Applied machine learning developers have a lot of open-source machine learning frameworks, e.g., ScikitLearn, Spark MLlib, ML.Net, etc. Frameworks provide a user-friendly high-level interface to algorithms, but implementations resort to low-level languages and optimizations. Such low-level C/C++, C#, or Java code is far from the original mathematical notation that is preferable for machine learning algorithms research and development. Semagle Framework makes the most of low-level C#-like constructs for performance optimization and high-level semi-mathematical F# notation for joining the algorithm blocks. Modularization of algorithms with fine-grained blocks makes research and development of new implementations for the same family of problems straightforward.
Materialized path or path enumeration model1 stores the path to the tree node as a string or a list by concatenating the keys of the nodes in the path. Simple node inserts and removals, child and other descendants list, and parent and ancestors list queries make materialized path model attractive for large-scale applications. Fetching a subtree or an entire three rows in a single query is easy. An obvious solution to restoring the structure of a tree or subtree is to build a key-value map and append the child to the specific parent. This approach requires additional memory and relies on mutable data structures. Instead, it is enough to sort the lines in nesting order and build the tree structure by adding children to the previous parent.
The idea of PassCard was born from the frustration with password management solutions. Technology should make our lives easier and better, and pervasive online services help us greatly, but we do not feel free anymore. Each time we read an email on parents’ notebooks or pay in the store with another card, we need to check our phones or write down passwords on a piece of paper.
Sooner or later
printfn style of logging becomes too cumbersome and you start to search for a logging library.
For F# developers the most obvious choice is Logary, but very soon you
find out that your Logary logging code is even less readable. In this article you will find F# tricks,
which helped me to create a neat abstraction for logging in Semagle Framework.
During last 10-15 years, machine learning gradually moved from academia to industry. There are many open source frameworks like Scikit Learn, Spark MLlib and hundreds of lines of R code available for applied developers. Yet experimenting and development of machine learning algorithms remain difficult problems. Functional programming languages like Haskell, OCaml and F# have appealing semi-mathematical notation that greatly simplifies machine learning algorithms implementation, but could meet required performance restrictions. Semagle F# Framework is a successful experiment that demonstrates how to create and refine the functional code for clarity and performance, and now its code is available on GitHub.