docs: Preparing release notes for v1.5.3 [WIP 4]

Signed-off-by: mandar2812 <mandar2812@gmail.com>

docs/releases/mydoc_release_notes_153.md

@@ -4,7 +4,35 @@
 
 ## Additions
 
+### Data Set API
 
+ The `DataSet` family of classes helps the user to create and transform potentially large number of data instances.
+ Users can create and perform complex transformations on data sets, using the `DataPipe` API or simple Scala functions.
+
+ ```scala
+ import _root_.io.github.mandar2812.dynaml.probability._
+ import _root_.io.github.mandar2812.dynaml.pipes._
+ import io.github.mandar2812.dynaml.tensorflow._
+
+
+ val random_numbers = GaussianRV(0.0, 1.0) :* GaussianRV(1.0, 2.0) 
+
+ //Create a data set.
+ val dataset1 = dtfdata.dataset(random_numbers.iid(10000).draw) 
+
+ val filter_gr_zero = DataPipe[(Double, Double), Boolean](c => c._1 > 0d && c._2 > 0d)
+
+ //Filter elements
+ val data_gr_zero = dataset1.filter(filter_gr_zero)
+
+ val abs_func: (Double, Double) => (Double, Double) = (c: (Double, Double)) => (math.abs(c._1), math.abs(c._2))
+
+ //Map elements
+ val data_abs     = dataset1.map(abs_func)
+
+ ```
+
+ Find out more about the `DataSet` API and its capabilities in the scala [docs]().
 
 ### Tensorflow Integration
 
@@ -69,6 +97,9 @@
 
  #### Dynamical Systems: Continuous Time RNN
 
+ Continuous time recurrent neural networks (CTRNN) are an important class of recurrent neural networks. They enable
+ the modelling of non-linear and potentially complex dynamical systems of multiple variables, with feedback.
+
   - Added CTRNN layer: `dtflearn.ctrnn`
 
   - Added CTRNN layer with inferable time step: `dtflearn.dctrnn`.

dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/tensorflow/data/DataSet.scala

@@ -51,17 +51,23 @@ class DataSet[X](val data: Iterable[X]) {
 
   lazy val size: Int = data.toSeq.length
 
+
+  def filter(filterFn: X => Boolean): DataSet[X] = DataSet[X](data.filter(filterFn))
+
   /**
     * Filter elements of this data set which satisfy
     * a predicate.
     * */
-  def filter(pipe: DataPipe[X, Boolean]): DataSet[X] = DataSet[X](self.data.filter(pipe(_)))
+  def filter(pipe: DataPipe[X, Boolean]): DataSet[X] = filter(pipe.run _)
+
+
+  def filterNot(filterFn: X => Boolean): DataSet[X] = DataSet[X](data.filterNot(filterFn))
 
   /**
     * Filter elements of this data set which does not
     * satisfy a predicate.
     * */
-  def filterNot(pipe: DataPipe[X, Boolean]): DataSet[X] = DataSet[X](self.data.filterNot(pipe(_)))
+  def filterNot(pipe: DataPipe[X, Boolean]): DataSet[X] = filterNot(pipe.run _)
 
   /**
     * Creates a new data set of type [[Y]]
@@ -101,6 +107,9 @@ class DataSet[X](val data: Iterable[X]) {
     * */
   def concatenate(other: DataSet[X]): DataSet[X] = DataSet[X](self.data ++ other.data)
 
+
+  def transform[Y](transformation: Iterable[X] => Iterable[Y]): DataSet[Y] = DataSet[Y](transformation(data))
+
   /**
     * Transform the underlying collection in a way that uses potentially all of its elements.
     * */