forked from personal/squiggle.c
add gamma distribution & documentation.
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README.md
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README.md
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@ -10,8 +10,10 @@ A self-contained C99 library that provides a subset of [Squiggle](https://www.sq
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- Because it will last long
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- Because it can fit in my head
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- Because if you can implement something in C, you can implement it anywhere else
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- Because it can be made faster if need be, e.g., with a multi-threading library like OpenMP, or by adding more algorithmic complexity
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- **Because there are few abstractions between it and the bare metal: C -> assembly = CPU instructions**, leading to fewer errors beyond the programmer's control.
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- Because it can be made faster if need be
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- e.g., with a multi-threading library like OpenMP, or by adding more algorithmic complexity
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- or more simply, by inlining the sampling functions (adding an `inline` directive before their function declaration)
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- **Because there are few abstractions between it and machine code** (C => assembly => machine code with gcc, or C => machine code, with tcc), leading to fewer errors beyond the programmer's control.
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## Getting started
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@ -79,8 +81,13 @@ Behaviour on error can be toggled by the `EXIT_ON_ERROR` variable. This library
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- [ ] Have some more complicated & realistic example
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- [ ] Add summarization functions: 90% ci (or all c.i.?)
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- [ ] Systematize references
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- [ ] Publish online
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- [ ] Add efficient sampling from a beta distribution
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- https://dl.acm.org/doi/10.1145/358407.358414
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- https://link.springer.com/article/10.1007/bf02293108
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- https://stats.stackexchange.com/questions/502146/how-does-numpy-generate-samples-from-a-beta-distribution
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- https://github.com/numpy/numpy/blob/5cae51e794d69dd553104099305e9f92db237c53/numpy/random/src/distributions/distributions.c
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- [ ] Support all distribution functions in <https://www.squiggle-language.com/docs/Api/Dist>
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- [ ] Support all distribution functions in <https://www.squiggle-language.com/docs/Api/Dist>, and do so efficiently
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@ -107,3 +114,5 @@ Behaviour on error can be toggled by the `EXIT_ON_ERROR` variable. This library
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- [x] Explain individual examples
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- [x] Rename functions to something more self-explanatory, e.g,. `sample_unit_normal`.
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- [x] Add summarization functions: mean, std
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- [x] Add sampling from a gamma distribution
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- https://dl.acm.org/doi/pdf/10.1145/358407.358414
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@ -18,12 +18,13 @@ DEPENDENCIES=$(MATH)
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# OPENMP=-fopenmp
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## Flags
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VERBOSE=#-v
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DEBUG= #'-g'
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STANDARD=-std=c99 ## gnu99 allows for nested functions.
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EXTENSIONS= #-fnested-functions
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WARNINGS=-Wall
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OPTIMIZED=-O3#-Ofast
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CFLAGS=$(DEBUG) $(STANDARD) $(EXTENSIONS) $(WARNINGS) $(OPTIMIZED)
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CFLAGS=$(VERBOSE) $(DEBUG) $(STANDARD) $(EXTENSIONS) $(WARNINGS) $(OPTIMIZED)
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## Formatter
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STYLE_BLUEPRINT=webkit
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BIN
references/marsaglia-gamma.pdf
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BIN
references/marsaglia-gamma.pdf
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36
squiggle.c
36
squiggle.c
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@ -73,6 +73,42 @@ float sample_to(float low, float high, uint32_t* seed)
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return sample_lognormal(logmean, logsigma, seed);
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}
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float sample_gamma(float alpha, uint32_t* seed){
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// A Simple Method for Generating Gamma Variables, Marsaglia and Wan Tsang, 2001
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// https://dl.acm.org/doi/pdf/10.1145/358407.358414
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// see also the references/ folder
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if(alpha >=1){
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float d, c, x, v, u;
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d = alpha - 1.0/3.0;
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c = 1.0/sqrt(9.0 * d);
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while(1){
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do {
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x = sample_unit_normal(seed);
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v = 1.0 + c * x;
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} while(v <= 0.0);
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v = pow(v, 3);
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u = sample_unit_uniform(seed);
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if( u < 1.0 - 0.0331 * pow(x, 4)){ // Condition 1
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// the 0.0331 doesn't inspire much confidence
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// however, this isn't the whole story
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// by knowing that Condition 1 implies condition 2
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// we realize that this is just a way of making the algorithm faster
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// i.e., of not using the logarithms
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return d*v;
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}
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if(log(u) < 0.5*pow(x,2) + d*(1.0 - v + log(v))){ // Condition 2
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return d*v;
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}
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}
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}else{
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return sample_gamma(1 + alpha, seed) * pow(sample_unit_uniform(seed), 1/alpha);
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// see note in p. 371 of https://dl.acm.org/doi/pdf/10.1145/358407.358414
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}
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}
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// Array helpers
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float array_sum(float* array, int length)
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{
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