2018-12-1

Author: Travis-MA

bsocore.cpp

@@ -2,7 +2,6 @@
 // Date: 2018-11-15 created, 2018-12-1 updated
 // BSO CORE
 
-
 #include <iostream>
 #include <vector>
 #include <math.h>
@@ -17,7 +16,10 @@
 using namespace std;
 #define INF 1000000000;
 
-// Gnerate an uniform distribution random variable in [0, 1]
+
+/*
+*  Gnerate an uniform distribution random variable in [0, 1]
+*/
 double uniRand()
 {
 	default_random_engine e;
@@ -27,6 +29,11 @@ double uniRand()
 	return u(e);
 }
 
+/* Generate a set of random value sample points
+*  [min, max]: the uniform distribution domain
+*  m: Sample point number
+*  n: Single point value dimension
+*/
 vector<Point> randomSet(double min, double max, int m, int n)
 {
 	vector<Point> po;
@@ -44,6 +51,11 @@ vector<Point> randomSet(double min, double max, int m, int n)
 	return po;
 }
 
+/* Generate a random value sample points
+*  [min, max]: the uniform distribution domain
+*  n: Point value dimension
+*  id: Point id
+*/
 Point randomPoint(double min, double max, int n, int id)
 {
 	vector<double> content;
@@ -57,6 +69,10 @@ Point randomPoint(double min, double max, int n, int id)
 	return point;
 }
 
+/* Sample point multiply with a number (value * coef)
+*  coef: 
+*  origin: 
+*/
 Point pointMtply(double coef, Point origin)
 {
 	int dim = origin.getDimension();
@@ -69,6 +85,10 @@ Point pointMtply(double coef, Point origin)
 	return ans;
 }
 
+/* Sample point multiply with each other (value * value)
+*  p1:
+*  p2:
+*/
 Point pointAdd(Point p1, Point p2)
 {
 	int dim = p1.getDimension();
@@ -97,7 +117,7 @@ int main(int argc, char *argv[])
 	const int max_iteration = 250; // Pick any deviations you want
 
 	const int max_run = 5; // Pick any laps you want
-	Function fun;
+	Function Fun;
 
 	// Runs off laps from here
 	for (int idx = 0; idx < max_run; idx++)
@@ -120,7 +140,7 @@ int main(int argc, char *argv[])
 		// Evaluate the fitness of n ideas
 		for (int times = 0; times < np; times++)
 		{
-			fit_popu[times] = fun.fun(popu[times]); 
+			fit_popu[times] = Fun.fun(popu[times]); 
 		}
 
 		int n_iteration = 0;
@@ -139,7 +159,6 @@ int main(int argc, char *argv[])
 			{
 				fit_values[clu] = INF;  // Assign a initial big fitness value as best fitness
 				number_in_cluster[clu] = kmeans.getCluster(clu).getSize();
-				//cout << "number_in_cluster: clu: " << clu << " value: " << number_in_cluster[clu] << endl;
 			}
 
 			for (int times = 0; times < np; times++)
@@ -157,11 +176,9 @@ int main(int argc, char *argv[])
 
 			// Check with every cluster
 			acculate_num_cluster[0] = 0;
-			//cout << "acculate_num_cluster: " << 0 << " value: " << acculate_num_cluster[0] << endl;
 			for (int times = 1; times < nc; times++)
 			{
 				acculate_num_cluster[times] = acculate_num_cluster[times - 1] + number_in_cluster[times - 1];
-				//cout << "acculate_num_cluster: " << times << " value: " << acculate_num_cluster[times] << endl;
 			}
 
 			// Evaluate the individuals
@@ -230,6 +247,7 @@ int main(int argc, char *argv[])
 					indi_1 = acculate_num_cluster[c1] + floor(uniRand() * number_in_cluster[c1]);
 					c2 = floor(uniRand() * nc);
 					indi_2 = acculate_num_cluster[c2] + floor(uniRand() * number_in_cluster[c2]);
+					
 					double tem = uniRand();
 					if (uniRand() < 0.5)
 					{
@@ -247,7 +265,7 @@ int main(int argc, char *argv[])
 				normal_distribution<double> n(0, 1);
 				indi_temp = pointAdd(indi_temp, pointMtply(n(e), stepSize));
 
-				double fv = fun.fun(indi_temp); 
+				double fv = Fun.fun(indi_temp); 
 
 				if (fv < fit_popu[times])
 				{
@@ -267,15 +285,14 @@ int main(int argc, char *argv[])
 			double min = INF;
 			for (int k = 0; k < fit_values.size(); k++)
 			{
-				//cout << "fv: " << fit_values[k] << endl;
 				if (min > fit_values[k]) min = fit_values[k];
 			}
 			best_fitness[n_iteration] = min;
 
 			n_iteration++;
 		}
 
-		cout << "bestfitness: ";
+		cout << "Best Fitness: ";
 		for (int succ = 0; succ < best_fitness.size(); succ++)
 		{
 			cout << best_fitness[succ] << " ";

function.cpp

@@ -1,25 +1,24 @@
+// Authors: Siqing Ma
+// Date: 2018-11-15 created, 2018-12-1 updated
+// BSO FUNCTION
+
+
 #include <iostream>
 #include <vector>
 #include <math.h>
 #include <stdlib.h>
 #include <time.h>
 #include <algorithm>
-#include <string>
 #include "kmeans.h"
 #include "function.h"
-#include <stack>
 
 
 using namespace std;
 
-Function::Function()
-{
-
-}
+Function::Function() {}
 
 double Function::fun(Point point)
 {
-	//cout << "fun: " << endl;
 	double z = 0;
 	for (int i = 0; i < 10; i++) 
 	{

kmeans.cpp

@@ -1,3 +1,9 @@
+// Authors: Siqing Ma
+// Date: 2018-11-15 created, 2018-12-1 updated
+// Kmeans algorithm 
+// Distance mode: "cityblock"
+// EmptyAction mode: "singleton"
+
 #include <iostream>
 #include <vector>
 #include <math.h>
@@ -7,11 +13,6 @@
 #include <string>
 #include "kmeans.h"
 using namespace std;
-/**
-Created:2018-11-17, Updated:2018-11-17
-Author: Siqing Ma
-Version: V0.1T
-**/
 
 Point::Point(int id_point, vector<double>& values)
 {
@@ -105,6 +106,7 @@ map<int, Point> Cluster::getPoints()
 }
 
 
+
 KMeans::KMeans(int nc, int np, int nd, int max_iterations, vector<Point> centers)
 {
 	this->nc = nc;
@@ -114,7 +116,6 @@ KMeans::KMeans(int nc, int np, int nd, int max_iterations, vector<Point> centers
 	this->init_centres = centers;
 }
 
-// return ID of nearest center 
 int KMeans::getNearestCentreId(Point point)
 {
 	double sum = 0.0, min_dist;