Student T Process Mixture

Author: mandar2812

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

@@ -21,6 +21,8 @@ package io.github.mandar2812.dynaml.models
 import breeze.linalg.DenseVector
 import breeze.stats.distributions.{ContinuousDistr, Moments}
 import io.github.mandar2812.dynaml.kernels.CovarianceFunction
+import io.github.mandar2812.dynaml.models.gp.{AbstractGPRegressionModel, GaussianProcessMixture}
+import io.github.mandar2812.dynaml.models.stp.{AbstractSTPRegressionModel, MVStudentsTModel, MVTMixture, StudentTProcessMixture}
 import io.github.mandar2812.dynaml.pipes.DataPipe
 import io.github.mandar2812.dynaml.probability._
 import io.github.mandar2812.dynaml.probability.distributions.HasErrorBars
@@ -150,6 +152,26 @@ I, Y, W <: ContinuousMixtureRV[_, _]] extends
   ContinuousProcessModel[Seq[(I, Y)], I, Y, W]
 
 
+object StochasticProcessMixtureModel {
+
+  def apply[T, I: ClassTag](
+    component_processes: Seq[AbstractGPRegressionModel[T, I]],
+    weights: DenseVector[Double]) =
+    new GaussianProcessMixture[T, I](component_processes, weights)
+
+  def apply[T, I: ClassTag](
+    component_processes: Seq[AbstractSTPRegressionModel[T, I]],
+    weights: DenseVector[Double]) =
+    new StudentTProcessMixture[T, I](component_processes, weights)
+
+
+  def apply[T, I: ClassTag](
+    component_processes: Seq[MVStudentsTModel[T, I]],
+    weights: DenseVector[Double]) =
+    new MVTMixture[T, I](component_processes, weights)
+
+}
+
 /**
   * A process which is a multinomial mixture of
   * continuous component processes.

dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/models/stp/StudentTProcessMixture.scala

@@ -0,0 +1,31 @@
+package io.github.mandar2812.dynaml.models.stp
+
+import breeze.linalg.DenseVector
+import io.github.mandar2812.dynaml.algebra.{PartitionedPSDMatrix, PartitionedVector}
+import io.github.mandar2812.dynaml.analysis.InnerProductPV
+import io.github.mandar2812.dynaml.models.GenContinuousMixtureModel
+import io.github.mandar2812.dynaml.probability.MultStudentsTPRV
+import io.github.mandar2812.dynaml.probability.distributions.BlockedMultivariateStudentsT
+import spire.algebra.VectorSpace
+
+import scala.reflect.ClassTag
+
+/**
+  * @author mandar2812 date 21/06/2017.
+  * */
+class StudentTProcessMixture[T, I: ClassTag](
+  override val component_processes: Seq[AbstractSTPRegressionModel[T, I]],
+  override val weights: DenseVector[Double]) extends
+  GenContinuousMixtureModel[
+    T, I, Double, PartitionedVector,
+    PartitionedPSDMatrix, BlockedMultivariateStudentsT,
+    MultStudentsTPRV, AbstractSTPRegressionModel[T, I]](
+    component_processes, weights) {
+
+  protected val blockSize: Int = component_processes.head._blockSize
+
+  override protected def toStream(y: PartitionedVector): Stream[Double] = y.toStream
+
+  override protected def getVectorSpace(num_dim: Int): VectorSpace[PartitionedVector, Double] =
+    InnerProductPV(num_dim, blockSize)
+}