Changes in 5928f77: added resources/ to windestimation's build.properties Change-Id: Ifb8e0026818c954b59e629dc6ab570592e0f2e3c

				java/com.sap.sailing.windestimation/build.properties
			
          @@ -1,4 +1,5 @@

          -source.. = src/

          +source.. = src/,\

          +           resources/

           output.. = bin/

           bin.includes = META-INF/,\

                          lib/smile-core-1.5.2.jar,\

				wiki/howto/windestimation.md
			
          @@ -1,6 +1,6 @@

           # Training of internal Wind Estimation models

          -This document describes the generation process of Machine Learning (ML) models which are used internally by wind estimation. It is highly recommended to proceed this howto step by step considering the order of sections. At the end of this howto, you will generate a file containing the representation of internal models used by ``com.sap.sailing.windestimation`` bundle. You can use this file to update the wind estimation models of a running server instance. If you are interested in a more advanced tutorial which requires all the execution steps contained in ``SimpleModelsTrainingPart...`` classes to be executed manually, then you might be interested in [Advanced Guide for training of internal Wind Estimation models](./windestimationAdvanced.md)

          +This document describes the generation process of Machine Learning (ML) models which are used internally by the maneuver-based wind estimation. It is highly recommended to process this howto step by step considering the order of sections. At the end of this howto, you will generate a file containing the representation of internal models used by ``com.sap.sailing.windestimation`` bundle. You can use this file to update the wind estimation models of a running server instance. If you are interested in a more advanced tutorial which requires all the execution steps contained in ``SimpleModelsTrainingPart...`` classes to be executed manually, then you might be interested in [Advanced Guide for training of internal Wind Estimation models](./windestimationAdvanced.md)

           ## Prerequisites

          @@ -16,9 +16,9 @@ To complete the training process successfully, you need to make sure that you ha

           ## Model training process

           1. Run ``com.sap.sailing.windestimation.model.SimpleModelsTrainingPart1`` as a normal Java Application. After this, all the necessary maneuver and wind data will be downloaded, pre-processed and maneuver classifiers get trained.

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

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

              ![Screenshot of graphical info dialog requesting to perform data cleansing for duration dimension](../images/windestimation/dialogRequestingDataCleansingForDurationDimension.jpg "Screenshot of graphical info dialog requesting to perform data cleansing for duration dimension")

          -   Press OK. Afterwards, a graphical window must open with two charts. The top chart is an XY-chart where the x-axis represents **seconds** and the y-axis represents various TWD delta-based measures (e.g. standard deviation or mean). Below the XY-chart, a histogram for the data points of the XY-chart is provided. You can zoom-in and zoom-out in each of the charts by mouse dragging. Be aware that currently, the zoom level of both charts is not synchronizing.

          +   Press OK. Afterwards, a graphical window must open with two charts. The top chart is an XY-chart where the x-axis represents **seconds** and the y-axis represents various TWD delta-based measures (e.g. standard deviation or mean). Below the XY-chart, a histogram for the data points of the XY-chart is provided. You can zoom-in and zoom-out in each of the charts by mouse dragging. Be aware that currently the zoom level of both charts is not synchronizing.

              ![Screenshot of graphical wind data visualization tool for duration dimension](../images/windestimation/aggregatedDurationBasedTwdDeltaTransitionBeforeDataCleansing.jpg "Screenshot of duration-based TWD delta visualization tool before data cleansing")

           3. Open your graphical MongoDB client and connect to ``windEstimation`` database hosted within 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 ``value``. Its corresponding metrics plotted in y-axis are the other attributes. ``std`` represents standard deviation (``Sigma`` curve in XY-chart) and ``std0`` represents standard deviation with zero as mean value (``Zero mean sigma`` curve in XY-chart).

