final class LassoRegression extends RegressionBase
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def
computeLoss(coefficients: DenseVector[Double], intercept: Double)(x: DenseMatrix[Double], y: DenseVector[Double], sampleWeights: DenseVector[Double]): Double
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def
fit(data: Matrix[Double], outputs: Vector[Double], sampleWeights: Vector[Double], fitIntercept: Boolean): RegressionResult
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fit(data: Matrix[Double], outputs: Vector[Double], fitIntercept: Boolean): RegressionResult
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def
normalizeSampleWeights(sampleWeights: DenseVector[Double]): DenseVector[Double]
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def
regress(x: DenseMatrix[Double], y: DenseVector[Double]): DenseVector[Double]
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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 viasum.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 viasum.vectorizeCols_Double(ClassTag[Double], Zero.DoubleZero, sum.helper_Double)
, which does not exist in breeze 1.2, thus causingjava.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 causejava.lang.NoSuchMethodError
when running on Spark 3.0.* and 3.1.*. Workaround: use reflection to construct the implementation.- Definition Classes
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