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io.github.mandar2812.dynaml.examples

TestGPHousing

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object TestGPHousing

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Type Members

  1. type Data = Stream[Pattern]

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  2. type DataAlt = Seq[Pattern]

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  3. type Features = DenseVector[Double]

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  4. type GPAlt = AbstractGPRegressionModel[DataAlt, Features]

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  5. type Kernel = LocalScalarKernel[Features]

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  6. type Output = Double

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  7. type Pattern = (Features, Output)

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  8. type PatternAlt = (Features, Features)

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  9. type PredictionsAndErrBars = Seq[(Features, Output, Output, Output, Output)]

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  10. type PredictionsAndOutputs = List[(Output, Output)]

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  11. type Scales = (GaussianScaler, GaussianScaler)

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  12. type TTData = (Data, Data)

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  4. def apply(kernel: Kernel, bandwidth: Double = 0.5, noise: Kernel, trainFraction: Double = 0.75, columns: List[Int] = ..., grid: Int = 5, step: Double = 0.2, globalOpt: String = "ML", stepSize: Double = 0.01, maxIt: Int = 300, policy: String = "GS"): Unit

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. val postScaling: StreamMapPipe[(Features, Features), (Features, Double)]

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  17. val preScaling: StreamMapPipe[(Features, Double), (Features, DenseVector[Double])]

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  18. def runExperiment(kernel: Kernel, bandwidth: Double = 0.5, noise: Kernel, num_training: Int = 200, columns: List[Int] = List(40,16,21,23,24,22,25), grid: Int = 5, step: Double = 0.2, globalOpt: String = "ML", pol: String = "GS", opt: Map[String, String]): Unit

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

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  20. def toString(): String

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  21. final def wait(): Unit

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

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  23. final def wait(arg0: Long): Unit

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