              ![Screenshot of MongoDB Compass with opened aggregatedDurationTwdTransition collection](../images/windestimation/mongoDbCompassWithOpenedAggregatedDurationTwdTransitionCollection.jpg "Screenshot of MongoDB Compass with opened aggregatedDurationTwdTransition collection")

...	...	@@ -1,4 +1,5 @@
1		-source.. = src/
	1	+source.. = src/,\
	2	+ resources/
2	3	output.. = bin/
3	4	bin.includes = META-INF/,\
4	5	lib/smile-core-1.5.2.jar,\

...	...	@@ -1,6 +1,6 @@
1	1	# Training of internal Wind Estimation models
2	2
3		-This document describes the generation process of Machine Learning (ML) models which are used internally by wind estimation. It is highly recommended to proceed this howto step by step considering the order of sections. At the end of this howto, you will generate a file containing the representation of internal models used by ``com.sap.sailing.windestimation`` bundle. You can use this file to update the wind estimation models of a running server instance. If you are interested in a more advanced tutorial which requires all the execution steps contained in ``SimpleModelsTrainingPart...`` classes to be executed manually, then you might be interested in [Advanced Guide for training of internal Wind Estimation models](./windestimationAdvanced.md)
	3	+This document describes the generation process of Machine Learning (ML) models which are used internally by the maneuver-based wind estimation. It is highly recommended to process this howto step by step considering the order of sections. At the end of this howto, you will generate a file containing the representation of internal models used by ``com.sap.sailing.windestimation`` bundle. You can use this file to update the wind estimation models of a running server instance. If you are interested in a more advanced tutorial which requires all the execution steps contained in ``SimpleModelsTrainingPart...`` classes to be executed manually, then you might be interested in [Advanced Guide for training of internal Wind Estimation models](./windestimationAdvanced.md)
4	4
5	5	## Prerequisites
6	6
...	...	@@ -16,9 +16,9 @@ To complete the training process successfully, you need to make sure that you ha
16	16	## Model training process
17	17
18	18	1. Run ``com.sap.sailing.windestimation.model.SimpleModelsTrainingPart1`` as a normal Java Application. After this, all the necessary maneuver and wind data will be downloaded, pre-processed and maneuver classifiers get trained.
19		-2. Make sure that the launched program does not get terminated by an uncaught exception. Wait until graphical info dialog shows up which requests you to perform data cleansing for duration dimension.
	19	+2. Make sure that the launched program does not get terminated by an uncaught exception. Wait until a graphical info dialog shows up which asks you to perform data cleansing for the duration dimension.
20	20	![Screenshot of graphical info dialog requesting to perform data cleansing for duration dimension](../images/windestimation/dialogRequestingDataCleansingForDurationDimension.jpg "Screenshot of graphical info dialog requesting to perform data cleansing for duration dimension")
21		- Press OK. Afterwards, a graphical window must open with two charts. The top chart is an XY-chart where the x-axis represents seconds and the y-axis represents various TWD delta-based measures (e.g. standard deviation or mean). Below the XY-chart, a histogram for the data points of the XY-chart is provided. You can zoom-in and zoom-out in each of the charts by mouse dragging. Be aware that currently, the zoom level of both charts is not synchronizing.
	21	+ Press OK. Afterwards, a graphical window must open with two charts. The top chart is an XY-chart where the x-axis represents seconds and the y-axis represents various TWD delta-based measures (e.g. standard deviation or mean). Below the XY-chart, a histogram for the data points of the XY-chart is provided. You can zoom-in and zoom-out in each of the charts by mouse dragging. Be aware that currently the zoom level of both charts is not synchronizing.
22	22	![Screenshot of graphical wind data visualization tool for duration dimension](../images/windestimation/aggregatedDurationBasedTwdDeltaTransitionBeforeDataCleansing.jpg "Screenshot of duration-based TWD delta visualization tool before data cleansing")
23	23	3. Open your graphical MongoDB client and connect to ``windEstimation`` database hosted within 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 ``value``. Its corresponding metrics plotted in y-axis are the other attributes. ``std`` represents standard deviation (``Sigma`` curve in XY-chart) and ``std0`` represents standard deviation with zero as mean value (``Zero mean sigma`` curve in XY-chart).
24	24	![Screenshot of MongoDB Compass with opened aggregatedDurationTwdTransition collection](../images/windestimation/mongoDbCompassWithOpenedAggregatedDurationTwdTransitionCollection.jpg "Screenshot of MongoDB Compass with opened aggregatedDurationTwdTransition collection")