Merge pull request #14 from max-acc/7-documentation

7 documentation

Author: max-acc

README.md

@@ -13,17 +13,44 @@
 ### Installing
 
 
-### Executing program
-
-
-### Error Codes
+### Executing the program
+#### Setting up the classification model
+Firstly you have to import a package called "classification" that contains all important functions for classifying a 
+dataset consisting of float values:   
+```import classification.ClassificationOfFloatValues;```<br>  
+The next step is to create an object for this classification (ob is used as a default name for an object):  
+```ClassificationOfFloatValues ob = new ClassificationOfFloatValues(dataset);```  
+The ```dataset``` variable should contain the name of the dataset that should be classified as a string. 
+The dataset has to be in the same folder as the main file.<br>  
+If the dataset has an index or a header (or both), it has to be indecaded by the user.  
+If there is a header you have to call ```ob.setIndex(true);``` or/and ```ob.setHeader(true);```.  
+The default value for these is ```false``` because it is expected that the dataset does not have an index or header.  
+Most datasets do have a header and an index so make sure, if your dataset has a header or an index, to include this part in your program.
+<br><br>
+
+#### Processing the data
+The following functions are required for classifying the data.  
+Firstly you have to call ```ob.dataProcessing();```  
+```ob.dataSubdivision();```  
+```ob.distanceClassification();```
+<br><br>
+
+#### Evaluating the Results
+For evaluating the predicted results you can call ```ob.evaluateResults();```.
+There are multiple ways to show how the results should be displayed.  
+The ```ob.setEvaluation(model)``` functions sets the evaluation models which are going to be calculated and printed.
+```model``` should contain one of the names below as a string.<br>  
+**Confusion Matrix**: Printing a normal confusion matrix for every class (size: class x class).  
+**Simple Confusion Matrix**: Printing a simplified confusion matrix for every class with true positives and false positives (size: class x 2).  
+**NormalizedConfusion Matrix**: Printing a normalized confusion matrix with the format of the confusion matrix as explained
+above. The values that are displayed a normalized (values between 0 and 1).
 
 
 ## Scripts
-
-
+There is a script that explains the programs function and also explains the data manipulation in detail.  
+You can find the description here.
 ## Help
-
+If you need help if applying the algorithm to your projects, feel free to ask.
 
 ## Authors
 
@@ -33,6 +60,10 @@ Contributors names and contact info
 	* [@max-acc](https://github.com/max-acc)
 
 ## Version History
+### Built v-0.1  
+The current built is v-0.1.  
+It is possible to classify a dataset which contains only float values.
+It is important to consider that the weight for every class is the same.
 
 
 ## License

script/Dokumentation.pdf

@@ -16,7 +16,7 @@ public static void main (String[] args) throws Exception {
         ob.setIndex(true);
         ob.setHeader(true);
 
-        // traingingData = percentage of training data <1
+        // trainingData = percentage of training data <1
         ob.dataValidation(0.7f);
         ob.dataProcessing();
         ob.dataSubdivision();