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io.github.mandar2812.dynaml.examples

AbottPowerPlant

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object AbottPowerPlant

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  1. type Data = Stream[(Features, Features)]

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  2. type Features = DenseVector[Double]

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  3. type GPModel = AbstractGPRegressionModel[Seq[(DenseVector[Double], Double)], DenseVector[Double]]

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  4. type Kernel = LocalScalarKernel[Features]

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  5. type Scales = (GaussianScaler, GaussianScaler)

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def apply(kernel: Kernel, noise: Kernel)(deltaT: Int, num_training: Int, num_test: Int, opt: Map[String, String]): (MVStudentsTModel[Data, Features], MultiRegressionMetrics)

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  5. def apply(kernel: Kernel, noise: Kernel, deltaT: Int = 2, timelag: Int = 0, stepPred: Int = 3, num_training: Int = 150, num_test: Int = 1000, column: Int = 7, opt: Map[String, String]): Seq[Seq[AnyVal]]

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. val deltaOperationARXMultiOutput: (List[Int], List[Int]) ⇒ DataPipe[Stream[(Double, DenseVector[Double])], Stream[(DenseVector[Double], DenseVector[Double])]]

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. val names: Map[Int, String]

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def runExperiment(kernel: Kernel, noise: Kernel, deltaT: Int = 2, timelag: Int = 0, stepPred: Int = 3, num_training: Int = 150, num_test: Int, column: Int = 7, opt: Map[String, String]): Seq[Seq[AnyVal]]

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  20. def runMOExperiment(kernel: Kernel, noise: Kernel, deltaT: Int = 2, num_training: Int = 150, num_test: Int, opt: Map[String, String]): (MVStudentsTModel[Data, Features], MultiRegressionMetrics)

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

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  22. def toString(): String

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  23. final def wait(): Unit

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

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

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