Uses of Interface
cc.mallet.grmm.types.Factor

Packages that use Factor

cc.mallet.grmm.inference
cc.mallet.grmm.inference.gbp
cc.mallet.grmm.learning
cc.mallet.grmm.types
cc.mallet.grmm.util

Uses of Factor in cc.mallet.grmm.inference

Methods in cc.mallet.grmm.inference that return Factor

Factor	MessageArray.ToMsgsIterator.currentMessage()
Factor	MessageArray.get(Factor from, Variable to)
Factor	MessageArray.get(java.lang.Object from, java.lang.Object to)
Factor	MessageArray.get(Variable from, Factor to)
Factor	JunctionTree.getCPF(VarSet c)
Factor	JunctionTree.getSepsetPot(VarSet v1, VarSet v2)
Factor	BruteForceInferencer.joint(FactorGraph model)
Factor	BruteForceInferencer.joint(JunctionTree jt)
Factor	AbstractBeliefPropagation.lookupMarginal(Variable var)
abstract Factor	AbstractInferencer.lookupMarginal(Variable variable)
Factor	JunctionTree.lookupMarginal(Variable var)
Factor	VariableElimination.lookupMarginal(Variable var)
Factor	Inferencer.lookupMarginal(Variable v) Returns the computed marginal of a given variable.
Factor	JunctionTreeInferencer.lookupMarginal(Variable var)
Factor	SamplingInferencer.lookupMarginal(Variable var)
Factor	BruteForceInferencer.lookupMarginal(Variable var)
Factor	AbstractBeliefPropagation.lookupMarginal(VarSet c)
Factor	AbstractInferencer.lookupMarginal(VarSet c)
Factor	Inferencer.lookupMarginal(VarSet varSet) Returns the computed marginal of a given clique in a graph.
Factor	JunctionTreeInferencer.lookupMarginal(VarSet varSet)
Factor	SamplingInferencer.lookupMarginal(VarSet varSet)
Factor	BruteForceInferencer.lookupMarginal(VarSet c)
Factor	AbstractBeliefPropagation.MessageStrategy.msgProduct(Factor product, int idx, int excludeMsgFrom)
Factor	AbstractBeliefPropagation.AbstractMessageStrategy.msgProduct(Factor product, int idx, int excludeMsgFrom)
Factor	MessageArray.ToMsgsIterator.next()
Factor	RandomGraphs.FactorGenerator.nextFactor(VarSet vars)
Factor	RandomGraphs.UniformFactorGenerator.nextFactor(VarSet vars)
Factor	TRP.query(DirectedModel m, Variable var)
Factor	VariableElimination.unnormalizedMarginal(FactorGraph model, Variable query) The bulk of the variable-elimination algorithm.

Methods in cc.mallet.grmm.inference with parameters of type Factor

Factor	MessageArray.get(Factor from, Variable to)
Factor	MessageArray.get(Variable from, Factor to)
int	MessageArray.getIndex(Factor from)
Factor	AbstractBeliefPropagation.MessageStrategy.msgProduct(Factor product, int idx, int excludeMsgFrom)
Factor	AbstractBeliefPropagation.AbstractMessageStrategy.msgProduct(Factor product, int idx, int excludeMsgFrom)
void	MessageArray.put(Factor from, Variable to, Factor msg)
void	MessageArray.put(int fromIdx, int toIdx, Factor msg)
void	MessageArray.put(Variable from, Factor to, Factor msg)
protected void	AbstractBeliefPropagation.sendMessage(FactorGraph mdl, Factor from, Variable to)
void	AbstractBeliefPropagation.MessageStrategy.sendMessage(FactorGraph mdl, Factor from, Variable to)
void	AbstractBeliefPropagation.SumProductMessageStrategy.sendMessage(FactorGraph mdl, Factor from, Variable to)
void	AbstractBeliefPropagation.MaxProductMessageStrategy.sendMessage(FactorGraph mdl, Factor from, Variable to)
protected void	AbstractBeliefPropagation.sendMessage(FactorGraph mdl, Variable from, Factor to)
void	AbstractBeliefPropagation.MessageStrategy.sendMessage(FactorGraph mdl, Variable from, Factor to)
void	AbstractBeliefPropagation.SumProductMessageStrategy.sendMessage(FactorGraph mdl, Variable from, Factor to)
void	AbstractBeliefPropagation.MaxProductMessageStrategy.sendMessage(FactorGraph mdl, Variable from, Factor to)
void	JunctionTree.setCPF(VarSet c, Factor pot)

