public override EvaluationResults EvaluateModelOnData(Converter <Leaf, SufficientStatistics> predMap, Converter <Leaf, SufficientStatistics> targMap)
{
int predCount = ModelScorer.PhyloTree.CountOfNonMissingLeaves(predMap);
int targCount = ModelScorer.PhyloTree.CountOfNonMissingLeaves(targMap);
int globalNonMissingCount = ModelScorer.PhyloTree.CountOfNonMissingLeaves(predMap, targMap);
MessageInitializerGaussian nullMessageInitializer = MessageInitializerGaussian.GetInstance(
predMap, targMap, (DistributionGaussianConditional)NullDistns[0], ModelScorer.PhyloTree.LeafCollection);
Score nullScore = ModelScorer.MaximizeLikelihood(nullMessageInitializer);
MessageInitializerGaussian altMessageInitializer = MessageInitializerGaussian.GetInstance(
predMap, targMap, (DistributionGaussianConditional)AltDistn, ModelScorer.PhyloTree.LeafCollection);
Score altScore = ModelScorer.MaximizeLikelihood(altMessageInitializer);
EvaluationResults evalResults = EvaluationResultsGaussian.GetInstance(this, nullScore, altScore, predCount, targCount, globalNonMissingCount, ChiSquareDegreesOfFreedom);
return(evalResults);
}
public override EvaluationResults EvaluateModelOnDataGivenParams(Converter <Leaf, SufficientStatistics> predMap, Converter <Leaf, SufficientStatistics> targMap, EvaluationResults previousResults)
{
int predCount = ModelScorer.PhyloTree.CountOfNonMissingLeaves(predMap);
int targCount = ModelScorer.PhyloTree.CountOfNonMissingLeaves(targMap);
int globalNonMissingCount = ModelScorer.PhyloTree.CountOfNonMissingLeaves(predMap, targMap);
MessageInitializerGaussian nullMessageInitializer = MessageInitializerGaussian.GetInstance(
predMap, targMap, (DistributionGaussianConditional)NullDistns[0], ModelScorer.PhyloTree.LeafCollection);
double nullLL = ModelScorer.ComputeLogLikelihoodModelGivenData(nullMessageInitializer, previousResults.NullScores[0].OptimizationParameters);
Score nullScore = Score.GetInstance(nullLL, previousResults.NullScores[0].OptimizationParameters, previousResults.NullScores[0].Distribution);
MessageInitializerGaussian altMessageInitializer = MessageInitializerGaussian.GetInstance(
predMap, targMap, (DistributionGaussianConditional)AltDistn, ModelScorer.PhyloTree.LeafCollection);
double altLL = ModelScorer.ComputeLogLikelihoodModelGivenData(altMessageInitializer, previousResults.AltScore.OptimizationParameters);
Score altScore = Score.GetInstance(altLL, previousResults.AltScore.OptimizationParameters, previousResults.AltScore.Distribution);
EvaluationResults evalResults = EvaluationResultsGaussian.GetInstance(this, nullScore, altScore, predCount, targCount, globalNonMissingCount, ChiSquareDegreesOfFreedom);
return(evalResults);
}
protected EvaluationResults CreateDummyResults(int predNonMissing, int targNonMissing, int globalNonMissingCount)
{
IDistributionSingleVariable nullDistn = NullDistns[0];
OptimizationParameterList nullParams = nullDistn.GetParameters();
foreach (OptimizationParameter param in nullParams)
{
param.Value = double.NegativeInfinity;
}
Score nullScore = Score.GetInstance(0, nullParams, nullDistn);
OptimizationParameterList altParams = AltDistn.GetParameters();
foreach (OptimizationParameter param in altParams)
{
param.Value = double.NegativeInfinity;
}
Score altScore = Score.GetInstance(0, altParams, AltDistn);
return(EvaluationResultsGaussian.GetInstance(this, nullScore, altScore, predNonMissing, targNonMissing, globalNonMissingCount, ChiSquareDegreesOfFreedom));
}
