Skip to main content

RcppAnnoy 0.0.9

(This article was first published on Thinking inside the box , and kindly contributed to R-bloggers)

An new version 0.0.9 of RcppAnnoy, our Rcpp-based R integration of the nifty Annoy library by Erik, is now on CRAN. Annoy is a small and lightweight C++ template header library for very fast approximate nearest neighbours.

This release corrects an issue for Windows users discovered by GitHub user ‘khoran’ who later also suggested the fix of binary mode. It upgrades to Annoy release 1.9.1 and brings its new Manhattan distance to RcppAnnoy. A number of unit tests were added as well, and we updated some packaging internals such as symbol registration.

And I presume I had a good streak emailing with Uwe’s robots as the package made it onto CRAN rather smoothly within ten minutes of submission:

RcppAnnou to CRAN 

Changes in this version are summarized here:

Changes in version 0.0.9 (2017-08-31)

  • Synchronized with Annoy upstream version 1.9.1

  • Minor updates in calls and tests as required by annoy 1.9.1

  • New Manhattan distance modules along with unit test code

  • Additional unit tests from upstream test code carried over

  • Binary mode is used for save (as suggested by @khoran in #21)

  • A new file init.c was added with calls to R_registerRoutines() and R_useDynamicSymbols()

  • Symbol registration is enabled in useDynLib

Courtesy of CRANberries, there is also a diffstat report for this release.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

To leave a comment for the author, please follow the link and comment on their blog: Thinking inside the box .
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...


from R-bloggers http://ift.tt/2wWzsIL
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Solving Van der Pol equation with ivp_solve

Van der Pol’s differential equation is The equation describes a system with nonlinear damping, the degree of damping given by μ. If μ = 0 the system is linear and undamped, but for positive μ the system is nonlinear and damped. We will plot the phase portrait for the solution to Van der Pol’s equation in Python using SciPy’s new ODE solver ivp_solve . The function ivp_solve does not solve second-order systems of equations directly. It solves systems of first-order equations, but a second-order differential equation can be recast as a pair of first-order equations by introducing the first derivative as a new variable. Since y is the derivative of x , the phase portrait is just the plot of ( x , y ). If μ = 0, we have a simple harmonic oscillator and the phase portrait is simply a circle. For larger values of μ the solutions enter limiting cycles, but the cycles are more complicated than just circles. Here’s the Python code that made the plot. from scipy import linspace from ...
Post a Comment
Read more

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...
Post a Comment
Read more