axle.ml

LinearRegression

trait LinearRegression extends AnyRef

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LinearRegression
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Type Members

  1. case class LinearEstimator [D] (featureExtractor: (D) ⇒ List[Double], featureNormalizer: FeatureNormalizer, θ: M[Double], objectiveNormalizer: FeatureNormalizer, errLog: List[Double]) extends Product with Serializable

  2. type M [T] = JblasMatrix[T]

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  3. def ## (): Int

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  6. def asInstanceOf [T0] : T0

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  7. def clone (): AnyRef

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  8. def cost (xi: M[Double], θ: M[Double], yi: Double): M[Double]

  9. def dTheta (X: M[Double], y: M[Double], θ: M[Double]): M[Double]

  10. def dθ (X: M[Double], y: M[Double], θ: M[Double]): M[Double]

  11. def eq (arg0: AnyRef): Boolean

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  12. def equals (arg0: Any): Boolean

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  13. def finalize (): Unit

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  14. def getClass (): java.lang.Class[_]

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  15. def gradientDescent (X: M[Double], y: M[Double], θ: M[Double], α: Double, iterations: Int): (M[Double], List[Double])

  16. def h (xi: M[Double], θ: M[Double]): M[Double]

  17. def hashCode (): Int

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  18. def isInstanceOf [T0] : Boolean

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  19. def ne (arg0: AnyRef): Boolean

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  20. def normalEquation (X: M[Double], y: M[Double]): M[Double]

  21. def notify (): Unit

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  22. def notifyAll (): Unit

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  23. def regression [D] (examples: Seq[D], numFeatures: Int, featureExtractor: (D) ⇒ List[Double], objectiveExtractor: (D) ⇒ Double, α: Double, iterations: Int): LinearEstimator[D]

  24. def synchronized [T0] (arg0: ⇒ T0): T0

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  25. def toString (): String

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  26. def wait (): Unit

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

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  28. def wait (arg0: Long): Unit

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