Uses of Factor in cc.mallet.grmm.inference.gbp

Methods in cc.mallet.grmm.inference.gbp that return Factor

Factor	ParentChildGBP.lookupMarginal(Variable variable)
Factor	ParentChildGBP.lookupMarginal(VarSet varSet)

Uses of Factor in cc.mallet.grmm.learning

Classes in cc.mallet.grmm.learning that implement Factor

static class

ACRF.UnrolledGraph

Methods in cc.mallet.grmm.learning that return Factor

Factor

ACRF.UnrolledVarSet.getFactor()

Methods in cc.mallet.grmm.learning with parameters of type Factor

ACRF.UnrolledVarSet

ACRF.UnrolledGraph.getUnrolledVarSet(Factor f)

Uses of Factor in cc.mallet.grmm.types

Subinterfaces of Factor in cc.mallet.grmm.types

interface	DiscreteFactor $Id: DiscreteFactor.java,v 1.1 2007/10/22 21:37:44 mccallum Exp $
interface	ParameterizedFactor A factor that supports taking derivatives with respect to its continuous variables.

Classes in cc.mallet.grmm.types that implement Factor

class	AbstractFactor An Abstract class from which new Factor classes can be subclassed.
class	AbstractTableFactor Class for a multivariate multinomial distribution.
class	Assignment An assignment to a bunch of variables.
class	BetaFactor $Id: BetaFactor.java,v 1.1 2007/10/22 21:37:44 mccallum Exp $
class	BinaryUnaryFactor A factor over a continuous variable theta and binary variables var.
class	BoltzmannPairFactor A factor over a continuous variable theta and binary variables var.
class	BoltzmannUnaryFactor A factor over a continuous variable theta and binary variables var.
class	ConstantFactor $Id: ConstantFactor.java,v 1.1 2007/10/22 21:37:44 mccallum Exp $
class	CPT $Id: CPT.java,v 1.1 2007/10/22 21:37:44 mccallum Exp $
class	DirectedModel Class for directed graphical models.
class	FactorGraph Class for undirected graphical models.
class	LogTableFactor Created: Jan 4, 2006
class	NormalFactor Multivariate Gaussian factor.
class	PottsTableFactor A factor over a continuous variable alpha and discrete variables x such that phi(x|alpha) is Potts.
class	SkeletonFactor A subclass of Factor in which all operations throw an UnsupportedOperationException.
class	TableFactor Created: Jan 4, 2006
class	UndirectedGrid A grid-shaped undirected graphical model.
class	UndirectedModel Class for pairwise undirected graphical models, also known as pairwise Markov random fields.
class	UniformFactor $Id: UniformFactor.java,v 1.1 2007/10/22 21:37:44 mccallum Exp $
class	UniNormalFactor Univariate Gaussian factor.

