📄 algorithmlbg.java,v
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enableControl();
}
d336 5a340 5 {
disableControl();
step2();
enableControl();
}
d343 6a348 6 {
disableControl();
step3();
enableControl();
}
d353 4a356 1
d388 1a388 1 // add all points from the forth data set
a396 1 *
a401 1 *
d418 1a418 1 int i=0;
d488 3a655 1 *
d658 1a658 2 * @@param DecisionRegion region: classified data sets
*
a707 1 *
d713 3a715 2 * @@return : standard deviation of the cluster
*
a754 1 *
d757 1a757 2 * @@param DecisionRegion region: stored data sets from the classification
*
d806 6a811 8 *
* Computes the k-mean decision region - nearest neighbor algorithm
*
* @@param Vector vec: vector of initial guesses
*
* @@return vector of desision region points
*
*/
a816 1
d829 2a830 2 double incrementY = (scale.ymax-scale.ymin)/outputHeight;
double incrementX = (scale.xmax-scale.xmin)/outputWidth;
d852 2a853 1 MyPoint pixel = new MyPoint(currentX, currentY); smallestSoFar = Double.MAX_VALUE;
d925 2a926 1 MyPoint mean4 = (MyPoint)point_means_d.elementAt(l);
d969 4@1.4log@some errors done during Java Style Documentation were rectified.@text@d2 1a2 1* AlgorithmLBG.java
d4 1a4 8* Created: 7/15/03
*
* Author: Phil Trasatti
*
* @@author Daniel May
*
* Class: AlgorithmLBG
*
a53 1 * method: initialize
d55 4a58 1 * @@param none
a60 4 * description:
* overrides the initialize() method in the base class. initializes
* member data and prepares for execution of first step. this method
* "resets" the algorithm.
d65 2a66 1 //System.out.println(algo_id + ": initialize()");
d81 1d101 1d105 1d112 1d116 1d120 1a125 1 * method: step1
d127 2a128 1 * @@param none
a129 3 *
* description:
* displays data sets from input box in output box.
d135 2a136 1 //System.out.println(algo_id + " : step1()");
d139 1d147 1d158 1d166 8a173 10 * method: step2
*
* @@param none
* @@return boolean
*
* description:
* computes the means of each data set and displays the means graphically
* and numerically
*
*/
d177 2a178 1 //System.out.println(algo_id + " : step2()");
d181 1d200 1a200 1 //cbinary_d.addElement(decision_regions_d);
d209 6a214 9 * method: step3
*
* @@param none
* @@return boolean
*
* description:
* Computes the Decision Regions and the associated errors.
*
*/
d218 2a219 1 //System.out.println(algo_id + " : step3()");
d224 2a225 1 pro_box_d.appendMessage(" Iteration " + (currObject+1) + "\n");
d254 2a255 1 for (int i=0; i<numclusters; i++) {
d282 2a283 1 for (int j=0; j<cluster.size(); j++) {
d310 5a314 5 message = new String(" Covariance matrix:\n" +
" " + c11
+ " " + c12 + "\n" +
" " + c21
+ " " + c22);
d334 6a339 6 " Total number of samples: " +
total + "\n" +
" Misclassified samples: " +
error + "\n" +
" Classification error: " +
MathUtil.setDecimal(err, 2) + "%");
a349 1 * method: run
d351 2a352 2 * @@param none
* @@return none
a353 3 * description:
* implementation of the run function from the Runnable interface.
