Added new grouped wavelet filters into DynaML Pipes library

tailhq · Dec 14, 2016 · 2219649 · 2219649
1 parent 0f04003
commit 2219649
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Showing 2 changed files with 7 additions and 3 deletions.
diff --git a/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/DynaMLPipe.scala b/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/DynaMLPipe.scala
@@ -26,7 +26,7 @@ import io.github.mandar2812.dynaml.models.gp.AbstractGPRegressionModel
 import io.github.mandar2812.dynaml.optimization.{CoupledSimulatedAnnealing, GPMLOptimizer, GloballyOptWithGrad, GridSearch}
 import io.github.mandar2812.dynaml.pipes.{DataPipe, ReversibleScaler, Scaler, StreamDataPipe}
 import io.github.mandar2812.dynaml.utils.{GaussianScaler, MVGaussianScaler, MinMaxScaler}
-import io.github.mandar2812.dynaml.wavelets.{HaarWaveletFilter, InverseHaarWaveletFilter}
+import io.github.mandar2812.dynaml.wavelets.{GroupedHaarWaveletFilter, HaarWaveletFilter, InvGroupedHaarWaveletFilter, InverseHaarWaveletFilter}
 import org.apache.log4j.Logger
 import org.renjin.script.RenjinScriptEngine
 import org.renjin.sexp._
@@ -553,6 +553,10 @@ object DynaMLPipe {
     * */
   val invHaarWaveletFilter = (order: Int) => InverseHaarWaveletFilter(order)
 
+  val groupedHaarWaveletFilter = (orders: Array[Int]) => GroupedHaarWaveletFilter(orders)
+
+  val invGroupedHaarWaveletFilter = (orders: Array[Int]) => InvGroupedHaarWaveletFilter(orders)
+
   def trainParametricModel[
   G, T, Q, R, S, M <: ParameterizedLearner[G, T, Q, R, S]
   ](regParameter: Double, step: Double = 0.05,

diff --git a/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/wavelets/HaarWaveletFilter.scala b/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/wavelets/HaarWaveletFilter.scala
@@ -84,7 +84,7 @@ case class GroupedHaarWaveletFilter(orders: Array[Int]) extends ReversibleScaler
 
   assert(partitionIndices.length == orders.length, "Number of partitions must be equal to number of variable groups")
 
-  override val i: Scaler[DenseVector[Double]] = InvGroupedHaarWaveletFilter(orders)
+  override val i: InvGroupedHaarWaveletFilter = InvGroupedHaarWaveletFilter(orders)
 
   override def run(data: DenseVector[Double]): DenseVector[Double] = DenseVector(
     partitionIndices.zip(componentFilters).map(limitsAndFilter => {
@@ -107,7 +107,7 @@ case class InvGroupedHaarWaveletFilter(orders: Array[Int]) extends Scaler[DenseV
 
   val partitionIndices: Array[(Int, Int)] =
     orders.map(twoExp).scanLeft(0)(_+_).sliding(2).map(c => (c.head, c.last)).toArray
-  
+
   assert(partitionIndices.length == orders.length, "Number of partitions must be equal to number of variable groups")
 
   override def run(data: DenseVector[Double]): DenseVector[Double] = DenseVector(