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AIMA-Java Core Algorithms from the book Artificial Intelligence a Modern Approach 3rd Ed.
package aima.core.probability.util;
import java.util.Collection;
import java.util.LinkedHashMap;
import java.util.LinkedHashSet;
import java.util.Map;
import java.util.Set;
import aima.core.probability.CategoricalDistribution;
import aima.core.probability.Factor;
import aima.core.probability.RandomVariable;
import aima.core.probability.domain.FiniteDomain;
import aima.core.probability.proposition.AssignmentProposition;
import aima.core.util.SetOps;
import aima.core.util.math.MixedRadixNumber;
/**
* A Utility Class for associating values with a set of finite Random Variables.
* This is also the default implementation of the CategoricalDistribution and
* Factor interfaces (as they are essentially dependent on the same underlying
* data structures).
*
* @author Ciaran O'Reilly
*/
public class ProbabilityTable implements CategoricalDistribution, Factor {
private double[] values = null;
//
private Map randomVarInfo = new LinkedHashMap();
private int[] radices = null;
private MixedRadixNumber queryMRN = null;
//
private String toString = null;
private double sum = -1;
/**
* Interface to be implemented by an object/algorithm that wishes to iterate
* over the possible assignments for the random variables comprising this
* table.
*
* @see ProbabilityTable#iterateOverTable(Iterator)
* @see ProbabilityTable#iterateOverTable(Iterator,
* AssignmentProposition...)
*/
public interface Iterator {
/**
* Called for each possible assignment for the Random Variables
* comprising this ProbabilityTable.
*
* @param possibleAssignment
* a possible assignment, ω, of variable/value pairs.
* @param probability
* the probability associated with ω
*/
void iterate(Map possibleAssignment,
double probability);
}
public ProbabilityTable(Collection vars) {
this(vars.toArray(new RandomVariable[vars.size()]));
}
public ProbabilityTable(RandomVariable... vars) {
this(new double[ProbUtil.expectedSizeOfProbabilityTable(vars)], vars);
}
public ProbabilityTable(double[] vals, RandomVariable... vars) {
if (null == vals) {
throw new IllegalArgumentException("Values must be specified");
}
if (vals.length != ProbUtil.expectedSizeOfProbabilityTable(vars)) {
throw new IllegalArgumentException("ProbabilityTable of length "
+ vals.length + " is not the correct size, should be "
+ ProbUtil.expectedSizeOfProbabilityTable(vars)
+ " in order to represent all possible combinations.");
}
if (null != vars) {
for (RandomVariable rv : vars) {
// Track index information relevant to each variable.
randomVarInfo.put(rv, new RVInfo(rv));
}
}
values = new double[vals.length];
System.arraycopy(vals, 0, values, 0, vals.length);
radices = createRadixs(randomVarInfo);
if (radices.length > 0) {
queryMRN = new MixedRadixNumber(0, radices);
}
}
public int size() {
return values.length;
}
//
// START-ProbabilityDistribution
@Override
public Set getFor() {
return randomVarInfo.keySet();
}
@Override
public boolean contains(RandomVariable rv) {
return randomVarInfo.keySet().contains(rv);
}
@Override
public double getValue(Object... assignments) {
return values[getIndex(assignments)];
}
@Override
public double getValue(AssignmentProposition... assignments) {
if (assignments.length != randomVarInfo.size()) {
throw new IllegalArgumentException(
"Assignments passed in is not the same size as variables making up probability table.");
}
int[] radixValues = new int[assignments.length];
for (AssignmentProposition ap : assignments) {
RVInfo rvInfo = randomVarInfo.get(ap.getTermVariable());
if (null == rvInfo) {
throw new IllegalArgumentException(
"Assignment passed for a variable that is not part of this probability table:"
+ ap.getTermVariable());
}
radixValues[rvInfo.getRadixIdx()] = rvInfo.getIdxForDomain(ap
.getValue());
}
return values[(int) queryMRN.getCurrentValueFor(radixValues)];
}
// END-ProbabilityDistribution
//
//
// START-CategoricalDistribution
public double[] getValues() {
return values;
}
@Override
public void setValue(int idx, double value) {
values[idx] = value;
