object
LinearRegression extends ObjectiveFunction[Double, Double]
Value Members
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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
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def
adaptParameterNames(s: Seq[String]): Seq[String]
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def
adaptPenalizationMask[T](batch: Batch[T]): Vec[Double]
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def
apply[T](b: Vec[Double], batch: Batch[T])(implicit arg0: MatOps[T]): Double
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
eval[T](estimates: Vec[Double], batch: Batch[T])(implicit arg0: MatOps[T]): Double
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def
finalize(): Unit
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def
generate(estimates: Vec[Double], data: Mat[Double], rng: () ⇒ Double): Vec[Double]
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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def
hessian[T](p: Vec[Double], batch: Batch[T])(implicit arg0: MatOps[T]): Mat[Double]
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final
def
isInstanceOf[T0]: Boolean
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def
jacobi[T](b: Vec[Double], batch: Batch[T])(implicit arg0: MatOps[T]): Vec[Double]
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def
minusHessianLargestEigenValue[T](p: Vec[Double], batch: Batch[T])(implicit arg0: MatOps[T]): Double
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
predict[T](estimates: Vec[Double], data: T)(implicit arg0: MatOps[T]): Vec[Double]
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def
predict(estimates: Vec[Double], data: Vec[Double]): Double
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def
predictMat(estimates: Vec[Double], data: Mat[Double]): Vec[Double]
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def
start(cols: Int): Vec[Double]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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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
Inherited from AnyRef
Inherited from Any
