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Johannes Theiner 2020-01-14 11:09:21 +01:00
commit 07bd6f6315
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40
clustering/DataFileReader.java Normal file
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import java.io.*;
import java.util.ArrayList;
public class DataFileReader {
static ArrayList<ArrayList<Double>> readFile(String file){
ArrayList<ArrayList<Double>> data = new ArrayList<ArrayList<Double>>();
//Count: leere Zeilen werden nicht mitverwendet
int count = 0;
try(BufferedReader stream = new BufferedReader(new FileReader(file))){
while(true){
String line = stream.readLine();
if(line == null){
break;
}
if(!line.contains("#")){
String[] parts = line.split("[ |\t]");
data.add(new ArrayList<Double>());
for(String s : parts){
try{
data.get(count).add(Double.parseDouble(s));
}catch(NumberFormatException e){
}
}
if(data.get(count).size() == 0){
data.remove(count);
}else{
count++;
}
}
}
}
catch(FileNotFoundException e){
e.printStackTrace();
System.out.println("file " + file + " not found");
} catch (IOException e) {
e.printStackTrace();
}
return data;
}
}

67
clustering/KMeans.java Normal file
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import java.util.ArrayList;
public class KMeans extends VarMin{
public KMeans(int k){
super(k);
}
@Override
public void clustering(){
initCluster();
System.out.println(getCompactness());
boolean change = true;
while(change){
change = false;
for(int i = 0; i < data.size();i++) {
int newCluster = getClosestCluster(data.get(i));
int oldCluster = clusterMap.get(i);
if(oldCluster != newCluster){
clusterMap.set(i, newCluster);
//update centroids
ArrayList<Double> p = new ArrayList<>(data.get(i));
ArrayList<Double> u1 = new ArrayList<>(centroids.get(oldCluster));
ArrayList<Double> u2 = new ArrayList<>(centroids.get(newCluster));
mulPoint(u1, (double)count[oldCluster]);
mulPoint(u2, (double)count[newCluster]);
addPoint(u2, p);
mulPoint(p, -1.0);
addPoint(u1, p);
mulPoint(u1,(1.0 / (count[oldCluster] - 1)));
mulPoint(u2,(1.0 / (count[newCluster] + 1)));
centroids.set(oldCluster, u1);
centroids.set(newCluster, u2);
count[oldCluster]--;
count[newCluster]++;
//centroids.get(oldCluster) = (1.0 / (count[oldCluster] - 1)) * (count[oldCluster] * centroids.get(oldCluster) - data.get(i));
//centroids.get(newCluster) = (1.0 / (count[newCluster] + 1)) * (count[newCluster] * centroids.get(newCluster) + data.get(i));
//calcCluster();
change = true;
}
}
System.out.println(getCompactness());
}
}
public static void main(String[] args) {
int k = 3;
if(args.length >= 1){
k = Integer.parseInt(args[0]);
}
KMeans kmeans = new KMeans(k);
ArrayList<ArrayList<Double>> data = DataFileReader.readFile("input.txt");
kmeans.setData(data);
kmeans.clustering();
ArrayList<ArrayList<ArrayList<Double>>> cluster1 = kmeans.getCluster();
System.out.println("Result:");
printCluster(cluster1);
writeToFile(cluster1, "cluster.txt");
}
}

