State of DynaML 2016
Summarizes some of the pet projects being tackled in DynaML
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.
A short tour of the enhancements which were completed.
January to June¶
v1.3.xseries with the following new additions
- 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
- Regularized Least Squares Solvers
- Gradient Descent
- Committee model solvers
- Linear Solvers for LSSVM
- Laplace approximation for GPs
- Data Pipes API
Migration to scala version 2.11.8
Started work on release
1.4.xseries with initial progress
- Migrated from Maven to Sbt.
- Set Ammonite as default REPL.
June to December¶
v1.4with the following features.
The following inference models have been added.
- LSSVM committees.
- Multi-output, multi-task Gaussian Process models as reviewed in Lawrence et. al.
- Student T Processes: single and multi output inspired from Shah, Ghahramani et. al
- Performance improvement to computation of marginal likelihood and posterior predictive distribution in Gaussian Process models.
- Posterior predictive distribution outputted by the
AbstractGPRegressionbase class is now changed to
MultGaussianRVwhich is added to the
LocallyStationaryKernelclasses in the kernel APIs, converted
LaplacianKernelto subclasses of
MLPKernelwhich implements the maximum likelihood perceptron kernel as shown here.
Added co-regionalization kernels which are used in Lawrence et. al to formulate kernels for vector valued functions. In this category the following co-regionalization kernels were implemented.
Improved performance when calculating kernel matrices for composite kernels.
:*operator to kernels so that one can create separable kernels used in co-regionalization models.
- 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.
ReversibleScalertraits to represent transformations which input and output into the same domain set, these traits are extensions of
Added Discrete Wavelet Transform based on the Haar wavelet.
Started work on
v1.4.1with the following progress
Linear Algebra API
Partitioned Matrices/Vectors and the following operations
- Addition, Subtraction
- Matrix, vector multiplication
- LU, Cholesky
- A\y, A\Y
- Added API end points for representing Measurable Functions of random variables.
- Added Matthews Correlation Coefficient calculation to
Data Pipes API
Encoder[S,D]traits which are reversible data pipes representing an encoding between types
- Added support for compiling basic R code with renjin. Run R code in the following manner:
1 2 3 4 5 6 7 8 9 10 11 12 13 14
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))")
Some projects being worked on right now are.
- Bayesian optimization using Gaussian Process models.
- Implementation of Neural Networks using the akka 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)