/** Reads the input data from the file and stores the data points in the vector */
public void readData() throws IOException {
BufferedReader in = new BufferedReader(new FileReader(this.inputFileName));
String line = "";
while ((line = in.readLine()) != null) {
// StringTokenizer st = new StringTokenizer(line, " \t\n\r\f,");
String[] st = line.split(":");
// 3维
if (st.length == 3) {
kMeansPoint dp =
new kMeansPoint(
Double.parseDouble(st[0]), Double.parseDouble(st[1]), Double.parseDouble(st[2]));
dp.assignToCluster(0);
this.kMeansPoints.add(dp);
}
if (st.length >= 2) {
List<Double> listST = new ArrayList<Double>();
for (String str : st) {
listST.add(Double.parseDouble(str));
}
kMeansPoint dp = new kMeansPoint(listST);
dp.assignToCluster(0);
this.kMeansPoints.add(dp);
}
}
in.close();
} // end of readData()
/**
* Assigns a data point to one of the k clusters based on its distance from the means of the
* clusters
*
* @param dp data point to be assigned
*/
public void assignToCluster(kMeansPoint dp) {
int currentCluster = dp.getClusterNumber();
if (dp.getListData().size() == 3) {
double minDistance = kMeansPoint.distance(dp, this.clusters[currentCluster].getMean());
;
for (int i = 0; i < this.k; i++)
if (kMeansPoint.distance(dp, this.clusters[i].getMean()) < minDistance) {
minDistance = kMeansPoint.distance(dp, this.clusters[i].getMean());
currentCluster = i;
}
} else {
double minDistance = kMeansPoint.comPointsDis(dp, this.clusters[currentCluster].getMean());
;
for (int i = 0; i < this.k; i++)
if (kMeansPoint.comPointsDis(dp, this.clusters[i].getMean()) < minDistance) {
minDistance = kMeansPoint.comPointsDis(dp, this.clusters[i].getMean());
currentCluster = i;
}
}
dp.assignToCluster(currentCluster);
} // end of assignToCluster
/**
* Main method -- to test the kMeans class
*
* @param args command line arguments
*/
public static void main(String[] args) {
// //对用户矩阵，从3到100个聚类数目，分别计算这个数目下的聚类结果，写入文件
// for(int z = 3; z <=100; z++){
//
// //计算z个聚类的聚类结果
// kMeans km = new kMeans(z, "D:/pca/pcauserM0.85.txt");
//
// try {
// km.readData();
// } catch (Exception e) {
// System.err.println(e);
// System.exit(-1);
// }
//
// km.runKMeans();
// System.out.println(km.getDataPoints().size());
// System.out.println(km.getDataPoints().get(0));
// System.out.println(km.getDataPoints().get(km.getDataPoints().size()-1));
//
// //把结果写入文件
//// Iterator i = this.kMeansPoints.iterator();
//// while (i.hasNext()){
//// //设置一个String用来写入到文件
//// String strWr = new String();
//// strWr = "\n" + (kMeansPoint)(i.next());
//// }
//
// //把结果写入文件
// for(int i = 0; i < km.getDataPoints().size(); i++){
// //设置一个String用来写入到文件
// String strWr = new String();
// kMeansPoint kmp = (kMeansPoint) km.getDataPoints().get(i);
// strWr = i+":"+ kmp.getClusterNumber();
//
// //设置一个路径用来保存文件
// String pathZ = "D:/pca/difClusters/kM_" + z + "kind_0.85_0.3user.txt";
// write(pathZ, strWr);
// }
// }
// //给电影矩阵聚类，从3到100个聚类数目，分别计算这个数目下的聚类结果，写入文件
// for(int z = 3; z <=100; z++){
// //计算z个聚类的聚类结果
// kMeans km = new kMeans(z, "D:/pca/pcauserMovieM0.85.txt");
//
// try {
// km.readData();
// } catch (Exception e) {
// System.err.println(e);
// System.exit(-1);
// }
//
// km.runKMeans();
// System.out.println(km.getDataPoints().size());
// System.out.println(km.getDataPoints().get(0));
// System.out.println(km.getDataPoints().get(km.getDataPoints().size()-1));
//
// //把结果写入文件
//// Iterator i = this.kMeansPoints.iterator();
//// while (i.hasNext()){
//// //设置一个String用来写入到文件
//// String strWr = new String();
//// strWr = "\n" + (kMeansPoint)(i.next());
//// }
//
// //把结果写入文件
// for(int i = 0; i < km.getDataPoints().size(); i++){
// //设置一个String用来写入到文件
// String strWr = new String();
