Changes in 2313376: Adjusted windestimation model training howto after recent refactoring

				wiki/howto/windestimation.md
			
          @@ -39,8 +39,8 @@ The following steps import all the data required from sapsailing.com into the lo

           2. Run *com.sap.sailing.windestimation.data.importer.PolarDataImporter*

           ## Maneuver classifiers training

          -1. Run *com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierTrainer*. Within the this step, the maneuver data is preprocessed and all maneuver classifiers are trained for each supported context

          -2. Optionally run *com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierScoring* to print the performance of the trained classifiers and to verify maneuver classification scoring

          +1. Run *com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierTrainer*. Within the this step, the maneuver data is preprocessed and all maneuver classifiers are trained for each supported context.

          +2. Optionally run *com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierScoring* to print the performance of the trained classifiers. After this step, a list with macro-averaged F2-score of each trained classifier will be stored in *./maneuverClassifierScores.csv*

           ## Duration-based TWD delta standard deviation regressor

          @@ -49,7 +49,7 @@ The following steps import all the data required from sapsailing.com into the lo

           3. Run *com.sap.sailing.windestimation.datavisualization.AggregatedDurationDimensionPlot* to visualize the wind data. 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 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 synchronized

           4. 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 total number of the points must not exceed 100.

           5. Delete all the instances within the collection which do not make sense. For this, use the data visualization tool from step 3 to identify such instances. Pay a special attention to the instances in the beginnning and end. Some of the instances are not representative due to small number of supporting instances which is visualized in the histogram. Restart the data visualization tool as often as need to visualize the changed data.

          -6. Open the source code of the class *com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelMetadata*. It is recommended to read JavaDoc of the class. Scroll down to the definition of the inner class/enum *DurationValueRange*. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error. Make sure that there are at least 2 data points available within each interval

          +6. Open the source code of the class *com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelContext*. Scroll down to the definition of the inner class/enum *DurationValueRange*. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error. Make sure that there are at least 2 data points available within each interval. Datapoint with x = 0, y = 0 will be created automatically.

           7. Run *com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionStdRegressorTrainer*

           8. Verify the trained regressor functions. They are printed in the console output of the previous step. For instance, you can visualize the polynoms by means of https://www.wolframalpha.com/

          @@ -62,7 +62,6 @@ The steps of this sections are similar to the steps of the previous section. It 

           3. Run *com.sap.sailing.windestimation.datavisualization.AggregatedDistanceDimensionPlot* to visualize the wind data. Here, the x-axis of the XY-chart represents **meters**

           4. Open your graphical MongoDB client and connect to *windEstimation* database hosted by your local MongoDB. Open collection *aggregatedDistanceTwdTransition* collection. Within the collection you will see all the instances/data points visualized in the previous step. The total number of the points must not exceed 100.

           5. Delete all the instances within the collection which do not make sense.

          -6. Open the source code of the class *com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelMetadata*. Scroll down to the definition of the inner class/enum *DistanceValueRange*. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error.

          -7. Open the source code of the class *com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionStdRegressorTrainer* and scroll down to *getTrainingDataForDistance()* method. The method returns the training datasets which will be used for the model training. Adjust the datasets accordingly so that at least two datasets intersect with [fromInclusive; toExclusive] of each specified interval in the previous step

          +6. Open the source code of the class *com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelContext*. Scroll down to the definition of the inner class/enum *DistanceValueRange*. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error.

           7. Run *com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionStdRegressorTrainer*

          -8. Verify the trained regressor functions. They are printed in the console output of previous step

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          +8. Verify the trained regressor functions. They are printed in the console output of the previous step.

          \ No newline at end of file

...	...	@@ -39,8 +39,8 @@ The following steps import all the data required from sapsailing.com into the lo
39	39	2. Run com.sap.sailing.windestimation.data.importer.PolarDataImporter
40	40
41	41	## Maneuver classifiers training
42		-1. Run com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierTrainer. Within the this step, the maneuver data is preprocessed and all maneuver classifiers are trained for each supported context
43		-2. Optionally run com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierScoring to print the performance of the trained classifiers and to verify maneuver classification scoring
	42	+1. Run com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierTrainer. Within the this step, the maneuver data is preprocessed and all maneuver classifiers are trained for each supported context.
	43	+2. Optionally run com.sap.sailing.windestimation.model.classifier.maneuver.ManeuverClassifierScoring to print the performance of the trained classifiers. After this step, a list with macro-averaged F2-score of each trained classifier will be stored in ./maneuverClassifierScores.csv
44	44
45	45	## Duration-based TWD delta standard deviation regressor
46	46
...	...	@@ -49,7 +49,7 @@ The following steps import all the data required from sapsailing.com into the lo
49	49	3. Run com.sap.sailing.windestimation.datavisualization.AggregatedDurationDimensionPlot to visualize the wind data. 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 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 synchronized
50	50	4. 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 total number of the points must not exceed 100.
51	51	5. Delete all the instances within the collection which do not make sense. For this, use the data visualization tool from step 3 to identify such instances. Pay a special attention to the instances in the beginnning and end. Some of the instances are not representative due to small number of supporting instances which is visualized in the histogram. Restart the data visualization tool as often as need to visualize the changed data.
52		-6. Open the source code of the class com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelMetadata. It is recommended to read JavaDoc of the class. Scroll down to the definition of the inner class/enum DurationValueRange. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error. Make sure that there are at least 2 data points available within each interval
	52	+6. Open the source code of the class com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionRegressorModelContext. Scroll down to the definition of the inner class/enum DurationValueRange. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error. Make sure that there are at least 2 data points available within each interval. Datapoint with x = 0, y = 0 will be created automatically.
53	53	7. Run com.sap.sailing.windestimation.model.regressor.twdtransition.DurationBasedTwdTransitionStdRegressorTrainer
54	54	8. Verify the trained regressor functions. They are printed in the console output of the previous step. For instance, you can visualize the polynoms by means of https://www.wolframalpha.com/
55	55
...	...	@@ -62,7 +62,6 @@ The steps of this sections are similar to the steps of the previous section. It
62	62	3. Run com.sap.sailing.windestimation.datavisualization.AggregatedDistanceDimensionPlot to visualize the wind data. Here, the x-axis of the XY-chart represents meters
63	63	4. Open your graphical MongoDB client and connect to windEstimation database hosted by your local MongoDB. Open collection aggregatedDistanceTwdTransition collection. Within the collection you will see all the instances/data points visualized in the previous step. The total number of the points must not exceed 100.
64	64	5. Delete all the instances within the collection which do not make sense.
65		-6. Open the source code of the class com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelMetadata. Scroll down to the definition of the inner class/enum DistanceValueRange. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error.
66		-7. Open the source code of the class com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionStdRegressorTrainer and scroll down to getTrainingDataForDistance() method. The method returns the training datasets which will be used for the model training. Adjust the datasets accordingly so that at least two datasets intersect with [fromInclusive; toExclusive] of each specified interval in the previous step
	65	+6. Open the source code of the class com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionRegressorModelContext. Scroll down to the definition of the inner class/enum DistanceValueRange. The enum defines the intervals for which a separate regressor model will be trained. Adjust the intervals accordingly in order to allow the regressor model to learn the data curve with minimal error.
67	66	7. Run com.sap.sailing.windestimation.model.regressor.twdtransition.DistanceBasedTwdTransitionStdRegressorTrainer
68		-8. Verify the trained regressor functions. They are printed in the console output of previous step
...	...	\ No newline at end of file
	0	+8. Verify the trained regressor functions. They are printed in the console output of the previous step.
...	...	\ No newline at end of file