//Add N instances, if selected instances is less than the user-defined minimum
public ObjectInstanceSelection AddInstances(ObjectInstanceSelection globalBestFlower, int Min)
{
int countSelected = globalBestFlower.Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
if (countSelected < Min)
{
//if there are less than N, add N instances, where N = the number of selected instances
diff = Min - countSelected;
while (c < diff)
{
if (globalBestFlower.Attribute_Values[d] == 1) //skip the already selected solutions
{
d++;
continue;
}
else //add instances to positions that are not selected; i.e. where instance mask is equal to 0
{
globalBestFlower.Attribute_Values[d] = 1;
c++; d++;
}
}
}
diff = globalBestFlower.Attribute_Values.Count(a => a == 1);
return(globalBestFlower);
}
//evaluate new bat solution, update better solution (if found), and get global best bat
public ObjectInstanceSelection EvaluateSolution(double[] batFitnessVal, double[] newBatFitnessVal, double globalBest, List <ObjectInstanceSelection> bats, List <ObjectInstanceSelection> newBats, ObjectInstanceSelection globalBestBat, double loudness)
{
double newBest = new double();
int maxIndex;
Random r = new Random();
//evaluate solution and update, if better solution is found
for (int i = 0; i < batFitnessVal.Count(); i++)
{
if (newBats[i].Fitness > bats[i].Fitness && r.NextDouble() < loudness)
{
bats[i] = new ObjectInstanceSelection(newBats[i].Attribute_Values, newBats[i].Attribute_Values_Continuous, newBats[i].Frequency, newBats[i].Velocity, newBats[i].Pointers, newBats[i].Fitness); //create a clone of flowers
batFitnessVal[i] = newBats[i].Fitness;
//bats[i] = newBats[i]; //update solution
}
}
//get blobal best flower
newBest = newBatFitnessVal.Max(); //get the flower with the highest fitness
if (newBest > globalBest)
{
globalBest = newBest;
maxIndex = Array.IndexOf(newBatFitnessVal, newBest); //select the index for the global best
globalBestBat = new ObjectInstanceSelection(newBats[maxIndex].Attribute_Values, newBats[maxIndex].Attribute_Values_Continuous, newBats[maxIndex].Frequency, newBats[maxIndex].Velocity, newBats[maxIndex].Pointers, newBats[maxIndex].Fitness); //create a clone of flowers; //select the global best flower
//globalBestBat = newBats[maxIndex]; //select the global best flower
}
return(globalBestBat);
}
//evaluate new flower solution, update better solution (if found), and get global best flower
public ObjectInstanceSelection EvaluateSolution(double[] flowerFitnessVal, double[] newflowerFitnessVal, double globalBest, List <ObjectInstanceSelection> flowers, List <ObjectInstanceSelection> newFlowers, ObjectInstanceSelection globalBestFlower)
{
double newBest = new double();
int maxIndex;
//evaluate solution and update, if better solution is found
for (int i = 0; i < flowerFitnessVal.Count(); i++)
{
if (newFlowers[i].Fitness > flowers[i].Fitness)
{
flowers[i] = new ObjectInstanceSelection(newFlowers[i].Attribute_Values, newFlowers[i].Attribute_Values_Continuous, newFlowers[i].Pointers, newFlowers[i].Fitness); //create a clone of flowers
flowerFitnessVal[i] = newFlowers[i].Fitness;
}
}
//get blobal best flower
newBest = newflowerFitnessVal.Max(); //get the flower with the highest fitness
if (newBest > globalBest)
{
globalBest = newBest;
maxIndex = Array.IndexOf(newflowerFitnessVal, newBest); //select the index for the global best
globalBestFlower = new ObjectInstanceSelection(newFlowers[maxIndex].Attribute_Values, newFlowers[maxIndex].Attribute_Values_Continuous, newFlowers[maxIndex].Pointers, newFlowers[maxIndex].Fitness); //create a clone of flowers; //select the global best flower
}
return(globalBestFlower);
}
/// <summary>
/// generating the initial locations of n spiders
/// </summary>
public List <ObjectInstanceSelection> InitializeBinarySpider(int nSpiders, int subsetSize, int probSize, Problem prob)
{
Random rnd = new Random();
List <int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
FireflyInstanceSelection fpa = new FireflyInstanceSelection();
List <ObjectInstanceSelection> attr_values = new List <ObjectInstanceSelection>();
int cnt1 = 0, cnt2 = 0, cnt3 = 0;
//create an array of size n for x and y
int[] xn = new int[subsetSize]; //instance mask
double[] xn_Con = new double[subsetSize]; //instance mask continuous
double freq = new double(); //initialize the frequency of all the bats to zero
double[] vel = new double[subsetSize]; //initialize the velocity of all the bats to zero
int[] pointers = new int[subsetSize]; //array contain pointer to actual individual instance represented in the instance mask
double spiderPosition = 0;
int k = 0;
int bound = 100;
for (int i = 0; i < nSpiders; i++)
{
xn = new int[subsetSize];
xn_Con = new double[subsetSize];
pointers = new int[subsetSize];
cnt1 = 0; cnt2 = 0; cnt3 = 0;
for (int j = 0; j < prob.Count; j++)
{
if (cnt1 < (0.7 * subsetSize) && prob.Y[rNum[j]] == -1) //select 70% positive instance of the subset
{
//xn_Con[cnt3] = rnd.NextDouble();
//xn[cnt3] = fi.Binarize(xn_Con[cnt3], rnd.NextDouble());
xn[cnt3] = rnd.Next(0, 2); //initialize each spider position.
pointers[cnt3] = rNum[j];
spiderPosition = rnd.NextDouble() * 2 * bound - bound; //generate position of spider
k++; cnt1++; cnt3++;
}
else if (cnt2 < (0.3 * subsetSize) && prob.Y[rNum[j]] == 1)
{
//xn_Con[cnt3] = rnd.NextDouble();
//xn[cnt3] = fi.Binarize(xn_Con[cnt3], rnd.NextDouble());
xn[cnt3] = rnd.Next(0, 2); //initialize each spider position.
