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Adjusted DistanceAndDurationAwareTransitionProbabilityCalculator to

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calculate p considering the the sd sum of both dimensions
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Vladislav Chumak committed 2019-01-29 10:45:35 +01:00
1 parent 0eedcde4f7
commit 79087bf878
1 file changed
+2 -14
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java/com.sap.sailing.windestimation/src/com/sap/sailing/windestimation/model/regressor/twdtransition/GaussianBasedTwdTransitionDistributionCache.java
+2 -14
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@@ -1,7 +1,6 @@
package com.sap.sailing.windestimation.model.regressor.twdtransition;
import com.sap.sailing.windestimation.data.TwdTransition;
import com.sap.sailing.windestimation.model.regressor.TrainableRegressorModel;
import com.sap.sailing.windestimation.model.store.ModelStore;
import smile.stat.distribution.GaussianDistribution;
@@ -28,19 +27,8 @@ public class GaussianBasedTwdTransitionDistributionCache {
}
public double getP(TwdTransition twdTransition) {
double durationBasedP = getPFromRegressorCache(durationBasedTwdTransitionRegressorCache, twdTransition);
double distanceBasedP = getPFromRegressorCache(distanceBasedTwdTransitionRegressorCache, twdTransition);
return durationBasedP + distanceBasedP / 2;
}
private double getPFromRegressorCache(
SingleDimensionBasedTwdTransitionRegressorCache<? extends SingleDimensionBasedTwdTransitionRegressorModelMetadata> regressorCache,
TwdTransition twdTransition) {
TrainableRegressorModel<TwdTransition, ? extends SingleDimensionBasedTwdTransitionRegressorModelMetadata> regressorModel = regressorCache
.getBestModel(twdTransition);
double[] x = regressorModel.getContextSpecificModelMetadata().getX(twdTransition);
double std = regressorModel.getValue(x);
double p = getGaussianP(std, twdTransition.getTwdChange().abs().getDegrees());
double stdSum = getCompoundDistance(twdTransition);
double p = getGaussianP(stdSum, twdTransition.getTwdChange().abs().getDegrees());
return p;
}