1313### Installing
1414
1515
16- ### Executing program
17- 18- 19- ### Error Codes
16+ ### Executing the program
17+ #### Setting up the classification model
18+ Firstly you have to import a package called "classification" that contains all important functions for classifying a
19+ dataset consisting of float values:
20+ ``` import classification.ClassificationOfFloatValues; ``` <br >
21+ The next step is to create an object for this classification (ob is used as a default name for an object):
22+ ``` ClassificationOfFloatValues ob = new ClassificationOfFloatValues(dataset); ```
23+ The ``` dataset ``` variable should contain the name of the dataset that should be classified as a string.
24+ The dataset has to be in the same folder as the main file.<br >
25+ If the dataset has an index or a header (or both), it has to be indecaded by the user.
26+ If there is a header you have to call ``` ob.setIndex(true); ``` or/and ``` ob.setHeader(true); ``` .
27+ The default value for these is ``` false ``` because it is expected that the dataset does not have an index or header.
28+ 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.
29+ <br ><br >
30+ 31+ #### Processing the data
32+ The following functions are required for classifying the data.
33+ Firstly you have to call ``` ob.dataProcessing(); ```
34+ ``` ob.dataSubdivision(); ```
35+ ``` ob.distanceClassification(); ```
36+ <br ><br >
37+ 38+ #### Evaluating the Results
39+ For evaluating the predicted results you can call ``` ob.evaluateResults(); ``` .
40+ There are multiple ways to show how the results should be displayed.
41+ The ``` ob.setEvaluation(model) ``` functions sets the evaluation models which are going to be calculated and printed.
42+ ``` model ``` should contain one of the names below as a string.<br >
43+ ** Confusion Matrix** : Printing a normal confusion matrix for every class (size: class x class).
44+ ** Simple Confusion Matrix** : Printing a simplified confusion matrix for every class with true positives and false positives (size: class x 2).
45+ ** NormalizedConfusion Matrix** : Printing a normalized confusion matrix with the format of the confusion matrix as explained
46+ above. The values that are displayed a normalized (values between 0 and 1).
2047
2148
2249## Scripts
23- 24- 50+ There is a script that explains the programs function and also explains the data manipulation in detail.
51+ You can find the description here.
2552## Help
26- 53+ If you need help if applying the algorithm to your projects, feel free to ask.
2754
2855## Authors
2956
@@ -33,6 +60,10 @@ Contributors names and contact info
3360 * [@max-acc](https://github.com/max-acc)
3461
3562## Version History
63+ ### Built v-0.1
64+ The current built is v-0.1.
65+ It is possible to classify a dataset which contains only float values.
66+ It is important to consider that the weight for every class is the same.
3667
3768
3869## License
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