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breeze.stats.regression.LeastSquares.scala Maven / Gradle / Ivy
package breeze.stats.regression
import breeze.generic.UFunc
import breeze.linalg._
import org.netlib.util.intW
import com.github.fommil.netlib.LAPACK.{getInstance=>lapack}
import java.util.Arrays
private object leastSquaresImplementation {
def doLeastSquares(data: DenseMatrix[Double], outputs: DenseVector[Double], workArray: Array[Double]): LeastSquaresRegressionResult = {
require(data.rows == outputs.size)
require(data.rows > data.cols+1)
require(workArray.length >= 2*data.rows*data.cols)
val info = new intW(0)
lapack.dgels("N", data.rows, data.cols, 1, data.data, data.rows, outputs.data, data.rows, workArray, workArray.length, info)
if (info.`val` < 0) {
throw new ArithmeticException("Least squares did not converge.")
}
val coefficients = new DenseVector[Double](Arrays.copyOf(outputs.data, data.cols))
var r2 = 0.0
for (i <- 0 until (data.rows - data.cols)) {
r2 = r2 + math.pow(outputs.data(data.cols+i), 2)
}
LeastSquaresRegressionResult(coefficients, r2)
}
}
case class LeastSquaresRegressionResult(coefficients: DenseVector[Double], rSquared: Double) extends RegressionResult[DenseVector[Double], Double] {
def apply(x: DenseVector[Double]): Double = coefficients.dot(x)
def apply(X: DenseMatrix[Double]): DenseVector[Double] = X*coefficients
}
object leastSquares extends UFunc {
implicit val matrixVectorWithWorkArray: Impl3[DenseMatrix[Double], DenseVector[Double], Array[Double], LeastSquaresRegressionResult] = new Impl3[DenseMatrix[Double], DenseVector[Double], Array[Double], LeastSquaresRegressionResult] {
def apply(data: DenseMatrix[Double], outputs: DenseVector[Double], workArray: Array[Double]): LeastSquaresRegressionResult = leastSquaresImplementation.doLeastSquares(data.copy, outputs.copy, workArray)
}
implicit val matrixVectorSpecifiedWork: Impl3[DenseMatrix[Double], DenseVector[Double], Int, LeastSquaresRegressionResult] = new Impl3[DenseMatrix[Double], DenseVector[Double], Int, LeastSquaresRegressionResult] {
def apply(data: DenseMatrix[Double], outputs: DenseVector[Double], workSize: Int): LeastSquaresRegressionResult = leastSquaresImplementation.doLeastSquares(data.copy, outputs.copy, new Array[Double](workSize))
}
implicit val matrixVector: Impl2[DenseMatrix[Double], DenseVector[Double], LeastSquaresRegressionResult] = new Impl2[DenseMatrix[Double], DenseVector[Double], LeastSquaresRegressionResult] {
def apply(data: DenseMatrix[Double], outputs: DenseVector[Double]): LeastSquaresRegressionResult = leastSquaresImplementation.doLeastSquares(data.copy, outputs.copy, new Array[Double](math.max(1, data.rows*data.cols*2)))
}
}
object leastSquaresDestructive extends UFunc {
implicit val matrixVectorWithWorkArray: Impl3[DenseMatrix[Double], DenseVector[Double], Array[Double], LeastSquaresRegressionResult] = new Impl3[DenseMatrix[Double], DenseVector[Double], Array[Double], LeastSquaresRegressionResult] {
def apply(data: DenseMatrix[Double], outputs: DenseVector[Double], workArray: Array[Double]): LeastSquaresRegressionResult = leastSquaresImplementation.doLeastSquares(data, outputs, workArray)
}
implicit val matrixVectorSpecifiedWork: Impl3[DenseMatrix[Double], DenseVector[Double], Int, LeastSquaresRegressionResult] = new Impl3[DenseMatrix[Double], DenseVector[Double], Int, LeastSquaresRegressionResult] {
def apply(data: DenseMatrix[Double], outputs: DenseVector[Double], workSize: Int): LeastSquaresRegressionResult = leastSquaresImplementation.doLeastSquares(data, outputs, new Array[Double](workSize))
}
implicit val matrixVector: Impl2[DenseMatrix[Double], DenseVector[Double], LeastSquaresRegressionResult] = new Impl2[DenseMatrix[Double], DenseVector[Double], LeastSquaresRegressionResult] {
def apply(data: DenseMatrix[Double], outputs: DenseVector[Double]): LeastSquaresRegressionResult = leastSquaresImplementation.doLeastSquares(data, outputs, new Array[Double](math.max(1, data.rows*data.cols*2)))
}
}