* determines what the current step is and calls the appropriate method.
d358 2a359 1 //System.out.println(algo_id + " : run()");
d381 1d387 2a388 1 public void generatePool() {
d392 2a393 1 if (data_pool_d.size() > 0) {
d399 2a400 1 for (int i=0; i<set1_d.size(); i++) {
d406 2a407 1 for (int i=0; i<set2_d.size(); i++) {
d413 2a414 1 for (int i=0; i<set3_d.size(); i++) {
d420 2a421 1 for (int i=0; i<set4_d.size(); i++) {
d427 2a428 1 * method: getClosestSet
a433 2 * method determines the closest data sets to the cluster
*
d435 2a436 1 public int getClosestSet(MyPoint mean) {
d496 2a497 1 if (dist1 < dist2 && dist1 < dist3 && dist1 < dist4) {
d503 2a504 1 if (dist2 < dist1 && dist2 < dist3 && dist2 < dist4) {
d510 2a511 1 if (dist3 < dist1 && dist3 < dist2 && dist3 < dist4) {
d520 2a521 1 public int displayClusterError(int closest, Vector cluster, int id) {
d532 4a535 4 if (closest == 1) {
for (int i=0; i<cluster.size(); total++, i++) {
d544 2a545 1 for (int j=0; j<set1_d.size(); j++) {
d553 2a554 1 if (cst.x == pnt.x && cst.y == pnt.y) {
d561 2a562 1 if (!present) {
d570 4a573 4 if (closest == 2) {
for (int i=0; i<cluster.size(); total++, i++) {
d582 2a583 2 for (int j=0; j<set2_d.size(); j++) {
d590 2a591 1 if (cst.x == pnt.x && cst.y == pnt.y) {
d598 2a599 1 if (!present) {
d607 4a610 4 if (closest == 3) {
for (int i=0; i<cluster.size(); total++, i++) {
d619 2a620 2 for (int j=0; j<set3_d.size(); j++) {
d627 2a628 1 if (cst.x == pnt.x && cst.y == pnt.y) {
d635 2a636 1 if (!present) {
d644 4a647 4 if (closest == 4) {
for (int i=0; i<cluster.size(); total++, i++) {
d656 2a657 2 for (int j=0; j<set4_d.size(); j++) {
d664 2a665 1 if (cst.x == pnt.x && cst.y == pnt.y) {
d672 2a673 1 if (!present) {
d685 2a686 1 * method: computeBinaryDeviates
a689 4 * @@return none
*
* method computes the binary deviates after each iteraion
*
d691 2a692 1 public void computeBinaryDeviates(Vector decisionRegions) {
d704 2a705 2 for (int i=0; i<numsets; i++) {
d708 3a710 1 Vector dataset = (Vector)((DataSet)decisionRegions.elementAt(i)).getDataSet();
d739 11a749 11 * method: clusterDeviation
*
* @@param Vector cluster: cluster of data points
* @@param Point: mean of the cluster
*
* @@return : standard deviation of the cluster
*
* method to calculate the standard deviation of the cluster
*
*/
public MyPoint clusterDeviation(Vector cluster, MyPoint mean) {
d758 2a759 2 for (int i=0; i<cluster.size(); i++) {
d786 8a793 10 * method: classify
*
* @@param DecisionRegion region: stored data sets from the classification
*
* @@return none
*
* method classifies the data sets based on the k-means iterative algorithm
*
*/
public void classify(Vector guesses) {
d797 2a798 2 for (int i=0; i<data_pool_d.size(); i++) {
d811 2a812 2 for (int j=0; j<guesses.size(); j++) {
d823 2a824 1 if(dist < smallestSoFar) {
d839 2a840 1 * method: getDecisionRegion
d842 1a842 1 * @@param Vector vec: vector of initial guesses
a845 2 * computes the k-mean decision region - nearest neighbor algorithm
*
d847 2a848 1 public Vector getDecisionRegion(Vector vec) {
d877 2a878 2 for(int i = 0; i < outputWidth; i++) {
d884 2a885 2 for(int j = 0; j < outputHeight; j++) {
d892 2a893 2 for (int k = 0; k < vec.size(); k++) {
d897 2a898 1 dist = MathUtil.distance(pixel.x, pixel.y, point.x, point.y);
d900 2a901 2 if(dist < smallestSoFar) {
d907 4a910 2 if(set1_d.size() > 0) {
MyPoint mean1 = (MyPoint)point_means_d.elementAt(l);
d913 3a915 2 mean1.x, mean1.y);
if(cdist < smallest) {
d924 4a927 2 if(set2_d.size() > 0) {
MyPoint mean2 = (MyPoint)point_means_d.elementAt(l);
d930 3a932 2 mean2.x, mean2.y);
if(cdist < smallest) {
d941 4a944 2 if(set3_d.size() > 0) {
MyPoint mean3 = (MyPoint)point_means_d.elementAt(l);
d947 3a949 2 mean3.x, mean3.y);
if(cdist < smallest) {
d958 2a959 1 if(set4_d.size() > 0) {
d963 3a965 2 mean4.x, mean4.y);
if(cdist < smallest) {
d987 2a988 1 if(j > 0 && i > 0) {
