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Clustering.cpp
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Clustering.cpp
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/*
@author Jonathan Vasquez
@date 4/19/16
@Project1
*/
#include "Clustering.h"
#include <cstdlib>
#include <iostream>
#include <fstream>
using namespace std;
/*
Takes in file on console sort of like in python. It is more convenient for user.
BONUS POINTS will be greatly appreciated
*/
Clustering::Clustering(char fp[])
{
CHECK=true;
fstream file(fp);//file from terminal/console
file>>num_of_points>>num_of_clusters;//reads points and data in this order
int index,i;
cluster= new double*[num_of_points];//creates cluster dynamic array
for(i=0;i<num_of_points;i++)
{
cluster[i]=new double[num_of_clusters+1];//counter
}
points = new double *[num_of_points];//updates points
for(i=0;i<num_of_points;i++)
{
points[i]=new double[2];
//Read the data set
file>>points[i][0]>>points[i][1];//input scheme
}
int arrIndex[num_of_clusters];
fill_n(arrIndex,num_of_clusters,0);
srand(time(0));//better than ran() because it updates regularly based on time()
centroids=new double*[num_of_clusters];//creates centroids
for(i=0;i<num_of_clusters;i++)
{
centroids[i]=new double[2];
//Picks random index for centroids
index=(rand()%num_of_points);
//makes sure no centroid is picked twice
while(arrIndex[(index%num_of_clusters)]!=0)
index++;
arrIndex[(index%num_of_clusters)]++;
//x coordinate
centroids[i][0]=points[index][0];
//y coordinate
centroids[i][1]=points[index][1];
}
file.close();//closes file safely
}
////////////////////////////////////////
/*
Destructor
*/
Clustering::~Clustering()
{
int i=0;
for(i=0;i<=num_of_clusters;i++)
delete [] cluster[i];
delete []cluster;
for(i=0;i<2;i++)
delete [] points[i];
delete [] points;
for(i=0;i<2;i++)
delete [] centroids[i];
delete [] centroids;
}
/*
Finds the Euclidean Distance
*/
double Clustering::euclidean_distance(double x1, double y1, double x2, double y2)
{
double z= x1>x2?x1-x2:x2-x1;
z+= (y1>y2?y1-y2:y2-y1);//ternary operator takes less space than bunch of conditions
//Returns the distance between the two points
return z;
}
void Clustering::perform_cluster()
{
int steps=1;
int old;
bool newCheck,check1;
while(CHECK)
{
check1=true;
newCheck=true;
for(int i=0;i<num_of_points;i++)
{
old=int(cluster[i][num_of_clusters]);
for(int j=0;j<num_of_clusters;j++)
{
//Write the distance data for each centroid
cluster[i][j]=euclidean_distance(points[i][0],points[i][1],centroids[j][0],centroids[j][1]);
}
//Set the respective centroid id
minimum_of_distance(cluster[i]);
// Mark if the centroid has changed
if(old==int(cluster[i][num_of_clusters]))
newCheck=true;
else
newCheck=false;
check1 = check1 & newCheck;
}
//Print step wise
print(steps);
getchar();
//Get the new centroids
set_centroids();
steps++;
//loop till there is no change in centroids
CHECK = !check1;
}
for(int i=0;i<num_of_clusters;i++)
{
cout<<"\nCluster "<<i+1<<" :"<<endl;
for(int j=0;j<num_of_points;j++)
{
if(cluster[j][num_of_clusters]==i)
cout<<points[j][0]<<","<<points[j][1]<<endl;
}
cout<<"\n*******************************************"<<endl;
}
}
void Clustering::minimum_of_distance(double temp[])
{
double min=temp[0];
temp[num_of_clusters]=0;
for(int j=1;j<num_of_clusters;j++)
{
if(min>temp[j])
{
//Set the minimum value
min=temp[j];
//Set the respective centroid id
temp[num_of_clusters]=j;
}
}
}
void Clustering::print(int step)
{
int i,j;
cout<<"Step Number: "<<step<<endl;
for(i=0;i<num_of_points;i++)
{
cout<<"Point "<<i<<": ";
for(j=0;j<num_of_clusters;j++)
{
cout<<cluster[i][j]<<" ";
}
cout<<cluster[i][j]+1<<" ";
cout<<endl;
}
cout<<"The centroids are:"<<endl;
for(i=0;i<num_of_clusters;i++)
cout<<centroids[i][0]<<" "<<centroids[i][1]<<endl;
cout<<"*****************************************\n"<<endl;
}
void Clustering::set_centroids()
{
int *total_points=new int[num_of_clusters];
int i,j;
//Fill total_points with 0
fill_n(total_points,num_of_clusters,0);//fill_n is used as an output scheme sort of like a complex cout<<
for(i=0;i<num_of_points;i++)
{
//Count the number of points in a cluster
total_points[int(cluster[i][num_of_clusters])]++;
}
for(i=0;i<num_of_clusters;i++)
{
double total_x=0;
double total_y=0;
for(j=0;j<num_of_points;j++)
{
//for the centroid id == cluster id
if(cluster[j][num_of_clusters]==i)
{
//Total x co-ordinate
total_x+=points[j][0];
//Total y co-ordinate
total_y+=points[j][1];
}
}
//Finding the mean on x and y co-ordinate
centroids[i][0]=total_x/total_points[i];
centroids[i][1]=total_y/total_points[i];
}
delete []total_points;
}