public static void Message_Tranfer_Arbol_Union(MarkovNet arboldeunion, Evidence evidence )
{
Generic(evidence,null,arboldeunion);
}
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);
}
}
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);
}
}
static void Generic(Evidence evidence, BayesianGraph poliarbol, MarkovNet arboldeunion)
{
//tamanno del grafo sea poliarbol o de racimos
var count = poliarbol == null
? arboldeunion.Count
: poliarbol.Count;
double realization_probalility = 1.0;
var nodesConditionalProbabilities = new double[count][];
var bayesianGraph = poliarbol ?? arboldeunion.BayesianNet;
bool traspasoEnPoliarbol = poliarbol != null;
if (evidence == null) return;
var deterministicEvidence = new KeyValuePair<BayesianNode, int>[evidence.EvidenceValues.Length];
//absorber evidencia
//no hay evidencia
for (int i = 0; i < count; i++)
deterministicEvidence[i] = new KeyValuePair<BayesianNode, int>(bayesianGraph[i], -1);
for (int j = 0; j < count; j++)
nodesConditionalProbabilities[j] = new double[bayesianGraph[j].ConditionalProbability.Length];
bool stocasticrealizations = false;
foreach (var item in EvidenceRealization(evidence, deterministicEvidence, 0))
{
stocasticrealizations = true;
if (traspasoEnPoliarbol)
Message_Tranfer_Poliarbol(poliarbol, item.Item1);
else
Message_Tranfer_Arbol_Union(arboldeunion, item.Item1);
realization_probalility = item.Item2;
for (int i = 0; i < nodesConditionalProbabilities.Length; i++)
for (int j = 0; j < nodesConditionalProbabilities[i].Length; j++)
{
nodesConditionalProbabilities[i][j] += bayesianGraph[i].ConditionalProbability[j] * realization_probalility;
}
}
//si no hubo evidencia estocastica es que se esta solicitando el traspaso de
//msges sin evidencia
if (stocasticrealizations)
{
for (int i = 0; i < nodesConditionalProbabilities.Length; i++)
for (int j = 0; j < nodesConditionalProbabilities[i].Length; j++)
bayesianGraph[i].ConditionalProbability[j] = nodesConditionalProbabilities[i][j];
}
else
{
if (traspasoEnPoliarbol)
Message_Tranfer_Poliarbol(poliarbol, deterministicEvidence);
else
Message_Tranfer_Arbol_Union(arboldeunion, deterministicEvidence);
}
}
public static void Message_Tranfer_Arbol_Union(MarkovNet arboldeunion, KeyValuePair<BayesianNode, int>[] evidence = null)
{
//absorber evidence
if (evidence != null && evidence.Length > 0)
{
arboldeunion.BayesianNet.Evidence = evidence.Select(x => x.Value).ToArray();
}
if (arboldeunion.Count() > ParalellStartNumber)
{
#region ParalellAlgorithm
arboldeunion.Debug = true;
var taskarr = new Task[arboldeunion.Count];
for (int i = 0; i < arboldeunion.Count; i++)
{
taskarr[i] = new Task(o =>
{
var node = o as BayesianNode;
if (node != null)
lock (node)
{
do
{
arboldeunion.Work(node);
} while (!node.Finished);
}
}, arboldeunion[i]);
taskarr[i].Start();
}
for (int i = 0; i < arboldeunion.Count*8; i++)
{
System.Threading.Thread.Sleep(300);
Increment();
}
Task.WaitAll(taskarr);
arboldeunion.Debug = false;
#endregion
}
else
#region DeterministicAlgorithm
while (!arboldeunion.AllConditionalProbabilitiesComputed())
{
foreach (var item in arboldeunion)
{
arboldeunion.Work(item);
}
Increment();
}
#endregion
foreach (var node in arboldeunion.BayesianNet)
arboldeunion.Marginalize_X(node);
}
