case class GridHyperOptimizer() extends HyperOptimizer with Product with Serializable
Brute force search over the grid of hypers
Created by maxhutch on 12/7/16.
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Instance Constructors
- new GridHyperOptimizer()
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- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
- Definition Classes
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- def addHyperGrid(name: String, values: Seq[Any]): GridHyperOptimizer.this.type
Add a 1D hyper range to the space searched by this optimizer
Add a 1D hyper range to the space searched by this optimizer
- name
of the hyper
- values
it takes, enumerated as a seq
- returns
calling instance
- Definition Classes
- HyperOptimizer
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- @native()
- var hyperGrids: Map[String, Seq[Any]]
The search space
The search space
- Definition Classes
- HyperOptimizer
- final def isInstanceOf[T0]: Boolean
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- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- def optimize(trainingData: Seq[(Vector[Any], Any)], numIterations: Int = 1, builder: (Map[String, Any]) => Learner): (Map[String, Any], Double)
Search by enumerating every combination of hyper values
Search by enumerating every combination of hyper values
- trainingData
the data to train/test on
- numIterations
ignored, since this is a brute force search
- returns
the best hyper map found in the search space
- Definition Classes
- GridHyperOptimizer → HyperOptimizer
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- final def synchronized[T0](arg0: => T0): T0
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- final def wait(): Unit
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- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(arg0: Long): Unit
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