k-均值算法的java实现

import java.io.BufferedReader;

import java.io.FileNotFoundException;

import java.io.FileReader;

import java.io.IOException;

public class KAverage {

private int sampleCount = 0;

private int dimensionCount = 0;

private int centerCount = 0;

private double[][] sampleValues;

private double[][] centers;

private double[][] tmpCenters;

private String dataFile = "";

/**

* 通过构造器传人数据文件

*/

public KAverage(String dataFile) throws NumberInvalieException {

this.dataFile = dataFile;

}

/**

* 第一行为s;d;c含义分别为样例的数目，每个样例特征的维数，聚类中心个数 文件格式为d[,d]...;d[,d]... 如：1,2;2,3;1,5

* 每一维之间用,隔开，每个样例间用;隔开。结尾没有';' 可以有多行

*/

private int initData(String fileName) {

String line;

String samplesValue[];

String dimensionsValue[] = new String[dimensionCount];

BufferedReader in;

try {

in = new BufferedReader(new FileReader(fileName));

} catch (FileNotFoundException e) {

e.printStackTrace();

return -1;

}

/*

* 预处理样本，允许后面几维为0时，不写入文件

*/

for (int i = 0; i < sampleCount; i++) {

for (int j = 0; j < dimensionCount; j++) {

sampleValues[i][j] = 0;

}

}

int i = 0;

double tmpValue = 0.0;

try {

line = in.readLine();

String params[] = line.split(";");

if (params.length != 3) {// 必须为3个参数，否则错误

return -1;

}

/**

* 获取参数

*/

this.sampleCount = Integer.parseInt(params[0]);

this.dimensionCount = Integer.parseInt(params[1]);

this.centerCount = Integer.parseInt(params[2]);

if (sampleCount <= 0 || dimensionCount <= 0 || centerCount <= 0) {

throw new NumberInvalieException("input number <= 0.");

}

if (sampleCount < centerCount) {

throw new NumberInvalieException(

"sample number < center number");

}

sampleValues = new double[sampleCount][dimensionCount + 1];

centers = new double[centerCount][dimensionCount];

tmpCenters = new double[centerCount][dimensionCount];

while ((line = in.readLine()) != null) {

samplesValue = line.split(";");

for (int j = 0; j < samplesValue.length; j++) {

dimensionsValue = samplesValue[j].split(",");

for (int k = 0; k < dimensionsValue.length; k++) {

tmpValue = Double.parseDouble(dimensionsValue[k]);

sampleValues[i][k] = tmpValue;

}

i++;

}

}

} catch (IOException e) {

e.printStackTrace();

return -2;

} catch (Exception e) {

e.printStackTrace();

return -3;

}

return 1;

}

/**

* 返回样本中第s1个和第s2个间的欧式距离

*/

private double getDistance(int s1, int s2) throws NumberInvalieException {

double distance = 0.0;

if (s1 < 0 || s1 >= sampleCount || s2 < 0 || s2 >= sampleCount) {

throw new NumberInvalieException("number out of bound.");

}

for (int i = 0; i < dimensionCount; i++) {

distance += (sampleValues[s1][i] - sampleValues[s2][i])

* (sampleValues[s1][i] - sampleValues[s2][i]);

}

return distance;

}

/**

* 返回给定两个向量间的欧式距离

*/

private double getDistance(double s1[], double s2[]) {

double distance = 0.0;

for (int i = 0; i < dimensionCount; i++) {

distance += (s1[i] - s2[i]) * (s1[i] - s2[i]);

}

return distance;

}

/**

* 更新样本中第s个样本的最近中心

*/

private int getNearestCenter(int s) {

int center = 0;

double minDistance = Double.MAX_VALUE;

double distance = 0.0;

for (int i = 0; i < centerCount; i++) {

distance = getDistance(sampleValues[s], centers[i]);

if (distance < minDistance) {

minDistance = distance;

center = i;

}

}

sampleValues[s][dimensionCount] = center;

return center;

}

/**

* 更新所有中心

*/

private void updateCenters() {

double center[] = new double[dimensionCount];

for (int i = 0; i < dimensionCount; i++) {

center[i] = 0;

}

int count = 0;

for (int i = 0; i < centerCount; i++) {

count = 0;

for (int j = 0; j < sampleCount; j++) {

if (sampleValues[j][dimensionCount] == i) {

count++;

for (int k = 0; k < dimensionCount; k++) {

center[k] += sampleValues[j][k];

}

}

}

for (int j = 0; j < dimensionCount; j++) {

centers[i][j] = center[j] / count;

}

}

}

/**

* 判断算法是否终止

*/

private boolean toBeContinued() {

for (int i = 0; i < centerCount; i++) {

for (int j = 0; j < dimensionCount; j++) {

if (tmpCenters[i][j] != centers[i][j]) {

return true;

}

}

}

return false;

}

/**

* 关键方法，调用其他方法，处理数据

*/

public void doCaculate() {

initData(dataFile);

for (int i = 0; i < centerCount; i++) {

for (int j = 0; j < dimensionCount; j++) {

centers[i][j] = sampleValues[i][j];

