/// <summary>
/// THe most challenging function. This calculates the probability for changing to each state, and the time left before changing.
/// It takes into account the fact that some states have been half in on state and half in another during it's wait.
/// </summary>
/// <param name="bayesNode">The node we are calculating the probability for.</param>
/// <returns></returns>
private BayesNode calculateNextProb(BayesNode bayesNode)
{
if (!isCurrentlyChangeable(bayesNode))
return bayesNode;
if(bayesNode.VariableName == "lightning")
Console.Write("f");
//I dunno... something default. Assuming default prob is 1.0/n
double[] probsSum = new double[bayesNode.States.Count];
for(uint i=0; i!=probsSum.Length; ++i)
if(i!=bayesNode.ObservedValue)
probsSum[i] = 1/(probsSum.Length-1);
probsSum[bayesNode.ObservedValue] = 1.0;
int count = 1;
double[] avgProb = new double[bayesNode.States.Count];
foreach (BayesEdge be in getNodesInfluences(bayesNode.VariableName).Values)
{
//The duration is stored as the negative sum, at the x,x index for state x.
string influencersState;
if (be.CIMs.ContainsKey(influencersState = be.NodeA.States[(int)be.NodeA.ObservedValue]))
{
for (int i = 0; i != probsSum.Length; ++i)
probsSum[i] += be.CIMs[influencersState][bayesNode.ObservedValue][i];
++count;
}
}
for (int i = 0; i != probsSum.Length; ++i)
avgProb[i] = probsSum[i]/count;
double time = avgProb[bayesNode.ObservedValue];
//this if statement is merely for initialization from what I remember.
if (bayesNode.timeLeft == 0)
{
bayesNode.timeLeft = time;
bayesNode.nextVarProbs = avgProb;
}
else
{
//this assumes timeLeft is being updated somewhere else.
//This is the amount of time left ratio. So the new state only affects the var as much as it should.
double modifier = bayesNode.timeLeft / (bayesNode.nextVarProbs[bayesNode.ObservedValue]);
//Apply modifier to probs
for(int i=0; i!=avgProb.Length; ++i)
avgProb[i] *= modifier;
//get new probs. Generally they shouldn't change much. Ie. if only 2 variables effect the node,
//then it may change a lot, but if 10 variables effect the node it will change very little (1/10).
for (int i = 0; i != avgProb.Length; ++i)
bayesNode.nextVarProbs[i] = (bayesNode.nextVarProbs[i] + avgProb[i]) / 2;
bayesNode.nextVarProbs[bayesNode.ObservedValue] = (bayesNode.timeLeft = bayesNode.nextVarProbs.Sum() - bayesNode.nextVarProbs[bayesNode.ObservedValue]);
}
return bayesNode;
}
private BayesNode linearlyInfer(BayesNode bayesNode)
{
Random r = new Random();
//Normalized dice roll. // -1, because we disclude the current state. We are CHANGING states.
double diceRoll = r.NextDouble() * (bayesNode.nextVarProbs.Sum() - 1);
double currentSum = 0.0;
for (uint i = 0; i != bayesNode.nextVarProbs.Length; ++i)
{
if(i != bayesNode.ObservedValue) //discluding the time element.
if (diceRoll <= (currentSum += bayesNode.nextVarProbs[i]))
{
bayesNode.ObservedValue = i; //set the new state.
break;
}
}
return bayesNode;
}
/// <summary>
/// checks to see if the node can change under the current circumstances.
/// </summary>
/// <param name="n">the node in question.</param>
/// <returns></returns>
public bool isCurrentlyChangeable(BayesNode n)
{
foreach (BayesEdge be in getNodesInfluences(n.VariableName).Values)
if (be.CIMs.ContainsKey(be.NodeA.States[(int)be.NodeA.ObservedValue]))
return true;
return false;
}
