case class LinearRegression(fv: Seq[(DenseVector[Double]) ⇒ DenseVector[Double]] = ..., inverseThreshold: Double = ...) extends Product with Serializable
Implements linear regression for solving XA = B where A and B are given matrices and X is solved for. If there is no solution to the system, X will be the matrix that minimizes the least squares error.
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- new LinearRegression(fv: Seq[(DenseVector[Double]) ⇒ DenseVector[Double]] = ..., inverseThreshold: Double = ...)
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final
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def
apply(AInv: DenseMatrix[Double], vb: Seq[DenseVector[Double]]): DenseMatrix[Double]
- See also
LinearRegression.apply()
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def
apply(va: Seq[DenseVector[Double]], vb: Seq[DenseVector[Double]]): DenseMatrix[Double]
- See also
LinearRegression.apply()
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final
def
asInstanceOf[T0]: T0
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def
buildMatrixFromColumns(va: Seq[DenseVector[Double]]): DenseMatrix[Double]
- See also
LinearRegression.buildMatrixFromColumns()
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def
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- val fv: Seq[(DenseVector[Double]) ⇒ DenseVector[Double]]
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- val inverseThreshold: Double
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