}

}

for (int i = 0; i < centerCount; i++) {

for (int j = 0; j < dimensionCount; j++) {

tmpCenters[i][j] = 0;

}

}

while (toBeContinued()) {

for (int i = 0; i < sampleCount; i++) {

getNearestCenter(i);

}

for (int i = 0; i < centerCount; i++) {

for (int j = 0; j < dimensionCount; j++) {

tmpCenters[i][j] = centers[i][j];

}

}

updateCenters();

System.out

.println("******************************************************");

showResultData();

}

}

/*

* 显示数据

*/

private void showSampleData() {

for (int i = 0; i < sampleCount; i++) {

for (int j = 0; j < dimensionCount; j++) {

if (j == 0) {

System.out.print(sampleValues[i][j]);

} else {

System.out.print("," + sampleValues[i][j]);

}

}

System.out.println();

}

}

/*

* 分组显示结果

*/

private void showResultData() {

for (int i = 0; i < centerCount; i++) {

System.out.println("第" + (i + 1) + "个分组内容为:");

for (int j = 0; j < sampleCount; j++) {

if (sampleValues[j][dimensionCount] == i) {

for (int k = 0; k <= dimensionCount; k++) {

if (k == 0) {

System.out.print(sampleValues[j][k]);

} else {

System.out.print("," + sampleValues[j][k]);

}

}

System.out.println();

}

}

}

}

public static void main(String[] args) {

/*

*也可以通过命令行得到参数

*/

String fileName = "D://eclipsejava//K-Average//src//sample.txt";

if(args.length > 0){

fileName = args[0];

}

try {

KAverage ka = new KAverage(fileName);

ka.doCaculate();

System.out

.println("***************************<<result>>**************************");

ka.showResultData();

} catch (Exception e) {

e.printStackTrace();

}

}

}

import java.io.BufferedReader;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;

public class KAverage {
private int sampleCount = 0;
private int dimensionCount = 0;
private int centerCount = 0;
private double[][] sampleValues;
private double[][] centers;
private double[][] tmpCenters;
private String dataFile = "";

/**
* 通过构造器传人数据文件
*/
public KAverage(String dataFile) throws NumberInvalieException {
this.dataFile = dataFile;
}

/**
* 第一行为s;d;c含义分别为样例的数目，每个样例特征的维数，聚类中心个数 文件格式为d[,d]...;d[,d]... 如：1,2;2,3;1,5
* 每一维之间用,隔开，每个样例间用;隔开。结尾没有';' 可以有多行
*/

private int initData(String fileName) {
String line;
String samplesValue[];
String dimensionsValue[] = new String[dimensionCount];
BufferedReader in;
try {
in = new BufferedReader(new FileReader(fileName));
} catch (FileNotFoundException e) {
e.printStackTrace();
return -1;
}
/*
* 预处理样本，允许后面几维为0时，不写入文件
*/
for (int i = 0; i < sampleCount; i++) {
for (int j = 0; j < dimensionCount; j++) {
sampleValues[i][j] = 0;
}
}

int i = 0;
double tmpValue = 0.0;
try {
line = in.readLine();
String params[] = line.split(";");
if (params.length != 3) {// 必须为3个参数，否则错误
return -1;
}
/**
* 获取参数
*/
this.sampleCount = Integer.parseInt(params[0]);
this.dimensionCount = Integer.parseInt(params[1]);
this.centerCount = Integer.parseInt(params[2]);
if (sampleCount <= 0 || dimensionCount <= 0 || centerCount <= 0) {
throw new NumberInvalieException("input number <= 0.");
}
if (sampleCount < centerCount) {
throw new NumberInvalieException(
"sample number < center number");
}

sampleValues = new double[sampleCount][dimensionCount + 1];
centers = new double[centerCount][dimensionCount];
tmpCenters = new double[centerCount][dimensionCount];

while ((line = in.readLine()) != null) {
samplesValue = line.split(";");
for (int j = 0; j < samplesValue.length; j++) {
dimensionsValue = samplesValue[j].split(",");
for (int k = 0; k < dimensionsValue.length; k++) {
tmpValue = Double.parseDouble(dimensionsValue[k]);
sampleValues[i][k] = tmpValue;
}
i++;
}
}

} catch (IOException e) {
e.printStackTrace();
return -2;
} catch (Exception e) {
e.printStackTrace();
return -3;
}
return 1;
}

/**
* 返回样本中第s1个和第s2个间的欧式距离
*/
private double getDistance(int s1, int s2) throws NumberInvalieException {
double distance = 0.0;
if (s1 < 0 || s1 >= sampleCount || s2 < 0 || s2 >= sampleCount) {
throw new NumberInvalieException("number out of bound.");
}
for (int i = 0; i < dimensionCount; i++) {
distance += (sampleValues[s1][i] - sampleValues[s2][i])
* (sampleValues[s1][i] - sampleValues[s2][i]);
}

return distance;
}

/**
* 返回给定两个向量间的欧式距离
*/
private double getDistance(double s1[], double s2[]) {
double distance = 0.0;
for (int i = 0; i < dimensionCount; i++) {
distance += (s1[i] - s2[i]) * (s1[i] - s2[i]);
}
return distance;
}

