private void doTraining(CreatePredictorVM vm, IEnumerable <PDBFile> trainingFiles, IDictionary <string, IDictionary <Residue, bool> > referenceInterface, IDictionary <string, IDictionary <string, ProteinGraph> > graphs)
{
var Feature = new AverageHydrophobicityFeature();
var values = new LinkedList <AgO <double, ResidueNode> >();
foreach (var trainingFile in trainingFiles)
{
var req = new RequestRasa(trainingFile);
req.RequestInDefaultContext();
var rasa = req.Rasavalues;
foreach (var entry in graphs[trainingFile.Name])
{
Feature.Compute(entry.Value);
//values.AddRange(entry.Value.Nodes.Select(node => new AgO<double, ResidueNode>(Feature.ValueOf(node.Data.Residue) * rasa[node.Data.Residue], node.D
//)));
}
}
Max = values.Max(v => v.Data1);
//var intervals = values.DivideByScoreAequidistant(val => val.Data1, vm.DivisionIntervals);
//var pts = new LinkedList<AgO<double, double>>();
//for (int i = 0; i < intervals.Length; i++)
//{
// var interval = intervals[i];
// var pt = new AgO<double, double>();
// pt.Data1 = ((double)i + 1) / intervals.Length;
// var iface = (interval.Sum(val => referenceInterface[val.Data2.Residue.PDB.Name][val.Data2.Residue] ? 1.0 : 0.0));
// pt.Data2 = iface / interval.Count();
// pts.Add(pt);
//}
//ValueFunction = new StepFunction(pts);
}
private Dictionary <string, Dictionary <Residue, bool> > doPrediction(PDBFile file, IDictionary <string, ProteinGraph> graphs)
{
var dict = new Dictionary <string, Dictionary <Residue, bool> >();
var Feature = new AverageHydrophobicityFeature();
var req = new RequestRasa(file);
req.RequestInDefaultContext();
var rasa = req.Rasavalues;
//Threshold = 0.5;
//foreach (var graph in graphs)
//{
// Feature.Compute(graph.Value);
// var valdict = new Dictionary<ResidueNode, double[]>();
// var edgeScores = new double[2, 2] { { 1.2, 0.6 }, { 0.6, 1.0 } };
// foreach (var node in graph.Value.Nodes)
// {
// var val = (Feature.ValueOf(node.Data.Residue) * rasa[node.Data.Residue]) / Max;
// valdict.Add(node, new double[2] { val, 1 - val });
// }
// var edgedict = new Dictionary<SimpleEdge<ResidueNode>, double[,]>();
// foreach (var edge in graph.Value.Edges)
// {
// edgedict.Add(edge, edgeScores);
// }
// var predDict = new Dictionary<Residue, bool>();
// dict.Add(graph.Key, predDict);
// var crfGraph = graph.Value.CreateGraph(valdict, edgedict);
// //var options = new MarginalizeByResamplingOptions(2, 200);
// //foreach (var node in crfNodes)
// //{
// // var probs = MarginalizeByResampling.Do(crfNodes, node, options);
// // predDict.Add(graph.Value.Nodes.First(nd => nd.Residue.Id.Equals(node.Id)).Residue, probs[0] >= Threshold);
// //}
// var result = SimpleSampling.Do(crfGraph, 2000);
// foreach (var node in crfGraph.Nodes)
// {
// var score1 = result[new Assign() { Node = node, Label = 0 }];
// var score2 = result[new Assign() { Node = node, Label = 1 }];
// var prob = score2 / (score1 + score2);
// predDict.Add(graph.Value.Nodes.First(nd => nd.Data.Residue.Id.Equals(node.Data.Id)).Residue, prob >= Threshold);
// }
//}
return(dict);
}
private void doTraining(CreatePredictorVM vm, IEnumerable <PDBFile> trainingFiles, IDictionary <string, IDictionary <Residue, bool> > referenceInterface, IDictionary <string, IDictionary <string, ProteinGraph> > graphs)
{
var Feature = new AverageHydrophobicityFeature();
var values = new LinkedList <AgO <double, ResidueNode> >();
foreach (var trainingFile in trainingFiles)
{
var req = new RequestRasa(trainingFile);
req.RequestInDefaultContext();
var rasa = req.Rasavalues;
foreach (var entry in graphs[trainingFile.Name])
{
Feature.Compute(entry.Value);
//values.AddRange(entry.Value.Nodes.Select(node => new AgO<double, ResidueNode>(Feature.ValueOf(node.Data.Residue) * rasa[node.Data.Residue], node)));
}
}
Max = values.Max(v => v.Data1);
}
private Dictionary <string, Dictionary <Residue, bool> > doPrediction(PDBFile file, IDictionary <string, ProteinGraph> graphs)
{
var dict = new Dictionary <string, Dictionary <Residue, bool> >();
var Feature = new AverageHydrophobicityFeature();
var req = new RequestRasa(file);
req.RequestInDefaultContext();
var rasa = req.Rasavalues;
foreach (var graph in graphs)
{
Feature.Compute(graph.Value);
var predDict = new Dictionary <Residue, bool>();
dict.Add(graph.Key, predDict);
foreach (var node in graph.Value.Nodes)
{
predDict.Add(node.Data.Residue, (Feature.ValueOf(node.Data.Residue) * rasa[node.Data.Residue]) / Max >= Threshold);
}
}
return(dict);
}