Methods in cc.mallet.grmm.types that return Factor

static Factor	Factors.asFactor(Inferencer inf) Adapter that allows an Inferencer to be treated as if it were a factor.
static Factor	Factors.average(Factor ptl1, Factor ptl2, double weight)
Factor	SkeletonFactor.duplicate()
Factor	CPT.duplicate()
Factor	UniNormalFactor.duplicate()
Factor	Factor.duplicate()
Factor	NormalFactor.duplicate()
Factor	BoltzmannUnaryFactor.duplicate()
abstract Factor	AbstractTableFactor.duplicate()
Factor	LogTableFactor.duplicate()
Factor	ConstantFactor.duplicate()
Factor	FactorGraph.duplicate() Returns a copy of this model.
Factor	UniformFactor.duplicate()
Factor	BinaryUnaryFactor.duplicate()
Factor	PottsTableFactor.duplicate()
Factor	TableFactor.duplicate()
Factor	Assignment.duplicate()
Factor	BoltzmannPairFactor.duplicate()
Factor	BetaFactor.duplicate()
Factor	CPT.extractMax(java.util.Collection vars)
Factor	Factor.extractMax(java.util.Collection vars) Returns a potential phi over the given variables obtained by taking phi (x) = max_[all v that contain x] this.prob (x)
Factor	AbstractTableFactor.extractMax(java.util.Collection vars)
Factor	FactorGraph.extractMax(java.util.Collection vars)
Factor	AbstractFactor.extractMax(java.util.Collection vars)
Factor	CPT.extractMax(Variable var)
Factor	Factor.extractMax(Variable var) Returns a potential phi over the given variables obtained by taking phi (x) = max_[all v that contain x] this.prob (x)
Factor	AbstractTableFactor.extractMax(Variable var)
Factor	FactorGraph.extractMax(Variable var)
Factor	AbstractFactor.extractMax(Variable var)
Factor	CPT.extractMax(Variable[] vars)
Factor	Factor.extractMax(Variable[] vars) Returns a potential phi over the given variables obtained by taking phi (x) = max_[all v that contain x] this.prob (x)
Factor	AbstractTableFactor.extractMax(Variable[] vars)
Factor	FactorGraph.extractMax(Variable[] vars)
Factor	AbstractFactor.extractMax(Variable[] vars)
protected Factor	SkeletonFactor.extractMaxInternal(VarSet varSet)
protected Factor	UniNormalFactor.extractMaxInternal(VarSet varSet)
protected Factor	NormalFactor.extractMaxInternal(VarSet varSet)
protected Factor	BoltzmannUnaryFactor.extractMaxInternal(VarSet varSet)
protected Factor	ConstantFactor.extractMaxInternal(VarSet varSet)
protected Factor	UniformFactor.extractMaxInternal(VarSet varSet)
protected Factor	BinaryUnaryFactor.extractMaxInternal(VarSet varSet)
protected Factor	PottsTableFactor.extractMaxInternal(VarSet varSet)
protected Factor	Assignment.extractMaxInternal(VarSet varSet)
protected Factor	BoltzmannPairFactor.extractMaxInternal(VarSet varSet)
protected Factor	BetaFactor.extractMaxInternal(VarSet varSet)
protected abstract Factor	AbstractFactor.extractMaxInternal(VarSet varSet)
Factor	FactorGraph.factorOf(java.util.Collection c) Searches the graphical model for a factor over the given collection of variables.
Factor	FactorGraph.factorOf(Variable var) Returns the factor for a given node.
Factor	FactorGraph.factorOf(Variable var1, Variable var2) Returns the factor defined over a given pair of variables.
Factor	FactorGraph.factorOf(VarSet varSet) Returns the factor in this graph, if any, whose domain is a given clique.
Factor	FactorGraph.getFactor(int i)
Factor	AbstractFactor.log()
static Factor	ConstantFactor.makeIdentityFactor()
static Factor	AbstractTableFactor.makeIdentityFactor(AbstractTableFactor copy) STATIC FACTORY METHODS
Factor	CPT.marginalize(java.util.Collection vars)
Factor	Factor.marginalize(java.util.Collection vars) Returns the marginal of this distribution over the given variables.
Factor	AbstractTableFactor.marginalize(java.util.Collection vars)
Factor	FactorGraph.marginalize(java.util.Collection vars)
Factor	AbstractFactor.marginalize(java.util.Collection vars)
Factor	CPT.marginalize(Variable var)
Factor	Factor.marginalize(Variable var) Returns the marginal of this distribution over one variable.
Factor	AbstractTableFactor.marginalize(Variable var)
Factor	FactorGraph.marginalize(Variable var)
Factor	AbstractFactor.marginalize(Variable var)
Factor	CPT.marginalize(Variable[] vars)
Factor	Factor.marginalize(Variable[] vars) Returns the marginal of this distribution over the given variables.
Factor	AbstractTableFactor.marginalize(Variable[] vars) Returns the marginal of this distribution over the given variables.
Factor	FactorGraph.marginalize(Variable[] vars)
Factor	AbstractFactor.marginalize(Variable[] vars)
protected abstract Factor	AbstractTableFactor.marginalizeInternal(AbstractTableFactor result)
protected Factor	LogTableFactor.marginalizeInternal(AbstractTableFactor result)
