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PREV NEXT | FRAMES NO FRAMES |
Packages that use Factor | |
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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 |
VariableElimination.lookupMarginal(Variable var)
|
Factor |
SamplingInferencer.lookupMarginal(Variable var)
|
Factor |
JunctionTreeInferencer.lookupMarginal(Variable var)
|
Factor |
JunctionTree.lookupMarginal(Variable var)
|
Factor |
Inferencer.lookupMarginal(Variable v)
Returns the computed marginal of a given variable. |
Factor |
BruteForceInferencer.lookupMarginal(Variable var)
|
abstract Factor |
AbstractInferencer.lookupMarginal(Variable variable)
|
Factor |
AbstractBeliefPropagation.lookupMarginal(Variable var)
|
Factor |
SamplingInferencer.lookupMarginal(VarSet varSet)
|
Factor |
JunctionTreeInferencer.lookupMarginal(VarSet varSet)
|
Factor |
Inferencer.lookupMarginal(VarSet varSet)
Returns the computed marginal of a given clique in a graph. |
Factor |
BruteForceInferencer.lookupMarginal(VarSet c)
|
Factor |
AbstractInferencer.lookupMarginal(VarSet c)
|
Factor |
AbstractBeliefPropagation.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 |
UniNormalFactor.duplicate()
|
Factor |
UniformFactor.duplicate()
|
Factor |
TableFactor.duplicate()
|
Factor |
SkeletonFactor.duplicate()
|
Factor |
PottsTableFactor.duplicate()
|
Factor |
NormalFactor.duplicate()
|
Factor |
LogTableFactor.duplicate()
|
Factor |
FactorGraph.duplicate()
Returns a copy of this model. |
Factor |
Factor.duplicate()
|
Factor |
CPT.duplicate()
|
Factor |
ConstantFactor.duplicate()
|
Factor |
BoltzmannUnaryFactor.duplicate()
|
Factor |
BoltzmannPairFactor.duplicate()
|
Factor |
BinaryUnaryFactor.duplicate()
|
Factor |
BetaFactor.duplicate()
|
Factor |
Assignment.duplicate()
|
abstract Factor |
AbstractTableFactor.duplicate()
|
Factor |
FactorGraph.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 |
CPT.extractMax(java.util.Collection vars)
|
Factor |
AbstractTableFactor.extractMax(java.util.Collection vars)
|
Factor |
AbstractFactor.extractMax(java.util.Collection vars)
|
Factor |
FactorGraph.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 |
CPT.extractMax(Variable var)
|
Factor |
AbstractTableFactor.extractMax(Variable var)
|
Factor |
AbstractFactor.extractMax(Variable var)
|
Factor |
FactorGraph.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 |
CPT.extractMax(Variable[] vars)
|
Factor |
AbstractTableFactor.extractMax(Variable[] vars)
|
Factor |
AbstractFactor.extractMax(Variable[] vars)
|
protected Factor |
UniNormalFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
UniformFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
SkeletonFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
PottsTableFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
NormalFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
ConstantFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
BinaryUnaryFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
BetaFactor.extractMaxInternal(VarSet varSet)
|
protected Factor |
Assignment.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 |
FactorGraph.marginalize(java.util.Collection vars)
|
Factor |
Factor.marginalize(java.util.Collection vars)
Returns the marginal of this distribution over the given variables. |
Factor |
CPT.marginalize(java.util.Collection vars)
|
Factor |
AbstractTableFactor.marginalize(java.util.Collection vars)
|
Factor |
AbstractFactor.marginalize(java.util.Collection vars)
|
Factor |
FactorGraph.marginalize(Variable var)
|
Factor |
Factor.marginalize(Variable var)
Returns the marginal of this distribution over one variable. |
Factor |
CPT.marginalize(Variable var)
|
Factor |
AbstractTableFactor.marginalize(Variable var)
|
Factor |
AbstractFactor.marginalize(Variable var)
|
Factor |
FactorGraph.marginalize(Variable[] vars)
|
Factor |
Factor.marginalize(Variable[] vars)
Returns the marginal of this distribution over the given variables. |
Factor |
