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Merge pull request #2 from max-acc/work-in-progress
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‎.gitignore‎

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out/

‎.idea/vcs.xml‎

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‎README.md‎

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# hello-world
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# <p align="center"><b>Classification of float values in Java</b></p>
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## Description
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## Getting Started
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### Dependencies
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### Installing
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### Executing program
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### Error Codes
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## Scripts
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## Help
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## Authors
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Contributors names and contact info
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* Max Wenk
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* [@max-acc](https://github.com/max-acc)
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## Version History
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## License
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This project is licensed under the "GNU Affero General Public License v3.0" License - see the LICENSE.md file for details.
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## Acknowledgments
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* [README Template](https://gist.github.com/DomPizzie/7a5ff55ffa9081f2de27c315f5018afc)

‎src/Main.java‎

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@@ -21,6 +21,8 @@ public static void main (String[] args) throws Exception {
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ob.dataSubdivision();
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//System.out.println(ob.feedback()[0][2]);
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ob.distanceClassification();
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ob.evaluateResults();
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}
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}

‎src/classification/ClassificationOfFloatValues.java‎

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@@ -27,6 +27,10 @@ public class ClassificationOfFloatValues {
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private boolean dataSubdivisionBool = false; // Data has been divided into training and test data
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private String MLAlgorithm; // Variable for saving which machine learning algorithm has been used
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// --- Classification result data variables
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private String[][][] predictedTestData;
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private int[][] sortedProbability;
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// Function to add the members of the class
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public float[][] output() { return this.predictorData; }
@@ -83,6 +87,13 @@ public void dataValidation (float trainingData) {
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// --- Functions for evaluating the machine learning results -------------------------------------------------------
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public void evaluateResults() {
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DATA_evaluation evaluationObject = new DATA_evaluation(this.testDataResults,
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this.columnCount - this.numberOfTrainingData,
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this.predictedTestData,
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this.sortedProbability);
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evaluationObject.confusionMatrix();
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}
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public void confusionMatrix() {
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if (this.MLAlgorithm == "DistanceClassification") {
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System.out.println("nice confusion");
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// Testing the distance classification model
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classificationObject.setTestData(this.testDataPredictors, this.testDataResults, this.rowCount, this.columnCount - this.numberOfTrainingData);
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classificationObject.testModel();
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// Get the test data
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this.predictedTestData = classificationObject.getPredictedTestData();
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this.sortedProbability = classificationObject.getSortedProbability();
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}
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}

‎src/classification/DATA_evaluation.java‎

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package classification;
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public class DATA_evaluation {
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private String[][][] predictedTestData;
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private int[][] sortedProbability;
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private int columnCount;
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private int numberOfClasses;
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private String [] testDataResults;
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private int[][] confustionMatrix = new int[3][2];
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protected DATA_evaluation(String[] testDataResults, int columnCount, String[][][] predictedTestData, int[][] sortedProbability) {
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this.testDataResults = testDataResults;
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this.columnCount = columnCount;
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this.predictedTestData = predictedTestData;
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this.sortedProbability = sortedProbability;
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}
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protected void confusionMatrix() {
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System.out.println(this.testDataResults[0]);
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System.out.println(this.columnCount);
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System.out.println(this.predictedTestData[0][0][0]);
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System.out.println(this.predictedTestData[0][0][1]);
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System.out.println(this.predictedTestData[0][1][0]);
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System.out.println(this.predictedTestData[0][1][1]);
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System.out.println(this.predictedTestData[0][2][0]);
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System.out.println(this.predictedTestData[0][2][1]);
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System.out.println(this.sortedProbability[0][0]);
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System.out.println(this.sortedProbability[0][1]);
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System.out.println(this.sortedProbability[0][2]);
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// Resetting the confusion matrix
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for (int i = 0; i < 3; i++) {
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for (int j = 0; j < 2; j++) {
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this.confustionMatrix[i][j] = 0;
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}
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}
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for (int i = 0; i < this.columnCount; i++) {
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if (this.testDataResults[i].equals(this.predictedTestData[i][this.sortedProbability[i][0]][0])) {
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this.confustionMatrix[this.sortedProbability[i][0]][0]++;
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}
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else {
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this.confustionMatrix[this.sortedProbability[i][0]][1]++;
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}
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}
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for (int i = 0; i < 3; i++) {
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for (int j = 0; j < 2; j++) {
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System.out.print(this.confustionMatrix[i][j] + " ");
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}
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System.out.println();
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}
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}
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}

