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Documentation Refactoring #526
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c7310d5
Documentation revision start
limarta 8043ab3
Doc changes
limarta 172e901
swap in
limarta 3f58b32
HMM example
limarta ad9760d
Serialization as an extension
limarta 3728b91
Edit docs. Add attribution
limarta dad5ec4
API listings
limarta 1c9f86e
Merge branch 'master' into doc_fix
limarta d2e5e88
Reformat code blocks
limarta 40483df
Delete docs/out.txt
limarta 9ee1e40
Remove files
limarta a722ace
Add instrutions
limarta a2db05b
Remove tutorial
limarta ef5f674
Directory changes
limarta 07117db
Fix gfi explanation code snippets. Other page fixes
limarta 0eda49a
How-to section
limarta ff26837
More how-tos
limarta 079881f
Ported SML tutorial. More directory refactoring
limarta 029ee99
Fix how to titles. Add MCMC description in API
limarta 62a69be
Added more undocumented fucntions. Edited docstrings. Minor config ch…
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| @@ -1,6 +1,7 @@ | ||
| [deps] | ||
| Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" | ||
| Gen = "ea4f424c-a589-11e8-07c0-fd5c91b9da4a" | ||
| Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" | ||
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| [compat] | ||
| Documenter = "0.27" | ||
| Documenter = "1" |
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| @@ -0,0 +1,16 @@ | ||
| # Website Docs | ||
| - `pages.jl` to find skeleton of website. | ||
| - `make.jl` to build the website index. | ||
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| # How to add tutorials | ||
| Currently you must write the tutorial directly in the docs rather than a source file (e.g. Quarto). See `getting_started` or `tutorials` for examples. | ||
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| Code snippets must use the triple backtick with a label to run. The environment carries over so long as the labels match. Example: | ||
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| ```@example tutorial_1 | ||
| x = rand() | ||
| ``` | ||
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| ```@example tutorial_1 | ||
| print(x) | ||
| ``` | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,53 @@ | ||
| pages = [ | ||
| "Home" => "index.md", | ||
| "Getting Started" => [ | ||
| "Example 1: Linear Regression" => "getting_started/linear_regression.md", | ||
| "Example 2: Hidden Markov Models" => "getting_started/hidden_markov_model.md", | ||
| ], | ||
| "Tutorials" => [ | ||
| "Basics" => [ | ||
| "tutorials/modeling_in_gen.md", | ||
| "tutorials/combinators.md", | ||
| "tutorials/gfi.md", | ||
| "tutorials/mcmc.md", | ||
| "tutorials/vi.md" | ||
| ], | ||
| "Advanced" => [ | ||
| "tutorials/modeling_in_gen.md", | ||
| "tutorials/trace_translators.md" | ||
| # "tutorials/data_driven_proposals.md", | ||
| ], | ||
| "Modeling Languages" => [ | ||
| "tutorials/languages/static_dsl.md", | ||
| "tutorials/languages/extending.md" | ||
| ], | ||
| ], | ||
| "Modeling Languages and APIs" => [ | ||
| "Generative Functions" => "api/gfi.md", | ||
| "Probability Distributions" => "api/distributions.md", | ||
| "Built-in Modeling Language" => "api/modeling.md", | ||
| "Generative Function Combinators" => "api/combinators.md", | ||
| "Choice Maps" => "api/choice_maps.md", | ||
| "Selections" => "api/selections.md", | ||
| "Optimizing Trainable Parameters" => "api/parameter_optimization.md", | ||
| "Trace Translators" => "api/trace_translators.md", | ||
| ], | ||
| "Standard Inference Library" => [ | ||
| "Importance Sampling" => "api/importance.md", | ||
| "MAP Optimization" => "api/map.md", | ||
| "Markov chain Monte Carlo" => "api/mcmc.md", | ||
| "MAP Optimization" => "api/map.md", | ||
| "Particle Filtering" => "api/pf.md", | ||
| "Variational Inference" => "api/vi.md", | ||
| "Learning Generative Functions" => "api/learning.md" | ||
| ], | ||
| "Internals" => [ | ||
| "Optimizing Trainable Parameters" => "api/internals/parameter_optimization.md", | ||
| "Modeling Language Implementation" => "api/internals/language_implementation.md" | ||
| ], | ||
| "Miscellanea" => [ | ||
| "Changelog" => "misc/changelog.md", | ||
| "Contributing" => "misc/contributing.md", | ||
| "References" => "misc/references.md" | ||
| ] | ||
| ] |
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| ## Generative Functions | ||
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| ```@docs | ||
| GenerativeFunction | ||
| ``` | ||
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| add example | ||
| ```julia | ||
| @gen function model() | ||
| end | ||
| ``` | ||
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| The complete set of methods in the generative function interface (GFI) is: | ||
| ```@docs | ||
| simulate | ||
| generate | ||
| update | ||
| regenerate | ||
| get_args | ||
| get_retval | ||
| get_choices | ||
| get_score | ||
| get_gen_fn | ||
| Base.getindex | ||
| project | ||
| propose | ||
| assess | ||
| has_argument_grads | ||
| accepts_output_grad | ||
| accumulate_param_gradients! | ||
| choice_gradients | ||
| get_params | ||
| ``` |
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| # Markov chain Monte Carlo (MCMC) | ||
| ```@docs | ||
| metropolis_hastings | ||
| mh | ||
| mala | ||
| hmc | ||
| elliptical_slice | ||
| @pkern | ||
| @kern | ||
| @rkern | ||
| reversal | ||
| involutive_mcmc | ||
