KernelOps based optimisations

1. Resorting to kernelOps for kernel addition and multiplication
2. Using scalaxy-streams optimise function

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

@@ -30,18 +30,27 @@ object KernelOps extends UFunc {
         firstKern: LocalScalarKernel[Index],
         otherKernel: LocalScalarKernel[Index]): CompositeCovariance[Index] =
         new CompositeCovariance[Index] {
-          override val hyper_parameters = firstKern.hyper_parameters ++ otherKernel.hyper_parameters
+
+          val (fID, sID) = (firstKern.toString.split("\\.").last, otherKernel.toString.split("\\.").last)
+
+          override val hyper_parameters =
+            firstKern.hyper_parameters.map(h => fID+"/"+h) ++
+              otherKernel.hyper_parameters.map(h => sID+"/"+h)
 
           override def evaluate(x: Index, y: Index) = firstKern.evaluate(x,y) + otherKernel.evaluate(x,y)
 
-          state = firstKern.state ++ otherKernel.state
+          state = firstKern.state.map(h => (fID+"/"+h._1, h._2)) ++ otherKernel.state.map(h => (sID+"/"+h._1, h._2))
 
-          blocked_hyper_parameters = firstKern.blocked_hyper_parameters ++ otherKernel.blocked_hyper_parameters
+          blocked_hyper_parameters =
+            firstKern.blocked_hyper_parameters.map(h => fID+"/"+h) ++
+              otherKernel.blocked_hyper_parameters.map(h => sID+"/"+h)
 
           override def setHyperParameters(h: Map[String, Double]): this.type = {
-            firstKern.setHyperParameters(h)
-            otherKernel.setHyperParameters(h)
-            super.setHyperParameters(h)
+            firstKern.setHyperParameters(h.filter(_._1.contains(fID))
+              .map(kv => (kv._1.split("/").tail.mkString("/"), kv._2)))
+            otherKernel.setHyperParameters(h.filter(_._1.contains(sID))
+              .map(kv => (kv._1.split("/").tail.mkString("/"), kv._2)))
+            this
           }
 
           override def gradient(x: Index, y: Index): Map[String, Double] =
@@ -63,18 +72,27 @@ object KernelOps extends UFunc {
       override def apply(firstKern: LocalScalarKernel[Index],
                          otherKernel: LocalScalarKernel[Index]): CompositeCovariance[Index] =
         new CompositeCovariance[Index] {
-          override val hyper_parameters = firstKern.hyper_parameters ++ otherKernel.hyper_parameters
+
+          val (fID, sID) = (firstKern.toString.split("\\.").last, otherKernel.toString.split("\\.").last)
+
+          override val hyper_parameters =
+            firstKern.hyper_parameters.map(h => fID+"/"+h) ++
+              otherKernel.hyper_parameters.map(h => sID+"/"+h)
 
           override def evaluate(x: Index, y: Index) = firstKern.evaluate(x,y) * otherKernel.evaluate(x,y)
 
-          state = firstKern.state ++ otherKernel.state
+          state = firstKern.state.map(h => (fID+"/"+h._1, h._2)) ++ otherKernel.state.map(h => (sID+"/"+h._1, h._2))
 
-          blocked_hyper_parameters = firstKern.blocked_hyper_parameters ++ otherKernel.blocked_hyper_parameters
+          blocked_hyper_parameters =
+            firstKern.blocked_hyper_parameters.map(h => fID+"/"+h) ++
+              otherKernel.blocked_hyper_parameters.map(h => sID+"/"+h)
 
           override def setHyperParameters(h: Map[String, Double]): this.type = {
-            firstKern.setHyperParameters(h)
-            otherKernel.setHyperParameters(h)
-            super.setHyperParameters(h)
+            firstKern.setHyperParameters(h.filter(_._1.contains(fID))
+              .map(kv => (kv._1.split("/").tail.mkString("/"), kv._2)))
+            otherKernel.setHyperParameters(h.filter(_._1.contains(sID))
+              .map(kv => (kv._1.split("/").tail.mkString("/"), kv._2)))
+            this
           }
 
           override def gradient(x: Index, y: Index): Map[String, Double] =

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

@@ -40,8 +40,8 @@ CovarianceFunction[Index, Double, DenseMatrix[Double]]
     *
     * */
   def +[T <: LocalScalarKernel[Index]](otherKernel: T)(implicit ev: ClassTag[Index]): CompositeCovariance[Index] =
-  new DecomposableCovariance(this, otherKernel)(DynaMLPipe.genericReplicationEncoder[Index](2))
-  //kernelOps.addLocalScKernels(this, otherKernel)
+  //new DecomposableCovariance(this, otherKernel)(DynaMLPipe.genericReplicationEncoder[Index](2))
+  kernelOps.addLocalScKernels(this, otherKernel)
 
