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Links 1 through 10 of 8973 daniel mackinlay's Bookmarks

Where I took my bookmarks when delicious lost features such as the amazing search and RSS. This site is now deprecated.

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Use your dynamic language (lua, in this case) to implement compiled DSL

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Rweave-like report generation for python science using a literate programming style

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What It Is The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it on the fly into code for its internal virtual machine (VM). Due to its integrated just-in-time (JIT) compiler, it does not require a compiler at runtime. Also,numexpr implements support for multi-threading computations straight into its internal virtual machine, written in C. This allows to bypass the GIL in Python, and allows near-optimal parallel performance in your vector expressions, most specially on CPU-bounded operations (memory-bounded ones were already the strong point of numexpr). See MultiThreadVM for more info on this.

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Parsers made with funcparserlib are pure-Python LL(*) parsers. It means that it's very easy to write them without thinking about look-aheads and all that hardcore parsing stuff. But the recursive descent parsing is a rather slow method compared to LL(k) or LR(k) algorithms. So the primary domain for funcparserlib is parsing little languages or external DSLs (domain specific languages). The library itself is very small. Its source code is only 0.5 KLOC, with lots of comments included. It features the longest parsed prefix error reporting, as well as a tiny lexer generator for token position tracking.

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successor to sweave in that it integrates R an LaTeX, but aims to be a more extensible superset, integrating python, HTML, markdown, restructuredtext...

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Forget-Table is a database for storing non-stationary categorical distributions that forget old observations responsibly. It has been designed to store millions of distributions and can be written to at a high volume. "Forgetting" from a distribution is done by simulating a Poisson process with a user-specified rate. This results in equalizing all bins in a distribution such that, if no new observations are added in, the distribution will approach uniform.

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understand regression and classification algorithms but visualising them in action

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microsoft's domain-specific probabilistic language

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probabilistic programming in scala, factor graph model

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