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Feature NumPy R contiguous (virtual) memory ✔ ✔ 'view' memory model ✔ ✘ subset-assignment ✔ ✔ vectorized operations ✔ ✔ memory-mapping ✔ ✘* broadcasting rules ✔ ✔ index arrays ✔ ✔ This comparison is current as of R 2.13.0, NumPy version 1.4.1, and other web resources to date. Because this post was motivated by a [...] [Read more...]

I’m not sure how I missed this package, but I am sure glad I’ve found it. The data.table package for R provides something of a reconceptualization of the standard data.frame object. Though it remains (mostly) compatible with data.frame. The advantage … Continue reading → [Read more...]

I came up with an idea to draw correlation network to get a grasp about relationship between a list of stocks. An alternative way to show correlation matrix would be head map, which can have limitations with big matrices (__100). Unfortunately,  ggplot2 package doesn’t have a easy way to draw ... [Read more...]

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Thanks to this post, I found OpenClassroom. In addition, thanks to Andrew Ng and his lectures, I took my first course in machine learning. These videos are quite easy to follow. Exercise 2 requires implementing gradient descent algorithm to model data with linear regression. Read More: 243 Words Totally [Read more...]

For its 20th anniversary, JCGS offers free access to papers, including Andrew’s discussion paper Why tables are really much better than graphs. (Another serious ending for an April fool joke!) Incidentally (or rather coincidentally), I received today the great news that our Using parallel computation to improve Independent Metropolis-Hastings ... [Read more...]

First you need to create a workflow in Knime. This is what i used. I loaded in the Iris data, renamed the tables for further use in my scripts and showed a view, or first did an R snippet to show a view afterwards. Once this is done, make sure ... [Read more...]

R can easily generate random samples from a whole library of probability distributions. We might want to do this to gain insight into the distribution's shape and properties. A tricky aspect of statistics is that results like the central limit theore... [Read more...]

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Recently, the good folks at Infochimps.com rolled out a series of new APIs to add to their already impressive set of data resources. I have been in a perpetual state of catch-up since the new year, so I have only now got around to adding some of these new ... [Read more...]

First of all, welcome to my blog! I will write posts about trading, quantitative and algorithmic trading, programming and everything else what is on my mind. Feel free to comment and give suggestions how to improve this blog. Thank you! Well, one of th... [Read more...]

I recently started using RStudio, the amazing new IDE for R. You can view all of RStudio's keyboard shortcuts by going to the help menu, but I made this printable reference for myself and thought I'd share it. I only included the Windows shortcuts, and... [Read more...]

I decided to spend a few hours this weekend writing the R code to scrape the individual statistics of NBA players (2010-11 only).  I originally planned to write up a few NBA-related analyses, but a friend was visiting from out … Continue reading → [Read more...]

What is JRI? An additional library and plugin for Eclipse to call r functions in a Java-application. And according to wikipedia it’s  a now obsolete API for invoking native C++ calls from Java that has long been supplanted by Java Native Interfa... [Read more...]

Here are the results of a "Curse of Dimensionality" homework assignment for Terran Lane's Introduction to Machine Learning class. Pretty pictures, interesting results, and a good exercise in explicit parallelism with R. It's neat to see distance scaling linearly with standard deviation, and linearly with the Lth-root for an L-norm ... [Read more...]

Here is a fast R function to extract exon locations from a BED12 file. Note that fast is a relative term, the function below is fast enough for me, may not be fast enough for others :) Anyway, a BED12 file typically has locations of genomic features (t... [Read more...]

Exercise 5.2 Improves the Logistic Regression implementation done in Exercise 4 by adding a regularization parameter that reduces the problem of over-fitting. We will be using Newton's Method. Data Here's the data we want to fit. # linear regression # load the data mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydHZPN2... [Read more...]

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