pointers[cnt3] = rNum[j];
spiderPosition = rnd.NextDouble() * 2 * bound - bound; //generate position of spider
k++; cnt2++; cnt3++;
}
if (cnt3 >= subsetSize)
{
break;
}
}
ObjectInstanceSelection OI = new ObjectInstanceSelection(xn, xn_Con, pointers, 0.0, spiderPosition);
attr_values.Add(OI);
}
return(attr_values);
}
/// <summary>
/// generating the initial locations of n flower
/// </summary>
public List <ObjectInstanceSelection> InitializeBinaryFlower(int nFlower, int subsetSize, int probSize, Problem prob)
{
Random rnd = new Random();
List <int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
FireflyInstanceSelection fpa = new FireflyInstanceSelection();
List <ObjectInstanceSelection> attr_values = new List <ObjectInstanceSelection>();
int cnt1 = 0, cnt2 = 0, cnt3 = 0;
//create an array of size n for x and y
int[] xn = new int[subsetSize]; //instance mask
double[] xn_Con = new double[subsetSize]; //instance mask continuous
int[] pointers = new int[subsetSize]; //array contain pointer to actual individual instance represented in the instance mask
int k = 0;
for (int i = 0; i < nFlower; i++)
{
xn = new int[subsetSize];
xn_Con = new double[subsetSize];
pointers = new int[subsetSize];
cnt1 = 0; cnt2 = 0; cnt3 = 0;
for (int j = 0; j < prob.Count; j++)
{
if (cnt1 < (0.7 * subsetSize) && prob.Y[rNum[j]] == -1) //select 70% positive instance of the subset
{
//xn[cnt3] = rnd.NextDouble() <= 0.5 ? 0 : 1;
xn[cnt3] = rnd.Next(0, 2);
//xn_Con[cnt3] = rnd.NextDouble();
//xn[cnt3] = fi.Binarize(xn_Con[cnt3], rnd.NextDouble());
pointers[cnt3] = rNum[j];
k++; cnt1++; cnt3++;
}
else if (cnt2 < (0.3 * subsetSize) && prob.Y[rNum[j]] == 1)
{
//xn[cnt3] = rnd.NextDouble() <= 0.5 ? 0 : 1;
xn[cnt3] = rnd.Next(0, 2);
//xn_Con[cnt3] = rnd.NextDouble();
//xn[cnt3] = fi.Binarize(xn_Con[cnt3], rnd.NextDouble());
pointers[cnt3] = rNum[j];
k++; cnt2++; cnt3++;
}
if (cnt3 >= subsetSize)
{
break;
}
}
ObjectInstanceSelection OI = new ObjectInstanceSelection(xn, xn_Con, pointers, 0.0);
attr_values.Add(OI);
}
return(attr_values);
}
/// <summary>
/// generating the initial locations of n bats
/// </summary>
public List <ObjectInstanceSelection> InitializeBinaryBat(int nBats, int subsetSize, int probSize, Problem prob)
{
Random rnd = new Random();
List <int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
FireflyInstanceSelection fpa = new FireflyInstanceSelection();
List <ObjectInstanceSelection> attr_values = new List <ObjectInstanceSelection>();
int cnt1 = 0, cnt2 = 0, cnt3 = 0;
//create an array of size n for x and y
int[] xn = new int[subsetSize]; //instance mask
double[] xn_Con = new double[subsetSize]; //instance mask continuous
double freq = new double(); //initialize the frequency of all the bats to zero
double[] vel = new double[subsetSize]; //initialize the velocity of all the bats to zero
int[] pointers = new int[subsetSize]; //array contain pointer to actual individual instance represented in the instance mask
int k = 0;
for (int i = 0; i < nBats; i++)
{
xn = new int[subsetSize];
xn_Con = new double[subsetSize];
pointers = new int[subsetSize];
cnt1 = 0; cnt2 = 0; cnt3 = 0;
for (int j = 0; j < prob.Count; j++)
{
if (cnt1 < (0.7 * subsetSize) && prob.Y[j] == -1) //select 70% negative instance (i.e. ham) of the subset
{
xn[cnt3] = rnd.Next(0, 2);
//xn[cnt3] = 0;
pointers[cnt3] = rNum[j];
k++; cnt1++; cnt3++;
}
else if (cnt2 < (0.3 * subsetSize) && prob.Y[j] == 1)
{
xn[cnt3] = rnd.Next(0, 2);
//xn[cnt3] = 0;
pointers[cnt3] = rNum[j];
k++; cnt2++; cnt3++;
}
if (cnt3 >= subsetSize)
{
break;
}
}
ObjectInstanceSelection OI = new ObjectInstanceSelection(xn, xn_Con, freq, vel, pointers, 0.0);
attr_values.Add(OI);
}
return(attr_values);
}
/// <summary>
/// generating the initial locations of n fireflies
/// </summary>
public List <ObjectInstanceSelection> init_ffa(int nFF, int subsetSize, int probSize, Problem prob)
{
Random rnd = new Random(); // Random rx = new Random(); Random ry = new Random();
List <int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
List <ObjectInstanceSelection> attr_values = new List <ObjectInstanceSelection>();
int cnt1 = 0, cnt2 = 0, cnt3 = 0;
//create an array of size n for x and y
int[] xn = new int[subsetSize]; //instance mask
int[] pointers = new int[subsetSize]; //array contain pointer to actual individual instance represented in the instance mask
int k = 0;
for (int i = 0; i < nFF; i++)
{
xn = new int[subsetSize];
pointers = new int[subsetSize];
cnt1 = 0; cnt2 = 0; cnt3 = 0;
for (int j = 0; j < prob.Count; j++)
{
if (cnt1 < (0.7 * subsetSize) && prob.Y[j] == 1) //select 70% positive instance of the subset
{
xn[cnt3] = rnd.Next(0, 2);
pointers[cnt3] = rNum[k];
k++; cnt1++; cnt3++;
}
else if (cnt2 < (0.3 * subsetSize) && prob.Y[j] == -1)
{
xn[cnt3] = rnd.Next(0, 2);
pointers[cnt3] = rNum[k];
k++; cnt2++; cnt3++;
}
if (cnt3 >= subsetSize)
{
break;
}
}
ObjectInstanceSelection OI = new ObjectInstanceSelection(0.0, 0.0, xn, pointers);
attr_values.Add(OI);
}
return(attr_values);
}
/// <summary>
/// This method ensures that the C and Gamma values do not go beyond specified range
/// </summary>
public void findrange(ObjectInstanceSelection fireflies, double minC, double maxC, double minG, double maxG)
{
if ((double)fireflies.cValue <= minC)
{
fireflies.cValue = minC;
}
if ((double)fireflies.cValue >= maxC)
{
fireflies.cValue = maxC;
}
if ((double)fireflies.GValue <= minG)
{
fireflies.GValue = minG;
}
if ((double)fireflies.GValue >= maxG)
{
fireflies.GValue = maxG;
}
}
public Problem CuckooSearch(Problem prob, out double storagePercentage)
{
int nNests = 5; //number of nests, or number of solutions
int subsetSize = 100;
int maxGen = 5; //maximum generation
double discoveryRate = 0.25; //discovery rate of alien eggs
double tolerance = Math.Exp(-5);
int lowerBound = -5;
int upperBound = 5;
int totalInstances = prob.X.Count(); //problem size
double[] cuckooFitnessVal = new double[nNests];
double[] newCuckooFitnessVal = new double[nNests];
ObjectInstanceSelection globalBestCuckoo = null;
double globalBest = double.MinValue;
Random rand = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> cuckoos = InitializeBinaryCuckoo(nNests, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newCuckoos = new List <ObjectInstanceSelection>(cuckoos.Count); //create a clone of bats
cuckoos.ForEach((item) =>
{
newCuckoos.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Pointers, item.Fitness)); //create a clone of flowers
});
cuckooFitnessVal = EvaluateObjectiveFunction(cuckoos, prob); //evaluate fitness value for all the bats
newCuckooFitnessVal = EvaluateObjectiveFunction(newCuckoos, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
CuckooFitness(cuckooFitnessVal, cuckoos); //fitness value for each bats
CuckooFitness(newCuckooFitnessVal, newCuckoos); //fitness value for new bats
globalBestCuckoo = EvaluateSolution(cuckooFitnessVal, newCuckooFitnessVal, globalBest, cuckoos, newCuckoos, globalBestCuckoo); //get the global best flower
globalBest = globalBestCuckoo.__Fitness;
//generate new solutions
double beta = 3 / 2;
double A = fp.Gamma(1 + beta) * Math.Sin(Math.PI * (beta / 2));
double B = fp.Gamma((1 + beta) / 2) * beta;
double C = (beta - 1) / 2;
double D = Math.Pow(2, C);
double E = A / (B * D);
double sigma = Math.Pow(E, (1 / beta));
double F;
double G;
double step;
double stepSize;
int x = 0;
for (int i = 0; i <= maxGen; i++)
{
for (int j = 0; j < nNests; j++)
{
for (int k = 0; k < subsetSize; k++)
{
F = SimpleRNG.GetNormal() * sigma;
G = SimpleRNG.GetNormal();
step = F / Math.Pow(Math.Abs(G), (1 / beta));
//In the next equation, the difference factor (s-best) means that when the solution is the best solution, it remains unchanged.
