public ProbabilityTable pointwiseProductPOS(
ProbabilityTable multiplier, params RandomVariable[] prodVarOrder)
{
ProbabilityTable product = new ProbabilityTable(prodVarOrder);
if (!product.randomVarInfo.keySet().Equals(
SetOps.union(new List <RandomVariable>(randomVarInfo.keySet()),
new List <RandomVariable>(multiplier.randomVarInfo
.keySet()))))
{
if (1 == product.getValues().Length)
{
product.getValues()[0] = getValues()[0] * multiplier.getValues()[0];
}
else
{
// Otherwise need to iterate through the product
// to calculate its values based on the terms.
Object[] term1Values = new Object[randomVarInfo.size()];
Object[] term2Values = new Object[multiplier.randomVarInfo
.size()];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// private int idx = 0;
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// int term1Idx = termIdx(term1Values, ProbabilityTable.this,
// possibleWorld);
// int term2Idx = termIdx(term2Values, multiplier,
// possibleWorld);
// product.getValues()[idx] = getValues()[term1Idx]
// * multiplier.getValues()[term2Idx];
// idx++;
// }
// private int termIdx(Object[] termValues, ProbabilityTable d,
// Map<RandomVariable, Object> possibleWorld) {
// if (0 == termValues.Length) {
// // The term has no variables so always position 0.
// return 0;
// }
// int i = 0;
// for (RandomVariable rv : d.randomVarInfo.keySet()) {
// termValues[i] = possibleWorld.get(rv);
// i++;
// }
// return d.getIndex(termValues);
// }
//};
//product.iterateOverTable(di);
// TODO
}
}
return(product);
}
public ProbabilityTable pointwiseProduct(ProbabilityTable multiplier)
{
List <RandomVariable> prodVars = SetOps.union(new List <RandomVariable>(randomVarInfo.Keys),
new List <RandomVariable>(multiplier.randomVarInfo.Keys));
return(pointwiseProductPOS(multiplier, prodVars
.ToArray()));
}
public FullJointDistributionModel(double[] values, params RandomVariable[] vars)
{
if (null == vars)
{
throw new ArgumentException(
"Random Variables describing the model's representation of the World need to be specified.");
}
distribution = new ProbabilityTable(values, vars);
representation = new Set<RandomVariable>();
for (int i = 0; i < vars.Length; i++)
{
representation.add(vars[i]);
}
//representation = Collections.unmodifiableSet(representation);
}
public CPT(RandomVariable on, double[] values,
params RandomVariable [] conditionedOn) {
this.on = on;
if (null == conditionedOn) {
conditionedOn = new RandomVariable[0];
}
RandomVariable[] tableVars = new RandomVariable[conditionedOn.Length + 1];
for (int i = 0; i < conditionedOn.Length; i++) {
tableVars[i] = conditionedOn[i];
parents.add(conditionedOn[i]);
}
tableVars[conditionedOn.Length] = on;
table = new ProbabilityTable(values, tableVars);
onDomain.AddRange(((FiniteDomain) on.getDomain()).getPossibleValues());
checkEachRowTotalsOne();
}
public ProbabilityTable sumOut(params RandomVariable[] vars)
{
Set <RandomVariable> soutVars = new Set <RandomVariable>(
this.randomVarInfo.keySet());
foreach (RandomVariable rv in vars)
{
soutVars.remove(rv);
}
ProbabilityTable summedOut = new ProbabilityTable(soutVars);
if (1 == summedOut.getValues().Length)
{
summedOut.getValues()[0] = getSum();
}
else
{
// Otherwise need to iterate through this distribution
// to calculate the summed out distribution.
