package parser

import java.nio.file.{Files, Paths}
import java.util.function.Consumer

import scala.collection.mutable.ArrayBuffer

/**
 * @author miniwolf
 */
object LinearRegression {
  val STEP_SIZE: Int = 15

  def main(args: Array[String]) {
    val x: ArrayBuffer[Int] = new ArrayBuffer[Int]()
    val y: ArrayBuffer[Int] = new ArrayBuffer[Int]()
    val res = new ArrayBuffer[Double]()

    Files.lines(Paths.get("input.csv")).forEach( new Consumer[String] {
      override def accept(line: String): Unit = {
      val res = line.split(";")
      x.+=:(res(0).toInt)
      y.+=:(res(1).toInt)
    } })

    var i = 0
    while ( i*STEP_SIZE-STEP_SIZE < x.length ) {
      val xk = new ArrayBuffer[Int](STEP_SIZE)
      val yk = new ArrayBuffer[Int](STEP_SIZE)
      for ( k <- 0 until STEP_SIZE ) {
        if ( (k + i * STEP_SIZE) < x.length - 1 ) {
          xk += x(k + i * STEP_SIZE)
          yk += y(k + i * STEP_SIZE)
        }
      }
      res.+=(linearReg(xk, yk))
      println()
      i += 1
    }

    res.foreach(println(_))

    def linearReg(xk: ArrayBuffer[Int], yk: ArrayBuffer[Int]): Double = {
      var sumx: Double = 0.0
      var sumy: Double = 0.0
      var sumx2: Double = 0.0
      xk.foreach { case d =>
        sumx += d
        sumx2 += d * d
      }

      // second pass: compute summary statistics
      yk.foreach(i => sumy += i)
      var i = 0
      val n = xk.length
      val xbar: Double = sumx / n
      val ybar: Double = sumy / n
      var xxbar: Double = 0.0
      var yybar: Double = 0.0
      var xybar: Double = 0.0
      var beta1: Double = 0.0
      var beta0: Double = 0.0
      while ( i < n ) {
        val xi = xk(i)
        val yi = yk(i)
        xxbar += (xi - xbar) * (xi - xbar)
        yybar += (yi - ybar) * (yi - ybar)
        xybar += (xi - xbar) * (yi - ybar)

        beta1 = xybar / xxbar
        beta0 = ybar - beta1 * xbar
        i += 1
        // print results
        println("y" + xi + " = " + beta1 + " * x + " + beta0)
      }
      beta1
    }
  }
}