CPT.marginalize(Variable[] vars)
|
Factor |
AbstractTableFactor.marginalize(Variable[] vars)
Returns the marginal of this distribution over the given variables. |
Factor |
AbstractFactor.marginalize(Variable[] vars)
|
protected Factor |
TableFactor.marginalizeInternal(AbstractTableFactor result)
|
protected Factor |
LogTableFactor.marginalizeInternal(AbstractTableFactor result)
|
protected abstract Factor |
AbstractTableFactor.marginalizeInternal(AbstractTableFactor result)
|
protected Factor |
UniNormalFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
UniformFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
SkeletonFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
PottsTableFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
NormalFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
ConstantFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
BinaryUnaryFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
BetaFactor.marginalizeInternal(VarSet varsToKeep)
|
protected Factor |
Assignment.marginalizeInternal(VarSet varsToKeep)
|
protected abstract Factor |
AbstractFactor.marginalizeInternal(VarSet varsToKeep)
|
Factor |
FactorGraph.marginalizeOut(Variable var)
|
Factor |
Factor.marginalizeOut(Variable var)
Returns the marginal distribution attained by summing out the given variable. |
Factor |
CPT.marginalizeOut(Variable var)
|
Factor |
AbstractTableFactor.marginalizeOut(Variable var)
|
Factor |
AbstractFactor.marginalizeOut(Variable var)
|
Factor |
FactorGraph.marginalizeOut(VarSet varset)
|
Factor |
Factor.marginalizeOut(VarSet varset)
Returns the marginal distribution attained by summing out the given set of variables. |
Factor |
CPT.marginalizeOut(VarSet varset)
|
Factor |
AbstractTableFactor.marginalizeOut(VarSet badVars)
|
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 |
FactorGraph.multiply(Factor dist)
|
Factor |
Factor.multiply(Factor dist)
Returns the elementwise product of this factor with another. |
Factor |
CPT.multiply(Factor dist)
|
Factor |
ConstantFactor.multiply(Factor other)
|
Factor |
BoltzmannUnaryFactor.multiply(Factor other)
|
Factor |
BoltzmannPairFactor.multiply(Factor other)
|
Factor |
AbstractTableFactor.multiply(Factor dist)
Returns the elementwise product of this potential and another one. |
Factor |
AbstractFactor.multiply(Factor dist)
|
static Factor |
Factors.multiplyAll(java.util.Collection factors)
|
Factor |
UniNormalFactor.normalize()
|
Factor |
UniformFactor.normalize()
|
Factor |
TableFactor.normalize()
Multiplies every entry in the potential by a constant such that all the entries sum to 1. |
Factor |
SkeletonFactor.normalize()
|
Factor |
PottsTableFactor.normalize()
|
Factor |
NormalFactor.normalize()
|
Factor |
LogTableFactor.normalize()
|
Factor |
FactorGraph.normalize()
|
Factor |
Factor.normalize()
Multiplies this potential by a constant such that it sums to 1. |
Factor |
CPT.normalize()
|
Factor |
ConstantFactor.normalize()
|
Factor |
BinaryUnaryFactor.normalize()
|
Factor |
BetaFactor.normalize()
|
Factor |
Assignment.normalize()
|
abstract Factor |
AbstractTableFactor.normalize()
|
protected Factor |
TableFactor.slice_general(Variable[] vars,
Assignment observed)
|
protected Factor |
LogTableFactor.slice_general(Variable[] vars,
Assignment observed)
|
protected abstract Factor |
AbstractTableFactor.slice_general(Variable[] vars,
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 Factor |
LogTableFactor.slice_onevar(Variable var,
Assignment observed)
|
protected abstract Factor |
AbstractTableFactor.slice_onevar(Variable var,
Assignment observed)
|
protected Factor |
TableFactor.slice_twovar(Variable v1,
Variable v2,
Assignment observed)
|
protected Factor |
LogTableFactor.slice_twovar(Variable v1,
Variable v2,
Assignment observed)
|
protected abstract Factor |
AbstractTableFactor.slice_twovar(Variable v1,
Variable v2,
Assignment observed)
|
Factor |
UniNormalFactor.slice(Assignment assn)
|
Factor |
UniformFactor.slice(Assignment assn)