Object[] termValues = new Object[summedOut.randomVarInfo
.size()];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// int i = 0;
// foreach (RandomVariable rv in summedOut.randomVarInfo.keySet()) {
// termValues[i] = possibleWorld.get(rv);
// i++;
// }
// summedOut.getValues()[summedOut.getIndex(termValues)] += probability;
// }
//};
//iterateOverTable(di);
//TODO:
}
return(summedOut);
}
public ProbabilityTable divideBy(ProbabilityTable divisor)
{
if (!randomVarInfo.keySet().containsAll(divisor.randomVarInfo.keySet()))
{
throw new IllegalArgumentException(
"Divisor must be a subset of the dividend.");
}
ProbabilityTable quotient = new ProbabilityTable(new List <RandomVariable>(randomVarInfo.Keys));
if (1 == divisor.getValues().Length)
{
double d = divisor.getValues()[0];
for (int i = 0; i < quotient.getValues().Length; i++)
{
if (0 == d)
{
quotient.getValues()[i] = 0;
}
else
{
quotient.getValues()[i] = getValues()[i] / d;
}
}
}
else
{
Set <RandomVariable> dividendDivisorDiff = SetOps
.difference(new List <RVInfo>(randomVarInfo.keySet()),
new List <RVInfo>(randomVarInfo.keySet()));
Map <RandomVariable, RVInfo> tdiff = null;
MixedRadixNumber tdMRN = null;
if (dividendDivisorDiff.size() > 0)
{
tdiff = new LinkedHashMap <RandomVariable, RVInfo>();
foreach (RandomVariable rv in dividendDivisorDiff)
{
tdiff.put(rv, new RVInfo(rv));
}
tdMRN = new MixedRadixNumber(0, createRadixs(tdiff));
}
Map <RandomVariable, RVInfo> diff = tdiff;
MixedRadixNumber dMRN = tdMRN;
int[] qRVs = new int[quotient.radices.Length];
MixedRadixNumber qMRN = new MixedRadixNumber(0,
quotient.radices);
//ProbabilityTable.Iterator divisorIterator = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// foreach (RandomVariable rv in possibleWorld.keySet()) {
// RVInfo rvInfo = quotient.randomVarInfo.get(rv);
// qRVs[rvInfo.getRadixIdx()] = rvInfo
// .getIdxForDomain(possibleWorld.get(rv));
// }
// if (null != diff) {
// // Start from 0 off the diff
// dMRN.setCurrentValueFor(new int[diff.size()]);
// do {
// for (RandomVariable rv : diff.keySet()) {
// RVInfo drvInfo = diff.get(rv);
// RVInfo qrvInfo = quotient.randomVarInfo.get(rv);
// qRVs[qrvInfo.getRadixIdx()] = dMRN
// .getCurrentNumeralValue(drvInfo
// .getRadixIdx());
// }
// updateQuotient(probability);
// } while (dMRN.increment());
// } else {
// updateQuotient(probability);
// }
// }
// //
//
//private void updateQuotient(double probability) {
// int offset = (int) qMRN.getCurrentValueFor(qRVs);
// if (0 == probability) {
// quotient.getValues()[offset] = 0;
// } else {
// quotient.getValues()[offset] += getValues()[offset]
// / probability;
// }
//}
////// };
// divisor.iterateOverTable(divisorIterator);
// TODO
}
return(quotient);
}
public Factor getFactorFor(params AssignmentProposition[] evidence) {
Set<RandomVariable> fofVars = new Set<RandomVariable>(
table.getFor());
foreach (AssignmentProposition ap in evidence) {
fofVars.Remove(ap.getTermVariable());
}
ProbabilityTable fof = new ProbabilityTable(new List<RandomVariable>(fofVars));
// Otherwise need to iterate through the table for the
// non evidence variables.
