jonnylaw

Harrier League Cross Country From Scratch The Harrier League is a cross country running league with seven fixtures across the North East of England in the 2017/18 season across the winter months from September ’17 until March ‘18.

The harrier league is unique to other cross country fixtures because the senior runners are divided up into slow, medium and fast packs. In the senior men’s race, the slow runners start first followed 2 minutes 30 seconds later by the medium pack runners, then a further 2 minutes 30

jonnylaw

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# Simulate Data From Regression DLM #
#####################################

regression_dlm = function(n = 6 * 24, psi, X) {
  theta = matrix(NA_real_, ncol = ncol(X), nrow = n)
  y = numeric(n)
  V = psi[1]
  W = diag(psi[2:3])
  theta[1,] = rnorm(2)

jonnylaw

import akka.stream.scaladsl._
import akka.actor.ActorSystem
import akka.NotUsed
import akka.stream._
import akka.util.ByteString
import java.nio.file.Paths
import scala.concurrent.Future

object DescriptiveStats extends App {

jonnylaw

package model

import breeze.stats.distributions._
import breeze.linalg._
import breeze.numerics.exp
import java.io.{File, PrintWriter}

object GaussianModel {
  case class Parameters(mu: DenseVector[Double], sigma: Double) {
    override def toString = s"${mu.data.mkString(", ")}, $sigma"

jonnylaw

import cats.implicits._
import cats.data.OptionT

object ErrorHandling {
  // Calculate the square root of a positive number or return an exception (which isn't obvious from the return type)
  def unsafe_sqrt(a: Double): Double = {
    if (a > 0) math.sqrt(a)
    else throw new Exception("Can't calculate square root of negative number")
  }

jonnylaw

type Likelihood = Double
case class Observation(y: Int, x: DenseVector[Double])
  
def likelihood(data: Vector[Observation]): Parameters => Likelihood = p => {
    val ll = data.
      map{ y => Poisson(exp(p.dot(y.x))).logProbabilityOf(y.y) }.
      sum

    exp(ll)
  }

jonnylaw

type Parameters = DenseVector[Double]

def proposal(delta: Double): Parameters => Rand[Parameters] = p => {
    for {
      d <- Gaussian(0, delta)
      params = p mapValues (d + _)
    } yield params
  }

jonnylaw

import breeze.stats.distributions.Gaussian
import Stream._
import java.io.{PrintWriter, File}

object MetropolisHastings {
  case class Parameter(mu: Double, sigma: Double) {
    override def toString = s"$mu, $sigma"
  }

  def sims(n: Int, params: Parameter): List[Double] = {