//Here the factor 0.01 comes from the fact that L/100 should the typical step size of walks/flights where L is the typical lenghtscale;
//otherwise, Levy flights may become too aggresive/efficient, which makes new solutions (even) jump out side of the design domain (and thus wasting evaluations).
stepSize = 0.01 * step * (cuckoos[j].Attribute_Values[k] - globalBestCuckoo.Attribute_Values[k]);
//Now the actual random walks or levyy flights
newCuckoos[j].Attribute_Values[k] = fi.Binarize((newCuckoos[j].Attribute_Values[k] + stepSize) * SimpleRNG.GetNormal(), rand.NextDouble());
if (cuckoos[j].Attribute_Values[k] == 1 && newCuckoos[j].Attribute_Values[k] == 0)
{
x++;
}
}
}
//discovery and randomization - replace some nest by constructing new solutions
newCuckoos = EmptyNest(cuckoos, newCuckoos, discoveryRate, subsetSize, nNests);
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(cuckoos, newCuckoos);
//evaluate new solution
newCuckooFitnessVal = EvaluateObjectiveFunction(newCuckoos, prob); //evaluate fitness value for all the bats
CuckooFitness(newCuckooFitnessVal, newCuckoos); //fitness value for new bats
globalBestCuckoo = EvaluateSolution(cuckooFitnessVal, newCuckooFitnessVal, globalBest, cuckoos, newCuckoos, globalBestCuckoo); //get the global best flower
globalBest = globalBestCuckoo.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int min = 40; //minimum number of selected instances
globalBestCuckoo = fpa.AddInstances(globalBestCuckoo, min);
Problem subBest = fi.buildModelMultiClass(globalBestCuckoo, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
public Problem Bat(Problem prob)
{
//default parameters
int populationSize = 5; //number of bats in the population
int maxGeneration = 100;
int subsetSize = 200;
double loudness = 0.5;
double pulseRate = 0.5;
int totalInstances = prob.X.Count(); //problem size
double frequencyMin = 0; //minimum frequency. Frequency range determine the scalings
double frequencyMax = 2; //maximum frequency.
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
double[] batFitnessVal = new double[populationSize];
double[] newbatFitnessVal = new double[populationSize];
double globalBest = double.MinValue;
ObjectInstanceSelection globalBestBat = null;
Random r = new Random();
//initialize population
List <ObjectInstanceSelection> bats = InitializeBat(populationSize, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newBats = new List <ObjectInstanceSelection>(bats.Count); //create a clone of bats
bats.ForEach((item) =>
{
newBats.Add(new ObjectInstanceSelection(item.__Attribute_Values, item.__Attribute_Values_Continuous, item.__Frequency, item.__Velocity, item.__Pointers, item.__Fitness)); //create a clone of flowers
});
batFitnessVal = fi.EvaluateObjectiveFunction(bats, prob); //evaluate fitness value for all the bats
newbatFitnessVal = fi.EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
BatFitness(batFitnessVal, bats); //fitness value for each bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.__Fitness;
//start bat algorithm
double rand = r.NextDouble(); //generate random number
for (int i = 0; i < maxGeneration; i++)
{
//loop over all bats or solutions
for (int j = 0; j < populationSize; j++)
{
bats[j].__Frequency = frequencyMin + (frequencyMin - frequencyMax) * rand; //adjust frequency
for (int k = 0; k < subsetSize; k++)
{
double randNum = SimpleRNG.GetNormal(); //generate random number with normal distribution
newBats[j].__Velocity[k] = bats[j].__Velocity[k] + (bats[j].__Attribute_Values_Continuous[k] - globalBestBat.Attribute_Values_Continuous[k]) * bats[j].__Frequency; //update velocity
newBats[j].__Attribute_Values_Continuous[k] = bats[j].__Attribute_Values_Continuous[k] + bats[j].__Velocity[k]; //update bat position in continuous space
newBats[j].__Attribute_Values_Continuous[k] = SimpleBounds(newBats[j].__Attribute_Values_Continuous[k], lowerBound, upperBound); //ensure that value does not go beyond defined boundary
if (rand > pulseRate) //The factor 0.001 limits the step sizes of random walks
{
newBats[j].__Attribute_Values_Continuous[k] = globalBestBat.Attribute_Values_Continuous[k] + 0.001 * randNum;
}
newBats[j].__Attribute_Values[k] = fi.Binarize(newBats[j].__Attribute_Values_Continuous[k], r.NextDouble()); //convert to binary
}
}
//evaluate new solution
newbatFitnessVal = fi.EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for all the bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.__Fitness;
}
//ensure that at least, 40 instances is selected for classification
int countSelected = globalBestBat.__Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
int Min = 40; //minimum number of selected instances
if (countSelected < Min)
{
//if there are less than N, add N instances, where N = the number of selected instances
diff = Min - countSelected;
while (c < diff)
{
if (globalBestBat.__Attribute_Values[d++] == 1)
{
continue;
}
else
{
globalBestBat.__Attribute_Values[d++] = 1;
c++;
}
}
}
Problem subBest = fi.buildModel(globalBestBat, prob); //build model for the best Instance Mast
return(subBest);
}
//Binary Bat
public Problem BinaryBat(Problem prob, out double storagePercentage)
{
//default parameters
int populationSize = 3; //number of bats in the population
int subsetSize = 100;
int maxGeneration = 3;
double loudness = 0.5;
double pulseRate = 0.5;
int totalInstances = prob.X.Count(); //problem size
double frequencyMin = 0; //minimum frequency. Frequency range determine the scalings
double frequencyMax = 2; //maximum frequency.