Object[] termValues = new Object[fofVars.Count];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// if (0 == termValues.length) {
// fof.getValues()[0] += probability;
// } else {
// int i = 0;
// for (RandomVariable rv : fof.getFor()) {
// termValues[i] = possibleWorld.get(rv);
// i++;
// }
// fof.getValues()[fof.getIndex(termValues)] += probability;
// }
// }
//};
//table.iterateOverTable(di, evidence);
// TODO: another screwed up iterator
return fof;
}
public CategoricalDistribution getConditioningCase(
params AssignmentProposition[] parentValues) {
if (parentValues.Length != parents.Count) {
throw new ArgumentException(
"The number of parent value arguments ["
+ parentValues.Length
+ "] is not equal to the number of parents ["
+ parents.Count + "] for this CPT.");
}
ProbabilityTable cc = new ProbabilityTable(getOn());
//ProbabilityTable.Iterator pti = new ProbabilityTable.Iterator() {
// private int idx = 0;
// public void iterate(Map<RandomVariable, Object> possibleAssignment,
// double probability) {
// cc.getValues()[idx] = probability;
// idx++;
// }
//};
//table.iterateOverTable(pti, parentValues);
// TODO: this is screwed up
return cc;
}
public CategoricalDistribution jointDistribution(
params IProposition[] propositions)
{
ProbabilityTable d = null;
IProposition conjProp = ProbUtil
.constructConjunction(propositions);
LinkedHashSet<RandomVariable> vars = new LinkedHashSet<RandomVariable>(
conjProp.getUnboundScope());
if (vars.Count > 0)
{
RandomVariable[] distVars = new RandomVariable[vars.Count];
vars.CopyTo(distVars);
ProbabilityTable ud = new ProbabilityTable(distVars);
Object[] values = new Object[vars.Count];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// if (conjProp.holds(possibleWorld)) {
// int i = 0;
// for (RandomVariable rv : vars) {
// values[i] = possibleWorld.get(rv);
// i++;
// }
// int dIdx = ud.getIndex(values);
// ud.setValue(dIdx, ud.getValues()[dIdx] + probability);
// }
// }
//};
//distribution.iterateOverTable(di);
// TODO:
d = ud;
}
else
{
// No Unbound Variables, therefore just return
// the singular probability related to the proposition.
d = new ProbabilityTable();
d.setValue(0, prior(propositions));
}
return d;
}
public ProbabilityTable pointwiseProductPOS(
ProbabilityTable multiplier, params RandomVariable[] prodVarOrder)
{
ProbabilityTable product = new ProbabilityTable(prodVarOrder);
if (!product.randomVarInfo.keySet().Equals(
SetOps.union(new List<RandomVariable>(randomVarInfo.keySet()),
new List<RandomVariable>(multiplier.randomVarInfo
.keySet()))))
{
if (1 == product.getValues().Length)
{
product.getValues()[0] = getValues()[0]*multiplier.getValues()[0];
}
else
{
// Otherwise need to iterate through the product
// to calculate its values based on the terms.
Object[] term1Values = new Object[randomVarInfo.size()];
Object[] term2Values = new Object[multiplier.randomVarInfo
.size()];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// private int idx = 0;
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// int term1Idx = termIdx(term1Values, ProbabilityTable.this,
// possibleWorld);
// int term2Idx = termIdx(term2Values, multiplier,
// possibleWorld);
// product.getValues()[idx] = getValues()[term1Idx]
// * multiplier.getValues()[term2Idx];
// idx++;
// }
// private int termIdx(Object[] termValues, ProbabilityTable d,
// Map<RandomVariable, Object> possibleWorld) {
// if (0 == termValues.Length) {
// // The term has no variables so always position 0.
