Moving components of Kalman filter into appropriate modules and packages

Author: mandar2812

firstOrderdlm/visualisedlm.R renamed to dynaml-core/scripts/visualisedlm.R

@@ -1,6 +1,11 @@
+package io.github.mandar2812.dynaml.models.lm
+
+import java.io.{File, PrintWriter}
+
 import breeze.stats.distributions.Gaussian
-import generalDLM._
-import java.io.{PrintWriter, File}
+import io.github.mandar2812.dynaml.models.lm.generalDLM._
+
+import scala.io.Source
 
 object KFilter {
 
@@ -54,17 +59,17 @@ object KFilter {
   val runKFilter = {
     val p = Parameters(3.0, 0.5, 0.0, 10.0)
     // simulate 16 different realisations of 100 observations, representing 16 stations
-    val observations = (1 to 16) map (id => (id -> simulate(p).take(100).toVector))
+    val observations = (1 to 16) map (id => id -> simulate(p).take(100).toVector)
 
     // filter for one station, using simulated data
     observations.
       filter{ case (id, _) => id == 1 }.
       flatMap{ case (_, d) => filterSeries(d)(p) }
 
     // or, read in data from the file we previously wrote
-    val data = io.Source.fromFile("firstOrderdlm.csv").getLines.toList.
+    val data = Source.fromFile("data/firstOrderdlmRes.csv").getLines.toList.
       map(l => l.split(",")).
-      map(r => (r(0).toInt -> Data(r(1).toDouble, r(2).toDouble, None)))
+      map(r => r(0).toInt -> Data(r(1).toDouble, r(2).toDouble, None))
 
     // filter for all stations, using data from file
     val filtered = data.
@@ -75,7 +80,7 @@ object KFilter {
         (id, filterSeries(data.sortBy(_.time))(p)) } // apply the filter to the sorted data
 
     // write the filter for all stations to a file
-    val pw = new PrintWriter(new File("filteredDlm.csv"))
+    val pw = new PrintWriter(new File("data/filteredDlmRes.csv"))
     pw.write(filtered.
       flatMap{ case (id, data) =>
           data map (x => id + ", " + x.toString)}.

firstOrderdlm/visualisemcmc.R renamed to dynaml-core/scripts/visualisemcmc.R

@@ -1,9 +1,12 @@
-import scalaz._
-import Scalaz._
+package io.github.mandar2812.dynaml.models.lm
+
 import java.io.{File, PrintWriter}
+import java.lang.Math.sqrt
+
 import breeze.stats.distributions.Gaussian
-import Math.sqrt
-import KFilter._
+
+import scalaz.Scalaz._
+
 
 object generalDLM {
   type Loglikelihood = Double
@@ -34,7 +37,7 @@ object generalDLM {
     // simulate 16 different realisations of 100 observations, representing 16 stations
     val observations = (1 to 16) map (id => (id, simulate(p).take(100).toVector))
 
-    val pw = new PrintWriter(new File("firstOrderdlm.csv"))
+    val pw = new PrintWriter(new File("data/firstOrderdlmRes.csv"))
     pw.write(
       observations.
         flatMap{ case (id, data) =>

firstOrderdlm/KFilter.scala renamed to dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/models/lm/KFilter.scala

@@ -1,11 +1,12 @@
-import breeze.stats.distributions.{Uniform, Gaussian, Gamma}
-import generalDLM._
-import KFilter._
+package io.github.mandar2812.dynaml.optimization
+
 import java.io.{File, PrintWriter}
 import breeze.numerics.log
-import breeze.linalg.{DenseMatrix, DenseVector, diag}
-import scalaz._
-import Scalaz._
+import breeze.stats.distributions.{Gaussian, Uniform}
+import io.github.mandar2812.dynaml.models.lm.KFilter
+import io.github.mandar2812.dynaml.models.lm.generalDLM._
+import KFilter._
+import scalaz.Scalaz._
 
 object mcmc {
   case class MetropolisState(params: Parameters, accepted: Int, ll: Loglikelihood)
@@ -56,7 +57,7 @@ object mcmc {
     println(s"Accepted: ${iters.last.accepted.toDouble/n}")
 
     // write the parameters to file
-    val pw = new PrintWriter(new File("mcmcOut.csv"))
+    val pw = new PrintWriter(new File("data/mcmcOutRes.csv"))
     pw.write(iters.map(_.params).mkString("\n"))
     pw.close()
   }

firstOrderdlm/generalDLM.scala renamed to dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/models/lm/generalDLM.scala

@@ -14,8 +14,8 @@ object Dependencies {
     "com.github.tototoshi" % "scala-csv_2.11" % "1.1.2" % "compile",
     "org.scala-lang" % "jline" % "2.11.0-M3" % "compile",
     "org.scalaforge" % "scalax" % "0.1" % "compile",
-    "org.scala-lang" % "scala-pickling_2.11" % "0.9.1" % "compile"
-
+    "org.scala-lang" % "scala-pickling_2.11" % "0.9.1" % "compile",
+    "org.scalaz" %% "scalaz-core" % "7.2.0"
   )
 
   val apacheSpark = Seq(
@@ -27,7 +27,8 @@ object Dependencies {
 
   val linearAlgebraDependencies = Seq(
     "org.scalanlp" % "breeze_2.11" % "0.11.2" % "compile",
-    "org.scalanlp" % "breeze-natives_2.11" % "0.11.2" % "compile")
+    "org.scalanlp" % "breeze-natives_2.11" % "0.11.2" % "compile"/*,
+    "org.spire-math" %% "spire" % "0.11.0"*/)
 
   val chartsDependencies = Seq(
     "com.github.wookietreiber" % "scala-chart_2.11" % "0.4.2" % "compile",