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
double[] batFitnessVal = new double[populationSize];
double[] newbatFitnessVal = new double[populationSize];
double globalBest = double.MinValue;
ObjectInstanceSelection globalBestBat = null;
Random r = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> bats = InitializeBinaryBat(populationSize, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newBats = new List <ObjectInstanceSelection>(bats.Count); //create a clone of bats
bats.ForEach((item) =>
{
newBats.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Frequency, item.Velocity, item.Pointers, item.Fitness)); //create a clone of flowers
});
batFitnessVal = EvaluateObjectiveFunction(bats, prob); //evaluate fitness value for all the bats
newbatFitnessVal = EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
BatFitness(batFitnessVal, bats); //fitness value for each bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.Fitness;
//start bat algorithm
double rand = r.NextDouble(); //generate random number
for (int i = 0; i < maxGeneration; i++)
{
//loop over all bats or solutions
for (int j = 0; j < populationSize; j++)
{
for (int k = 0; k < subsetSize; k++)
{
bats[j].Frequency = frequencyMin + (frequencyMin - frequencyMax) * r.NextDouble(); //Adjust frequency
double randNum = SimpleRNG.GetNormal(); //generate random number with normal distribution
newBats[j].Velocity[k] = newBats[j].Velocity[k] + (bats[j].Attribute_Values[k] - globalBestBat.Attribute_Values[k]) * bats[j].Frequency; //update velocity
//newBats[j].Attribute_Values[k] = fpa.ConvertToBinary(newBats[j].Velocity[k], newBats[j].Attribute_Values[k]); //update bat position in the binary space
newBats[j].Attribute_Values[k] = TransferFunction(newBats[j].Velocity[k], newBats[j].Attribute_Values[k]); //update bat position in the binary space
if (rand > pulseRate)
{
newBats[j].Attribute_Values[k] = globalBestBat.Attribute_Values[k]; //change some of the dimensions of the position vector with some dimension of global best. Refer to reference for more explaination
}
}
}
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(bats, newBats);
//evaluate new solution
newbatFitnessVal = EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for all the bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int min = 15; //minimum number of selected instances
globalBestBat = fpa.AddInstances(globalBestBat, min);
Problem subBest = fi.buildModelMultiClass(globalBestBat, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
public Problem SocialSpider(Problem prob, out double storagePercentage)
{
int nSpiders = 5; //population size of spiders
int subsetSize = 100;
int totalInstances = prob.X.Count();;
int bound = 100, maxGen = 5;
double r_a = 1; //This parameter controls the attenuation rate of the vibration intensity over distance
double p_c = 0.7; // p_c describes the probability of changing mask of spider
double p_m = 0.1; // This is also a user-controlled parameter defined in (0, 1). It controls the probability of assigning a one or zero to each bit of a mask
bool info = true;
double[][] globalBestPosition = new double[1][];
double[] targetIntensity = new double[nSpiders]; //best vibration for each spider
//double[] targetPosition = new double[nSpiders]; //target position for each spider
double[,] mask = new double[nSpiders, subsetSize];
double[,] newMask = new double[nSpiders, subsetSize];
double[,] movement = new double[nSpiders, subsetSize];
double[] inactive = new double[nSpiders];
double[] spiderFitnessVal = new double[nSpiders];
double[] newSpiderFitnessVal = new double[nSpiders];
ObjectInstanceSelection globalBestSpider = null;
double globalBest = double.MinValue;
Random rand = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> spiders = InitializeBinarySpider(nSpiders, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newSpiders = new List <ObjectInstanceSelection>(spiders.Count); //create a clone of bats
spiders.ForEach((item) =>
{
newSpiders.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Pointers, item.Fitness, item.Position)); //create a clone of flowers
});
spiderFitnessVal = EvaluateObjectiveFunction(spiders, prob); //evaluate fitness value for all the bats
newSpiderFitnessVal = EvaluateObjectiveFunction(newSpiders, prob); //evaluate fitness value for new spiders. Note: this will be the same for this function call, since pollination has not occur
SpiderFitness(spiderFitnessVal, spiders); //fitness value for each spiders
SpiderFitness(newSpiderFitnessVal, newSpiders); //fitness value for new spider
globalBestSpider = EvaluateSolution(spiderFitnessVal, newSpiderFitnessVal, globalBest, spiders, newSpiders, globalBestSpider); //get the global best spider
globalBest = globalBestSpider.Fitness;
double[] standDev = new double[subsetSize];
List <double> listPositions = new List <double>();
List <double[]> spiderPositions = new List <double[]>();
//calculate the standard deviation of all spider positions
for (int a = 0; a < subsetSize; a++)
{
double[] sPositions = new double[nSpiders];
for (int b = 0; b < nSpiders; b++)
{
sPositions[b] = spiders[b].Attribute_Values[a]; //get all spider positions column wise
//sPositions[b] = spiders[b].Attribute_Values_Continuous[a]; //get all spider positions column wise
}
spiderPositions.Add(sPositions); //save positions in list
}
for (int a = 0; a < subsetSize; a++)
{
standDev[a] = getStandardDeviation(spiderPositions[a].ToList()); //calculate standard deviation for each spider solution
}
double baseDistance = standDev.Average(); //calculate the mean of standev
//compute paired euclidean distances of all vectors in spider; similar to pdist function in matlab. Reference: http://www.mathworks.com/help/stats/pdist.html
int n = (nSpiders * (nSpiders - 1)) / 2; //total number of elements array dist.
double[] euclidenDist = new double[n]; //Note that, this is array for paired eucliden distance, similar to pdist() function in matlab.
