Affichage des articles dont le libellé est scala vs java. Afficher tous les articles
Affichage des articles dont le libellé est scala vs java. Afficher tous les articles

jeudi, décembre 08, 2011

Simple linear regression in Scala

Here is how to compute Simple linear regression in Scala.
Class LinearRegression takes in n measurements from a List[(x: Double, y: Double)] and computes the line that best fits the data according to the least squares metric.
This Scala program is the scala translation of the java program available at http://introcs.cs.princeton.edu/java/97data/LinearRegression.java.html .
class LinearRegression(val pairs: List[(Double,Double)]) { 
 val size = pairs.size
 println("pairs = " + pairs)

 // first pass: read in data, compute xbar and ybar
 val sums = pairs.foldLeft(new X_X2_Y(0D,0D,0D))(_ + new X_X2_Y(_))
 val bars = (sums.x / size, sums.y / size)

 // second pass: compute summary statistics
 val sumstats = pairs.foldLeft(new X2_Y2_XY(0D,0D,0D))(_ + new X2_Y2_XY(_, bars))

 val beta1 = sumstats.xy / sumstats.x2
 val beta0 = bars._2 - (beta1 * bars._1)
 val betas = (beta0, beta1)

 println("y = " + ("%4.3f" format beta1) + " * x + " + ("%4.3f" format beta0))

 // analyze results
 val correlation = pairs.foldLeft(new RSS_SSR(0D,0D))(_ + RSS_SSR.build(_, bars, betas))
 val R2 = correlation.ssr / sumstats.y2
 val svar = correlation.rss / (size - 2)
 val svar1 = svar / sumstats.x2
 val svar0 = ( svar / size ) + ( bars._1 * bars._1 * svar1)
 val svar0bis = svar * sums.x2 / (size * sumstats.x2)
 println("R^2                 = " + R2)
 println("std error of beta_1 = " + Math.sqrt(svar1))
 println("std error of beta_0 = " + Math.sqrt(svar0))
 println("std error of beta_0 = " + Math.sqrt(svar0bis))
 println("SSTO = " + sumstats.y2)
 println("SSE  = " + correlation.rss)
 println("SSR  = " + correlation.ssr)
}

object RSS_SSR {
 def build(p: (Double,Double), bars: (Double,Double), betas: (Double,Double)): RSS_SSR = {
  val fit = (betas._2 * p._1) + betas._1
  val rss = (fit-p._2) * (fit-p._2)
  val ssr = (fit-bars._2) * (fit-bars._2)
  new RSS_SSR(rss, ssr)
 }
}

class RSS_SSR(val rss: Double, val ssr: Double) {
 def +(p: RSS_SSR): RSS_SSR = new RSS_SSR(rss+p.rss, ssr+p.ssr)
}

class X_X2_Y(val x: Double, val x2: Double, val y: Double) {
 def this(p: (Double,Double)) = this(p._1, p._1*p._1, p._2)
 def +(p: X_X2_Y): X_X2_Y = new X_X2_Y(x+p.x,x2+p.x2,y+p.y)
}

class X2_Y2_XY(val x2: Double, val y2: Double, val xy: Double) {
 def this(p: (Double,Double), bars: (Double,Double)) = this((p._1-bars._1)*(p._1-bars._1), (p._2-bars._2)*(p._2-bars._2),(p._1-bars._1)*(p._2-bars._2))
 def +(p: X2_Y2_XY): X2_Y2_XY = new X2_Y2_XY(x2+p.x2,y2+p.y2,xy+p.xy)
}