Uses of Class
cc.mallet.grmm.types.Variable

Packages that use Variable

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

Uses of Variable in cc.mallet.grmm.inference

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

Variable

JunctionTreeInferencer.pickVertexToRemove(org._3pq.jgrapht.UndirectedGraph mdl, java.util.ArrayList lst)

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

VarSet	JunctionTree.findCluster(Variable[] vars) Returns a cluster in the tree that contains exactly the given variables, or null if no such cluster exists.
VarSet	JunctionTree.findParentCluster(Variable var) Returns a cluster in the tree that contains var.
Factor	MessageArray.get(Factor from, Variable to)
Factor	MessageArray.get(Variable from, Factor to)
int	MessageArray.getIndex(Variable to)
static double	Utils.localMagnetization(Inferencer inferencer, Variable var)
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)
void	MessageArray.put(Factor from, Variable to, Factor msg)
void	MessageArray.put(Variable from, Factor to, Factor msg)
Factor	TRP.query(DirectedModel m, Variable var)
static TableFactor	RandomGraphs.randomNodePotential(java.util.Random r, Variable var)
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)
static int[]	Utils.toSizesArray(Variable[] vars)
Factor	VariableElimination.unnormalizedMarginal(FactorGraph model, Variable query) The bulk of the variable-elimination algorithm.

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

Methods in cc.mallet.grmm.inference.gbp with parameters of type Variable

Factor

ParentChildGBP.lookupMarginal(Variable variable)

Uses of Variable in cc.mallet.grmm.learning

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

Variable	ACRF.UnrolledGraph.get(int idx)
Variable	ACRF.UnrolledGraph.varOfIndex(int t, int j)

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

int	ACRF.UnrolledGraph.getIndex(Variable var)
int	ACRF.UnrolledGraph.observedValue(Variable var)
void	ACRF.UnrolledGraph.setObserved(Variable var, int outcome)

Constructors in cc.mallet.grmm.learning with parameters of type Variable

ACRF.UnrolledVarSet(ACRF.UnrolledGraph graph, ACRF.Template tmpl, Variable[] vars, FeatureVector fv)

Uses of Variable in cc.mallet.grmm.types

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

static Variable[]	Factors.computeVars(Factor result)
static Variable[]	Factors.continuousVarsOf(Factor fg)
static Variable[]	Factors.discreteVarsOf(Factor fg)
Variable	FactorGraph.findVariable(java.lang.String name) Searches this model for a variable with a given name.
Variable	AbstractTableFactor.findVariable(java.lang.String name)
Variable	VarSet.get(int idx) Returns the variable in this clique at index idx.
Variable	UnmodifiableVarSet.get(int idx)
Variable	Universe.get(int idx)
Variable	ListVarSet.get(int idx)
Variable	HashVarSet.get(int idx)
Variable	FactorGraph.get(int index) Returns a variable from this model with a given index.
Variable	BitVarSet.get(int idx)
Variable	UndirectedGrid.get(int x, int y)
Variable	CPT.getChild()
Variable	FactorGraph.getVariable(int i)
Variable	Factor.getVariable(int i)
Variable	CPT.getVariable(int i)
Variable	Assignment.getVariable(int i)
Variable	AbstractTableFactor.getVariable(int i)
Variable	AbstractFactor.getVariable(int i)
Variable[]	Assignment.getVars() Returns all variables which are assigned to.
Variable[]	VarSet.toVariableArray() Returns the variables in this clique as an array, that should not be modified.
Variable[]	UnmodifiableVarSet.toVariableArray()
Variable[]	ListVarSet.toVariableArray()
Variable[]	HashVarSet.toVariableArray()
Variable[]	BitVarSet.toVariableArray()

