Uses of Class

Packages that use Variable

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)