Tag: R

Feed: Featured Blog Posts - Data Science Central. Author: Sandipan Dey. In this article a few popular image processing problems along with their solutions are going to be discussed. Python image processing libraries are going to be used to solve these problems. Image Transformations and Warping 0. Display RGB image color channels in 3D A gray-scale image can be thought of a 2-D function f(x,y) of the pixel locations (x,y), that maps each pixel into its corresponding gray level (an integer in [0,255], e.g.,). For an RGB image there are 3 such functions, f_R(x,y), f_G(x.y), f_B(x.y). matplotlib’s 3-D plot functions can be used to plot each function. The following ... Read More

Feed: R-bloggers. Author: xi'an. I came across this question on X validated that wondered about one of our examples in Monte Carlo Statistical Methods. We have included a section on Laplace approximations in the Monte Carlo integration chapter, with a bit of reluctance on my side as this type of integral approximation does not directly connect to Monte Carlo methods. Even less in the case of the example as we aimed at replacing a coverage probability for a Gamma distribution with a formal Laplace approximation. Formal due to the lack of asymptotics, besides the length of the interval (a,b) which ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. Featured Resources and Technical Contributions  Solving Image Processing Problems with Python - Part 1 Getting Started with NLP: Simple Topic Modeling in R - Part 1 Deploying Machine Learning Models on Spark Structured Streaming  General Myths to avoid in Data Science and Machine Learning  Neural Networks from a Bayesian Perspective  Comparison of the Top Cloud APIs for Computer Vision  A Little College Sports Analysis, but ... Read More

Feed: R-bloggers. Author: Anisa Dhana. In comparison with other statistical software (e.g., SAS, STATA, and SPSS), R is the best for data visualization. Therefore, in all posts I have written for DataScience+ I take advantage of R and make plots using ggplot2 to visualize all the findings. For example, previously I plotted the percentiles of body mass index in the NHANES 2005-2014 and got exactly same results as the paper published in JAMA. In this post, I will make a map of the prevalence of Alzheimer disease mortality by the state in the USA. The Centers for Disease Control and ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . Note: Columnstore.xml modifications you manually made are not automatically carried forward on an upgrade. These modifications will need to be incorporated back into .XML once the upgrade has occurred. The previous configuration file will be saved as /usr/local/mariadb/columnstore/etc/Columnstore.xml.rpmsave. If you have specified a root database password (which is good practice), then you must configure a .my.cnf file with user credentials for the upgrade process to use. Create a .my.cnf file in the user home directory with 600 file permissions with the following content (updating PASSWORD as appropriate): [mysqladmin] user = root password ... Read More

Feed: R-bloggers. Author: Jozef's Rblog. In this summertime post in the case4base series, we will look at useful tools in base R, which let us profile our code without any extra packages needed to be installed. We will cover simple and easy to use speed profiling, more complex profiling of performance and memory and, as always, look at alternatives to base R as well, with a special shout out to profiling integration in RStudio. Base function system.time returns the difference between two proc.time calls within which it evaluates an expression provided as argument. The simplest usage can be seen below: ... Read More

Feed: R-bloggers. Author: Thinking inside the box. A new RcppArmadillo release 0.9.100.5.0, based on the new Armadillo release 9.100.5 from earlier today, is now on CRAN and in Debian. It once again follows our (and Conrad’s) bi-monthly release schedule. Conrad started with a new 9.100.* series a few days ago. I ran reverse-depends checks and found an issue which he promptly addressed; CRAN found another which he also very promptly addressed. It remains a true pleasure to work with such experienced professionals as Conrad (with whom I finally had a beer around the recent useR! in his home town) and ... Read More

Feed: Planet big data. Author: David Smith. A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications I've noted over the past month or so. Open Source AI, ML & Data Science News ONNX Model Zoo is now available, providing a library of pre-trained state-of-the-art models in deep learning in the ONNX format. In the 2018 IEEE Spectrum Top Programming Language rankings, Python takes the top spot and R ranks #7. Julia 1.0 has been released, marking the stabilization of the scientific computing ... Read More

Feed: R-bloggers. Author: . Topics and Categories in the Russian Troll Tweets I decided to return to the analysis I conducted for the IRA tweets dataset. (You can read up on that analysis and R code here.) Specifically, I returned to the LDA results, which looked like they lined up pretty well with the account categories identified by Darren Linvill and Patrick Warren. But with slightly altered code, we can confirm that or see if there’s more to the topics data than meets the eye. (Spoiler alert: There is more than meets the eye.) I reran much of the original ... Read More

Feed: R-bloggers. Author: Ilya Kipnis. This is a review of James Picerno’s Quantitative Investment Portfolio Analytics in R. Overall, it’s about as fantastic a book as you can get on portfolio optimization until you start getting into corner cases stemming from large amounts of assets. Here’s a quick summary of what the book covers: 1) How to install R. 2) How to create some rudimentary backtests. 3) Momentum. 4) Mean-Variance Optimization. 5) Factor Analysis 6) Bootstrapping/Monte-Carlo simulations. 7) Modeling Tail Risk 8) Risk Parity/Vol Targeting 9) Index replication 10) Estimating impacts of shocks 11) Plotting in ggplot 12) Downloading/saving data ... Read More