This repository demonstrates a simple example of the LVQ4J library usage on the Iris Data Set, which is, perhaps, the best known study case in the machine classification field.
Step 1. Download or clone this repository.
git clone https://github.com/MeGysssTaa/lvq4j-example-irisStep 2. Build the example.
cd lvq4j-example-iris ./gradlew buildStep 3. Download the Iris Data Set.
Step 4. Run the example:
java -jar build/libs/lvq4j-example-iris-1.0.0.jar <train data file>Profit! If you did everything correctly, the output you'll see will be similar to this:
lvq4j-example-iris $ java -jar build/libs/lvq4j-example-iris-1.0.0.jar /mnt/e/iris.csv 19:44:56.689 [main] INFO Main - Successfully read 150 Iris data records 19:44:56.709 [main] INFO Main - Neural network will be training asynchronously! 19:44:56.710 [Iris Train Thread] INFO LVQ4J - Normalized input in 0 millis with function me.darksidecode.lvq4j.NormalizationFunction$$Lambda$24/1209669119 19:44:56.711 [Iris Train Thread] INFO LVQ4J - Initialized weights in 0 millis with strategy me.darksidecode.lvq4j.WeightsInitializer$$Lambda$17/1884122755 19:44:56.711 [Iris Train Thread] INFO LVQ4J - Neural network will begin training from scratch. 19:44:56.753 [Iris Train Thread] INFO Train Finish Listener - The neural network has finished training! 19:44:56.754 [Iris Train Thread] INFO Train Finish Listener - =============================================== 19:44:56.755 [Iris Train Thread] INFO Train Finish Listener - SUMMARY 19:44:56.756 [Iris Train Thread] INFO Train Finish Listener - Overall accuracy: 98.0% 19:44:56.757 [Iris Train Thread] INFO Train Finish Listener - Accuracy per cluster (per Iris species): 19:44:56.757 [Iris Train Thread] INFO Train Finish Listener - 0: 100.0% 19:44:56.757 [Iris Train Thread] INFO Train Finish Listener - 1: 96.0% 19:44:56.757 [Iris Train Thread] INFO Train Finish Listener - 2: 98.0% 19:44:56.757 [Iris Train Thread] INFO Train Finish Listener - =============================================== 19:44:56.758 [Iris Train Thread] INFO LVQ4J - Training completed. It took 46 millis to run 188 iterations for a final error square sum of 13.05253438308561 See LVQ4J Wiki and try playing with the code, and then write an own classifier that makes use of LVQ4J.
- If you have any questions or issues, don't hesitate to open an issue!
- If you believe something isn't working as intended, and you know how to fix it, or if you have some ideas for improvements, please create a pull request.