Introduction to Phylanx Coding

In this post I’ll go through the simple implementation of LRA (Logistic Regression Algorithm) to outline the Phylanx architecture and also demonstrate how one might go about writing their own programs in Phylanx. The complete version of the code discussed in this post can be found in the project’s GitHub repository under examples/algorithms directory and the corresponding dataset can be found here. Continue reading

Phylanx Report: October 2017

The second month of work focused on

  • creating a minimal set of primitives (driven by the use case of implementing a full Logistic Regression Training Algorithm – LRA)
  • Migrating the code base from using the Eigen library for all matrix related operations to Blaze, adapting the build system
  • Refactoring the compilation subsystem of Phylanx to enable support for higher order functions

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Phylanx Seminar

Friday the Phylanx team at LSU held a seminar on the current theory, techniques, and methodology used by the Phylanx project. During the lecture Hartmut laid out the scope of the challenge we are trying to solve and the three components of the project as we have them today. Additionally, he explained the grammar we are using to describe expression trees and the current and future role Python is playing in the project.

You can watch the seminar, as well as, follow along with the overheads and examples provided below.

Seminar Video: