# DominationLossExample

#### class DominationLossExample extends Example

Implements the domination loss function: it penalizes models that rank any of the badCandidates above any of the goodCandidates.

The actual loss used in this version is the maximum (margin-augmented) difference between goodCandidates and badCandidates.

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### Instance Constructors

1. #### new DominationLossExample(goodCandidates: Seq[variable.Var], badCandidates: Seq[variable.Var], model: model.Model with model.Parameters)

goodCandidates

The examples in the positive class

The examples in the negative class

model

The model

You probably don't want to use this.

### Value Members

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

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2. #### final def !=(arg0: Any): Boolean

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3. #### final def ##(): Int

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4. #### final def ==(arg0: AnyRef): Boolean

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5. #### final def ==(arg0: Any): 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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7. #### final def asInstanceOf[T0]: T0

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8. #### def clone(): AnyRef

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9. #### final def eq(arg0: AnyRef): Boolean

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10. #### def equals(arg0: Any): Boolean

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11. #### def finalize(): Unit

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15. #### final def ne(arg0: AnyRef): Boolean

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16. #### final def notify(): Unit

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17. #### final def notifyAll(): Unit

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18. #### final def synchronized[T0](arg0: ⇒ T0): T0

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