# MIRA

#### class MIRA extends MarginScaled

The MIRA algorithm

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### Instance Constructors

1. #### new MIRA(C: Double = 1.0)

C

The regularization constant. Doesn't really need tuning.

### 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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6. #### val C: Double

The regularization constant.

The regularization constant. Doesn't really need tuning.

Definition Classes
MIRAMarginScaled
7. #### final def asInstanceOf[T0]: T0

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

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protected[java.lang]
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weights

The weights

rate

The learning rate

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

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

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

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13. #### def finalizeWeights(weights: WeightsSet): Unit

Once learning is done, the weights should be copied back into normal tensors.

Once learning is done, the weights should be copied back into normal tensors.

weights

The weights

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14. #### final def getClass(): Class[_]

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15. #### def hashCode(): Int

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16. #### def initializeWeights(weights: WeightsSet): Unit

Some optimizers swap out weights with special purpose tensors for e.

Some optimizers swap out weights with special purpose tensors for e.g. efficient scoring while learning.

weights

The weights

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17. #### def isConverged: Boolean

Online optimizers generally don't converge

Online optimizers generally don't converge

returns

Always false

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18. #### final def isInstanceOf[T0]: Boolean

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19. #### var it: Int

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20. #### def lRate(weights: WeightsSet, gradient: WeightsMap, value: Double): Double

Override this method to change the learning rate

Override this method to change the learning rate

weights

The weights

value

The value

returns

The learning rate

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

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

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

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Override this method do to some transformation to the gradient before going on with optimization

Override this method do to some transformation to the gradient before going on with optimization

weights

The weights

Definition Classes
25. #### def reset(): Unit

To override if you want to reset internal state.

To override if you want to reset internal state.

Definition Classes
26. #### final def step(weights: WeightsSet, gradient: WeightsMap, value: Double): Unit

Should not be overriden.

Should not be overriden. The main flow of a GradientStep optimizer.

weights

The weights

value

The value

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

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28. #### def toString(): String

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29. #### final def wait(): Unit

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30. #### final def wait(arg0: Long, arg1: Int): Unit

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31. #### final def wait(arg0: Long): Unit

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