public override Dictionary <string, SufficientStatistics> GenerateRandomMapping(Dictionary <string, SufficientStatistics> realCaseIdToNonMissingValue, ref Random random)
{
//!!!!put check in to make sure ISufficientSTatistics is reall BooleanStatistics
Converter <Leaf, SufficientStatistics> leafToDistnClassFunction = PhyloDDriver.CreateSufficientStatisticsMap(realCaseIdToNonMissingValue);
PhyloTree tree = _modelScorer.PhyloTree;
MessageInitializer messageInitializer = MessageInitializerDiscrete.GetInstance(leafToDistnClassFunction, _discreteDistribution, new int[] { 1, 1 }, tree.LeafCollection);
Score score = _modelScorer.MaximizeLikelihood(messageInitializer);
double percentNonMissing = (double)tree.CountOfNonMissingLeaves(realCaseIdToNonMissingValue) /
(double)SpecialFunctions.Count(tree.LeafCollection);
double equilibrium = score.OptimizationParameters[(int)DistributionDiscreteConditional.ParameterIndex.Equilibrium].Value;
double lambda = score.OptimizationParameters[(int)DistributionDiscreteConditional.ParameterIndex.Lambda].Value;
Dictionary <string, BooleanStatistics> randomCaseIdToNonMissingValue = tree.EvolveBinaryTree(equilibrium, lambda, 1 - percentNonMissing, ref random);
Dictionary <string, SufficientStatistics> converted;
SpecialFunctions.ConvertDictionaryToBaseClasses(randomCaseIdToNonMissingValue, out converted);
return(converted);
}
protected override string CreateReportLine(
ModelScorer modelScorer,
PhyloTree phyloTree,
RowData rowAndTargetData,
UniversalWorkList workList,
int rowIndex, int workListCount, int workIndex)
{
//!!!there is very similar code in ModelTesterDiscrete.cs
Dictionary <string, string> row = rowAndTargetData.Row;
string predictorVariable = row[PhyloTree.PredictorVariableColumnName];
string targetVariable = row[PhyloTree.TargetVariableColumnName]; // e.g. A@182 (amino acid "A" at position 182)
int nullIndex = int.Parse(row[PhyloTree.NullIndexColumnName]);
//Dictionary<string, bool> caseIdToNonNullPredictorValue = workList.NullIndexToPredictorToCaseIdToNonMissingValue[nullIndex][predictorVariable];
Dictionary <string, SufficientStatistics> caseIdToNonNullPredictorValue = rowAndTargetData.PredictorData; //workList.GetCaseIdToNonMissingValueForNullIndexAndPredictorVariable(nullIndex, predictorVariable);
Dictionary <string, SufficientStatistics> caseIdToNonMissingTargetValue = rowAndTargetData.TargetData;
Converter <Leaf, SufficientStatistics> targetDistributionMap = CreateSufficientStatisticsMap(caseIdToNonMissingTargetValue);
Converter <Leaf, SufficientStatistics> predictorDistributionClassFunction = CreateSufficientStatisticsMap(caseIdToNonNullPredictorValue);
int[] predictorCounts = phyloTree.CountsOfLeaves(predictorDistributionClassFunction);
int predictorFalseNameCount = predictorCounts[(int)DistributionDiscreteBinary.DistributionClass.False];
int predictorTrueNameCount = predictorCounts[(int)DistributionDiscreteBinary.DistributionClass.True];
int targetNonMissingCount = phyloTree.CountOfNonMissingLeaves(caseIdToNonMissingTargetValue);
int globalNonMissingCount = phyloTree.GlobalNonMissingCount(predictorDistributionClassFunction, targetDistributionMap);
StringBuilder stringBuilder = new StringBuilder(
SpecialFunctions.CreateTabString(
this, rowIndex, workListCount, workIndex, nullIndex, predictorVariable,
predictorFalseNameCount,
predictorTrueNameCount,
predictorTrueNameCount + predictorFalseNameCount,
targetVariable,
targetNonMissingCount,
globalNonMissingCount,
""));
bool ignoreRow = false;
foreach (int count in predictorCounts)
{
if (count == 0)
{
ignoreRow = true;
}
}
if (ignoreRow)
{
CompleteRowWithNaN(stringBuilder);
}
else
{
List <double> logLikelihoodList = new List <double>();
MessageInitializer messageInitializer =
modelScorer.CreateMessageInitializer(predictorDistributionClassFunction, targetDistributionMap, NullModelDistribution);
NullModelDistribution.InitialParamVals = null;
foreach (bool useParameter in new bool[] { false, true })
{
Score score = modelScorer.ScoreModel(messageInitializer, useParameter);
stringBuilder.Append(SpecialFunctions.CreateTabString(score, ""));
Debug.Write(SpecialFunctions.CreateTabString(score, ""));
logLikelihoodList.Add(score.Loglikelihood);
AltModelDistribution.InitialParamVals = score.OptimizationParameters;
}
double diff = logLikelihoodList[1] - logLikelihoodList[0];
double pValue = SpecialFunctions.LogLikelihoodRatioTest(Math.Max(diff, 0), ChiSquareDegreesOfFreedom);
stringBuilder.Append(SpecialFunctions.CreateTabString(diff, pValue));
Debug.WriteLine(SpecialFunctions.CreateTabString(diff, pValue));
}
return(stringBuilder.ToString());
}