protected Factor	TableFactor.marginalizeInternal(AbstractTableFactor result)
protected Factor	SkeletonFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	UniNormalFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	NormalFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	BoltzmannUnaryFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	ConstantFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	UniformFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	BinaryUnaryFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	PottsTableFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	Assignment.marginalizeInternal(VarSet varsToKeep)
protected Factor	BoltzmannPairFactor.marginalizeInternal(VarSet varsToKeep)
protected Factor	BetaFactor.marginalizeInternal(VarSet varsToKeep)
protected abstract Factor	AbstractFactor.marginalizeInternal(VarSet varsToKeep)
Factor	CPT.marginalizeOut(Variable var)
Factor	Factor.marginalizeOut(Variable var) Returns the marginal distribution attained by summing out the given variable.
Factor	AbstractTableFactor.marginalizeOut(Variable var)
Factor	FactorGraph.marginalizeOut(Variable var)
Factor	AbstractFactor.marginalizeOut(Variable var)
Factor	CPT.marginalizeOut(VarSet varset)
Factor	Factor.marginalizeOut(VarSet varset) Returns the marginal distribution attained by summing out the given set of variables.
Factor	AbstractTableFactor.marginalizeOut(VarSet badVars)
Factor	FactorGraph.marginalizeOut(VarSet varset)
Factor	AbstractFactor.marginalizeOut(VarSet varset)
static Factor	Factors.mix(AbstractTableFactor f1, AbstractTableFactor f2, double alpha) Returns a new Factor F = alpha * f1 + (1 - alpha) * f2.
Factor	CPT.multiply(Factor dist)
Factor	Factor.multiply(Factor dist) Returns the elementwise product of this factor with another.
Factor	AbstractTableFactor.multiply(Factor dist) Returns the elementwise product of this potential and another one.
Factor	ConstantFactor.multiply(Factor other)
Factor	FactorGraph.multiply(Factor dist)
Factor	AbstractFactor.multiply(Factor dist)
static Factor	Factors.multiplyAll(java.util.Collection factors)
Factor	SkeletonFactor.normalize()
Factor	CPT.normalize()
Factor	UniNormalFactor.normalize()
Factor	Factor.normalize() Multiplies this potential by a constant such that it sums to 1.
Factor	NormalFactor.normalize()
Factor	BoltzmannUnaryFactor.normalize()
abstract Factor	AbstractTableFactor.normalize()
Factor	LogTableFactor.normalize()
Factor	ConstantFactor.normalize()
Factor	FactorGraph.normalize()
Factor	UniformFactor.normalize()
Factor	BinaryUnaryFactor.normalize()
Factor	PottsTableFactor.normalize()
Factor	TableFactor.normalize() Multiplies every entry in the potential by a constant such that all the entries sum to 1.
Factor	Assignment.normalize()
Factor	BoltzmannPairFactor.normalize()
Factor	BetaFactor.normalize()
protected abstract Factor	AbstractTableFactor.slice_general(Variable[] vars, Assignment observed)
protected Factor	LogTableFactor.slice_general(Variable[] vars, Assignment observed)
protected Factor	TableFactor.slice_general(Variable[] vars, Assignment observed)
protected abstract Factor	AbstractTableFactor.slice_onevar(Variable var, Assignment observed)
protected Factor	LogTableFactor.slice_onevar(Variable var, Assignment observed)
protected Factor	TableFactor.slice_onevar(Variable var, Assignment observed) Creates a new potential from another by restricting it to a given assignment.
protected abstract Factor	AbstractTableFactor.slice_twovar(Variable v1, Variable v2, Assignment observed)
protected Factor	LogTableFactor.slice_twovar(Variable v1, Variable v2, Assignment observed)
protected Factor	TableFactor.slice_twovar(Variable v1, Variable v2, Assignment observed)
Factor	SkeletonFactor.slice(Assignment assn)
Factor	CPT.slice(Assignment assn)
Factor	UniNormalFactor.slice(Assignment assn)
Factor	Factor.slice(Assignment assn)
Factor	NormalFactor.slice(Assignment assn)
Factor	BoltzmannUnaryFactor.slice(Assignment assn)
Factor	AbstractTableFactor.slice(Assignment assn) Creates a new potential that is equal to this one, restricted to a given assignment.
Factor	ConstantFactor.slice(Assignment assn)
Factor	FactorGraph.slice(Assignment assn)
Factor	UniformFactor.slice(Assignment assn)
Factor	BinaryUnaryFactor.slice(Assignment assn)
Factor	PottsTableFactor.slice(Assignment assn)
Factor	Assignment.slice(Assignment assn)
Factor	BoltzmannPairFactor.slice(Assignment assn)
Factor	BetaFactor.slice(Assignment assn)
Factor	FactorGraph.slice(Assignment assn, java.util.Map toSlicedMap)

