public static GWGraph <TrainingInputNodeData, TrainingInputEdgeData, TrainingInputGraphData> ExampleData()
{
var rdm = new Random();
var graph = GWGraphpackageOne.GWString <TrainingInputNodeData, TrainingInputEdgeData, TrainingInputGraphData>(6);
graph.Data = new TrainingInputGraphData();
graph.Data.ObservationToCRFScoreProbability = new double[5, 5];
for (int i = 0; i < 5; i++)
{
for (int k = 0; k < 5; k++)
{
graph.Data.ObservationToCRFScoreProbability[i, k] = rdm.NextDouble();
}
}
foreach (var node in graph.Nodes)
{
node.Data = new TrainingInputNodeData();
node.Data.Observation = rdm.Next(5);
}
foreach (var edge in graph.Edges)
{
edge.Data = new TrainingInputEdgeData();
}
return(graph);
}
static void Main(string[] args)
{
CRFBase.Build.Do();
// als Referenz folgende Klasse:
//var oldWorkFlow = new WorkflowPede();
//WorkflowPede.Start(null);
{ // example Gradient Descent
//var request = new ExecuteGradientDescent();
//request.Request();
//Console.Write(request.Result);
}
{ // example viterbi
var graph = GWGraphpackageOne.GWString <CRFNodeData, CRFEdgeData, CRFGraphData>(12);
foreach (var node in graph.Nodes)
{
node.Data = new CRFNodeData();
node.Data.Scores = new double[] { 1, 2 };
}
foreach (var edge in graph.Edges)
{
edge.Data = new CRFEdgeData();
edge.Data.Scores = new double[, ] {
{ -11, 1 }, { 2, -22 }
};
}
var request = new SolveInference(graph, 2, 100);
request.Request();
foreach (var entry in request.Solution.Labeling)
{
Console.Write(entry);
Console.ReadKey();
}
}
}