Improved simple models training with corresponding wiki howto

+ LoggingUtil.logInfo("CSV with evaluation results have been stored in: " + csvFile.getAbsolutePath());

+import com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelContext;

+import com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelContext.DistanceValueRange;

+import com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelContext;

+import com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelContext.DurationValueRange;

+ private static ExecutorService executorService;

+ showInfoAboutIntervalAdjustments(DurationBasedTwdTransitionRegressorModelContext.class,

+ DurationValueRange.class);

+ showInfoAboutIntervalAdjustments(DistanceBasedTwdTransitionRegressorModelContext.class,

+ DistanceValueRange.class);

+ + "\" which do not make sense. E.g. values which are represented by a small number of supporting instances (see histogram), values which cause implausible zig zag sections within zero-mean standard deviation curve and etc. Close the graphical tool, when you are done to resume the model training.");

+ }

+

+ private static void showInfoAboutIntervalAdjustments(Class<?> classToAdjust, Class<?> valueRangeEnum) {

+ JOptionPane.showMessageDialog(null, "Now, open the source code of the class \"" + classToAdjust.getName()

+ + "\". Scroll down to the definition of the inner enum \"" + valueRangeEnum.getSimpleName()

+ + "\", read its JavaDoc and adjust its interval definitions so that each interval can be learned by the adjusted regressor model configuration with minimal error. Press OK ONLY after you are done.");

+ executorService.shutdown();

+ if (executorService == null || executorService.isShutdown()) {

+ createNewThreadPool();

+ }

+ private static void createNewThreadPool() {

+ executorService = new ThreadPoolExecutor(NUMBER_OF_THREADS, NUMBER_OF_THREADS, 0, TimeUnit.MILLISECONDS,

+ new ArrayBlockingQueue<>(NUMBER_OF_THREADS), new ThreadPoolExecutor.CallerRunsPolicy());

+ }

+

+* 16 GB RAM

+* ~24 operation hours of your computer

+

+## Model training process

+1. Run ``com.sap.sailing.windestimation.model.SimpleModelsTraining`` as normal Java Application. This program downloads all the necessary maneuver and wind data, pre-processes them and initiates training of maneuver classifiers.

+2. Make sure that the launched program does not get termined by an uncaught exception. Wait until graphical info dialog shows up which requests you to perform data cleansing for duration dimension and press OK.

+ 

+ A Swing-based GUI-Window must open with two charts, one XY-chart where the x-axis represents **seconds**, and the y-axis represents TWD delta-based series measures (e.g. standard deviation or mean). Below the chart, a histogram for the data points of the XY-Chart is provided. You can zoom-in and zoom-out in each of the chart by mouse dragging. Be aware that currently, the zoom level of both charts is not synchronizing.

+ 

+3. Open your graphical MongoDB client and connect to ``windEstimation`` database hosted by your local MongoDB. Open the collection with name ``aggregatedDurationTwdTransition``. Within the collection you will see all the instances/data points visualized in the previous step. The attribute used for the x-axis is represented by ``value``.

+ 

+4. Delete all the instances within the collection which do not make sense. For this, use the data visualization tool from step 2 to identify such instances. Some of the instances are not representative due to the small number of supporting instances which is visualized in the histogram. Such instances can produce unreasonable bumps in the XY-chart. The desired output of this step is that the series curve ``Zero mean sigma`` looks smooth and always growing, like depicted below:

+ 

+ Use the ``Refresh charts`` button as often as needed to update the charts with the modified data in MongoDB. Close the graphical visualization tool window after you are done with data cleansing to resume the training process. Confirm the confirmation dialog after you have finished the data cleansing of duration dimension:

+ 

+5. A new information dialog shows up (do not press OK yet!) requesting you to open the source code of the class ``com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelContext``. Open it and scroll down to the definition of the inner enum ``DurationValueRange``. The enum defines the intervals for which a separate regressor model will be trained. Read the Javadoc of ``DurationValueRange`` and adjust the intervals accordingly in order to allow the regressor model to learn the ``Zero mean sigma`` curve with minimal error. You can also configurate the polynomial which will be used for regressor training. Make sure that there are at least 2 data points available within each interval. The datapoint with x = 0, y = 0 will be created automatically. Press OK in information dialog after you are done.

+6. A graphical info dialog shows up which requests you to perform data cleansing for *distance* dimension. Press OK. All steps for data cleansing for the distance dimension are very similar to the data cleansing steps step 2. until step 5. for the duration dimension. Thus, consult these steps to complete data cleansing and models configuration for the distance duration. The unit used for distance representation is **meters**. The collection name required in step 3. is ``aggregatedDistanceTwdTransition``. The class required in step 5. is ``com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelContext`` and its inner enum is ``DistanceValueRange``.

+7. Wait until model training finishes and the program terminates normally. A new file with serialized representation of internal wind estimation models should be located in ``./windEstimationModels.dat``. The absolute path of the file must be printed in the console output of the program. You can upload the file via HTTP POST to http://sapsailing.com/windestimation/api/windestimation_data (see ``com.sap.sailing.windestimation.jaxrs.api.WindEstimationDataResource``) to update the wind estimation of a server instance.

+8. Optionally, run ``com.sap.sailing.windestimation.evaluation.WindEstimatorManeuverNumberDependentEvaluationRunner`` as normal Java Application to evaluate the wind estimation with the new trained models. The evaluation score will be stored as CSV in ``./maneuverNumberDependentEvaluation.csv``.