Refactor to IID Random Variable hierarchy

Author: mandar2812

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

@@ -18,9 +18,9 @@ under the License.
 * */
 package io.github.mandar2812.dynaml.probability
 
-import breeze.stats.distributions.{ContinuousDistr, Density, Rand}
+import breeze.stats.distributions.{ContinuousDistr, Density, DiscreteDistr, Rand}
 import io.github.mandar2812.dynaml.pipes.DataPipe
-import io.github.mandar2812.dynaml.probability.distributions.{AbstractContinuousDistr, GenericDistribution}
+import io.github.mandar2812.dynaml.probability.distributions.{AbstractContinuousDistr, AbstractDiscreteDistr, GenericDistribution}
 
 /**
   * An independent and identically distributed
@@ -86,6 +86,16 @@ D, +Dist <: Density[D] with Rand[D],
   }
 }
 
+object IIDRandomVarDistr {
+  def apply[D, Dist <: Density[D] with Rand[D]](base : RandomVarWithDistr[D, Dist])(n: Int)
+  : IIDRandomVarDistr[D, Dist, RandomVarWithDistr[D, Dist]] =
+    new IIDRandomVarDistr[D, Dist, RandomVarWithDistr[D, Dist]] {
+      val baseRandomVariable = base
+      val num = n
+    }
+
+}
+
 /**
   * An IID Random variable constructed
   * from a continuous random variable having
@@ -113,19 +123,39 @@ D, +Distr <: ContinuousDistr[D],
 
 }
 
-object IIDRandomVarDistr {
-  def apply[D, Dist <: Density[D] with Rand[D]](base : RandomVarWithDistr[D, Dist])(n: Int) =
-    new IIDRandomVarDistr[D, Dist, RandomVarWithDistr[D, Dist]] {
-      val baseRandomVariable = base
-      val num = n
-    }
+object IIDContinuousRVDistr {
 
   def apply[
   D, Distr <: ContinuousDistr[D],
-  R <: ContinuousRVWithDistr[D, Distr]](base: R)(n: Int) = new IIDContinuousRVDistr[D, Distr, R] {
-    override val baseRandomVariable = base
-    override val num = n
+  R <: ContinuousRVWithDistr[D, Distr]](base: R)(n: Int): IIDContinuousRVDistr[D, Distr, R] =
+    new IIDContinuousRVDistr[D, Distr, R] {
+      override val baseRandomVariable = base
+      override val num = n
+    }
+
+
+}
+
+trait IIDDiscreteRVDistr[
+D, +Distr <: DiscreteDistr[D],
++R <: DiscreteRVWithDistr[D, Distr]] extends
+  IIDRandomVarDistr[D, Distr, R] {
+
+  override val underlyingDist = new AbstractDiscreteDistr[Stream[D]] {
+    override def probabilityOf(x: Stream[D]) = x.map(baseRandomVariable.underlyingDist.probabilityOf).product
+
+    override def draw() = Stream.tabulate[D](num)(_ => baseRandomVariable.underlyingDist.draw())
   }
+}
+
+object IIDDiscreteRVDistr {
+  def apply[
+  D, Distr <: DiscreteDistr[D],
+  R <: DiscreteRVWithDistr[D, Distr]](base: R)(n: Int): IIDDiscreteRVDistr[D, Distr, R] =
+    new IIDDiscreteRVDistr[D, Distr, R] {
+      override val baseRandomVariable = base
+      override val num = n
+    }
 
 }
 

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

@@ -235,22 +235,76 @@ trait RandomVarWithDistr[Domain, +Dist <: Density[Domain] with Rand[Domain]]
 
 }
 
+/**
+  * A continuous random variable that has an associated
+  * probability density function.
+  *
+  * @tparam Domain Support/Sample space of the random variable
+  * @tparam Distr Type of the probability density as a subtype of breeze [[ContinuousDistr]]
+  * */
 trait ContinuousRVWithDistr[Domain, +Distr <: ContinuousDistr[Domain]] extends
   ContinuousRandomVariable[Domain] with
   RandomVarWithDistr[Domain, Distr] {
 
