Added status update

docs/_data/tags.yml

@@ -9,3 +9,4 @@ allowed-tags:
   - pipes
   - workflow
   - probability
+  - news

docs/_posts/2016-12-11-dynaml-new-features.md

@@ -0,0 +1,141 @@
+---
+title:  "2016: State of DynaML"
+published: true
+permalink: new_features_dec_2016.html
+summary: "Summarizes some of the pet projects being tackled in DynaML"
+sidebar: mydoc_sidebar
+tags: [news]
+---
+
+The past year has seen DynaML grow by leaps and bounds, this post hopes to give you an update about what has been achieved
+and a taste for what is to come.
+
+## Completed Features
+
+A short tour of the enhancements which were completed.
+
+### January to June
+
+- Released `v1.3.x` series with the following new additions
+
+  _Models_
+
+  * Regularized Least Squares
+  * Logistic and Probit Regression
+  * Feed Forward Neural Nets
+  * Gaussian Process (GP) classification and NARX based models
+  * Least Squares Support Vector Machines (LSSVM) for classification and regression
+  * Meta model API, committee models
+
+  _Optimization Primitives_
+
+  * Regularized Least Squares Solvers
+  * Gradient Descent
+  * Committee model solvers
+  * Linear Solvers for LSSVM
+  * Laplace approximation for GPs
+
+  _Miscellaneous_
+
+  * Data Pipes API
+  * Migration to scala version 2.11.8
+
+- Started work on release `1.4.x` series with initial progress
+
+  _Improvements_
+
+  * Migrated from Maven to Sbt.
+  * Set [Ammonite](http://www.lihaoyi.com/Ammonite/) as default REPL.
+
+### June to December
+
+- Released `v1.4` with the following features.
+
+  _Models_
+
+  The following inference models have been added.
+
+  * LSSVM committees.
+  * Multi-output, multi-task _Gaussian Process_ models as reviewed in [Lawrence et. al](https://arxiv.org/abs/1106.6251).
+  * _Student T Processes_: single and multi output inspired from [Shah, Ghahramani et. al](https://www.cs.cmu.edu/~andrewgw/tprocess.pdf)
+  * Performance improvement to computation of _marginal likelihood_ and _posterior predictive distribution_ in Gaussian Process models.
+  * Posterior predictive distribution outputted by the ```AbstractGPRegression``` base class is now changed to ```MultGaussianRV``` which is added to the ```dynaml.probability``` package.
+
+  _Kernels_
+
+  * Added ```StationaryKernel``` and ```LocallyStationaryKernel``` classes in the kernel APIs, converted ```RBFKernel```, ```CauchyKernel```, ```RationalQuadraticKernel``` & ```LaplacianKernel``` to subclasses of ```StationaryKernel```
+
+  * Added ```MLPKernel``` which implements the _maximum likelihood perceptron_ kernel as shown [here](http://gpss.cc/gpuqss16/slides/gp_gpss16_session2.pdf).
+
+  * Added _co-regionalization kernels_ which are used in [Lawrence et. al](https://arxiv.org/abs/1106.6251) to formulate kernels for vector valued functions. In this category the following co-regionalization kernels were implemented.
+    - ```CoRegRBFKernel```
+    - ```CoRegCauchyKernel```
+    - ```CoRegLaplaceKernel```
+    - ```CoRegDiracKernel```
+
+    * Improved performance when calculating kernel matrices for composite kernels.
+
+    * Added ```:*``` operator to kernels so that one can create separable kernels used in _co-regionalization models_.
+
+    _Optimization_
+
+    * Improved performance of ```CoupledSimulatedAnnealing```, enabled use of 4 variants of _Coupled Simulated Annealing_, adding the ability to set annealing schedule using so called _variance control_ scheme as outlined in [de-Souza, Suykens et. al](ftp://ftp.esat.kuleuven.be/sista/sdesouza/papers/CSA2009accepted.pdf).
+
+    _Pipes_
+
+    * Added ```Scaler``` and ```ReversibleScaler``` traits to represent transformations which input and output into the same domain set, these traits are extensions of ```DataPipe```.
+
+    * Added _Discrete Wavelet Transform_ based on the _Haar_ wavelet.
+
+- Started work on `v1.4.1` with the following progress
+
+  _Linear Algebra API_
+
+  * Partitioned Matrices/Vectors and the following operations
+
+    1. Addition, Subtraction
+    2. Matrix, vector multiplication
+    3. LU, Cholesky
+    4. A\y, A\Y
+
+  _Probability API_
+
+  * Added API end points for representing Measurable Functions of random variables.
+
+  _Model Evaluation_
+
+  * Added Matthews Correlation Coefficient calculation to `BinaryClassificationMetrics` via the `matthewsCCByThreshold` method  
+
+  _Data Pipes API_
+
+  * Added `Encoder[S,D]` traits which are reversible data pipes representing an encoding between types `S` and `D`.
+
+  _Miscellaneous_
+
+  1. Updated ```ammonite``` version to `0.8.1`
+  2. Added support for compiling basic R code with [renjin](http://www.renjin.org). Run R code in the following manner:
+
+```scala
+val toRDF = csvToRDF("dfWine", ';')
+val wine_quality_red = toRDF("data/winequality-red.csv")
+//Descriptive statistics
+val commands: String = """
+print(summary(dfWine))
+print("\n")
+print(str(dfWine))
+"""
+r(commands)
+//Build Linear Model
+val modelGLM = rdfToGLM("model", "quality", Array("fixed.acidity", "citric.acid", "chlorides"))
+modelGLM("dfWine")
+//Print goodness of fit
+r("print(summary(model))")
+```
+
+## Ongoing Work
+
+Some projects being worked on right now are.
+
+* Bayesian optimization using Gaussian Process models.
+* Implementation of Neural Networks using the [akka](http://akka.io) actor API.
+* Implementation of kernels which can be decomposed on data dimensions $$k((x_1, x_2), (y_1, y_2)) = k_1(x_1, y_1) + k_2(x_2, y_2)$$