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Settings can now be used in retrieval processors

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wind range setting is now usable and in effect
This commit is contained in:
Frederik Petersen
2015-09-14 14:03:23 +02:00
parent f0f99bb059
commit 46e20e3af9
29 changed files with 255 additions and 35 deletions
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java/com.sap.sailing.polars/src/com/sap/sailing/polars/clusters/SpeedClusterGroup.java
-85
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@@ -1,85 +0,0 @@
package com.sap.sailing.polars.clusters;
import java.util.ArrayList;
import java.util.Collection;
import com.sap.sailing.domain.common.Speed;
import com.sap.sailing.domain.common.impl.KnotSpeedImpl;
import com.sap.sse.datamining.data.Cluster;
import com.sap.sse.datamining.data.ClusterBoundary;
import com.sap.sse.datamining.impl.data.ClusterWithLowerAndUpperBoundaries;
import com.sap.sse.datamining.impl.data.ComparableClusterBoundary;
import com.sap.sse.datamining.impl.data.ComparisonStrategy;
import com.sap.sse.datamining.impl.data.FixClusterGroup;
public class SpeedClusterGroup extends FixClusterGroup<Speed> {
private static final long serialVersionUID = -3428022721991223921L;
/**
* A {@link SpeedClusterGroup} lets the user set the level mids for all its clusters. The Boundaries will
* automatically be determined. They are in the middle between each level mid but only if the distance between level
* mid and boundary is smaller or equal {@code maxDistance}
*
*
* @param messageKey
* @param levelMidsInKnots
* sorted low -> high. E.g. [2,4,6,10,15,20,30]
* @param maxDistanceInKnots
* the clusters will max span <-maxDistanceInKnots-|mid|-maxDistanceinKnots->
*/
public SpeedClusterGroup(String messageKey, double[] levelMidsInKnots, double maxDistanceInKnots) {
super(messageKey, createClustersForLevelMids(levelMidsInKnots, maxDistanceInKnots));
}
private static Collection<Cluster<Speed>> createClustersForLevelMids(double[] levelMidsInKnots,
double maxDistanceInKnots) {
ArrayList<Cluster<Speed>> clusterList = new ArrayList<Cluster<Speed>>();
for (int index = 0; index < levelMidsInKnots.length; index++) {
ClusterBoundary<Speed> lowerBoundary = createLowerBoundary(levelMidsInKnots, maxDistanceInKnots, index);
ClusterBoundary<Speed> upperBoundary = createUpperBoundary(levelMidsInKnots, maxDistanceInKnots, index);
Cluster<Speed> cluster = new ClusterWithLowerAndUpperBoundaries<Speed>(levelMidsInKnots[index] + "kn",
lowerBoundary, upperBoundary);
clusterList.add(cluster);
}
return clusterList;
}
private static ClusterBoundary<Speed> createUpperBoundary(double[] levelMidsInKnots, double maxDistanceInKnots,
int index) {
ClusterBoundary<Speed> upperBoundary;
double upperBoundaryValue;
if (index == levelMidsInKnots.length - 1) {
upperBoundaryValue = levelMidsInKnots[index] + maxDistanceInKnots;
} else {
double biggestPossibleUpperBoundary = levelMidsInKnots[index] + maxDistanceInKnots;
double midBetweenCurrentLevelMidAndUpperLevelMid = levelMidsInKnots[index]
+ (0.5 * (levelMidsInKnots[index + 1] - levelMidsInKnots[index]));
upperBoundaryValue = biggestPossibleUpperBoundary < midBetweenCurrentLevelMidAndUpperLevelMid ? biggestPossibleUpperBoundary
: midBetweenCurrentLevelMidAndUpperLevelMid;
}
upperBoundary = new ComparableClusterBoundary<Speed>(new KnotSpeedImpl(upperBoundaryValue),
ComparisonStrategy.LOWER_THAN);
return upperBoundary;
}
private static ClusterBoundary<Speed> createLowerBoundary(double[] levelMidsInKnots, double maxDistanceInKnots,
int index) {
ClusterBoundary<Speed> lowerBoundary;
double lowerBoundaryValue;
if (index > 0) {
double lowestPossibleLowerBoundary = levelMidsInKnots[index] - maxDistanceInKnots;
double midBetweenLowerLevelMidAndCurrentLevelMid = levelMidsInKnots[index - 1]
+ (0.5 * (levelMidsInKnots[index] - levelMidsInKnots[index - 1]));
lowerBoundaryValue = lowestPossibleLowerBoundary > midBetweenLowerLevelMidAndCurrentLevelMid ? lowestPossibleLowerBoundary
: midBetweenLowerLevelMidAndCurrentLevelMid;
} else {
double lowestPossibleLowerBoundary = levelMidsInKnots[index] - maxDistanceInKnots;
lowerBoundaryValue = lowestPossibleLowerBoundary <= 0 ? 0 : lowestPossibleLowerBoundary;