int kk = 0;
for (int i = 0; i < nSpiders; i++)
{
for (int j = 1 + i; j < nSpiders; j++)
{
//this distance is in pairs -> 1,0; 2,0; 3,0,...n,0; 2,1; 3,1; 4,1,...n,1;.... It is similar to pdist function in matlab
//euclidenDist[kk++] = computeEuclideanDistance(spiders[j].Attribute_Values_Continuous, spiders[i].Attribute_Values_Continuous); //generate a vibration for each spider position
euclidenDist[kk++] = computeEuclideanDistance(spiders[j].Attribute_Values, spiders[i].Attribute_Values); //generate a vibration for each spider position
//distance[i][j] = computeEuclideanDistance(spiders[i].Attribute_Values, spiders[j].Attribute_Values);
}
}
double[,] distance = SquareForm(euclidenDist, nSpiders); //Convert vibration to square matix, using SquareForm() function in matlab. Reference: see Squareform function in google
//double[,] intensityReceive = new double[nSpiders, nSpiders];
double[][] intensityReceive = new double[nSpiders][];
for (int a = 0; a < maxGen; a++)
{
for (int j = 0; j < nSpiders; j++)
{
//calculate the intensity for all the generated vibrations
intensityReceive[j] = new double[nSpiders];
double A = (spiders[j].Fitness + Math.Exp(-100)) + 1;
double intensitySource = Math.Log(1 / A);
for (int k = 0; k < nSpiders; k++)
{
double intensityAttenuation = Math.Exp(-distance[j, k] / (baseDistance * r_a));
//intensityReceive[j, k] = intensitySource * intensityAttenuation; //intensity for each spider vibration
intensityReceive[j][k] = intensitySource * intensityAttenuation; //intensity for each spider vibration
}
}
//select strongest vibration from intensity
int row = intensityReceive.GetLength(0);
int column = intensityReceive[0].Count();
//IEnumerable<double> bestReceive = Enumerable.Range(0, row).Select(i => Enumerable.Range(0, column).Select(j => intensityReceive[i, j]).Max()); //get the max value in each row
IEnumerable <double> bestReceive = Enumerable.Range(0, row).Select(i => Enumerable.Range(0, column).Select(j => intensityReceive[i][j]).Max()); //get the max value in each row
//IEnumerable<int> bestReceiveIndex = Enumerable.Range(0, row).Select(i => Enumerable.Range(0, column).Select(j => intensityReceive[i, j]).Max()); //get the max value in each row
//get the index of the strongest vibration
int[] maxIndex = new int[nSpiders];
for (int i = 0; i < nSpiders; i++)
{
maxIndex[i] = Array.IndexOf(intensityReceive[i], bestReceive.ElementAt(i));
}
//Store the current best vibration
int[] keepTarget = new int[nSpiders];
int[] keepMask = new int[nSpiders];
double[,] targetPosition = new double[nSpiders, subsetSize];
for (int i = 0; i < nSpiders; i++)
{
if (bestReceive.ElementAt(i) <= targetIntensity[i])
{
keepTarget[i] = 1;
}
inactive[i] = inactive[i] * keepTarget[i] + keepTarget[i];
targetIntensity[i] = (targetIntensity[i] * keepTarget[i]) + bestReceive.ElementAt(i) * (1 - keepTarget[i]);
if (rand.NextDouble() < Math.Pow(p_c, inactive[i]))
{
keepMask[i] = 1;
}
inactive[i] = inactive[i] * keepMask[i];
for (int j = 0; j < subsetSize; j++)
{
//newSpiders[i].Attribute_Values[j] = fi.Binarize(newSpiders[i].Attribute_Values[j] * spiders[maxIndex[i]].Attribute_Values[j] * (1 - keepTarget[i]), rand.NextDouble()); //update solution
targetPosition[i, j] = targetPosition[i, j] * keepTarget[i] + spiders[maxIndex[i]].Attribute_Values[j] * (1 - keepTarget[i]);
//targetPosition[i, j] = targetPosition[i, j] * keepTarget[i] + spiders[maxIndex[i]].Attribute_Values_Continuous[j] * (1 - keepTarget[i]);
newMask[i, j] = Math.Ceiling(rand.NextDouble() + rand.NextDouble() * p_m - 1);
mask[i, j] = keepMask[i] * mask[i, j] + (1 - keepMask[i]) * newMask[i, j]; //update dimension mask of spider
}
}
//Reshuffule the Spider solution
//Method: randomly generated positions pointing to rows and columns in the solution space. With the pointers, we can acess indivdual indices(or positions) in the solution
double[,] randPosition = GenerateRandomSpiderPosition(nSpiders, subsetSize, spiders);
//generate psfo, and perform random walk
double[,] followPosition = new double[nSpiders, subsetSize];
for (int i = 0; i < nSpiders; i++)
{
for (int j = 0; j < subsetSize; j++)
{
followPosition[i, j] = mask[i, j] * randPosition[i, j] + (1 - mask[i, j]) * targetPosition[i, j];
movement[i, j] = rand.NextDouble() * movement[i, j] + (followPosition[i, j] - spiders[i].Attribute_Values[j]) * rand.NextDouble(); //perform random movement
//movement[i, j] = rand.NextDouble() * movement[i, j] + (followPosition[i, j] - spiders[i].Attribute_Values_Continuous[j]) * rand.NextDouble(); //perform random movement
//newSpiders[i].Attribute_Values[j] = fi.Binarize(newSpiders[i].Attribute_Values_Continuous[j] + movement[i, j], rand.NextDouble()); //actual random walk
newSpiders[i].Attribute_Values[j] = fi.Binarize(newSpiders[i].Attribute_Values[j] + movement[i, j], rand.NextDouble()); //actual random walk
}
}
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(spiders, newSpiders);
//evaluate new solution
newSpiderFitnessVal = EvaluateObjectiveFunction(newSpiders, prob); //evaluate fitness value for all the bats
SpiderFitness(newSpiderFitnessVal, newSpiders); //fitness value for new bats
globalBestSpider = EvaluateSolution(spiderFitnessVal, newSpiderFitnessVal, globalBest, spiders, newSpiders, globalBestSpider); //get the global best flower
globalBest = globalBestSpider.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int Min = 15; //minimum number of selected instances
globalBestSpider = fpa.AddInstances(globalBestSpider, Min);
Problem subBest = fi.buildModelMultiClass(globalBestSpider, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
/// <summary>
/// generating the initial locations of n Cuckoo
/// </summary>
public List <ObjectInstanceSelection> InitializeBinaryCuckoo(int nNests, int subsetSize, int probSize, Problem prob)
{
//Random rnd = new Random();
//List<int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
List <ObjectInstanceSelection> attr_values = new List <ObjectInstanceSelection>();
//int cnt1 = 0, cnt2 = 0, cnt3 = 0;
//create an array of size n for x and y
Random rnd = new Random();
//List<int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
int[] xn = new int[subsetSize]; //instance mask
double[] xn_Con = new double[subsetSize]; //instance mask continuous
//int[] pointers = new int[subsetSize]; //array contain pointer to actual individual instance represented in the instance mask
List <double> classes = fi.getClassLabels(prob.Y); //get the class labels
int nClass = classes.Count;
int div = subsetSize / nClass;
//double freq = new double(); //initialize the frequency of all the bats to zero
//double[] vel = new double[subsetSize]; //initialize the velocity of all the bats to zero
//select pointers to instances for all the particles
//int k = 0;
if (nClass > 2) //do this for multi-class problems
{
int[] pointers = Training.AssignClassPointers_MultipleClass(prob, subsetSize, probSize); //array contain pointer to actual individual instance represented in the instance mask
for (int a = 0; a < nNests; a++)
{
xn = new int[subsetSize]; //instance mask
xn_Con = new double[subsetSize]; //instance mask continuous
for (int j = 0; j < subsetSize; j++)
{
xn[j] = rnd.Next(0, 2);
}