// return 0;
// }
// int i = 0;
// for (RandomVariable rv : d.randomVarInfo.keySet()) {
// termValues[i] = possibleWorld.get(rv);
// i++;
// }
// return d.getIndex(termValues);
// }
//};
//product.iterateOverTable(di);
// TODO
}
}
return product;
}
public ProbabilityTable pointwiseProduct(ProbabilityTable multiplier)
{
List<RandomVariable> prodVars = SetOps.union(new List<RandomVariable>(randomVarInfo.Keys),
new List<RandomVariable>(multiplier.randomVarInfo.Keys));
return pointwiseProductPOS(multiplier, prodVars
.ToArray());
}
public ProbabilityTable divideBy(ProbabilityTable divisor)
{
if (!randomVarInfo.keySet().containsAll(divisor.randomVarInfo.keySet()))
{
throw new IllegalArgumentException(
"Divisor must be a subset of the dividend.");
}
ProbabilityTable quotient = new ProbabilityTable(new List<RandomVariable>(randomVarInfo.Keys));
if (1 == divisor.getValues().Length)
{
double d = divisor.getValues()[0];
for (int i = 0; i < quotient.getValues().Length; i++)
{
if (0 == d)
{
quotient.getValues()[i] = 0;
}
else
{
quotient.getValues()[i] = getValues()[i]/d;
}
}
}
else
{
Set<RandomVariable> dividendDivisorDiff = SetOps
.difference(new List<RVInfo>(randomVarInfo.keySet()),
new List<RVInfo>(randomVarInfo.keySet()));
Map<RandomVariable, RVInfo> tdiff = null;
MixedRadixNumber tdMRN = null;
if (dividendDivisorDiff.size() > 0)
{
tdiff = new LinkedHashMap<RandomVariable, RVInfo>();
foreach (RandomVariable rv in dividendDivisorDiff)
{
tdiff.put(rv, new RVInfo(rv));
}
tdMRN = new MixedRadixNumber(0, createRadixs(tdiff));
}
Map<RandomVariable, RVInfo> diff = tdiff;
MixedRadixNumber dMRN = tdMRN;
int[] qRVs = new int[quotient.radices.Length];
MixedRadixNumber qMRN = new MixedRadixNumber(0,
quotient.radices);
//ProbabilityTable.Iterator divisorIterator = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// foreach (RandomVariable rv in possibleWorld.keySet()) {
// RVInfo rvInfo = quotient.randomVarInfo.get(rv);
// qRVs[rvInfo.getRadixIdx()] = rvInfo
// .getIdxForDomain(possibleWorld.get(rv));
// }
// if (null != diff) {
// // Start from 0 off the diff
// dMRN.setCurrentValueFor(new int[diff.size()]);
// do {
// for (RandomVariable rv : diff.keySet()) {
// RVInfo drvInfo = diff.get(rv);
// RVInfo qrvInfo = quotient.randomVarInfo.get(rv);
// qRVs[qrvInfo.getRadixIdx()] = dMRN
// .getCurrentNumeralValue(drvInfo
// .getRadixIdx());
// }
// updateQuotient(probability);
// } while (dMRN.increment());
// } else {
// updateQuotient(probability);
// }
// }
// //
//
//private void updateQuotient(double probability) {
// int offset = (int) qMRN.getCurrentValueFor(qRVs);
// if (0 == probability) {
// quotient.getValues()[offset] = 0;
// } else {
// quotient.getValues()[offset] += getValues()[offset]
// / probability;
// }
//}
////// };
// divisor.iterateOverTable(divisorIterator);
// TODO
}
return quotient;
}
public ProbabilityTable sumOut(params RandomVariable[] vars)
{
Set<RandomVariable> soutVars = new Set<RandomVariable>(
this.randomVarInfo.keySet());
foreach (RandomVariable rv in vars)
{
soutVars.remove(rv);
}
ProbabilityTable summedOut = new ProbabilityTable(soutVars);
if (1 == summedOut.getValues().Length)
{
summedOut.getValues()[0] = getSum();
}
else
{
// Otherwise need to iterate through this distribution
// to calculate the summed out distribution.
Object[] termValues = new Object[summedOut.randomVarInfo
.size()];
//ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
// public void iterate(Map<RandomVariable, Object> possibleWorld,
// double probability) {
// int i = 0;
// foreach (RandomVariable rv in summedOut.randomVarInfo.keySet()) {
// termValues[i] = possibleWorld.get(rv);
// i++;
// }
// summedOut.getValues()[summedOut.getIndex(termValues)] += probability;
// }
//};
//iterateOverTable(di);
//TODO:
}
return summedOut;
}
