final class LassoRegression extends RegressionBase

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Instance Constructors

  1. new LassoRegression(alpha: Double, maxIterations: Int = 1000, tol: Double = 1E-5)

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  4. final def asInstanceOf[T0]: T0
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  6. def computeLoss(coefficients: DenseVector[Double], intercept: Double)(x: DenseMatrix[Double], y: DenseVector[Double], sampleWeights: DenseVector[Double]): Double
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    protected
    Definition Classes
    LassoRegressionRegressionBase
  7. final def eq(arg0: AnyRef): Boolean
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  9. def finalize(): Unit
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  10. def fit(data: Matrix[Double], outputs: Vector[Double], sampleWeights: Vector[Double], fitIntercept: Boolean): RegressionResult
    Definition Classes
    RegressionBase
  11. def fit(data: Matrix[Double], outputs: Vector[Double], fitIntercept: Boolean): RegressionResult
    Definition Classes
    RegressionBase
  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. final def ne(arg0: AnyRef): Boolean
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  16. def normalizeSampleWeights(sampleWeights: DenseVector[Double]): DenseVector[Double]
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    protected
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    LassoRegressionRegressionBase
  17. final def notify(): Unit
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  18. final def notifyAll(): Unit
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  19. def regress(x: DenseMatrix[Double], y: DenseVector[Double]): DenseVector[Double]
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    protected
    Definition Classes
    LassoRegressionRegressionBase
  20. implicit lazy val sumImpl: breeze.linalg.sum.Impl[BroadcastedColumns[DenseMatrix[Double], DenseVector[Double]], Transpose[DenseVector[Double]]]

    Provides an implementation for sum operation of BroadcastedColumns in breeze.

    Provides an implementation for sum operation of BroadcastedColumns in breeze. Spark 3.0.* and 3.1.* depends on breeze 1.0 and Spark 3.2.* depends on breeze 1.2, and there is a breaking change in the way the implicit sum implementation is provided. In breeze 1.0, the implementation is constructed via sum.vectorizeCols_Double(ClassTag[Double], Zero.DoubleZero, sum.helper_Double), while in breeze 1.2, it's constructed via sum.vectorizeCols_Double(sum.helper_Double) If our code is compiled against Spark 3.2.0/breeze 1.0, the scala compiler implicitly constructs the implementation via sum.vectorizeCols_Double(ClassTag[Double], Zero.DoubleZero, sum.helper_Double), which does not exist in breeze 1.2, thus causing java.lang.NoSuchMethodError when running on Spark 3.2.0. Conversely, if our code is compiled against Spark 3.2.0/breeze 1.2, it will cause java.lang.NoSuchMethodError when running on Spark 3.0.* and 3.1.*. Workaround: use reflection to construct the implementation.

    Definition Classes
    RegressionBase
  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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