public override ChartElement Build()
{
BayesianNode node = new BayesianNode();
node.Name = Name;
node.PinSize = new Size(5, 5);
node.BackColor = Color.LightBlue;
node.Display = new Rectangle(Center.X - 100, Center.Y - 25, 130, 30);
node.ShowOutputPins = false;
node.ShowInputPins = false;
return(node);
}
public void Marginalize_X(BayesianNode Xj)
{
BayesianClique Ci = null;
int min = int.MaxValue;
foreach (var c in TreeCluster)
{
if (c.Nodes.Contains(Xj) && c.Nodes.Count < min)
{
Ci = c;
}
}
if (Ci != null)
{
Marginalize_Xj(Xj, Ci);
}
}
private void Marginalize_Xj(BayesianNode Xj, BayesianClique Ci)
{
if (TreeCluster == null)
{
;
}
//var clique = TreeCluster.Aggregate(default(BayesianClique), (x, y) => (x == null || y.Nodes.Contains(Xj) && y.Nodes.Count < x.Nodes.Count) ? y : x);
//if (clique == default(BayesianClique)) throw new Exception("El nodo no pertenece al arbol de union");
//var index = clique.Nodes.IndexOf(Xj);
var index = Ci.Nodes.IndexOf(Xj);
for (int i = 0; i < Xj.ConditionalProbability.Length; i++)
{
Xj.ConditionalProbability[i] = Math.Round(Ci.ConditionalProbability.Where(x => StringToList(x.Key)[index] == i).Select(y => y.Value).Sum(), 3);
}
}
public override ChartElement Build()
{
BayesianNode node = new BayesianNode();
node.Name = Name;
node.PinSize = new Size(5, 5);
node.BackColor = Color.LightBlue;
node.Display = new Rectangle(Center.X - 100, Center.Y - 25, 130, 30);
node.ShowOutputPins = false;
node.ShowInputPins = false;
return node;
}
private void computarProbabilidadesToolStripMenuItem_Click(object sender, EventArgs e)
{
tabControl1.SelectTab(0);
try
{
if (CheckConsistency())
{
//obtener la evidencia
if (flowChartViewer1.Charts.Count > 0 && Evidence.Count > 0)
GetEvidence();
//transformar el grafo del visor a un grafo de nodos bayesianos
var graph = new BayesianGraph();
//
var dict = new Dictionary<BayesianNode, BayesianNodeChartElement>(flowChartViewer1.Charts.Count);
BayesianNode node;
var dict2 = new Dictionary<BayesianNodeChartElement, BayesianNode>(flowChartViewer1.Charts.Count);
graph.Clear();
foreach (var item in flowChartViewer1.Charts.Cast<BayesianNodeChartElement>())
{
node = new BayesianNode(item.Parents.Count, item.Childs.Count(), item.States.Count,
item.Parents.Select(x => x.States.Count).ToArray()) { Probabilities = item.Condicional_Probabilities, Name = item.Name };
dict.Add(node, item);
dict2.Add(item,node);
item.CreateConditionalProbabilities();
graph.AddVertex(node);
}
foreach (var bnodechart in flowChartViewer1.Charts.Cast<BayesianNodeChartElement>())
{
foreach (var child in bnodechart.Childs)
{
graph.AddEdge(dict2[bnodechart], dict2[child]);
}
}
//ejecutar el traspaso de mensajes en un poliarbol o en una red multiplemente conexa
progressBar1.Value = 5;
//if(MessageTransfer.ParalellStartNumber>graph.Count)
MessageTransfer.Increment += UpdateProgress;
if (graph.Arbol())
{
if (Evidence.Count > 0)
MessageTransfer.Message_Tranfer_Poliarbol(graph, Evidence[EvidenceIndex]);
else
MessageTransfer.Message_Tranfer_Poliarbol(graph);
foreach (var nodebayesian in graph)
dict[nodebayesian].SetConditionalProbabilities(nodebayesian.ConditionalProbability);
}
else
{
var markovnet = new MarkovNet(graph);
if (Evidence.Count > 0)
MessageTransfer.Message_Tranfer_Arbol_Union(markovnet, Evidence[EvidenceIndex]);
else
MessageTransfer.Message_Tranfer_Arbol_Union(markovnet);
foreach (var nodebayesian in markovnet.BayesianNet)
dict[nodebayesian].SetConditionalProbabilities(nodebayesian.ConditionalProbability);
}
timer1.Stop();
progressBar1.Value=100;
Tests.Add(new BayesianTest()
{
Evidence = Evidence[EvidenceIndex].Clone(),
Probabilities =
graph.Select(x => x.ConditionalProbability.Clone() as double[]).ToArray()
});
TestsIndex = Tests.Count - 1;
ShowConditionalProbabilities();
}
}
catch (Exception ex)
{
MessageBox.Show("Hubo algun problema al calcular las probabilidades condicionales. " + ex.Message);
}
}