 
   /**
@@ -52,9 +52,10 @@ CovarianceFunction[Index, Double, DenseMatrix[Double]]
     *
     * */
   def *[T <: LocalScalarKernel[Index]](otherKernel: T)(implicit ev: ClassTag[Index]): CompositeCovariance[Index] =
-  new DecomposableCovariance(this, otherKernel)(
+  kernelOps.multLocalScKernels(this, otherKernel)
+  /*new DecomposableCovariance(this, otherKernel)(
     DynaMLPipe.genericReplicationEncoder[Index](2),
-    Reducer.:*:)
+    Reducer.:*:)*/
 
   def :*[T1](otherKernel: LocalScalarKernel[T1]): CompositeCovariance[(Index, T1)] =
     new TensorCombinationKernel[Index, T1](this, otherKernel)
@@ -100,6 +101,8 @@ class DecomposableCovariance[S](kernels: LocalScalarKernel[S]*)(
     k.state.map(h => (id+"/"+h._1, h._2))
   }).reduceLeft(_++_)
 
+  val encodingTuple = encoding*encoding
+
   override val hyper_parameters: List[String] = kernels.map(k => {
     val id = k.toString.split("\\.").last
     k.hyper_parameters.map(h => id+"/"+h)
@@ -110,6 +113,16 @@ class DecomposableCovariance[S](kernels: LocalScalarKernel[S]*)(
     k.blocked_hyper_parameters.map(h => id+"/"+h)
   }).reduceLeft(_++_)
 
+  def kernelBind = DataPipe((xy: (Array[S], Array[S])) => {
+    optimize {
+      (xy._1, xy._2, kernels.map(k => k.evaluate _ ))
+        .zipped
+        .map((x, y, k) => k(x, y))
+    }
+  })
+
+  def kernelPipe = encodingTuple > kernelBind > reducer
+
   override def repr: DecomposableCovariance[S] = this
 
   override def setHyperParameters(h: Map[String, Double]): DecomposableCovariance.this.type = {
@@ -128,17 +141,7 @@ class DecomposableCovariance[S](kernels: LocalScalarKernel[S]*)(
     this
   }
 
-  override def evaluate(x: S, y: S): Double = {
-    val (xs, ys) = (encoding*encoding)((x,y))
-      reducer(
-        optimize {
-          xs.zip(ys).zip(kernels).map(coupleAndKern => {
-            val (u,v) = coupleAndKern._1
-            coupleAndKern._2.evaluate(u,v)
-          })
-        }
-      )
-  }
+  override def evaluate(x: S, y: S): Double = kernelPipe run (x,y)
 
   override def gradient(x: S, y: S): Map[String, Double] = reducer match {
     case SumReducer =>
@@ -153,12 +156,6 @@ class DecomposableCovariance[S](kernels: LocalScalarKernel[S]*)(
         val (u,v) = coupleAndKern._1
         coupleAndKern._2.gradient(u,v).mapValues(_ * this.evaluate(x,y)/coupleAndKern._2.evaluate(x,y))
       }).reduceLeft(_++_)
-    case _: Reducer =>
-      val (xs, ys) = (encoding*encoding)((x,y))
-      xs.zip(ys).zip(kernels).map(coupleAndKern => {
-        val (u,v) = coupleAndKern._1
-        coupleAndKern._2.gradient(u,v)
-      }).reduceLeft(_++_)
   }
 }
 
@@ -214,7 +211,5 @@ class TensorCombinationKernel[R, S](
     case ProductReducer =>
       firstK.gradient(x._1, y._1).map(k => (k._1, k._2*secondK.evaluate(x._2, y._2))) ++
         secondK.gradient(x._2, y._2).map(k => (k._1, k._2*firstK.evaluate(x._1, y._1)))
-    case _: Reducer =>
-      firstK.gradient(x._1, y._1) ++ secondK.gradient(x._2, y._2)
   }
 }

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

@@ -192,10 +192,11 @@ package object utils {
 
   def getTypeTag[T: ru.TypeTag](obj: T) = ru.typeTag[T]
 
-  def combine[A](xs: Traversable[Traversable[A]]): Seq[Seq[A]] =
+  def combine[A](xs: Traversable[Traversable[A]]): Seq[Seq[A]] = optimize {
     xs.foldLeft(Seq(Seq.empty[A])) {
       (x, y) => for (a <- x.view; b <- y) yield a :+ b
     }
+  }
 
   def downloadURL(url: String, saveAs: String): Unit =
     new URL(url) #> new File(saveAs) !!