Text Processing using the textTinyPy package in Python

This blog post (which has many similarities with the previous one) explains the functionality of the textTinyPy package which can be installed from pypi using,

• pip install textTinyPy

The package has been tested on Linux using python 2.7. It is based on the same C++ source code as the textTinyR package, but it has a slightly different structure and it’s wrapped in Python using Cython. It will work properly only if the following requirements are satisfied / installed:

Text Processing using the textTinyR package

This blog post is about my recently released package on CRAN, textTinyR. The following notes and examples are based mainly on the package Vignette.

The advantage of the textTinyR package lies in its ability to process big text data files in batches efficiently. For this purpose, it offers functions for splitting, parsing, tokenizing and creating a vocabulary. Moreover, it includes functions for building either a document-term matrix or a term-document matrix and extracting information from those (term-associations, most frequent terms). Lastly, it embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. The source code is based mainly on C++11 and exported in R through the Rcpp, RcppArmadillo and BH packages.

Clustering using the ClusterR package

This blog post is about clustering and specifically about my recently released package on CRAN, ClusterR. The following notes and examples are based mainly on the package Vignette.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

Kernel k nearest neighbors

This blog post is about my recently released package on CRAN, KernelKnn. The package consists of three functions KernelKnn, KernelKnnCV and knn.index.dist. It also includes two data sets (housing data, ionosphere), which will be used here to illustrate the functionality of the package.

k nearest neighbors

In pattern recognition the k nearest neighbors (KNN) is a non-parametric method used for classification and regression. Although KNN belongs to the 10 most influential algorithms in data mining, it is considered as one of the simplest in machine learning.

OpenImageR, an image processing toolkit

This blog post is about my recently released package on CRAN , OpenImageR. The package supports functions for image pre-processing, filtering and image recognition and it uses RccpArmadillo extensively to reduce the execution time of computationally intesive functions. OpenImageR can be split in 3 parts : basic functions (convolution, cropImage, down_sample_image, flipImage, gamma_correction, imageShow, image_thresholding, List_2_Array, MinMaxObject, NormalizeObject, readImage, resizeImage, rgb_2gray, rotateFixed, rotateImage, writeImage), image filtering (Augmentation, delationErosion, edge_detection, translation, uniform_filter, ZCAwhiten) and image recognition (average_hash, dhash, hash_apply, HOG, HOG_apply, invariant_hash, phash). The following code snippets explain the functionality of the OpenImageR package in more detail,