08 Aug 2018
This blog post illustrates the new functionality of the OpenImageR package (Gabor Feature Extraction). The Gabor features have been used extensively in image analysis and processing (Character and Face recognition). Gabor (Nobel prize winner, an electrical engineer, and physicist) used the following wording which I think it’s worth mentioning in this blog post, “You can’t predict the future, but you can invent it.” (source).
In the following lines I’ll describe the GaborFeatureExtract R6 class which includes the following methods,
26 Jul 2018
In this blog post, I’ll explain how someone can take advantage of Singularity to make R or Python packages available as an image file to users. This is a necessity if the specific R or Python package is difficult to install across different operating systems making that way the installation process cumbersome. Lately, I’ve utilized the reticulate package in R (it provides an interface between R and Python) and I realized from first hand how difficult it is, in some cases, to install R and Python packages and make them work nicely together in the same operating system. This blog post by no means presents the potential of Singularity or containerization tools, such as docker, but it’s mainly restricted to package distribution / deployment.
05 Jul 2018
As of 2018-06-17 the elmNN package was archived and due to the fact that it was one of the machine learning functions that I used when I started learning R (it returns the output results pretty fast too) plus that I had to utilize the package last week for a personal task I decided to reimplement the R code in Rcpp. It didn’t take long because the R package was written, initially by the author, in a clear way. In the next lines I’ll explain the differences and the functionality just for reference.
04 Apr 2018
This blog post discuss the new functionality, which is added in the textTinyR package (version 1.1.0). I’ll explain some of the functions by using the data and pre-processing steps of this blog-post.
27 Feb 2018
The nmslibR package is a wrapper of NMSLIB, which according to the authors “… is a similarity search library and a toolkit for evaluation of similarity search methods. The goal of the project is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods”.
I’ve searched for some time (before wrapping NMSLIB) for a nearest neighbor library which can work with high dimensional data and can scale with big datasets. I’ve already written a package for k-nearest-neighbor search (KernelKnn), however, it’s based on brute force and unfortunately, it requires a certain computation time if the data consists of many rows. The nmslibR package, besides the main functionality of the NMSLIB python library, also includes an Approximate Kernel k-nearest function, which as I will show in the next lines is both fast and accurate. A comparison of NMSLIB with other popular approximate k-nearest-neighbor methods can be found here.