reinitLazyValues();
}
@Override
public double getSum() {
if (-1 == sum) {
sum = 0;
for (int i = 0; i < values.length; i++) {
sum += values[i];
}
}
return sum;
}
@Override
public ProbabilityTable normalize() {
double s = getSum();
if (s != 0 && s != 1.0) {
for (int i = 0; i < values.length; i++) {
values[i] = values[i] / s;
}
reinitLazyValues();
}
return this;
}
@Override
public int getIndex(Object... assignments) {
if (assignments.length != randomVarInfo.size()) {
throw new IllegalArgumentException(
"Assignments passed in is not the same size as variables making up the table.");
}
int[] radixValues = new int[assignments.length];
int i = 0;
for (RVInfo rvInfo : randomVarInfo.values()) {
radixValues[rvInfo.getRadixIdx()] = rvInfo
.getIdxForDomain(assignments[i]);
i++;
}
return (int) queryMRN.getCurrentValueFor(radixValues);
}
@Override
public CategoricalDistribution marginal(RandomVariable... vars) {
return sumOut(vars);
}
@Override
public CategoricalDistribution divideBy(CategoricalDistribution divisor) {
return divideBy((ProbabilityTable) divisor);
}
@Override
public CategoricalDistribution multiplyBy(CategoricalDistribution multiplier) {
return pointwiseProduct((ProbabilityTable) multiplier);
}
@Override
public CategoricalDistribution multiplyByPOS(
CategoricalDistribution multiplier, RandomVariable... prodVarOrder) {
return pointwiseProductPOS((ProbabilityTable) multiplier, prodVarOrder);
}
@Override
public void iterateOver(CategoricalDistribution.Iterator cdi) {
iterateOverTable(new CategoricalDistributionIteratorAdapter(cdi));
}
@Override
public void iterateOver(CategoricalDistribution.Iterator cdi,
AssignmentProposition... fixedValues) {
iterateOverTable(new CategoricalDistributionIteratorAdapter(cdi),
fixedValues);
}
// END-CategoricalDistribution
//
//
// START-Factor
@Override
public Set getArgumentVariables() {
return randomVarInfo.keySet();
}
@Override
public ProbabilityTable sumOut(RandomVariable... vars) {
Set soutVars = new LinkedHashSet(
this.randomVarInfo.keySet());
for (RandomVariable rv : vars) {
soutVars.remove(rv);
}
final 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.
final Object[] termValues = new Object[summedOut.randomVarInfo
.size()];
ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
public void iterate(Map possibleWorld,
double probability) {
int i = 0;
for (RandomVariable rv : summedOut.randomVarInfo.keySet()) {
termValues[i] = possibleWorld.get(rv);
i++;
}
summedOut.getValues()[summedOut.getIndex(termValues)] += probability;
}
};
iterateOverTable(di);
}
return summedOut;
}
@Override
public Factor pointwiseProduct(Factor multiplier) {
return pointwiseProduct((ProbabilityTable) multiplier);
}
@Override
public Factor pointwiseProductPOS(Factor multiplier,
RandomVariable... prodVarOrder) {
return pointwiseProductPOS((ProbabilityTable) multiplier, prodVarOrder);
}
@Override
public void iterateOver(Factor.Iterator fi) {
iterateOverTable(new FactorIteratorAdapter(fi));
}
@Override
public void iterateOver(Factor.Iterator fi,
AssignmentProposition... fixedValues) {
iterateOverTable(new FactorIteratorAdapter(fi), fixedValues);
}
// 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(Iterator pti) {
Map possibleWorld = new LinkedHashMap();
MixedRadixNumber mrn = new MixedRadixNumber(0, radices);
do {
for (RVInfo rvInfo : randomVarInfo.values()) {
possibleWorld.put(rvInfo.getVariable(), rvInfo
.getDomainValueAt(mrn.getCurrentNumeralValue(rvInfo
.getRadixIdx())));
}
pti.iterate(possibleWorld, values[mrn.intValue()]);
} while (mrn.increment());
}
/**
* 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(Iterator pti,
AssignmentProposition... fixedValues) {
Map possibleWorld = new LinkedHashMap();
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.
for (AssignmentProposition ap : fixedValues) {
if (!randomVarInfo.containsKey(ap.getTermVariable())) {
throw new IllegalArgumentException("Assignment proposition ["
+ ap + "] does not belong to this probability table.");
}
possibleWorld.put(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.size()) {
// Then only 1 iteration call is required.
