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

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` | `CPT.containsVar(Variable var)`
`boolean` | `Factor.containsVar(Variable var)` Returns whether the potential is over the given variable.
`boolean` | `AbstractTableFactor.containsVar(Variable var)` Returns true iff this potential is over the given variable
`boolean` | `FactorGraph.containsVar(Variable v1)` Returns whether this variable is part of the model.
`boolean` | `Assignment.containsVar(Variable var)` Returns true if this assignment specifies a value for `var`
`boolean` | `AbstractFactor.containsVar(Variable var)`
`protected abstract AbstractTableFactor` | `AbstractTableFactor.createBlankSubset(Variable[] vars)`
`protected AbstractTableFactor` | `LogTableFactor.createBlankSubset(Variable[] vars)`
`protected AbstractTableFactor` | `TableFactor.createBlankSubset(Variable[] 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)`
`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` | `FactorGraph.getIndex(Variable var)` Returns a unique numeric index for a variable in this model.
`int` | `Universe.getIndex(Variable var)`
`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` | `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)`
`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)`
`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 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)`
`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 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, Variable sigma)`
`BoltzmannUnaryFactor(Variable var, Variable alpha)`
`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)`

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