURALNET_H
+
+#include <vector>
+#include <fstream>
+#include <math.h>
+#include "utils.h"
+
+using namespace std;
+
+// Neuron Struct
+struct SNeuron
+{
+    int m_NumInputs; //the number of inputs into the neuron
+    vector<float>  m_vecWeight; //the weights for each input
+    SNeuron(int NumInputs); //ctor
+};
+
+
+// Neuron Layer Struct
+struct SNeuronLayer
+{
+    int m_NumNeurons; //number of neurons in layer
+    vector<SNeuron>     m_vecNeurons; //the layer of neurons
+    SNeuronLayer(int NumNeurons, int NumInputsPerNeuron);
+};
+
+
+// Neural Net
+class CNeuralNet
+{   
+private:   
+    int m_NumInputs;
+    int m_NumOutputs;
+    int m_NumHiddenLayers;
+    int m_NeuronsPerHiddenLyr;
+    vector<SNeuronLayer> m_vecLayers;//storage for each neuron layer 
+
+public:
+    CNeuralNet();
+    void CreateNet();
+    vector<float> GetWeights()const; //weights from ANN
+    int GetNumberOfWeights()const; //returns number of weights in net
+    void PutWeights(vector<float> &weights); //replaces weights
+    vector<float> Update(vector<float> &inputs); //gets outputs from net    
+    inline float Sigmoid(float activation, float response); //sigmoid function
+};
+
+#endif
\ No newline at end of file

diff -r 000000000000 -r ac93d9db8dbd Code/Ping.lib
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/Ping.lib	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,1 @@
+http://developer.mbed.org/users/rosienej/code/Ping/#6996f66161d7

diff -r 000000000000 -r ac93d9db8dbd Code/m3pimaze.lib
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/m3pimaze.lib	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,1 @@
+http://developer.mbed.org/users/jonmarsh/code/m3pimaze/#e1dac380452c

