📄 kmeanspoint.java
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/*
* Represents an abstraction for a data point in two dimensional space
*
*/
public class kMeansPoint {
/** Value in dimension x */
private int x;
/** Value in dimension y */
private int y;
/** Assigned cluster */
private int clusterNumber;
/**
* Creates a new instance of data point
*
* @param _x value in dimension x
* @param _y value in dimension y
*/
public kMeansPoint(int _x, int _y) {
this.x = _x;
this.y = _y;
this.clusterNumber=0;
} // end of kMeansPoint()
/**
* Assigns the data point to a cluster
*
* @param _clusterNumber the cluster to which this data point is to be assigned
*/
public void assignToCluster(int _clusterNumber) {
this.clusterNumber = _clusterNumber;
} // end of assignToCluster()
/**
* Returns the cluster to which the data point belongs
*
* @return the cluster number to which the data point belongs
*/
public int getClusterNumber() {
return this.clusterNumber;
} // end of getClusterNumber()
/**
* Returns the value of data point in x dimension
*
* @return the value in x dimension
*/
public int getX() {
return this.x;
} // end of getX()
/**
* Returns the value of data point in y dimension
*
* @return the value in y dimension
*/
public int getY() {
return this.y;
} // end of getY()
/**
* Returns the distance between two data points
*
* @param dp1 the first data point
* @param dp2 the second data point
* @return the distance between the two data points
*/
public static double distance(kMeansPoint dp1, kMeansPoint dp2) {
double result = 0;
double resultX = dp1.getX() - dp2.getX();
double resultY = dp1.getY() - dp2.getY();
result = Math.sqrt(resultX*resultX + resultY*resultY);
return result;
} // end of distance()
/**
* Returns a string representation of this kMeansPoint
*
* @return a string representation of this data point
*/
public String toString(){
return "(" + this.x + "," + this.y + ")[" + this.clusterNumber + "]";
} // end of toString()
} // end of class
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