| ``` |
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| ## Trace Translators | ||
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| ```@docs | ||
| @transform | ||
| @read | ||
| @write | ||
| @copy | ||
| pair_bijections! | ||
| is_involution! | ||
| inverse | ||
| DeterministicTraceTranslator | ||
| GeneralTraceTranslator | ||
| SimpleExtendingTraceTranslator | ||
| SymmetricTraceTranslator | ||
| ``` |
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| ## Variational inference | ||
| There are two procedures in the inference library for performing black box variational inference. | ||
| Each of these procedures can also train the model using stochastic gradient descent, as in a variational autoencoder. | ||
| ```@docs | ||
| black_box_vi! | ||
| black_box_vimco! | ||
| ``` |
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| @media all and (max-width: 560px) { | ||
| header.navigation { | ||
| position: fixed !important; | ||
| left:0; | ||
| top: 0; | ||
| width: 100%; | ||
| } | ||
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| header.navigation div.container { | ||
| margin-left: 0rem; | ||
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| header.navigation div.container nav.navbar { | ||
| min-height: 1rem !important; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav { | ||
| min-height: 1rem !important; | ||
| margin-left: 0.5rem !important; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav li.small-item { | ||
| visibility: visible !important; | ||
| display: block !important; | ||
| margin: 0.5rem; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav li.nav-item { | ||
| visibility: hidden; | ||
| display: none; | ||
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| header.navigation div.container nav.navbar ul.navbar-nav li.nav-item a { | ||
| visibility: hidden; | ||
| display: none; | ||
| } | ||
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| html:not(.theme--documenter-dark) body #documenter .docs-main { | ||
| margin-top: 2rem !important; | ||
| } | ||
| } | ||
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| @media all and (max-width: 1055px) and (min-width: 561px){ | ||
| header.navigation { | ||
| position: fixed !important; | ||
| left:0; | ||
| top: 0; | ||
| width: 100%; | ||
| } | ||
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| header.navigation div.container { | ||
| margin-left: 0rem; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav { | ||
| width: 80% !important; | ||
| } | ||
| } | ||
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| @media all and (min-width: 1056px) { | ||
| header.navigation { | ||
| position: fixed !important; | ||
| left:0; | ||
| top: 0; | ||
| width: 100%; | ||
| } | ||
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| header.navigation div.container { | ||
| margin-left: 18rem; | ||
| } | ||
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| } | ||
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| html.theme--documenter-dark header.navigation { | ||
| background-color: #1f2424 !important; | ||
| } | ||
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| html.theme--documenter-dark header.navigation div.container { | ||
| border-bottom: 1px solid #5e6d6f; | ||
| } | ||
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| html.theme--documenter-dark header.navigation div.container nav.navbar { | ||
| background-color: #1f2424 !important; | ||
| } | ||
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| html.theme--documenter-dark header.navigation div.container nav.navbar ul.navbar-nav li.nav-item a.nav-link { | ||
| color: white; | ||
| transition: color 100ms; | ||
| } | ||
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| html.theme--documenter-dark header.navigation div.container nav.navbar ul.navbar-nav li.nav-item a.nav-link:hover { | ||
| color: #0aa8a7 | ||
| } | ||
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| html header.navigation { | ||
| background-color: white !important; | ||
| } | ||
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| html header.navigation div.container { | ||
| border-bottom: 1px solid #dbdbdb; | ||
| } | ||
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| html header.navigation div.container nav.navbar ul.navbar-nav li.nav-item a.nav-link { | ||
| color: #222; | ||
| transition: color 100ms; | ||
| } | ||
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| html header.navigation div.container nav.navbar ul.navbar-nav li.nav-item a.nav-link:hover { | ||
| color: #0aa8a7 | ||
| } | ||
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| header.navigation { | ||
| z-index: 3; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav { | ||
| margin-left: 4rem; | ||
| min-height: 3.25rem; | ||
| width: 70%; | ||
| display: flex; | ||
| align-self: auto; | ||
| flex-direction: row; | ||
| justify-content: space-around; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav li.nav-item { | ||
| align-self: stretch; | ||
| align-content: space-around; | ||
| justify-content: center; | ||
| display: flex; | ||
| flex-direction: column; | ||
| } | ||
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| header.navigation div.container nav.navbar ul.navbar-nav li.small-item { | ||
| visibility: hidden; | ||
| display: none; | ||
| } |
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