protected override double[][] DefaultDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList discreteParameters)
{
double lambda = discreteParameters[(int)ParameterIndex.Lambda].Value;
double x = discreteParameters[(int)ParameterIndex.Equilibrium].Value;
// in phylogenies, Lambda = site-specific mutation rate (in literature, "gamma"), Length = time*average mutation rate,
// e^(-lambda*t) = Pr[no mutation), and 1 - e^(-lambda*t) = Pr[mutation]
double expExp = 1.0 - Math.Exp(-lambda * branchOrLeaf.Length);
//Debug.Assert(expExp > 0);
// in phylogenies, X = equilibrium frequency of this A.A.
double[][] yDist = new double[][] { new double[2], new double[2] };
yDist[0][1] = x * expExp; // Pr[mutation AND mutationToTrue]
yDist[0][0] = 1.0 - yDist[0][1]; // that is, Pr[mutation AND mutationToFalse] + Pr[no mutation] = (1-X)*expExp + (1 - expExp) = 1 - X*expExp
yDist[1][0] = (1 - x) * expExp; // Pr[mutation AND mutationToFalse]
yDist[1][1] = 1.0 - yDist[1][0];
#if DEBUG
//foreach (double[] dArr in yDist)
//{
// foreach (double d in dArr)
// Debug.Assert(!double.IsNaN(d) && d >= 0 && d <= 1);
//}
#endif
return(yDist);
}
//public virtual bool UsePredictorVariable(bool useParameter)
//{
// return useParameter;
//}
public double[][] CreateDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList discreteParameters,
Converter <Leaf, SufficientStatistics> predictorClassFunction)
{
if (branchOrLeaf is Branch)
{
return(CreateDistribution((Branch)branchOrLeaf, discreteParameters));
}
else
{
return(CreateDistribution((Leaf)branchOrLeaf, discreteParameters, predictorClassFunction));
}
}
public ConditionalGaussianDistribution CreateDistributionGaussianOrNull(
BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters, Converter <Leaf, SufficientStatistics> predictorLeafToBoolStats)
{
if (branchOrLeaf is Branch)
{
return(CreateDistributionGaussianOrNull((Branch)branchOrLeaf, gaussianParameters));
}
else
{
return(CreateDistributionGaussianOrNull((Leaf)branchOrLeaf, gaussianParameters, predictorLeafToBoolStats));
}
}
public static Branch GetBranchInstance(StreamReader streamreader, bool isRoot)
{
Branch branch = new Branch();
branch.IsRoot = isRoot;
//!!!fix it so that it can parse empty lists
bool first = true;
while (true)
{
char peek = Peek(streamreader);
if (!first && peek == ')')
{
break;
}
char c = Read(streamreader);
if (first)
{
SpecialFunctions.CheckCondition(c == '(');
first = false;
}
else
{
SpecialFunctions.CheckCondition(c == ',');
}
branch.BranchOrLeafCollection.Add(BranchOrLeaf.GetInstance(streamreader, false));
}
char rightChar = Read(streamreader);
SpecialFunctions.CheckCondition(rightChar == ')');
return(branch);
}
protected abstract double[][] DefaultDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList discreteParameters);
protected override ConditionalGaussianDistribution GetPlainConditionalGaussianDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters)
{
double alphaTimesVariance = gaussianParameters["AlphaVariance"].Value;
ConditionalGaussianDistribution plainConditionalDistribution = ConditionalGaussianDistribution.GetInstance();
// 0 (const part of mean) ax + b, Mean := b
plainConditionalDistribution.Mean = 0;
// 1 (ax + b = x), linearCoefficent := a.
