documentation and clean-up of DATA_evaluation.java

Author: Maximilian Wenk

src/classification/DATA_evaluation.java

@@ -11,6 +11,7 @@ public class DATA_evaluation {
 
     private boolean createConfusionMatrix = false;
 
+    // --- Creating a constructor for data evaluation
     protected DATA_evaluation(String[] testDataResults, int columnCount, String[][][] predictedTestData, int[][] sortedProbability, int numberOfClasses) {
         this.testDataResults    = testDataResults;
         this.columnCount        = columnCount;
@@ -23,20 +24,23 @@ protected DATA_evaluation(String[] testDataResults, int columnCount, String[][][
 
     // --- Function for creating a basic confusion matrix for further calculations -------------------------------------
     private void confusionMatrix() {
-
         // Resetting the confusion matrix
         for (int i = 0; i < this.numberOfClasses; i++) {
             for (int j = 0; j < this.numberOfClasses; j++) {
                 this.confustionMatrix[i][j] = 0;
             }
         }
 
+        // Going through every column of the data (only test data)
         for (int i = 0; i < this.columnCount; i++) {
+            // Compare the predicted result with the actual result
             if (this.testDataResults[i].equals(this.predictedTestData[i][this.sortedProbability[i][0]][0])) {
+                // If true: The data has been predicted truly
                 this.confustionMatrix[this.sortedProbability[i][0]][this.sortedProbability[i][0]]++;
             }
+            // If the predicted data does not match the actual result, it has been predicted falsely
             else {
-
+                // Writing data as a false prediction to the confusion matrix
                 for (int j = 0; j < this.numberOfClasses; j++) {
                     if (this.testDataResults[i].equals(this.predictedTestData[i][this.sortedProbability[i][j]][0])) {
                         this.confustionMatrix[this.sortedProbability[i][0]][this.sortedProbability[i][j]]++;
@@ -48,10 +52,12 @@ private void confusionMatrix() {
 
     // --- Printing a basic confusion matrix
     protected void getConfusionMatrix() {
+        // If the confusion matrix has not been created yet, it will be
         if (!this.createConfusionMatrix) {
             confusionMatrix();
         }
 
+        // Printing every element of the confusion matrix
         for (int i = 0; i < this.numberOfClasses; i++) {
             for (int j = 0; j < this.numberOfClasses; j++) {
                 System.out.print(confustionMatrix[i][j] + " ");
@@ -62,26 +68,32 @@ protected void getConfusionMatrix() {
 
     // --- Printing a simple confusion matrix
     protected void getConfusionMatrixSimple() {
+        // If the confusion matrix has not been created yet, it will be
         if (!this.createConfusionMatrix) {
             confusionMatrix();
         }
 
+        // Creating an array for calculating a simple confusion matrix
         int[][] confusionMatrixSimple = new int[this.numberOfClasses][2];
         for (int i = 0; i < this.numberOfClasses; i++) {
             for (int j = 0; j < 2; j++) {
                 confusionMatrixSimple[i][j] = 0;
             }
         }
 
+        // Going through every column of the data (only test data)
         for (int i = 0; i < this.columnCount; i++) {
+            // If the data has been predicted correctly it is TT
             if (this.testDataResults[i].equals(this.predictedTestData[i][this.sortedProbability[i][0]][0])) {
                 confusionMatrixSimple[this.sortedProbability[i][0]][0]++;
             }
+            // If the data has been predicted correctly it is TN
             else {
                 confusionMatrixSimple[this.sortedProbability[i][0]][1]++;
             }
         }
 
+        // Printing every element of the simple confusion matrix
         for (int i = 0; i < this.numberOfClasses; i++) {
             for (int j = 0; j < 2; j++) {
                 System.out.print(confusionMatrixSimple[i][j] + " ");
@@ -94,12 +106,17 @@ protected void getConfusionMatrixSimple() {
 
     // --- Printing a normalized confusion matrix
     protected void getConfusionMatrixNormalized() {
+        // If the confusion matrix has not been created yet, it will be
         if (!this.createConfusionMatrix) {
             confusionMatrix();
         }
+
+        // Creating an array for calculating a normalized confusion matrix
         float[][] confusionMatrixNormalized = new float[this.numberOfClasses][this.numberOfClasses];
 
+        // Going through every column of the data (only test data)
         for (int i = 0; i < this.numberOfClasses; i++) {
+            // Adding all predicted values per class
             int tempCount = 0;
             for (int j = 0; j < this.numberOfClasses; j++) {
                 tempCount += this.confustionMatrix[i][j];
@@ -108,12 +125,14 @@ protected void getConfusionMatrixNormalized() {
             if (tempCount == 0) {
                 continue;
             }
+
+            // Dividing the predicted data per class
             for (int j = 0; j < this.numberOfClasses; j++) {
                 confusionMatrixNormalized[i][j] = (float)this.confustionMatrix[i][j] / (float)tempCount;
             }
-
         }
 
+        // Printing every element of the normalized confusion matrix
         for (int i = 0; i < this.numberOfClasses; i++) {
             for (int j = 0; j < this.numberOfClasses; j++) {
                 System.out.print(confusionMatrixNormalized[i][j] + " ");