squiggle.c/ROADMAP.md

Roadmap

To do

• Big refactor
  • Come up with a better headline example; fermi paradox paper is too complicated
  • Make README.md less messy
  • Give examples of new functions
  • Reference commit with cdf functions, even though deleted
• Post on suckless subreddit
• Drive in a few more real-life applications
  • US election modelling?
• Look into using size_t instead of int for sample numbers
• Reorganize code a little bit to reduce usage of gcc's nested functions
• Rename examples

Done

• Document print stats
• Document rudimentary algebra manipulations for normal/lognormal
• Think through whether to delete cdf => samples function => not for now
• Think through whether to:
  • simplify and just abort on error
  • complexify and use boxes for everything
  • leave as is
  • Offer both options
• Add more functions to do algebra and get the 90% c.i. of normals, lognormals, betas, etc.
  • Think through which of these make sense.
• Systematize references
• Think through seed initialization
• Document parallelism
• Document confidence intervals
• Add example for only one sample
• Add example for many samples
• Use gcc extension to define functions nested inside main.
• Chain various sample_mixture functions
• Add beta distribution
  • See https://stats.stackexchange.com/questions/502146/how-does-numpy-generate-samples-from-a-beta-distribution for a faster method.
• Use OpenMP for acceleration
• Add function to get sample when given a cdf
• Don't have a single header file.
• Structure project a bit better
• Simplify PROCESS_ERROR macro
• Add README
  • Schema: a function which takes a sample and manipulates it,
  • and at the end, an array of samples.
  • Explain boxes
  • Explain nested functions
  • Explain exit on error
  • Explain individual examples
• Rename functions to something more self-explanatory, e.g,. sample_unit_normal.
• Add summarization functions: mean, std
• Add sampling from a gamma distribution
  • https://dl.acm.org/doi/pdf/10.1145/358407.358414
• Explain correlated samples
• Test summary statistics for each of the distributions.
  • For uniform
  • For normal
  • For lognormal
  • For lognormal (to syntax)
  • For beta distribution
• Clarify gamma/standard gamma
• Add efficient sampling from a beta distribution
  • https://dl.acm.org/doi/10.1145/358407.358414
  • https://link.springer.com/article/10.1007/bf02293108
  • https://stats.stackexchange.com/questions/502146/how-does-numpy-generate-samples-from-a-beta-distribution
  • 5cae51e794/numpy/random/src/distributions/distributions.c
• Pontificate about lognormal tests
• Give warning about sampling-based methods.
• Have some more complicated & realistic example
• Add summarization functions: 90% ci (or all c.i.?)
• Link to the examples in the examples section.
• Add a few functions for doing simple algebra on normals, and lognormals
  • Add prototypes
  • Use named structs
  • Add to header file
  • Provide example algebra
  • Add conversion between 90% ci and parameters.
  • Use that conversion in conjunction with small algebra.
  • Consider ergonomics of using ci instead of c_i
    • use named struct instead
    • demonstrate and document feeding a struct directly to a function; my_function((struct c_i){.low = 1, .high = 2});
  • Move to own file? Or signpost in file? => signposted in file.
• Write twitter thread: now here; retweets appreciated.
• Write better confidence interval code that:
  • Gets number of samples as an input
  • Gets either a sampler function or a list of samples
  • is O(n), not O(nlog(n))
  • Parallelizes stuff

Discarded

• Disambiguate sample_laplace--successes vs failures || successes vs total trials as two distinct and differently named functions
• Support all distribution functions in https://www.squiggle-language.com/docs/Api/Dist
• Add a custom preprocessor to allow simple nested functions that don't rely on local scope?
• Add tests in Stan?
• Test results for lognormal manipulations
• Consider desirability of defining shortcuts for algebra functions. Adds a level of magic, though.
• Think about whether to write a simple version of this for uxn, a minimalist portable programming stack which, sadly, doesn't have doubles (64 bit floats)