/**
* 更新样本中第s个样本的最近中心
*/
private int getNearestCenter(int s) {
int center = 0;
double minDistance = Double.MAX_VALUE;
double distance = 0.0;
for (int i = 0; i < centerCount; i++) {
distance = getDistance(sampleValues[s], centers[i]);
if (distance < minDistance) {
minDistance = distance;
center = i;
}
}
sampleValues[s][dimensionCount] = center;
return center;
}

/**
* 更新所有中心
*/
private void updateCenters() {
double center[] = new double[dimensionCount];
for (int i = 0; i < dimensionCount; i++) {
center[i] = 0;
}
int count = 0;
for (int i = 0; i < centerCount; i++) {
count = 0;
for (int j = 0; j < sampleCount; j++) {
if (sampleValues[j][dimensionCount] == i) {
count++;
for (int k = 0; k < dimensionCount; k++) {
center[k] += sampleValues[j][k];
}
}
}
for (int j = 0; j < dimensionCount; j++) {
centers[i][j] = center[j] / count;
}
}
}

/**
* 判断算法是否终止
*/
private boolean toBeContinued() {
for (int i = 0; i < centerCount; i++) {
for (int j = 0; j < dimensionCount; j++) {
if (tmpCenters[i][j] != centers[i][j]) {
return true;
}
}
}
return false;
}

/**
* 关键方法，调用其他方法，处理数据
*/
public void doCaculate() {
initData(dataFile);

for (int i = 0; i < centerCount; i++) {
for (int j = 0; j < dimensionCount; j++) {
centers[i][j] = sampleValues[i][j];
}
}
for (int i = 0; i < centerCount; i++) {
for (int j = 0; j < dimensionCount; j++) {
tmpCenters[i][j] = 0;
}
}

while (toBeContinued()) {
for (int i = 0; i < sampleCount; i++) {
getNearestCenter(i);
}
for (int i = 0; i < centerCount; i++) {
for (int j = 0; j < dimensionCount; j++) {
tmpCenters[i][j] = centers[i][j];
}
}
updateCenters();
System.out
.println("******************************************************");
showResultData();
}
}

/*
* 显示数据
*/
private void showSampleData() {
for (int i = 0; i < sampleCount; i++) {
for (int j = 0; j < dimensionCount; j++) {
if (j == 0) {
System.out.print(sampleValues[i][j]);
} else {
System.out.print("," + sampleValues[i][j]);
}
}
System.out.println();
}
}

/*
* 分组显示结果
*/
private void showResultData() {
for (int i = 0; i < centerCount; i++) {
System.out.println("第" + (i + 1) + "个分组内容为:");
for (int j = 0; j < sampleCount; j++) {
if (sampleValues[j][dimensionCount] == i) {
for (int k = 0; k <= dimensionCount; k++) {
if (k == 0) {
System.out.print(sampleValues[j][k]);
} else {
System.out.print("," + sampleValues[j][k]);
}
}
System.out.println();
}
}
}
}

public static void main(String[] args) {
/*
*也可以通过命令行得到参数
*/
String fileName = "D://eclipsejava//K-Average//src//sample.txt";
if(args.length > 0){
fileName = args[0];
}

try {
KAverage ka = new KAverage(fileName);
ka.doCaculate();
System.out
.println("***************************<<result>>**************************");
ka.showResultData();
} catch (Exception e) {
e.printStackTrace();
}
}
}

Java代码



/*

* 根据自己的需要定义一些异常，使得系统性更强

*/

public class NumberInvalieException extends Exception {

private String cause;

public NumberInvalieException(String cause){

if(cause == null || "".equals(cause)){

this.cause = "unknow";

}else{

this.cause = cause;

}

}

@Override

public String toString() {

return "Number Invalie!Cause by " + cause;

}

}

/*
* 根据自己的需要定义一些异常，使得系统性更强
*/
public class NumberInvalieException extends Exception {
private String cause;

public NumberInvalieException(String cause){
if(cause == null || "".equals(cause)){
this.cause = "unknow";
}else{
this.cause = cause;
}
}
@Override
public String toString() {
return "Number Invalie!Cause by " + cause;
}
}

测试数据

20;2;4

0,0;1,0;0,1;1,1;2,1;1,2;2,2;3,2;6,6;7,6

8,6;6,7;7,7;8,7;9,7;7,8;8,8;9,8;8,9;9,9

测试结果

***************************<<result>>**************************

第1个分组内容为:

0.0,0.0,0.0

1.0,0.0,0.0

0.0,1.0,0.0

1.0,1.0,0.0

2.0,1.0,0.0

1.0,2.0,0.0

2.0,2.0,0.0

3.0,2.0,0.0

第2个分组内容为:

6.0,6.0,1.0

7.0,6.0,1.0

8.0,6.0,1.0

6.0,7.0,1.0

7.0,7.0,1.0

8.0,7.0,1.0

9.0,7.0,1.0

7.0,8.0,1.0

8.0,8.0,1.0

9.0,8.0,1.0

8.0,9.0,1.0

9.0,9.0,1.0