# SemiSupervisedLikelihoodExample

#### class SemiSupervisedLikelihoodExample[A <: Iterable[variable.Var], B <: model.Model] extends Example

Maximum likelihood in one semi supervised setting. It does constrained inference and maximizes the likelihood of it when compared with unconstrained inference. This can implement Generalized Expectation and other algorithms.

A

The type of the labels

B

The type of the model

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### Instance Constructors

1. #### new SemiSupervisedLikelihoodExample(labels: A, model: B, inferConstrained: Infer[A, B], inferUnconstrained: Infer[A, B])

labels

The labels

model

The model

inferConstrained

Inference with constraints

inferUnconstrained

Inference without constraints. Parameters will be learned to match these two inferences.

### Value Members

1. #### final def !=(arg0: AnyRef): Boolean

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Put objective value and gradient into the accumulators.

Put objective value and gradient into the accumulators. Either argument can be null if they don't need to be computed.

value

Accumulator to hold value

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