diff -r 000000000000 -r ac93d9db8dbd Code/main.cpp
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/main.cpp	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,130 @@
+/*
+    This file is part of mbeddedNets.
+
+    mbeddedNets is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    mbeddedNets is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+    
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+//
+//  Description: For use with the m3pi in conjunction with Ping))) sensors.
+//               Creates user defined neural nets, iterating through each to
+//               complete a task. Reward is calculated using reflactance sensors
+//               on underside of m3pi chassis.
+
+#include <vector>
+#include <math.h>
+#include <cstdlib>
+#include <ctime>
+
+#include "mbed.h"
+#include "m3pimaze.h"
+#include "Ping.h"
+#include "CNeuralNet.h"
+#include "CGenAlg.h"
+#include "utils.h"
+
+const unsigned int NBots = 6; //set number of networks each bot has
+const unsigned int Gens = 48; //number of generations to run
+const float MAX = 0.5;
+const unsigned int CSz = 90; //half of container size in cm, limiting squashing function
+
+ m3pi m3pi(p23,p9,p10);
+ Ping Pinger1(p15);
+ Ping Pinger2(p16);
+ LocalFileSystem local("local");//output storage
+
+int main()
+{
+     float pr1; //right
+     float pr2; //left
+     Pinger1.Send();//send ping
+     Pinger2.Send();    
+     wait_ms(30);
+     pr1 = Pinger1.Read_cm();
+     pr2 = Pinger2.Read_cm();
+     pr1 = pr1 * 100;//for seeding random # generator
+     pr2 = pr2 * 100;
+     
+     srand(int(pr2*pr1));
+     FILE *fp = fopen("/local/out.txt", "a");
+     fprintf(fp, "Bot tick fit rIn lIn out1 out2");
+     fprintf(fp, "\n");
+     fclose(fp);
+     
+     CNeuralNet myNets[NBots]; //networks
+     vector<SGenome> botGenes; //genes
+     CGenAlg* m_pGA; //genetic algorithm
+     CGenAlg pGA(NBots, 0.1, 0.7, myNets[0].GetNumberOfWeights()); //nets,mutrate,crossrate,weightsinnn
+     m_pGA = &pGA;
+     botGenes = m_pGA->GetChromos();
+     
+     for (int i=0; i<NBots; i++){
+         myNets[i].PutWeights(botGenes[i].vecWeights);
+     }
+    
+    for(int j=0; j<Gens; j++){  
+        for(int i=0; i<NBots; i++){
+            //int fit = 0;
+            float fit = 0;
+            int ticks = 128;
+            int sensors[5];
+            m3pi.locate(0,1);
+            m3pi.sensor_auto_calibrate();//calibrate reflectance sensors
+            
+            //check if goal found
+            while(ticks >= 0){       
+                Pinger1.Send();    
+                Pinger2.Send();
+                wait_ms(30);
+                pr1 = Pinger1.Read_cm();
+                pr2 = Pinger2.Read_cm();
+                if(pr1 < 10 || pr2 < 10){
+                    break;  
+                }
+                Squash(pr1,CSz,-1,1);
+                Squash(pr2,CSz,-1,1);
+                vector<float> inputs; //inputs to network
+                inputs.push_back(pr1);
+                inputs.push_back(pr2);
+                vector<float> output = myNets[i].Update(inputs);//get outputs from network
+                m3pi.right_motor(output[0] * MAX);
+                m3pi.left_motor(output[1] * MAX);
+                m3pi.readsensor(sensors);
+                if(sensors[0] > 600 && sensors[1] > 600 && sensors[2] > 600 && sensors[3] > 600 && sensors[4] > 600){
+                    fit=384.0;
+                    FILE *fp = fopen("/local/out.txt", "a");
+                    fprintf(fp, "%d, %d, %f, %f, %f, %f, %f", i, ticks, fit, pr1, pr2, output[0], output[1]);
+                    fprintf(fp, "\n");
+                    fclose(fp);
+                    break;
+                }
+                FILE *fp = fopen("/local/out.txt", "a");
+                fprintf(fp, "%d, %d, %f, %f, %f, %f, %f", i, ticks, fit, pr1, pr2, output[0], output[1]);
+                fprintf(fp, "\n");
+                fclose(fp);
+                ticks--;
+                //fit++;
+                fit += (output[0] + output[1]);
+                wait(0.1);
+            }
+            m3pi.right_motor(0);//stop bot
+            m3pi.left_motor(0);
+            botGenes[i].dFitness = fit;
+            wait(6); //reset robot position
+        }
+        botGenes = m_pGA->Epoch(botGenes);
+        for (int i=0; i<NBots; ++i){
+            myNets[i].PutWeights(botGenes[i].vecWeights);
+        }     
+    }      
+    return 0;
+}

diff -r 000000000000 -r ac93d9db8dbd Code/mbed.bld
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/mbed.bld	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,1 @@
+http://mbed.org/users/mbed_official/code/mbed/builds/4fc01daae5a5
\ No newline at end of file

diff -r 000000000000 -r ac93d9db8dbd Code/utils.h
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Code/utils.h	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,27 @@
+#ifndef UTILS_H
+#define UTILS_H
+
+#include <stdlib.h>
+#include <math.h>
+#include <vector>
+
+using namespace std;
+
+// Random number functions
+
+//returns a random integer between x and y
+inline int RandInt(int x,int y) {return rand()%(y-x+1)+x;}
+
+//returns a random float between zero and 1
+inline float RandFloat() {return (rand())/(RAND_MAX+1.0);}
+
+//returns a random float in the range -1 < n < 1
+inline float RandomClamped() {return RandFloat() - RandFloat();}
+
+inline void Squash(float &arg, int obj, float min, float max){
+    arg = arg/obj - 1;
+    if (arg < min){arg = min;}
+    if (arg > max){arg = max;}
+}
+
+#endif
\ No newline at end of file

diff -r 000000000000 -r ac93d9db8dbd gpl.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/gpl.txt	Thu Apr 23 20:16:51 2015 +0000
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+
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+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.