plainConditionalDistribution.LinearCoefficent = 1;
plainConditionalDistribution.Variance = alphaTimesVariance * branchOrLeaf.Length;
return(plainConditionalDistribution);
}
protected override ConditionalGaussianDistribution GetPlainConditionalGaussianDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters)
{
double alphaTimesVariance = gaussianParameters["AlphaVariance"].Value;
double mean = gaussianParameters["Mean"].Value;
double alpha = gaussianParameters["Alpha"].Value;
ConditionalGaussianDistribution plainConditionalDistribution = ConditionalGaussianDistribution.GetInstance();
double root1MinusAlphaTimesBranchLength = Math.Sqrt(1 - alpha * branchOrLeaf.Length);
// ax + b, Mean := b
plainConditionalDistribution.Mean = mean * (1.0 - root1MinusAlphaTimesBranchLength);
// ax + b = x, LinearCoefficent := a
plainConditionalDistribution.LinearCoefficent = root1MinusAlphaTimesBranchLength;
plainConditionalDistribution.Variance = alphaTimesVariance * branchOrLeaf.Length;
return(plainConditionalDistribution);
}
protected abstract ConditionalGaussianDistribution GetPlainConditionalGaussianDistribution(
BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters);
protected override ConditionalGaussianDistributionParams GetPlainConditionalGaussianDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters)
{
// NOTE: For BM, interpret Alpha as AlphaTimesVariance. Alpha is only used to multiply against variance, and searching is easier we we do
// it this way, as otherwise alphaTimesVariance is affected by both alpha and variance when we're searching.
double alphaTimesVariance = gaussianParameters["Alpha"].Value;
//double alpha = gaussianParameters["Alpha"].Value;
//double variance = gaussianParameters["Variance"].Value;
// 0 (const part of mean) ax + b, Mean := b
double mean = 0;
// 1 (ax + b = x), linearCoefficent := a.
double linearCoefficient = 1;
//double conditionalVariance = alpha * variance * branchOrLeaf.Length;
double conditionalVariance = alphaTimesVariance * branchOrLeaf.Length;
ConditionalGaussianDistributionParams plainConditionalDistribution =
ConditionalGaussianDistributionParams.GetInstance(mean, conditionalVariance, linearCoefficient);
return(plainConditionalDistribution);
}
protected override ConditionalGaussianDistributionParams GetPlainConditionalGaussianDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters)
{
double variance = gaussianParameters["Variance"].Value;
ConditionalGaussianDistributionParams plainConditionalDistribution =
ConditionalGaussianDistributionParams.GetInstance(0, variance, 1);
return(plainConditionalDistribution);
}
protected override ConditionalGaussianDistributionParams GetPlainConditionalGaussianDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList gaussianParameters)
{
double variance = gaussianParameters["Variance"].Value;
//double alphaTimesVariance = gaussianParameters["AlphaVariance"].Value;
double mean = gaussianParameters["Mean"].Value;
double alpha = gaussianParameters["Alpha"].Value;
double fOfBranchLength = FOfBranchLength(branchOrLeaf.Length, alpha);
//ConditionalGaussianDistributionParams plainConditionalDistribution = ConditionalGaussianDistributionParams.GetInstance();
//double root1MinusAlphaTimesBranchLength = Math.Sqrt(1 - alpha * branchOrLeaf.Length);
double root1MinusFofBranchLength = Math.Sqrt(1 - FOfBranchLength(branchOrLeaf.Length, alpha));
// ax + b, Mean := b
double meanForGaussDistParams = mean * (1.0 - root1MinusFofBranchLength);
//double meanForGausDistParams = mean * (1.0 - root1MinusAlphaTimesBranchLength);
// ax + b = x, LinearCoefficent := a
double linearCoeffForGaussDistParams = root1MinusFofBranchLength;
//double linearCoeffForGausDistParams = root1MinusAlphaTimesBranchLength;
//double varForGausDistParams = alphaTimesVariance * branchOrLeaf.Length;
double varianceForGaussDistParams = fOfBranchLength * variance;
ConditionalGaussianDistributionParams plainConditionalDistribution =
ConditionalGaussianDistributionParams.GetInstance(meanForGaussDistParams, varianceForGaussDistParams, linearCoeffForGaussDistParams);
return(plainConditionalDistribution);
}
protected override double[][] DefaultDistribution(BranchOrLeaf branchOrLeaf, OptimizationParameterList parameters)
{
double[][] P = GetTransitionProbabilityMatrix(parameters, branchOrLeaf.Length);
return(P);
}
