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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.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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6.  def constantStep(s: Double): (Long, IndexedSeq[Double]) => Double
7.  def countAdaptiveStep(a: Double, b: Double, c: Double = 1.0): (Long, IndexedSeq[Double]) => Double
8.  def dot(x: IndexedSeq[Double], y: IndexedSeq[Double]): Double
9.  final def eq(arg0: AnyRef): Boolean

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10.  def equals(arg0: AnyRef): Boolean

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11.  def finalize(): Unit

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

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13.  def hashCode(): Int

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14.  final def isInstanceOf[T0]: Boolean

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15.  val linearGradient: (IndexedSeq[Double], (Double, IndexedSeq[Double])) => IndexedSeq[Double]

    constructs the gradient for linear regression.

    constructs the gradient for linear regression. the Pos type is (Double, IndexedSeq[Double]) note the LAST element in the weights is the constant term. and note that the length of the IndexedSeq in the tuple is one less than the weights (we don't carry the constant term)

16.  final def ne(arg0: AnyRef): Boolean

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17.  final def notify(): Unit

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18.  final def notifyAll(): 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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24.  def weightsOf[T](s: SGD[T]): Option[IndexedSeq[Double]]