1) Aggregating of build tasks 2) Added Haar Wavelet transform

Author: mandar2812

build.sbt

@@ -12,7 +12,7 @@ packageDescription := "DynaML is a scala library/repl for implementing and worki
   "which can be extended easily to implement advanced models for small and large scale applications.\n\n"+
   "But the library can also be used as an educational/research tool for data analysis."
 
-val mainVersion = "v1.4-beta.8"
+val mainVersion = "v1.4-beta.9"
 
 val dataDirectory = settingKey[File]("The directory holding the data files for running example scripts")
 

dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/DynaMLPipe.scala

@@ -309,6 +309,31 @@ object DynaMLPipe {
   def duplicate[Source, Destination](pipe: DataPipe[Source, Destination]) =
     DataPipe(pipe, pipe)
 
+  /**
+    * Constructs a data pipe which performs discrete Haar wavelet transform
+    * on the original signal.
+    * */
+  def waveletFilter(order: Int) = DataPipe((signal: DenseVector[Double]) => {
+    //Check size of signal before constructing DWT matrix
+    assert(
+      signal.length == math.pow(2.0, order).toInt,
+      "Length of signal must be dyadic power: 2^"+order
+    )
+
+    // Now construct DWT matrix
+    val invSqrtTwo = 1.0/math.sqrt(2.0)
+
+    val rowFactors = (0 until order).reverse.map(i => {
+      (1 to math.pow(2.0, i).toInt).map(k =>
+        invSqrtTwo/math.sqrt(order-i))})
+      .reduceLeft((a,b) => a ++ b).reverse
+
+    val appRowFactors = Seq(rowFactors.head) ++ rowFactors
+
+    val dwtvec = utils.haarMatrix(math.pow(2.0, order).toInt)*signal
+
+    dwtvec.mapPairs((row, v) => v*appRowFactors(row))
+  })
 
   def trainParametricModel[
   G, T, Q, R, S, M <: ParameterizedLearner[G, T, Q, R, S]

dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/utils/package.scala

@@ -18,11 +18,13 @@ under the License.
 * */
 package io.github.mandar2812.dynaml
 
-import java.io.{FileWriter, BufferedWriter, File}
-import breeze.linalg.{DenseMatrix, DenseVector}
-import com.github.tototoshi.csv.{QUOTE_NONNUMERIC, DefaultCSVFormat, CSVReader}
+import java.io.{BufferedWriter, File, FileWriter}
+
+import breeze.linalg.{DenseMatrix, DenseVector, kron}
+import com.github.tototoshi.csv.{CSVReader, DefaultCSVFormat, QUOTE_NONNUMERIC}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
+
 import scala.io.Source
 import scala.reflect.runtime.{universe => ru}
 import scala.annotation.tailrec
@@ -216,5 +218,26 @@ package object utils {
 
     transformData(tFunc)(lines)
   }
+
+  /**
+    * Construct a haar transform matrix of size n
+    *
+    * NOTE: n must be a power of 2.
+    *
+    * */
+  def haarMatrix(n: Int) = {
+    val pos = DenseMatrix(Array(1.0, 1.0))
+    val neg = DenseMatrix(Array(-1.0, 1.0))
+    val hMat = DenseMatrix(Array(1.0, 1.0), Array(-1, 1.0))
+    def haarMatrixAcc(i: Int, hMatAcc: DenseMatrix[Double]): DenseMatrix[Double] = i match {
+      case `n` => hMatAcc
+      case index =>
+        haarMatrixAcc(i*2,
+          DenseMatrix.vertcat[Double](
+            kron(hMatAcc, pos),
+            kron(DenseMatrix.eye[Double](i), neg)))
+    }
+    haarMatrixAcc(2, hMat)
+  }
 }