210
clustering/VarMin.java Normal file
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import java.io.BufferedWriter;
import java.io.FileNotFoundException;
import java.io.FileWriter;
import java.io.IOException;
import java.lang.reflect.Array;
import java.util.ArrayList;
public class VarMin {
public ArrayList<ArrayList<Double>> data;
public ArrayList<ArrayList<Double>> centroids;
public ArrayList<Integer> clusterMap;
int[] count;
int k;
int dim;
public VarMin(int k){
this.k = k;
this.dim = 0;
}
//euklidische distanz
public double distance(ArrayList<Double> p1, ArrayList<Double> p2){
int count = Math.min(p1.size(), p2.size());
double sum = 0;
for(int i = 0; i < count;i++){
sum += Math.pow(p1.get(i) - p2.get(i), 2);
}
return Math.sqrt(sum);
}
//p1 += p2
public void addPoint(ArrayList<Double> p1, ArrayList<Double> p2){
int count = Math.min(p1.size(), p2.size());
for(int i = 0; i < count;i++){
p1.set(i, p1.get(i) + p2.get(i));
}
}
//p1 *= v
public void mulPoint(ArrayList<Double> p1, Double v){
for(int i = 0; i < p1.size();i++){
p1.set(i, p1.get(i) * v);
}
}
private void setParams(int k, int dim){
this.k = k;
this.dim = dim;
centroids = new ArrayList<ArrayList<Double>>();
for(int i = 0; i < k;i++){
centroids.add(new ArrayList<Double>());
for(int j = 0; j < dim;j++){
centroids.get(i).add(0.0);
}
}
clusterMap = new ArrayList<Integer>();
count = new int[k];
}
public void setData(ArrayList<ArrayList<Double>> data){
dim = data.get(0).size();
this.data = data;
setParams(k,dim);
for(int i = 0; i < data.size();i++){
clusterMap.add(0);
}
}
public int getClosestCluster(ArrayList<Double> p){
double minDist = Double.MAX_VALUE;
int minIndex = 0;
for(int c = 0; c < k;c++){
double dist = distance(p, centroids.get(c));
if(dist < minDist) {
minDist = dist;
minIndex = c;
}
}
return minIndex;
}
public void initCluster(){
int pointsPerCluster = data.size() / k;
for(int i = 0; i < data.size();i++){
int c = i / pointsPerCluster;
addPoint(centroids.get(c), data.get(i));
clusterMap.set(i, c);
}
for(int c = 0; c < k;c++){
mulPoint(centroids.get(c), 1.0 / pointsPerCluster);
count[c] = pointsPerCluster;
}
}
public void calcCluster(){
for(int c = 0; c < k;c++){
mulPoint(centroids.get(c), 0.0);
}
for(int c = 0; c < k;c++){
count[c] = 0;
}
for(int i = 0; i < data.size();i++){
int c = clusterMap.get(i);
addPoint(centroids.get(c), data.get(i));
count[c]++;
}
for(int c = 0; c < k;c++){
if(count[c] == 0){
mulPoint(centroids.get(c),0.0);
}else{
mulPoint(centroids.get(c), 1.0 / count[c]);
}
}
}
public void clustering(){
initCluster();
System.out.println(getCompactness());
//iteration
boolean change = true;
while(change){
change = false;
for(int i = 0; i < data.size();i++) {
int newCluster = getClosestCluster(data.get(i));
if(clusterMap.get(i) != newCluster){
change = true;
}
clusterMap.set(i, newCluster);
}
calcCluster();
System.out.println(getCompactness());
}
}
public double getCompactness(){
double sum = 0;
for(int i = 0; i < data.size();i++){
sum += distance(data.get(i), centroids.get(clusterMap.get(i)));
}
return sum;
}
public ArrayList<ArrayList<ArrayList<Double>>> getCluster(){
ArrayList<ArrayList<ArrayList<Double>>> cluster = new ArrayList<>();
for(int i = 0; i < k;i++){
cluster.add(new ArrayList<>());
}
for(int i = 0; i < data.size();i++) {
int c = clusterMap.get(i);
cluster.get(c).add(data.get(i));
}
return cluster;
}
public static void printCluster(ArrayList<ArrayList<ArrayList<Double>>> cluster){
int i = 0;
for(ArrayList<ArrayList<Double>> c : cluster){
System.out.print("Cluster" + ++i + ": ");
for(ArrayList<Double> p : c){
System.out.print("(");
for (int j = 0; j < p.size();j++) {
System.out.print(p.get(j));
if(j < p.size() - 1){
System.out.print(",");
}
}
System.out.print("); ");
}
System.out.println();
}
}
public static void writeToFile(ArrayList<ArrayList<ArrayList<Double>>> cluster, String file){
try(BufferedWriter stream = new BufferedWriter(new FileWriter(file))) {
for(ArrayList<ArrayList<Double>> c : cluster){
for(ArrayList<Double> p : c){
for (int j = 0; j < p.size();j++) {
stream.write(p.get(j).toString() + " ");
}
stream.write("\n");
}
stream.write("\n");
}
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
}
public static void main(String[] args) {
int k = 3;
if(args.length >= 1){
k = Integer.parseInt(args[0]);
}
VarMin varmin = new VarMin(k);
ArrayList<ArrayList<Double>> data = DataFileReader.readFile("input.txt");
varmin.setData(data);
varmin.clustering();
ArrayList<ArrayList<ArrayList<Double>>> cluster1 = varmin.getCluster();
System.out.println("Result:");
printCluster(cluster1);
writeToFile(cluster1, "cluster.txt");
}
}

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clustering/show.py Normal file
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import matplotlib.pyplot as plt
import numpy as np
a = open("cluster.txt").read()
a = a.split("\n\n")
data = []
for b in a:
cluster = []
for c in b.split("\n"):
d = c.split(" ")
vec = []
for e in d:
if e != "":
vec.append(float(e))
if len(vec) != 0:
cluster.append(vec)
if len(cluster) != 0:
data.append(cluster)
for c in data:
xs = [x[0] for x in c]
ys = [x[1] for x in c]
plt.subplot()
plt.plot(xs, ys, "o")
plt.show()