//Training.InstanceMask_MultipleClass(prob, subsetSize, probSize, out xn); //initialize instance mask
ObjectInstanceSelection OI = new ObjectInstanceSelection(xn, xn_Con, pointers, 0.0);
attr_values.Add(OI);
}
}
else //do this for binary class problem
{
int[] pointers = Training.AssignClassPointersBinary(prob, probSize, subsetSize); //array contain pointer to actual individual instance represented in the instance mask
for (int i = 0; i < nNests; i++)
{
xn = new int[subsetSize];
xn_Con = new double[subsetSize];
//pointers = new int[subsetSize];
//cnt1 = 0; cnt2 = 0; cnt3 = 0;
for (int j = 0; j < subsetSize; j++)
{
xn[j] = rnd.Next(0, 2);
}
//Training.InstanceMask_Binary(prob, subsetSize, pointers, out xn);
ObjectInstanceSelection OI = new ObjectInstanceSelection(xn, xn_Con, pointers, 0.0);
attr_values.Add(OI);
//for (int j = 0; j < prob.Count; j++)
//{
// if (cnt1 < (0.7 * subsetSize) && prob.Y[rNum[j]] == -1) //select 70% positive instance of the subset
// {
// xn[cnt3] = rnd.Next(0, 2);
// pointers[cnt3] = rNum[j];
// k++; cnt1++; cnt3++;
// }
// else if (cnt2 < (0.3 * subsetSize) && prob.Y[rNum[j]] == 1)
// {
// xn[cnt3] = rnd.Next(0, 2);
// pointers[cnt3] = rNum[j];
// k++; cnt2++; cnt3++;
// }
// if (cnt3 >= subsetSize)
// break;
//}
}
}
return(attr_values);
}
//flower pollination algorithm by Yang
public Problem FlowerPollination(Problem prob)
{
int nargin = 0, totalInstances = prob.X.Count(), maxGeneration = 500;
int numOfFlower = 10; //population size
double probabilitySwitch = 0.8; //assign probability switch
int subsetSize = 200; //dimension for each flower
double[] flowerFitnessVal = new double[numOfFlower];
double[] newFlowerFitnessVal = new double[numOfFlower];
FireflyInstanceSelection fw = new FireflyInstanceSelection();
double globalBest = double.MinValue;
double newBest = new double();
ObjectInstanceSelection globalBestFlower = null;
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
int maxIndex;
//inittalize flowers, and get global best
List <ObjectInstanceSelection> flowers = InitializeFlower(numOfFlower, subsetSize, totalInstances, prob); //initialize solution
List <ObjectInstanceSelection> newFlowers = new List <ObjectInstanceSelection>(flowers.Count); //create a clone of flowers
flowers.ForEach((item) =>
{
newFlowers.Add(new ObjectInstanceSelection(item.__Attribute_Values, item.__Attribute_Values_Continuous, item.__Pointers, item.__Fitness)); //create a clone of flowers
});
flowerFitnessVal = fw.EvaluateObjectiveFunction(flowers, prob); //evaluate fitness value for all the flowers
newFlowerFitnessVal = fw.EvaluateObjectiveFunction(newFlowers, prob); //evaluate fitness value for new flowers. Note: this will be the same for this function call, since pollination has not occur
FlowerFitness(flowerFitnessVal, flowers); //fitness value for each flower
FlowerFitness(newFlowerFitnessVal, newFlowers); //fitness value for new flower
globalBestFlower = EvaluateSolution(flowerFitnessVal, newFlowerFitnessVal, globalBest, flowers, newFlowers, globalBestFlower); //get the global best flower
globalBest = flowerFitnessVal.Max();
//start flower algorithm
Random r = new Random();
double[] levy = new double[subsetSize];
for (int i = 0; i < maxGeneration; i++)
{
double rand = r.NextDouble();
if (rand > probabilitySwitch) //global pollination
{
//global pollination
for (int j = 0; j < numOfFlower; j++)
{
levy = LevyFlight(subsetSize);
for (int k = 0; k < subsetSize; k++)
{
double A = levy[k] * (flowers[j].__Attribute_Values_Continuous[k] - globalBestFlower.__Attribute_Values_Continuous[k]);
double B = flowers[j].__Attribute_Values_Continuous[k] + A;
A = SimpleBounds(B, lowerBound, upperBound); //ensure that value does not go beyond defined boundary
newFlowers[j].__Attribute_Values_Continuous[k] = A;
newFlowers[j].__Attribute_Values[k] = fw.Binarize(B, r.NextDouble()); //convert to binary
}
}
}
else //local pollination
{
for (int j = 0; j < numOfFlower; j++)
{
List <int> randNum = Training.GetRandomNumbers(2, numOfFlower); //generate 2 distinct random numbers
double epsilon = rand;
//local pollination
for (int k = 0; k < subsetSize; k++)
{
double A = flowers[j].__Attribute_Values_Continuous[k] + epsilon * (flowers[randNum[0]].__Attribute_Values_Continuous[k] - flowers[randNum[1]].__Attribute_Values_Continuous[k]); //randomly select two flowers from neighbourhood for pollination
A = SimpleBounds(A, lowerBound, upperBound); //ensure that value does not exceed defined boundary
newFlowers[j].__Attribute_Values_Continuous[k] = A; //save computation
newFlowers[j].__Attribute_Values[k] = fw.Binarize(A, r.NextDouble()); //convert to binary
}
}
}
//evaluate new solution
newFlowerFitnessVal = fw.EvaluateObjectiveFunction(newFlowers, prob); //evaluate fitness value for all the flowers
FlowerFitness(newFlowerFitnessVal, newFlowers); //fitness value for new flower
globalBestFlower = EvaluateSolution(flowerFitnessVal, newFlowerFitnessVal, globalBest, flowers, newFlowers, globalBestFlower); //Evaluate solution, update better solution and get global best flower
globalBest = flowerFitnessVal.Max();
}
//ensure that at least, 40 instances is selected for classification
int countSelected = globalBestFlower.__Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
int Min = 40; //minimum number of selected instances
if (countSelected < Min)
{
//if there are less than N, add N instances, where N = the number of selected instances
diff = Min - countSelected;
while (c < diff)
{
if (globalBestFlower.__Attribute_Values[d++] == 1)
{
continue;
}
else
{
globalBestFlower.__Attribute_Values[d++] = 1;
c++;
}
}
}
Problem subBest = fw.buildModel(globalBestFlower, prob); //build model for the best Instance Mast
return(subBest);
}
//flower pollination algorithm by Yang
public Problem BinaryFlowerPollination(Problem prob, out double storagePercentage)
{
int nargin = 0, totalInstances = prob.X.Count();
int maxGeneration = 3;
int numOfFlower = 3; //population size
int subsetSize = 100; //dimension for each flower
double probabilitySwitch = 0.8; //assign probability switch
double[] flowerFitnessVal = new double[numOfFlower];
double[] newFlowerFitnessVal = new double[numOfFlower];
double globalBest = double.MinValue;
double newBest = new double();
ObjectInstanceSelection globalBestFlower = null;
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
int maxIndex;
//inittalize flowers, and get global best
List <ObjectInstanceSelection> flowers = InitializeBinaryFlower(numOfFlower, subsetSize, totalInstances, prob); //initialize solution
List <ObjectInstanceSelection> newFlowers = new List <ObjectInstanceSelection>(flowers.Count); //create a clone of flowers
flowers.ForEach((item) =>
{
newFlowers.Add(new ObjectInstanceSelection(item.__Attribute_Values, item.__Attribute_Values_Continuous, item.__Pointers, item.__Fitness)); //create a clone of flowers
});
flowerFitnessVal = EvaluateObjectiveFunction(flowers, prob); //evaluate fitness value for all the flowers
newFlowerFitnessVal = EvaluateObjectiveFunction(newFlowers, prob); //evaluate fitness value for new flowers. Note: this will be the same for this function call, since pollination has not occur
FlowerFitness(flowerFitnessVal, flowers); //fitness value for each flower
FlowerFitness(newFlowerFitnessVal, newFlowers); //fitness value for new flower