Methods in cc.mallet.grmm.types with parameters of type Factor

void	FactorGraph.addFactor(Factor factor) Adds a factor to the model.
void	UndirectedModel.addFactor(Factor factor)
protected void	FactorGraph.afterFactorAdd(Factor factor) Performs operations on a factor after it has been added to the model, such as caching.
protected void	DirectedModel.afterFactorAdd(Factor factor)
boolean	CPT.almostEquals(Factor p)
boolean	Factor.almostEquals(Factor p) Returns whether this is almost equal to another potential.
boolean	AbstractTableFactor.almostEquals(Factor p)
boolean	FactorGraph.almostEquals(Factor p)
boolean	AbstractFactor.almostEquals(Factor p)
boolean	SkeletonFactor.almostEquals(Factor p, double epsilon)
boolean	CPT.almostEquals(Factor p, double epsilon)
boolean	UniNormalFactor.almostEquals(Factor p, double epsilon)
boolean	Factor.almostEquals(Factor p, double epsilon)
boolean	NormalFactor.almostEquals(Factor p, double epsilon)
boolean	BoltzmannUnaryFactor.almostEquals(Factor p, double epsilon)
boolean	AbstractTableFactor.almostEquals(Factor p, double epsilon)
boolean	ConstantFactor.almostEquals(Factor p, double epsilon)
boolean	FactorGraph.almostEquals(Factor p, double epsilon)
boolean	UniformFactor.almostEquals(Factor p, double epsilon)
boolean	BinaryUnaryFactor.almostEquals(Factor p, double epsilon)
boolean	PottsTableFactor.almostEquals(Factor p, double epsilon)
boolean	Assignment.almostEquals(Factor p, double epsilon)
boolean	BoltzmannPairFactor.almostEquals(Factor p, double epsilon)
boolean	BetaFactor.almostEquals(Factor p, double epsilon)
static Factor	Factors.average(Factor ptl1, Factor ptl2, double weight)
protected void	FactorGraph.beforeFactorAdd(Factor factor) Performs checking of a factor before it is added to the model.
protected void	DirectedModel.beforeFactorAdd(Factor factor)
static int[]	Factors.computeSizes(Factor result)
static Variable[]	Factors.computeVars(Factor result)
static Variable[]	Factors.continuousVarsOf(Factor fg)
static double	Factors.corr(Factor factor)
static Variable[]	Factors.discreteVarsOf(Factor fg)
void	CPT.divideBy(Factor pot)
void	UniNormalFactor.divideBy(Factor f)
void	Factor.divideBy(Factor pot) Computes this /= pot
void	NormalFactor.divideBy(Factor f)
void	AbstractTableFactor.divideBy(Factor pot) Does the conceptual equivalent of this /= pot.
void	FactorGraph.divideBy(Factor pot)
void	UniformFactor.divideBy(Factor other)
void	BetaFactor.divideBy(Factor f)
void	AbstractFactor.divideBy(Factor pot)
int	FactorGraph.getIndex(Factor factor)
Factor	CPT.multiply(Factor dist)
Factor	Factor.multiply(Factor dist) Returns the elementwise product of this factor with another.
Factor	AbstractTableFactor.multiply(Factor dist) Returns the elementwise product of this potential and another one.
Factor	ConstantFactor.multiply(Factor other)
Factor	FactorGraph.multiply(Factor dist)
Factor	AbstractFactor.multiply(Factor dist)
static DiscreteFactor	TableFactor.multiplyAll(Factor[] phis)
void	CPT.multiplyBy(Factor pot)
void	UniNormalFactor.multiplyBy(Factor f)
void	Factor.multiplyBy(Factor pot) Does this *= pot.
void	NormalFactor.multiplyBy(Factor f)
void	AbstractTableFactor.multiplyBy(Factor pot) Does the conceptual equivalent of this *= pot.
void	ConstantFactor.multiplyBy(Factor other)
void	FactorGraph.multiplyBy(Factor pot)
void	UniformFactor.multiplyBy(Factor other)
void	BetaFactor.multiplyBy(Factor f)
void	AbstractFactor.multiplyBy(Factor pot)
static double	Factors.mutualInformation(Factor factor) Given a joint distribution over two variables, returns their mutual information.
static double	Factors.oneDistance(Factor bel1, Factor bel2)
void	AbstractTableFactor.plusEquals(Factor f)
double	PottsTableFactor.secondDerivative(Factor q, Variable param, Assignment theta)
double	ParameterizedFactor.sumGradLog(Factor q, Variable param, Assignment assn) Computes the expected derivative of the log factor value.
double	BoltzmannUnaryFactor.sumGradLog(Factor q, Variable param, Assignment paramAssn)
double	BinaryUnaryFactor.sumGradLog(Factor q, Variable param, Assignment paramAssn)
double	PottsTableFactor.sumGradLog(Factor q, Variable param, Assignment theta)
double	BoltzmannPairFactor.sumGradLog(Factor q, Variable param, Assignment paramAssn)

Constructors in cc.mallet.grmm.types with parameters of type Factor

FactorGraph(Factor[] factors)

Uses of Factor in cc.mallet.grmm.util

Classes in cc.mallet.grmm.util that implement Factor

class

LabelsAssignment A special kind of assignment for Variables that can be arranged in a LabelsSequence.