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

int	Universe.add(Variable var)
void	FactorGraph.addFactor(Variable var1, Variable var2, double[] probs)
void	Assignment.addRow(Variable[] vars, double[] values)
void	Assignment.addRow(Variable[] vars, int[] values)
void	Assignment.addRow(Variable[] vars, java.lang.Object[] values)
java.util.List	FactorGraph.allFactorsContaining(Variable var)
java.util.List	FactorGraph.allFactorsOf(Variable var) Returns a list of all factors in the graph whose domain is exactly the specified var.
boolean	FactorGraph.containsVar(Variable v1) Returns whether this variable is part of the model.
boolean	Factor.containsVar(Variable var) Returns whether the potential is over the given variable.
boolean	CPT.containsVar(Variable var)
boolean	Assignment.containsVar(Variable var) Returns true if this assignment specifies a value for var
boolean	AbstractTableFactor.containsVar(Variable var) Returns true iff this potential is over the given variable
boolean	AbstractFactor.containsVar(Variable var)
protected AbstractTableFactor	TableFactor.createBlankSubset(Variable[] vars)
protected AbstractTableFactor	LogTableFactor.createBlankSubset(Variable[] vars)
protected abstract AbstractTableFactor	AbstractTableFactor.createBlankSubset(Variable[] 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)
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.
int	Assignment.get(int ridx, Variable var) Returns the value of var in this assigment.
int	Assignment.get(Variable var)
VarSet	FactorGraph.getAdjacentVertices(Variable var) Returns all variables that are adjacent to a given variable in this graph---that is, the set of all variables that share a factor with this one.
int[]	Assignment.getColumnInt(Variable x1)
CPT	DirectedModel.getCptofVar(Variable node) Returns the conditional distribution P ( node | Parents (node) )
int	FactorGraph.getDegree(Variable var) Returns the degree of a given variable in this factor graph, that is, the number of factors in which the variable is an argument.
double	Assignment.getDouble(int ridx, Variable var) Returns the value of var in this assigment.
double	Assignment.getDouble(Variable var)
int	Universe.getIndex(Variable var)
int	FactorGraph.getIndex(Variable var) Returns a unique numeric index for a variable in this model.
java.lang.Object	Assignment.getObject(int ri, Variable var)
java.lang.Object	Assignment.getObject(Variable var)
boolean	FactorGraph.isAdjacent(Variable v1, Variable v2) Returns whether two variables are adjacent in the model's graph.
boolean	UndirectedModel.isConnected(Variable v1, Variable v2)
static LogTableFactor	LogTableFactor.makeFromLogMatrix(Variable[] vars, Matrix values)
static LogTableFactor	LogTableFactor.makeFromLogValues(Variable[] vars, double[] vals)
static LogTableFactor	LogTableFactor.makeFromLogValues(Variable v, double[] vals)
static LogTableFactor	LogTableFactor.makeFromMatrix(Variable[] vars, SparseMatrixn values)
static LogTableFactor	LogTableFactor.makeFromValues(Variable[] vars, double[] vals)
static LogTableFactor	LogTableFactor.makeFromValues(Variable var, double[] vals2)
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)
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)
static CPT	Factors.normalizeAsCpt(AbstractTableFactor ptl, Variable var)
void	FactorGraph.remove(Variable var) Removes a variable from this model, along with all of its factors.
double	PottsTableFactor.secondDerivative(Factor q, Variable param, Assignment theta)
void	Assignment.setDouble(int ridx, Variable var, double value)
void	Assignment.setValue(int ridx, Variable var, int value)
void	Assignment.setValue(Variable var, int value)
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)
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 Variable

AbstractTableFactor(Variable var)
Creates an identity potential over the given variable.

AbstractTableFactor(Variable[] allVars)
Creates an identity potential with the given variables.

AbstractTableFactor(Variable[] allVars, double[] probs)
Creates a potential with the given variables and the given probabilities.

AbstractTableFactor(Variable[] allVars, Matrix probsIn)
Creates a potential with the given variables and the given probabilities.

AbstractTableFactor(Variable var, double[] values)

Assignment(Variable[] vars, double[] outcomes)
Creates an assignemnt for the given variables.

Assignment(Variable[] vars, int[] outcomes)
Creates an assignemnt for the given variables.

Assignment(Variable var, double outcome)

Assignment(Variable var, int outcome)

BetaFactor(Variable var, double alpha, double beta)

BetaFactor(Variable var, double alpha, double beta, double min, double max)

BinaryUnaryFactor(Variable var, Variable theta1, Variable theta2)

BoltzmannPairFactor(Variable x1, Variable x2, double sigma)

BoltzmannUnaryFactor(Variable var, double theta)

CPT(DiscreteFactor subFactor, Variable child)

DirectedModel(Variable[] vars)

FactorGraph(Variable[] vars)
Create a model with the variables given.

HashVarSet(Variable[] vars)

LogTableFactor(Variable var)

LogTableFactor(Variable[] allVars)

PottsTableFactor(Variable x1, Variable x2, Variable alpha)

PottsTableFactor(VarSet xs, Variable alpha)

TableFactor(Variable var)

TableFactor(Variable[] allVars)

TableFactor(Variable[] allVars, double[] probs)

TableFactor(Variable[] allVars, Matrix probsIn)

TableFactor(Variable var, double[] values)

UndirectedModel(Variable[] vars)

UniformFactor(Variable var, double min, double max)

UniformFactor(Variable var, double min, double max, double val)

UniNormalFactor(Variable var, double mean, double variance)

Uses of Variable in cc.mallet.grmm.util

Methods in cc.mallet.grmm.util that return Variable

Variable

LabelsAssignment.varOfIndex(int t, int j)

Methods in cc.mallet.grmm.util with parameters of type Variable

Label

LabelsAssignment.labelOfVar(Variable var)