globalBestFlower = EvaluateSolution(flowerFitnessVal, newFlowerFitnessVal, globalBest, flowers, newFlowers, globalBestFlower); //get the global best flower
globalBest = flowerFitnessVal.Max();
//start flower algorithm
Random r = new Random(); int x = 0;
double[] levy = new double[subsetSize];
for (int i = 0; i < maxGeneration; i++)
{
double rand = r.NextDouble();
if (rand > probabilitySwitch) //do global pollination, to produce new pollen solution
{
levy = LevyFlight(subsetSize);
for (int j = 0; j < numOfFlower; j++)
{
for (int k = 0; k < subsetSize; k++)
{
double A = levy[k] * (flowers[j].Attribute_Values[k] - globalBestFlower.Attribute_Values[k]);
double B = flowers[j].Attribute_Values[k] + A; //new pollen solution
//double A = levy[k] * (flowers[j].Attribute_Values_Continuous[k] - globalBestFlower.Attribute_Values_Continuous[k]);
//double B = flowers[j].Attribute_Values_Continuous[k] + A;
newFlowers[j].Attribute_Values[k] = ConvertToBinary(B, r.NextDouble()); //convert to binary
//newFlowers[j].__Attribute_Values[k] = TransferFunction(B, newFlowers[j].__Attribute_Values[k]); //update flower position in the binary space
}
List <int> randNum = Training.GetRandomNumbers(2, numOfFlower); //generate 2 distinct random numbers
for (int k = 0; k < subsetSize; k++)
{
double A = flowers[j].Attribute_Values[k] + (r.NextDouble() * (flowers[randNum[0]].Attribute_Values[k] - flowers[randNum[1]].Attribute_Values[k])); //randomly select two flowers from neighbourhood for pollination
//double A = flowers[j].Attribute_Values_Continuous[k] + r.NextDouble() * (flowers[randNum[0]].Attribute_Values_Continuous[k] - flowers[randNum[1]].Attribute_Values_Continuous[k]); //randomly select two flowers from neighbourhood for pollination
newFlowers[j].Attribute_Values[k] = ConvertToBinary(A, r.NextDouble()); //convert to binary
//newFlowers[j].__Attribute_Values[k] = TransferFunction(A, newFlowers[j].__Attribute_Values[k]); //update flower position in the binary space
}
}
}
else // //do local pollination, to produce new pollen solution
{
for (int j = 0; j < numOfFlower; j++)
{
List <int> randNum = Training.GetRandomNumbers(2, numOfFlower); //generate 2 distinct random numbers
for (int k = 0; k < subsetSize; k++)
{
double A = flowers[j].Attribute_Values[k] + r.NextDouble() * (flowers[randNum[0]].Attribute_Values[k] - flowers[randNum[1]].Attribute_Values[k]); //randomly select two flowers from neighbourhood for pollination
//double A = flowers[j].Attribute_Values_Continuous[k] + r.NextDouble() * (flowers[randNum[0]].Attribute_Values_Continuous[k] - flowers[randNum[1]].Attribute_Values_Continuous[k]); //randomly select two flowers from neighbourhood for pollination
newFlowers[j].Attribute_Values[k] = ConvertToBinary(A, r.NextDouble()); //convert to binary
//newFlowers[j].__Attribute_Values[k] = TransferFunction(A, newFlowers[j].__Attribute_Values[k]); //update flower position in the binary space
}
}
}
//Select best solutions from the original population and matured population for the next generation;
SelectBestSolution(flowers, newFlowers);
//evaluate new solution
newFlowerFitnessVal = EvaluateObjectiveFunction(newFlowers, prob); //evaluate fitness value for all the flowers
FlowerFitness(newFlowerFitnessVal, newFlowers); //fitness value for new flower
globalBestFlower = EvaluateSolution(flowerFitnessVal, newFlowerFitnessVal, globalBest, flowers, newFlowers, globalBestFlower); //Evaluate solution, update better solution and get global best flower
globalBest = globalBestFlower.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int min = 15; //minimum number of selected instances
globalBestFlower = AddInstances(globalBestFlower, min);
Problem subBest = fi.buildModelMultiClass(globalBestFlower, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
//build model for multi class problems
public Problem buildModelMultiClass(ObjectInstanceSelection firefly, Problem prob)
{
int tNI = firefly.Attribute_Values.Count(); //size of each Instance Mask
List <double> y = new List <double>();
List <Node[]> x = new List <Node[]>();
bool pos = false, neg = false;
List <double> classes = getClassLabels(prob.Y); //get the class labels
int nClass = classes.Count; //count the number of classes
int[] classCount = new int[nClass];
//building model for each instance in instance mask in each firefly object
for (int j = 0; j < tNI; j++)
{
if (firefly.__Attribute_Values[j] == 1) //if instance is selected, use for classification
{
int p = firefly.__Pointers[j];
x.Add(prob.X[p]);
y.Add(prob.Y[p]);
for (int i = 0; i < nClass; i++)
{
if (prob.Y[p] == classes[i])
{
classCount[i]++; //count the total number of instances in each class
}
}
}
else
{
continue;
}
}
Node[][] X = new Node[x.Count][];
double[] Y = new double[y.Count];
//ensuring that the subproblem consist of both positive and negative instance
int k = 0;
if (classCount.Sum() == 0) //if the sum is zero, then no instance was selected
{
return(null);
}
else //ensure that instance mask contains at least, one of each class instance
{
for (int a = 0; a < nClass; a++)
{
if (classCount[a] == 0)
{
int m = 0;
for (int i = 0; i < prob.Count; i++) //if no instance in this class, search the whole subproblem and insert one instance in the kth position of subproblem
{
if (prob.Y[i] == classes[a])
{
x[k] = prob.X[i]; //insert negative instance in the first and second position
y[k] = prob.Y[i]; //insert label
k++; m++;
}
if (m == 2)
{
break;
}
}
}
}
}
x.CopyTo(X); //convert from list to double[] array
y.CopyTo(Y);
Problem subProb = new Problem(X.Count(), Y, X, X[0].GetLength(0));
return(subProb);
}
/// <summary>
/// Main part of the Firefly Algorithm
/// </summary>
//public Problem firefly_simple(List<double> avgAcc, List<double> CValues, List<double> GValues, Problem prob)
public Problem firefly_simple(Problem prob, out double storagePercentage)
{
//int nF = 9; //number of instances
int nI = prob.X.Count(); //total number of instance in dataset
int nFF = 5; //number of fireflies. Note: NFF * subsetsize must not be greater than Size of training dataset
int subsetSize = 100; //size of each firefly Instance Mask
int MaxGeneration = 5; //number of pseudo time steps
int[] range = new int[4] {
-5, 5, -5, 5
}; //range=[xmin xmax ymin ymax]
double alpha = 0.2; //Randomness 0--1 (highly random)
double gamma = 1.0; //Absorption coefficient
int[] xn = new int[subsetSize];
double[] xo = new double[subsetSize];
double[] Lightn = new double[nFF];
double[] Lighto = new double[nFF];
double[] fitnessVal = new double[nFF];
double globalbestIntensity;
ObjectInstanceSelection globalBest = null;
//generating the initial locations of n fireflies
List <ObjectInstanceSelection> fireflies = init_ffa(nFF, subsetSize, nI, prob);
ObjectInstanceSelection[] fireflyBackup = new ObjectInstanceSelection[fireflies.Count];
ObjectInstanceSelection[] fireflyBest = new ObjectInstanceSelection[fireflies.Count];
List <int> changedIndex = new List <int>(); //changedIndex keeps track of the index of fireflies that has been moved
double newBestIntensity = new double();
int maxIndex;
bool stopSearch = false; //stopsearch is will be set to true when the a firefly with classification accuracy = 100 is found.
