public static void Message_Tranfer_Arbol_Union(MarkovNet arboldeunion, Evidence evidence )
{
Generic(evidence,null,arboldeunion);
}
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, Evidence evidence)
{
Generic(evidence, null, arboldeunion);
}
/// <summary>
/// Metodo que calcula, mediante el traspaso de mensajes en un poliarbol,
/// las probabilidades condicionales de un nodo dada la evidencia.
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="poliarbol"></param>
/// <param name="evidence">Tiene tantos elementos como nodos el poliarbol.
/// En la posicion i-esima guarda el valor del estado de la variable i si esta forma parte de la evidencia;
/// -1 en otro caso.
/// </param>
public static void Message_Tranfer_Poliarbol(BayesianGraph poliarbol, Evidence evidence)
{
Generic(evidence, poliarbol, null);
}
/// <summary>
/// Realiza todas las realizaciones de una evidencia estocastica
/// </summary>
/// <param name="evidence"></param>
/// <param name="realization"></param>
/// <param name="pos"></param>
/// <returns></returns>
static IEnumerable <Tuple <KeyValuePair <BayesianNode, int>[], double> > EvidenceRealization(Evidence evidence, KeyValuePair <BayesianNode, int>[] realization, int pos, double probability = 1.0)
{
if (pos == realization.Length)
{
if (realization.Any(x => x.Value >= 0))
{
yield return(new Tuple <KeyValuePair <BayesianNode, int>[], double>(realization, probability));
}
yield break;
}
//en cada paso recursivo se escoge un nodo para su seleccion de estado
//por cada probabilidad de estar en un estado de el nodo en la posicion pos
bool outofevidence = true;
for (int i = 0; i < evidence.EvidenceValues[pos].Length; i++)
{
if (evidence.EvidenceValues[pos][i] > 0)
{
realization[pos] = new KeyValuePair <BayesianNode, int>(realization[pos].Key, i);
outofevidence = false;
var p = probability * evidence.EvidenceValues[pos][i];
foreach (var item in EvidenceRealization(evidence, realization, pos + 1, p))
{
yield return(item);
}
}
}
//si no esta en la evidencia entonces relization[pos]=bayesiannode,-1
if (outofevidence)
{
foreach (var item in EvidenceRealization(evidence, realization, pos + 1, probability))
{
yield return(item);
}
}
}
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);
}
}
/// <summary>
/// Metodo que calcula, mediante el traspaso de mensajes en un poliarbol,
/// las probabilidades condicionales de un nodo dada la evidencia.
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="poliarbol"></param>
/// <param name="evidence">Tiene tantos elementos como nodos el poliarbol.
/// En la posicion i-esima guarda el valor del estado de la variable i si esta forma parte de la evidencia;
/// -1 en otro caso.
/// </param>
public static void Message_Tranfer_Poliarbol(BayesianGraph poliarbol, Evidence evidence)
{
Generic(evidence, poliarbol, null);
}
/// <summary>
/// Realiza todas las realizaciones de una evidencia estocastica
/// </summary>
/// <param name="evidence"></param>
/// <param name="realization"></param>
/// <param name="pos"></param>
/// <returns></returns>
static IEnumerable<Tuple<KeyValuePair<BayesianNode, int>[],double>> EvidenceRealization(Evidence evidence, KeyValuePair<BayesianNode, int>[] realization, int pos,double probability=1.0)
{
if (pos == realization.Length)
{
if(realization.Any(x=>x.Value>=0))
yield return new Tuple<KeyValuePair<BayesianNode, int>[], double>(realization,probability);
yield break;
}
//en cada paso recursivo se escoge un nodo para su seleccion de estado
//por cada probabilidad de estar en un estado de el nodo en la posicion pos
bool outofevidence = true;
for (int i = 0; i < evidence.EvidenceValues[pos].Length; i++)
{
if (evidence.EvidenceValues[pos][i] > 0)
{
realization[pos] = new KeyValuePair<BayesianNode, int>(realization[pos].Key, i);
outofevidence = false;
var p = probability*evidence.EvidenceValues[pos][i];
foreach (var item in EvidenceRealization(evidence, realization, pos + 1,p))
yield return item;
}
}
//si no esta en la evidencia entonces relization[pos]=bayesiannode,-1
if(outofevidence)
foreach (var item in EvidenceRealization(evidence, realization, pos + 1,probability))
yield return item;
}