// kMeansPoint kmp = (kMeansPoint) km.getDataPoints().get(i);
// strWr = i+":"+ kmp.getClusterNumber();
//
// //设置一个路径用来保存文件
// String pathZ = "D:/pca/difMovieClusters/kM_" + z + "kind_0.85_0.3movie.txt";
// write(pathZ, strWr);
// }
// }
// 给用户-电影类别矩阵聚类，从3到100个聚类数目，分别计算这个数目下的聚类结果，写入文件
for (int z = 238; z <= 300; z++) {
// 计算z个聚类的聚类结果
kMeans km = new kMeans(z, "D:/pca/recommendWork/pcamovieByAdd1Matrix0.85.txt");
try {
km.readData();
} catch (Exception e) {
System.err.println(e);
System.exit(-1);
}
km.runKMeans();
System.out.println(km.getDataPoints().size());
System.out.println(km.getDataPoints().get(0));
System.out.println(km.getDataPoints().get(km.getDataPoints().size() - 1));
// 把结果写入文件
// Iterator i = this.kMeansPoints.iterator();
// while (i.hasNext()){
// //设置一个String用来写入到文件
// String strWr = new String();
// strWr = "\n" + (kMeansPoint)(i.next());
// }s
// 把结果写入文件
for (int i = 0; i < km.getDataPoints().size(); i++) {
// 设置一个String用来写入到文件
String strWr = new String();
kMeansPoint kmp = (kMeansPoint) km.getDataPoints().get(i);
strWr = i + ":" + kmp.getClusterNumber();
// 设置一个路径用来保存文件
String pathZ = "D:/pca/recommendWork/difMovClusters/kM_" + z + "kind_0.85_clu_movie.txt";
write(pathZ, strWr);
}
}
} // end of main()
/**
* Updates the means of all k clusters, and returns if they have changed or not
*
* @return have the updated means of the clusters changed or not
*/
private boolean updateMeans() {
boolean reply = false;
if (((kMeansPoint) this.kMeansPoints.get(0)).getListData().size() == 3) {
double[] x = new double[this.k];
double[] y = new double[this.k];
double[] z = new double[this.k];
int[] size = new int[this.k];
kMeansPoint[] pastMeans = new kMeansPoint[this.k];
for (int i = 0; i < this.k; i++) {
x[i] = 0.0;
y[i] = 0.0;
z[i] = 0.0;
size[i] = 0;
pastMeans[i] = this.clusters[i].getMean();
}
Iterator i = this.kMeansPoints.iterator();
while (i.hasNext()) {
kMeansPoint dp = (kMeansPoint) (i.next());
int currentCluster = dp.getClusterNumber();
x[currentCluster] += dp.getX();
y[currentCluster] += dp.getY();
z[currentCluster] += dp.getZ();
size[currentCluster]++;
}
for (int j = 0; j < this.k; j++)
if (size[j] != 0) {
x[j] /= size[j];
y[j] /= size[j];
z[j] /= size[j];
kMeansPoint temp = new kMeansPoint(x[j], y[j], z[j]);
temp.assignToCluster(j);
this.clusters[j].setMean(temp);
if (kMeansPoint.distance(pastMeans[j], this.clusters[j].getMean()) != 0) reply = true;
}
} else {
double[][] douAVE =
new double[((kMeansPoint) this.kMeansPoints.get(0)).getListData().size()][this.k];
int[] size = new int[this.k];
kMeansPoint[] pastMeans = new kMeansPoint[this.k];
for (int i = 0; i < this.k; i++) {
for (int j = 0; j < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); j++) {
douAVE[j][i] = 0.0;
}
size[i] = 0;
pastMeans[i] = this.clusters[i].getMean();
}
Iterator i = this.kMeansPoints.iterator();
while (i.hasNext()) {
kMeansPoint dp = (kMeansPoint) (i.next());
int currentCluster = dp.getClusterNumber();
for (int j = 0; j < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); j++) {
douAVE[j][currentCluster] += dp.getListData().get(j);
}
size[currentCluster]++;
}
for (int j = 0; j < this.k; j++)
if (size[j] != 0) {
for (int k = 0; k < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); k++) {
douAVE[k][j] /= size[j];
}
List<Double> listKM = new ArrayList<Double>();
for (int k = 0; k < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); k++) {
listKM.add(douAVE[k][j]);
}
kMeansPoint temp = new kMeansPoint(listKM);
temp.assignToCluster(j);
this.clusters[j].setMean(temp);
if (kMeansPoint.comPointsDis(pastMeans[j], this.clusters[j].getMean()) != 0) reply = true;
}
}
return reply;
} // end of updateMeans()