|
Factor |
SkeletonFactor.slice(Assignment assn)
|
Factor |
PottsTableFactor.slice(Assignment assn)
|
Factor |
NormalFactor.slice(Assignment assn)
|
Factor |
FactorGraph.slice(Assignment assn)
|
Factor |
Factor.slice(Assignment assn)
|
Factor |
CPT.slice(Assignment assn)
|
Factor |
ConstantFactor.slice(Assignment assn)
|
Factor |
BinaryUnaryFactor.slice(Assignment assn)
|
Factor |
BetaFactor.slice(Assignment assn)
|
Factor |
Assignment.slice(Assignment assn)
|
Factor |
AbstractTableFactor.slice(Assignment assn)
Creates a new potential that is equal to this one, restricted to a given assignment. |
Factor |
FactorGraph.slice(Assignment assn,
java.util.Map toSlicedMap)
|
Methods in cc.mallet.grmm.types with parameters of type Factor | |
---|---|
void |
UndirectedModel.addFactor(Factor factor)
|
void |
FactorGraph.addFactor(Factor factor)
Adds a factor to the model. |
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 |
FactorGraph.almostEquals(Factor p)
|
boolean |
Factor.almostEquals(Factor p)
Returns whether this is almost equal to another potential. |
boolean |
CPT.almostEquals(Factor p)
|
boolean |
AbstractTableFactor.almostEquals(Factor p)
|
boolean |
AbstractFactor.almostEquals(Factor p)
|
boolean |
UniNormalFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
UniformFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
SkeletonFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
PottsTableFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
NormalFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
FactorGraph.almostEquals(Factor p,
double epsilon)
|
boolean |
Factor.almostEquals(Factor p,
double epsilon)
|
boolean |
CPT.almostEquals(Factor p,
double epsilon)
|
boolean |
ConstantFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
BoltzmannUnaryFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
BoltzmannPairFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
BinaryUnaryFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
BetaFactor.almostEquals(Factor p,
double epsilon)
|
boolean |
Assignment.almostEquals(Factor p,
double epsilon)
|
boolean |
AbstractTableFactor.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 |
UniNormalFactor.divideBy(Factor f)
|
void |
UniformFactor.divideBy(Factor other)
|
void |
NormalFactor.divideBy(Factor f)
|
void |
FactorGraph.divideBy(Factor pot)
|
void |
Factor.divideBy(Factor pot)
Computes this /= pot |
void |
CPT.divideBy(Factor pot)
|
void |
BetaFactor.divideBy(Factor f)
|
void |
AbstractTableFactor.divideBy(Factor pot)
Does the conceptual equivalent of this /= pot. |
void |
AbstractFactor.divideBy(Factor pot)
|
int |
FactorGraph.getIndex(Factor factor)
|
Factor |
FactorGraph.multiply(Factor dist)
|
Factor |
Factor.multiply(Factor dist)
Returns the elementwise product of this factor with another. |
Factor |
CPT.multiply(Factor dist)
|
Factor |
ConstantFactor.multiply(Factor other)
|
Factor |
BoltzmannUnaryFactor.multiply(Factor other)
|
Factor |
BoltzmannPairFactor.multiply(Factor other)
|
Factor |
AbstractTableFactor.multiply(Factor dist)
Returns the elementwise product of this potential and another one. |
Factor |
AbstractFactor.multiply(Factor dist)
|
static DiscreteFactor |
TableFactor.multiplyAll(Factor[] phis)
|
void |
UniNormalFactor.multiplyBy(Factor f)
|
void |
UniformFactor.multiplyBy(Factor other)
|
void |
NormalFactor.multiplyBy(Factor f)
|
void |
FactorGraph.multiplyBy(Factor pot)
|
void |
Factor.multiplyBy(Factor pot)
Does this *= pot. |
void |
CPT.multiplyBy(Factor pot)
|
void |
ConstantFactor.multiplyBy(Factor other)
|
void |
BetaFactor.multiplyBy(Factor f)
|
void |
AbstractTableFactor.multiplyBy(Factor pot)
Does the conceptual equivalent of this *= pot. |
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 |
PottsTableFactor.sumGradLog(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 |
BinaryUnaryFactor.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. |
|
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