Friends don't let friends program drunk. On Fri, Dec 7, 2012 at 8:31 PM, Michael Wilber <mwil...@uccs.edu> wrote: > This is such an exciting time to be a Racket programmer! Thanks for all > your work, Neil. > > During winter break once I'm finished with grad school apps, I'll > convert my final Signals and Systems Matlab project to racket/math to > get a feel for the library. > > One note off-hand: I really don't like the (array #[#[#[...]]]) syntax. > In Matlab, a 2D array is just: > [1 2; 3 4] > That's 10 characters. In Python, the equivalent array is (including > whitespace): > [[1, 2], [3, 4]] > But in Racket, the equivalent array is: > (array #[#[1 1] #[3 4]]) > This syntax, especially the visual weight of all the hash marks #[#[, > makes it harder for me to visually scan the line and see the actual > array values. To see this, note that I miscopied one of the numbers > when I wrote the racket array. How many seconds did it take you to find > my mistake? Now imagine doing that at 3AM when drunk and you get an idea > of my reason for saying this. This is the kind of syntax that will trip > up undergraduates who are trying to debug their programs. > > Another syntax might be something akin to vectors: #[[1 2] [3 4]] > > Or just (array (1 2) (3 4)) > > Or maybe using a : or something to separate rows for 2D arrays? > (array 1 2 : 3 4) > > (array 1 0 0 0 0 0 > : 0 1 0 0 0 0 > : 0 0 1 0 0 0 > : 0 0 0 1 0 0 > : 0 0 0 0 1 0 > : 0 0 0 0 0 1) > > All of these feel much less "racket-y," but they would eliminate all the > visual cruft. > > > Neil Toronto <neil.toro...@gmail.com> writes: >> I've just pushed the last commits that make the new math library ready >> for wider testing. Almost everything ready for use is documented, the >> tests keep passing, and everything *seems* to work. >> >> We (the Racket development team) need people to use the heck out of it, >> to find the majority of the remaining logic and design errors before the >> next release. This is a big chunk of new code - ~20k lines in ~100 >> nontrivial files - so the more eyes, the better. >> >> If all you have time for is checking documentation, we'll take it. The >> latest docs are here: >> >> http://pre.racket-lang.org/docs/html/math/ >> >> The nightly builds are here: >> >> http://pre.racket-lang.org/installers/ >> >> For Windows and Mac, the nightlies should include two new libraries used >> by `math/bigfloat': libgmp and libmpfr. Linux users almost certainly >> already have them; many packages (e.g. gcc) depend on libmpfr. >> >> If you prefer to build from source, clone the git repository here: >> >> https://github.com/plt/racket >> >> If you're building on Windows or Mac, you can download pre-built libgmp >> and libmpfr from here: >> >> http://download.racket-lang.org/libs/10/ >> >> ************ >> >> Once you have a pre-release build, do (require math), and away you go! >> >> Well, it's probably not obvious where to start. The following are the >> modules that `math' re-exports, and for some, what needs testing. Please >> feel free to skim. In decreasing order of attention they need: >> >> (require math/array) >> >> NumPy/SciPy/Repa-like arrays, Typed-Racket-style. Provides a >> covariant `Array' type and several mutable subtypes, array literals, >> functional constructors, pointwise operations with automatic >> broadcasting in SciPy/NumPy or permissive mode, slicing, indexing >> tricks, folds, easy arbitrary transformations, and parallel FFT. >> >> This one needs a lot of usability testing. In particular... >> >> We're trying something that's been done only once before, in >> Haskell's Repa: by default, arrays' elements are *non-strict*, >> meaning that they're recomputed when referenced. Cool things about >> this: pretty much everything can in principle be parallelized, and >> most intermediate arrays use almost no memory. But it requires more >> thought from users about when arrays should be made strict (computed >> and stored all at once). If it totally sucks, we can change it. If >> it's a worthwhile imposition, we'll leave it. If it's a wash, we >> could parameterize the default behavior. >> >> Also, I've been considering allowing negative axis numbers and row >> indexes. Need feedback on how helpful it would be vs. confusing and >> error-hiding. >> >> Lastly, I'm looking for a dual of `array-axis-reduce' (a kind of >> fold) that makes it easy to write `list-array->array', the inverse of >> the existing `array->list-array'. If you're into functional design >> puzzles, give this one a shot. >> >> (require math/bigfloat) >> >> Floating-point numbers with arbitrarily large precision and >> large exponents. Also elementary and special functions, whose results >> are proved to be correct in many theses and dissertations. This is a >> Racket interface to MPFR (www.mpfr.org). >> >> This module needs platform-specific testing. >> >> I think we've verified that `math/bigfloat' works on all the >> supported 64-bit platforms, but not whether it works on supported >> 32-bit platforms. It should. ;) >> >> There's an error in the current nightly build, which causes an >> infinite loop when libmpfr isn't installed and a bigfloat function is >> applied. I just pushed a fix; it should fail properly tomorrow. >> >> (require math/distributions) >> >> Probability distributions: discrete, integer-valued and real-valued. >> Distribution objects can compute pdfs, cdfs and inverse cdfs, >> optionally in log space, and for cdfs and inverse cdfs, optionally >> for upper tails. They can also generate samples. >> >> Design ideas are welcome. More distributions are planned. Let me know >> which you need, and I'll concentrate on those first. >> >> Watch out, R. Our gamma cdf is more accurate. >> >> (require math/special-functions) >> >> Non-elementary functions like gamma, erf, zeta, Lambert W, and >> incomplete gamma and beta integrals. Most of these should be fairly >> accurate; i.e. they compute answers with apparently < 5 ulps error >> for the majority of their domains. But floating-point domains are >> huge, so the more use, the better. >> >> (require math/number-theory) >> >> Number theory! Chinese Remainder solutions, coprimality and primality >> testing, factorization, congruence arithmetic parameterized on a >> current modulus, integer nth roots, multiplicative functions, common >> number sequences (Bernoulli, Eulerian, tangent, etc.), combinatorics, >> primitive roots, and more. >> >> Please thank Jens Axel Søgaard, who wrote this module, for his >> excellent work. >> >> (require math/statistics) >> >> Functions to compute summary values for collections of data, and to >> manage weighted samples. Every function for which it makes sense >> accepts weighted values, including the O(1)-space running statistics >> update. >> >> I'm in the middle of documenting this. I'll get around to documenting >> correlation, kth-value order statistics (via quickselect), and >> counting/binning at least by Dec. 18. >> >> (require math/flonum) >> >> Re-exports `racket/flonum' along with many more floating-point >> goodies. If you're a floating-point nut, you'll love it. If you're >> not, at least check out `flsum'. If you're doing statistics, look >> into log-space arithmetic (`lg+' and friends). >> >> (require math/base) >> >> Re-exports `racket/math'; also exports some more constants like >> gamma.0 (Euler-Mascheroni), a bit more float-complex support, inverse >> hyperbolic functions, large random numbers, error measurement. >> >> Lastly, there's `math/matrix', which is currently not re-exported by >> `math' because I haven't reviewed it yet. This is more from Jens Axel, >> and if it's like his other fine work, it's correct and well-tested. I'll >> probably get to it by Christmas. >> >> To sum up, there are 8 modules that need pounding on, and we need YOU to >> do some pounding. If you're not terribly busy, please pick one that >> looks interesting/fun/useful, and try it out. >> >> Thanks! >> >> Neil ⊥ >> ____________________ >> Racket Users list: >> http://lists.racket-lang.org/users > > ____________________ > Racket Users list: > http://lists.racket-lang.org/users
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