public virtual void SetModelData(NetModelData priors)
{
genesT1Prior.ObservedValue = priors.genesT1Dist;
alphaPrior.ObservedValue = priors.alphaDist;
betaPrior.ObservedValue = priors.betaDist;
wPrior.ObservedValue = priors.wDist;
}
public virtual void SetModelDataInit(NetModelData priors)
{
genesT1Prior.ObservedValue = priors.genesT1Dist;
alphaPrior.ObservedValue = priors.alphaDist;
betaPrior.ObservedValue = priors.betaDist;
//wPrior.ObservedValue = priors.wDist;
Range geneRange = new Range(priors.wDist.Length);
Range geneWeightRange = new Range(priors.wDist.Length - 1);
// w[geneRange][geneWeightRange] = Variable.GaussianFromMeanAndVariance(0, Variable.GammaFromShapeAndRate(1, 1)).ForEach(geneRange, geneWeightRange);
//wPrior.ObservedValue = Variable.GaussianFromMeanAndVariance(0, Variable.GammaFromShapeAndRate(1, 1)).ForEach(geneRange, geneWeightRange);
}
static void Main(string[] args)
{
double[][] trainingData = new double[2][];
trainingData[0] = new double[] { 0.5, 0.6, 0.7, 0.24 };
trainingData[1] = new double[] { 0.4, 0.3, 0.55, 0.95 };
Gaussian alphaPrior = Gaussian.FromMeanAndVariance(1, 0.002);
Gaussian betaPrior = Gaussian.FromMeanAndVariance(1, 0.002);
Gaussian genesT1Prior = Gaussian.FromMeanAndVariance(1, 0.002);
Gaussian wPrior = Gaussian.FromMeanAndVariance(0, 1);
int nGenes = trainingData[0].Length;
NetModelData initPriors = new NetModelData(
Util.ArrayInit(nGenes, u => genesT1Prior),
Util.ArrayInit(nGenes, u => alphaPrior),
Util.ArrayInit(nGenes, u => betaPrior),
Util.ArrayInit(nGenes, u => Util.ArrayInit(nGenes - 1, t => wPrior))
); // w -> variance : Variable.GammaFromShapeAndRate(1, 1)
//Train the model
PertNetModel pertNetModel = new PertNetModel();
Console.WriteLine("number of genes: " + trainingData[0].Length);
pertNetModel.CreateModel(trainingData[0].Length);
pertNetModel.SetModelData(initPriors);
NetModelData posteriors1 = pertNetModel.InferModelData(trainingData);
Gaussian[][] x = posteriors1.wDist;
for (int i = 0; i < posteriors1.wDist.Length; i++)
{
for (int j = 0; j < posteriors1.wDist[i].Length; j++)
{
Console.WriteLine(posteriors1.wDist[i][j]);
}
}
//Console.WriteLine("Inferred w = " + posteriors1.wDist);
// Console.WriteLine("===================");
// Console.WriteLine(pertNetModel.w);
//////////////////////////
double[][] trainingData2 = new double[2][];
trainingData2[0] = new double[] { 0.25, 0.16, 0.73, 0.4 };
trainingData2[1] = new double[] { 0.94, 0.43, 0.25, 0.65 };
pertNetModel.SetModelData(posteriors1);
NetModelData posteriors2 = pertNetModel.InferModelData(trainingData2);
Console.ReadLine();
}