}
lowerBoundary = new ComparableClusterBoundary<Speed>(new KnotSpeedImpl(lowerBoundaryValue),
ComparisonStrategy.GREATER_EQUALS_THAN);
return lowerBoundary;
}
}
java/com.sap.sailing.polars/src/com/sap/sailing/polars/mining/CubicRegressionPerCourseProcessor.java
+14 -1
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@@ -16,8 +16,9 @@ import com.sap.sailing.domain.common.LegType;
import com.sap.sailing.domain.common.Speed;
import com.sap.sailing.domain.common.Tack;
import com.sap.sailing.domain.polars.NotEnoughDataHasBeenAddedException;
import com.sap.sse.datamining.components.AdditionalResultDataBuilder;
import com.sap.sailing.domain.polars.PolarsChangedListener;
import com.sap.sse.common.settings.SerializableSettings;
import com.sap.sse.datamining.components.AdditionalResultDataBuilder;
import com.sap.sse.datamining.components.Processor;
import com.sap.sse.datamining.factories.GroupKeyFactory;
import com.sap.sse.datamining.impl.components.GroupedDataEntry;
@@ -192,4 +193,16 @@ public class CubicRegressionPerCourseProcessor implements
return null;
}
@Override
public SerializableSettings getSettings() {
// TODO Auto-generated method stub
return null;
}
@Override
public void setSettings(SerializableSettings settings) {
// TODO Auto-generated method stub
}
}
java/com.sap.sailing.polars/src/com/sap/sailing/polars/mining/MovingAverageProcessorImpl.java
+13
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@@ -22,6 +22,7 @@ import com.sap.sailing.domain.tracking.WindWithConfidence;
import com.sap.sailing.polars.regression.MovingAverageBoatSpeedEstimator;
import com.sap.sse.common.TimePoint;
import com.sap.sse.common.Util.Pair;
import com.sap.sse.common.settings.SerializableSettings;
import com.sap.sse.datamining.components.AdditionalResultDataBuilder;
import com.sap.sse.datamining.data.Cluster;
import com.sap.sse.datamining.data.ClusterGroup;
@@ -220,4 +221,16 @@ public class MovingAverageProcessorImpl implements MovingAverageProcessor {
return speedClusterGroup;
}
@Override
public SerializableSettings getSettings() {
// TODO Auto-generated method stub
return null;
}
@Override
public void setSettings(SerializableSettings settings) {
// TODO Auto-generated method stub
}
}
java/com.sap.sailing.polars/src/com/sap/sailing/polars/mining/SpeedClusterGroupFromWindSteppingCreator.java
-21
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@@ -1,21 +0,0 @@
package com.sap.sailing.polars.mining;
import com.sap.sailing.domain.common.Speed;
import com.sap.sailing.domain.common.impl.WindSpeedSteppingWithMaxDistance;
import com.sap.sailing.polars.clusters.SpeedClusterGroup;
import com.sap.sse.datamining.data.ClusterGroup;
public class SpeedClusterGroupFromWindSteppingCreator {
public static ClusterGroup<Speed> createSpeedClusterGroupFrom(WindSpeedSteppingWithMaxDistance windStepping) {
double maxDistance = windStepping.getMaxDistance();
double[] rawIntegerStepping = windStepping.getRawStepping();
double[] rawDoubleLevelMids = new double[rawIntegerStepping.length];
for (int i = 0; i < rawIntegerStepping.length; i++) {
rawDoubleLevelMids[i] = rawIntegerStepping[i];
}
return new SpeedClusterGroup("SpeedClusterGroup", rawDoubleLevelMids, maxDistance);
}
}
java/com.sap.sailing.polars/src/com/sap/sailing/polars/mining/SpeedRegressionPerAngleClusterProcessor.java
+13
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@@ -21,6 +21,7 @@ import com.sap.sailing.domain.polars.NotEnoughDataHasBeenAddedException;
import com.sap.sailing.domain.polars.PolarsChangedListener;
import com.sap.sailing.polars.regression.IncrementalLeastSquares;
import com.sap.sailing.polars.regression.impl.IncrementalAnyOrderLeastSquaresImpl;
import com.sap.sse.common.settings.SerializableSettings;
import com.sap.sse.datamining.components.AdditionalResultDataBuilder;
import com.sap.sse.datamining.components.Processor;
import com.sap.sse.datamining.data.Cluster;
@@ -216,4 +217,16 @@ public class SpeedRegressionPerAngleClusterProcessor implements
return angleClusterGroup;
}
@Override
public SerializableSettings getSettings() {
// TODO Auto-generated method stub
return null;
}
@Override
public void setSettings(SerializableSettings settings) {
// TODO Auto-generated method stub
}
}