pti.iterate(possibleWorld, getValue(fixedValues));
} else {
// Else iterate over the non-fixed values
Set freeVariables = SetOps.difference(
this.randomVarInfo.keySet(), possibleWorld.keySet());
Map freeVarInfo = new LinkedHashMap();
// Remove the fixed Variables
for (RandomVariable fv : 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
for (RVInfo freeRVI : 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 ProbabilityTable divideBy(ProbabilityTable divisor) {
if (!randomVarInfo.keySet().containsAll(divisor.randomVarInfo.keySet())) {
throw new IllegalArgumentException(
"Divisor must be a subset of the dividend.");
}
final ProbabilityTable quotient = new ProbabilityTable(randomVarInfo
.keySet());
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 dividendDivisorDiff = SetOps
.difference(this.randomVarInfo.keySet(),
divisor.randomVarInfo.keySet());
Map tdiff = null;
MixedRadixNumber tdMRN = null;
if (dividendDivisorDiff.size() > 0) {
tdiff = new LinkedHashMap();
for (RandomVariable rv : dividendDivisorDiff) {
tdiff.put(rv, new RVInfo(rv));
}
tdMRN = new MixedRadixNumber(0, createRadixs(tdiff));
}
final Map diff = tdiff;
final MixedRadixNumber dMRN = tdMRN;
final int[] qRVs = new int[quotient.radices.length];
final MixedRadixNumber qMRN = new MixedRadixNumber(0,
quotient.radices);
ProbabilityTable.Iterator divisorIterator = new ProbabilityTable.Iterator() {
public void iterate(Map possibleWorld,
double probability) {
for (RandomVariable rv : 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);
}
return quotient;
}
public ProbabilityTable pointwiseProduct(final ProbabilityTable multiplier) {
Set prodVars = SetOps.union(randomVarInfo.keySet(),
multiplier.randomVarInfo.keySet());
return pointwiseProductPOS(multiplier, prodVars
.toArray(new RandomVariable[prodVars.size()]));
}
public ProbabilityTable pointwiseProductPOS(
final ProbabilityTable multiplier, RandomVariable... prodVarOrder) {
final ProbabilityTable product = new ProbabilityTable(prodVarOrder);
if (!product.randomVarInfo.keySet().equals(
SetOps.union(randomVarInfo.keySet(), multiplier.randomVarInfo
.keySet()))) {
throw new IllegalArgumentException(
"Specified list deatailing order of mulitplier is inconsistent.");
}
// If no variables in the product
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.
final Object[] term1Values = new Object[randomVarInfo.size()];
final Object[] term2Values = new Object[multiplier.randomVarInfo
.size()];
ProbabilityTable.Iterator di = new ProbabilityTable.Iterator() {
private int idx = 0;
public void iterate(Map 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 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);
}
return product;
}
@Override
public String toString() {
if (null == toString) {
StringBuilder sb = new StringBuilder();
sb.append("<");
for (int i = 0; i < values.length; i++) {
if (i > 0) {
sb.append(", ");
}
sb.append(values[i]);
}
sb.append(">");
toString = sb.toString();
}
return toString;
}
//
// PRIVATE METHODS
//
private void reinitLazyValues() {
sum = -1;
toString = null;
}
private int[] createRadixs(Map mapRtoInfo) {
int[] r = new int[mapRtoInfo.size()];
// Read in reverse order so that the enumeration
// through the distributions is of the following
// order using a MixedRadixNumber, e.g. for two Booleans:
// X Y
// true true
// true false
// false true
// false false
// which corresponds with how displayed in book.
int x = mapRtoInfo.size() - 1;
for (RVInfo rvInfo : mapRtoInfo.values()) {
r[x] = rvInfo.getDomainSize();
rvInfo.setRadixIdx(x);
x--;
}
return r;
}
private class RVInfo {
private RandomVariable variable;
private FiniteDomain varDomain;
private int radixIdx = 0;
public RVInfo(RandomVariable rv) {
variable = rv;
varDomain = (FiniteDomain) variable.getDomain();
}
public RandomVariable getVariable() {
return variable;
}
public int getDomainSize() {
return varDomain.size();
}
public int getIdxForDomain(Object value) {
return varDomain.getOffset(value);
}
public Object getDomainValueAt(int idx) {
return varDomain.getValueAt(idx);
}
public void setRadixIdx(int idx) {
radixIdx = idx;
}
public int getRadixIdx() {
return radixIdx;
}
}
private class CategoricalDistributionIteratorAdapter implements Iterator {
private CategoricalDistribution.Iterator cdi = null;
public CategoricalDistributionIteratorAdapter(
CategoricalDistribution.Iterator cdi) {
this.cdi = cdi;
}
public void iterate(Map possibleAssignment,
double probability) {
cdi.iterate(possibleAssignment, probability);
}
}
private class FactorIteratorAdapter implements Iterator {
private Factor.Iterator fi = null;
public FactorIteratorAdapter(Factor.Iterator fi) {
this.fi = fi;
}
public void iterate(Map possibleAssignment,
double probability) {
fi.iterate(possibleAssignment, probability);
}
}
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