d990 2a991 1 associated != outputCanvas[i - 1][j]) {
a1027 1
a1028 1
a1029 1
a1030 14
@1.3log@changed the comments (few). The lot of documentation needs to be done.the comments comply with Java Documentation format.@text@d237 2a238 2 * compute the new means of the classified data
*
d662 1a662 1 * @@return the misclassified samples
@1.2log@Algorithm Complete and debug statements commented@text@d1 13a13 13//----------------------------------------------------------------------
// AlgorithmLBG.java
//
// Created: 7/15/03
//
// Author: Phil Trasatti
//
// Last Edited by: Daniel May
//
// Class: AlgorithmLBG
//
//
//---------------------------------------------------------------------
d60 11a70 11 //---------------------------------------------------------------
// method: initialize
//
// arguments: none
// return : boolean
//
// description:
// overrides the initialize() method in the base class. initializes
// member data and prepares for execution of first step. this method
// "resets" the algorithm.
//---------------------------------------------------------------
d127 10a136 10 //---------------------------------------------------------------
// method: step1
//
// arguments: none
// return : boolean
//
// description:
// displays data sets from input box in output box.
//
//---------------------------------------------------------------
d166 11a176 11 //---------------------------------------------------------------
// method: step2
//
// arguments: none
// return : boolean
//
// description:
// computes the means of each data set and displays the means graphically
// and numerically
//
//---------------------------------------------------------------
d209 10a218 10 //---------------------------------------------------------------
// method: step3
//
// arguments: none
// return : boolean
//
// description:
// Computes the Decision Regions and the associated errors.
//
//---------------------------------------------------------------
d237 2a238 2 // compute the new means of the classified data
//
d349 10a358 10 //---------------------------------------------------------------
// method: run
//
// arguments: none
// return : none
//
// description:
// implementation of the run function from the Runnable interface.
// determines what the current step is and calls the appropriate method.
//---------------------------------------------------------------
d422 10a431 10
// method: getClosestSet
//
// arguments:
// Point mean: mean of the cluster
//
// return : closest data set to the cluster
//
// method determines the closest data sets to the cluster
//
d662 1a662 1 // return the misclassified samples
d667 10a676 9 // method: computeBinaryDeviates
//
// arguments:
// DecisionRegion region: classified data sets
//
// return : none
//
// method computes the binary deviates after each iteraion
//
d720 12a731 11
// method: clusterDeviation
//
// arguments:
// Vector cluster: cluster of data points
// Point: mean of the cluster
//
// return : standard deviation of the cluster
//
// method to calculate the standard deviation of the cluster
//
d768 10a777 9 // method: classify
//
// arguments:
// DecisionRegion region: stored data sets from the classification
//
// return : none
//
// method classifies the data sets based on the k-means iterative algorithm
//
d822 10a831 9 // method: getDecisionRegion
//
// arguments:
// Vector vec: vector of initial guesses
//
// return : vector of desision region points
//
// computes the k-mean decision region - nearest neighbor algorithm
//
@1.1log@Initial revision@text@d74 1a74 1 System.out.println(algo_id + ": initialize()");
d140 1a140 1 System.out.println(algo_id + " : step1()");
d180 1a180 1 System.out.println(algo_id + " : step2()");
d222 1a222 1 System.out.println(algo_id + " : step3()");
d362 1a362 1 System.out.println(algo_id + " : run()");
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