+  self  =>
+
   override val underlyingDist: Distr
 
   override val sample = DataPipe(() => underlyingDist.sample())
 
-  override def iid(n: Int) = IIDRandomVarDistr[Domain, Distr, this.type](this)(n)
+  override def iid(n: Int) = IIDContinuousRVDistr[Domain, Distr, self.type](self)(n)
+
+  def :*[OtherDomain, OtherDistr <: ContinuousDistr[OtherDomain]](
+    other: ContinuousRVWithDistr[OtherDomain, OtherDistr]) = {
+
+    val productDensity = new ContinuousDistr[(Domain, OtherDomain)] {
+
+      override def unnormalizedLogPdf(x: (Domain, OtherDomain)) =
+        self.underlyingDist.logPdf(x._1) + other.underlyingDist.logPdf(x._2)
+
+      override def logNormalizer = self.underlyingDist.logNormalizer + other.underlyingDist.logNormalizer
+
+      override def draw() = (self.underlyingDist.draw, other.underlyingDist.draw)
+    }
+
+    RandomVariable(productDensity)
+  }
+
+}
+
+trait DiscreteRVWithDistr[Domain, +Distr <: DiscreteDistr[Domain]] extends
+  RandomVarWithDistr[Domain, Distr] {
+
+  self  =>
+
+  override val underlyingDist: Distr
+
+  override val sample = DataPipe(() => underlyingDist.sample())
+
+  override def iid(n: Int) = IIDDiscreteRVDistr[Domain, Distr, self.type](self)(n)
+
+  def :*[OtherDomain, OtherDistr <: DiscreteDistr[OtherDomain]](
+    other: DiscreteRVWithDistr[OtherDomain, OtherDistr]) = {
+
+    val productDensity = new DiscreteDistr[(Domain, OtherDomain)] {
+
+      override def probabilityOf(x: (Domain, OtherDomain)) =
+        self.underlyingDist.probabilityOf(x._1)*other.underlyingDist.probabilityOf(x._2)
+
+      override def draw() = (self.underlyingDist.draw, other.underlyingDist.draw)
+    }
+
+    RandomVariable(productDensity)
+  }
+
 }
 
 /**
   * A continuous random variable that has an associated
   * probability density function.
   *
   * */
+@deprecated("ContinuousDistrRV is deprecated as of DynaML v1.5, prefer ContinuousRVWithDistr")
 trait ContinuousDistrRV[Domain] extends
   ContinuousRVWithDistr[Domain, ContinuousDistr[Domain]] { self =>
 
@@ -356,9 +410,10 @@ object RandomVariable {
     * @param d A breeze discrete distribution
     * @return A discrete random variable instance.
     * */
-  def apply[O](d: DiscreteDistr[O]): DiscreteDistrRV[O] = new DiscreteDistrRV[O] {
-    override val underlyingDist = d
-  }
+  def apply[O](d: DiscreteDistr[O]): DiscreteRVWithDistr[O, DiscreteDistr[O]] =
+    new DiscreteRVWithDistr[O, DiscreteDistr[O]] {
+      override val underlyingDist = d
+    }
 
   def apply[O](g: ThreadedBufferedRand[O]) = new ThreadedRandomVariable[O](g)
 

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

@@ -44,9 +44,10 @@ object MultStudentsTRV {
   }
 }
 
-case class MultStudentsTPRV(mu: Double,
-                            mean: PartitionedVector,
-                            covariance: PartitionedPSDMatrix)(
+case class MultStudentsTPRV(
+  mu: Double,
+  mean: PartitionedVector,
+  covariance: PartitionedPSDMatrix)(
   implicit ev: Field[PartitionedVector])
   extends AbstractStudentsTRandVar[PartitionedVector, PartitionedPSDMatrix, BlockedMultivariateStudentsT](mu) {