globalbestIntensity = double.MinValue;
//Iterations or pseudo time marching
for (int i = 0; i < MaxGeneration; i++)
{
//Evaluate objective function
fitnessVal = this.EvaluateObjectiveFunction(fireflies, prob); //evaluate objective function for each firefly
//stop searching if firefly has found the best c and G value that yields 100%
for (int t = 0; t < fitnessVal.Count(); t++)
{
//double predAccr = avgAcc[changedIndex[t]] * 100;
double predAccr = fitnessVal[t] * 100;
if (predAccr == 100) //if prediction accuracy is equal to 100, stop searching and select the firefly that gives this accuracy
{
globalBest = fireflies[changedIndex[t]];
stopSearch = true;
break;
}
}
//stop searching if firefly has found the best c and G value that yields 100%
if (stopSearch == true)
{
break;
}
//fitnessVal = this.EvaluateObjectiveFunction(fireflies, avgAcc, prob); //evaluate objective function for each firefly
newBestIntensity = fitnessVal.Max(); //get the firefly with the highest light intensity
if (newBestIntensity > globalbestIntensity)
{
globalbestIntensity = newBestIntensity;
maxIndex = Array.IndexOf(fitnessVal, newBestIntensity); //select the index for the global best
globalBest = fireflies[maxIndex]; //select the global best firefly
//bestC = (double)fireflies[maxIndex].cValue; //save the C value for the global best
//bestGamma = (double)fireflies[maxIndex].GValue; //save the Gamma for the global best
}
fireflies.CopyTo(fireflyBackup); fitnessVal.CopyTo(Lighto, 0); fitnessVal.CopyTo(Lightn, 0); //creating duplicates
//Lightn.CopyTo(Lighto, 0);
changedIndex.Clear();
ffa_move(Lightn, fireflyBackup, Lighto, alpha, gamma, fireflies, prob);
fireflies.CopyTo(fireflyBackup); //backing up the current positions of the fireflies
Lightn.CopyTo(Lighto, 0); //backing up the current intensities of the fireflies
}
//ensure that at least, 40 instances is selected for classification
int countSelected = globalBest.__Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
int Min = 15; //minimum number of selected instances
if (countSelected < Min)
{
diff = Min - countSelected;
//if there are less than 40, add N instances, where N = the number of selected instances and 40
while (c < diff)
{
if (globalBest.__Attribute_Values[d++] == 1)
{
continue;
}
else
{
globalBest.__Attribute_Values[d++] = 1;
c++;
}
}
}
Problem subBest = buildModelMultiClass(globalBest, prob); //model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
//build model for binary problems
public Problem buildModel(ObjectInstanceSelection firefly, Problem prob)
{
int tNI = firefly.Attribute_Values.Count(); //size of each Instance Mask
List <double> y = new List <double>();
List <Node[]> x = new List <Node[]>();
bool pos = false, neg = false;
//building model for each instance in instance mask in each firefly object
for (int j = 0; j < tNI; j++)
{
if (firefly.__Attribute_Values[j] == 1) //if instance is selected, use for classification
{
int p = firefly.__Pointers[j];
x.Add(prob.X[p]);
y.Add(prob.Y[p]);
if (prob.Y[p] == 1)
{
pos = true;
}
else if (prob.Y[p] == -1)
{
neg = true;
}
}
else
{
continue;
}
}
Node[][] X = new Node[x.Count][];
double[] Y = new double[y.Count];
//ensuring that the subproblem consist of both positive and negative instance
int k = 0;
int countP = y.Count(r => r == 1); //counting the total number of positive instance in the subpeoblem
int countN = y.Count(r => r == -1); //counting the total number of negative instance in the subproble
if (pos == false && neg == false) //if no instance (positive and negative) was selected, return null. Don't perform any computation
{
return(null);
}
else if (pos == false || countP <= 1) //if pos == false, then no positive instance is in the subproblem
{
for (int i = 0; i < prob.Count; i++) //if no positive instance, search the whole subproblem and insert two postive instance in the first and second position of subproblem
{
if (prob.Y[i] == 1)
{
x[k] = prob.X[i]; //insert negative instance in the first and second position
y[k] = prob.Y[i]; //insert label
k++;
}
if (k == 2)
{
break;
}
}
}
else if (neg == false || countN <= 1) //if neg == false, then no negative instance is in the subproblem
{
k = 0;
for (int i = 0; i < prob.Count; i++) //if no negative instance, search the whole subproblem and insert two negative instances in the first and second position of subproblem
{
if (prob.Y[i] == -1)
{
x[k] = prob.X[i]; //insert negative instance in the first and second position
y[k] = prob.Y[i]; //insert label
k++;
}
if (k == 2)
{
break;
}
}
}
x.CopyTo(X); //convert from list to double[] array
y.CopyTo(Y);
Problem subProb = new Problem(X.Count(), Y, X, X[0].GetLength(0));
return(subProb);
}
