/** * Pointwise multiplication of this Factor by a given multiplier, creating a * new Factor representing the product of the two.<br> * <br> * see: AIMA3e Figure 14.10 page 527.<br> * <br> * Note: Default Factor multiplication is not commutative. The reason is * because the order of the variables comprising a Factor dictate the * ordering of the values for that factor. The default order of the * variables of the Factor returned is the order of the variables as they * are seen, as read from the left to right term, for e.g.: <br> * <br> * f<sub>1</sub>(Y)f<sub>2</sub>(X, Y)<br> * <br> * would give a Factor of the following form: <br> * Y, X<br> * <br> * i.e. an ordered union of the variables from the two factors. <br> * To override the default order of the product use pointwiseProductPOS(). * * @param multiplier * * @return a new Factor representing the pointwise product of this and the * passed in multiplier. The order of the variables comprising the * product factor is the ordered union of the left term (this) and * the right term (multiplier). * * @see Factor#pointwiseProductPOS(Factor, RandomVariable...) */ abstract public Factor pointwiseProduct(Factor multiplier);
/**
* Pointwise Multiplication - Product Order Specified (POS).<br>
* <br>
* see: AIMA3e Figure 14.10 page 527.<br>
* <br>
* Pointwise multiplication of this Factor by a given multiplier, creating a
* new Factor representing the product of the two. The order of the
* variables comprising the product will match those specified.
*
* @param multiplier
* @param prodVarOrder
* the order the variables comprising the product are to be in.
*
* @return a new Factor representing the pointwise product of this and the
* passed in multiplier. The order of the variables comprising the
* product distribution is the order specified.
*
* @see Factor#pointwiseProduct(Factor)
*/
abstract public Factor pointwiseProductPOS(Factor multiplier,
params RandomVariable[] prodVarOrder);
/**
* Iterate over all possible values assignments for the Random Variables
* comprising this ProbabilityTable that are not in the fixed set of values.
* This allows you to iterate over a subset of possible combinations.
*
* @param pti
* the ProbabilityTable Iterator to iterate
* @param fixedValues
* Fixed values for a subset of the Random Variables comprising
* this Probability Table.
*/
public void iterateOverTable(Factor.Iterator pti,
params AssignmentProposition[] fixedValues)
{
Map<RandomVariable, Object> possibleWorld = new LinkedHashMap<RandomVariable, Object>();
MixedRadixNumber tableMRN = new MixedRadixNumber(0, radices);
int[] tableRadixValues = new int[radices.Length];
// Assert that the Random Variables for the fixed values
// are part of this probability table and assign
// all the fixed values to the possible world.
foreach (AssignmentProposition ap in fixedValues)
{
if (!randomVarInfo.ContainsKey(ap.getTermVariable()))
{
throw new ArgumentException("Assignment proposition ["
+ ap + "] does not belong to this probability table.");
}
possibleWorld.Add(ap.getTermVariable(), ap.getValue());
RVInfo fixedRVI = randomVarInfo.get(ap.getTermVariable());
tableRadixValues[fixedRVI.getRadixIdx()] = fixedRVI
.getIdxForDomain(ap.getValue());
}
// If have assignments for all the random variables
// in this probability table
if (fixedValues.Length == randomVarInfo.Count)
{
// Then only 1 iteration call is required.
pti.iterate(possibleWorld, getValue(fixedValues));
}
else
{
// Else iterate over the non-fixed values
List<RandomVariable> freeVariables = SetOps.difference(
new List<RandomVariable>(randomVarInfo.Keys), new List<RandomVariable>(possibleWorld.Keys));
Map<RandomVariable, RVInfo> freeVarInfo = new LinkedHashMap<RandomVariable, RVInfo>();
// Remove the fixed Variables
foreach (RandomVariable fv in freeVariables)
{
freeVarInfo.put(fv, new RVInfo(fv));
}
int[] freeRadixValues = createRadixs(freeVarInfo);
MixedRadixNumber freeMRN = new MixedRadixNumber(0, freeRadixValues);
Object fval = null;
// Iterate through all combinations of the free variables
do
{
// Put the current assignments for the free variables
// into the possible world and update
// the current index in the table MRN
foreach (RVInfo freeRVI in freeVarInfo.values())
{
fval = freeRVI.getDomainValueAt(freeMRN
.getCurrentNumeralValue(freeRVI.getRadixIdx()));
possibleWorld.put(freeRVI.getVariable(), fval);
tableRadixValues[randomVarInfo.get(freeRVI.getVariable())
.getRadixIdx()] = freeRVI.getIdxForDomain(fval);
}
pti.iterate(possibleWorld, values[(int) tableMRN
.getCurrentValueFor(tableRadixValues)]);
} while (freeMRN.increment());
}
}
public FactorIteratorAdapter(Factor.Iterator fi)
{
this.fi = fi;
}
// END-Factor
//
/**
* Iterate over all the possible value assignments for the Random Variables
* comprising this ProbabilityTable.
*
* @param pti
* the ProbabilityTable Iterator to iterate.
*/
public void iterateOverTable(Factor.Iterator pti)
{
Map<RandomVariable, Object> possibleWorld = new LinkedHashMap<RandomVariable, Object>();
MixedRadixNumber mrn = new MixedRadixNumber(0, radices);
do
{
foreach (RVInfo rvInfo in randomVarInfo.Values)
{
possibleWorld.put(rvInfo.getVariable(), rvInfo
.getDomainValueAt(mrn.getCurrentNumeralValue(rvInfo
.
getRadixIdx
())));
}
pti.iterate(possibleWorld, values[mrn.intValue()]);
} while (mrn.increment());
}
public void iterateOver(Factor.Iterator fi,
params AssignmentProposition[] fixedValues)
{
iterateOverTable(new FactorIteratorAdapter(fi), fixedValues);
}
public void iterateOver(Factor.Iterator fi)
{
iterateOverTable(new FactorIteratorAdapter(fi));
}
public Factor pointwiseProductPOS(Factor multiplier,
params RandomVariable[] prodVarOrder)
{
return pointwiseProductPOS((ProbabilityTable) multiplier, prodVarOrder);
}
public Factor pointwiseProduct(Factor multiplier)
{
return pointwiseProduct((ProbabilityTable) multiplier);
}