‎src/classification/DistanceClassification.java‎

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@@ -21,7 +21,8 @@ public class DistanceClassification {
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private int testDataRowCount;
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private int testDataColumnCount;
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private String[][] predictedTestData;
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private String[][][] predictedTestData;
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private int[][] sortedProbability;
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protected DistanceClassification(float [][] trainingDataPredictors, String [] trainingDataResults, int rowCount, int columnCount, float density) {
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return this.sortedClassificationData;
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}
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protected String[][][] getPredictedTestData() {
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return this.predictedTestData;
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}
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protected int[][] getSortedProbability() {
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return this.sortedProbability;
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}
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protected float[][] getFeatureMean() {
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return this.featureMean;
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}
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}
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protected void testModel() {
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testClassifcationModel();
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testClassificationModel();
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}
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private void getClassificationClasses() {
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}
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}
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private void testClassifcationModel() {
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this.predictedTestData = new String[testDataColumnCount][2];
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private void testClassificationModel() {
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this.predictedTestData = new String[this.testDataColumnCount][this.numberOfClasses][2];
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this.sortedProbability = new int[this.testDataColumnCount][this.numberOfClasses];
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// Check the distance for every class
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for (int i = 0; i < this.testDataColumnCount; i++) {
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float[][] tempDelta = new float[this.numberOfClasses][this.rowCount];
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for (int j = 0; j < this.numberOfClasses; j++) {
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for (int k = 0; k < this.rowCount -1; k++) {
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tempDelta[j][k] = this.featureMean[j][k] - this.testDataPredictors[i][k];
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}
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155-
tempDelta[j][this.rowCount -1] = (float) Math.sqrt(
156-
Math.pow(tempDelta[j][0], 2) +
157-
Math.pow(tempDelta[j][1], 2) +
158-
Math.pow(tempDelta[j][2], 2) +
159-
Math.pow(tempDelta[j][3], 2));
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System.out.print(classes[j] + " ");
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System.out.println(tempDelta[j][this.rowCount -1]);
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}
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this.predictedTestData[i][j][0] = this.classes[j];
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float tempCalcDistance = 0;
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/*
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for (int j = 0; j < this.numberOfClasses; j++) {
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for (int k = 0; k < this.rowCount -1; k++) {
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System.out.print(tempDelta[j][k]);
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System.out.print(" ");
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tempCalcDistance += (float) Math.pow(tempDelta[j][k], 2);
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}
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System.out.println();
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}
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*/
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this.predictedTestData[i][0] = "best result";
175-
this.predictedTestData[i][1] = "probability";
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tempDelta[j][this.rowCount -1] = (float) Math.sqrt(tempCalcDistance);
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this.predictedTestData[i][j][1] = Float.toString(tempDelta[j][this.rowCount -1]);
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178-
//this.predictedTestData[i] = "best result";
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}
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System.out.print(" " + this.predictedTestData[i][j][0] + " ");
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System.out.println(this.predictedTestData[i][j][1]);
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}
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float min = Float.valueOf(this.predictedTestData[i][0][1]);
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int tempIndex = 0;
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this.sortedProbability[i][0] = tempIndex;
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for (int j = 0; j < this.numberOfClasses; j++) {
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if (min > Float.valueOf(this.predictedTestData[i][j][1])) {
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min = Float.valueOf(this.predictedTestData[i][j][1]);
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tempIndex = j;
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}
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}
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this.sortedProbability[i][0] = tempIndex;
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for (int j = 1; j < this.numberOfClasses; j++) {
195+
tempIndex = 0;
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this.sortedProbability[i][j] = tempIndex;
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min = Float.valueOf(this.predictedTestData[i][this.sortedProbability[i][j-1]][1]);
198+
for (int k = 0; k < this.numberOfClasses; k++) {
199+
if ((min > Float.valueOf(this.predictedTestData[i][k][1]) &&
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Float.valueOf(this.predictedTestData[i][k][1]) > Float.valueOf(this.predictedTestData[i][this.sortedProbability[i][j-1]][1])) ||
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(Float.valueOf(this.predictedTestData[i][k][1]) > Float.valueOf(this.predictedTestData[i][this.sortedProbability[i][j-1]][1]) &&
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min == Float.valueOf(this.predictedTestData[i][this.sortedProbability[i][j-1]][1]))) {
203+
if (min > Float.valueOf(this.predictedTestData[i][k][1]) &&
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Float.valueOf(this.predictedTestData[i][k][1]) > Float.valueOf(this.predictedTestData[i][this.sortedProbability[i][j-1]][1])) {
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}
206+
if (Float.valueOf(this.predictedTestData[i][k][1]) > Float.valueOf(this.predictedTestData[i][this.sortedProbability[i][j-1]][1]) &&
207+
k != this.sortedProbability[i][j-1]) {
208+
}
209+
min = Float.valueOf(this.predictedTestData[i][k][1]);
210+
tempIndex = k;
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}
212+
}
213+
this.sortedProbability[i][j] = tempIndex;
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}
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}
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}
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}

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