WardBrian

The Genealogy of Programming Languages (30K Ft Edition)

Bob Muller — Jan. 2020

1600                                                   Symbolic Algebra

...                                                         |
                                                                           Set Theory
1879                                                   Symbolic Logic

WardBrian

"""
The "doll house" interface focuses trying to give you
everything you might need wrapped up nicely. It is built on
the "lego" interface.
"""


from lego import constrain, init_random, unconstrain

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from jax import random, jit
import jax.numpy as jnp
from jax.scipy import stats

from util import (
    ravelize_function,
    make_log_density,
    constrain,
    positive,
    real,

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FetchContent_Declare(boost_ut
        GIT_REPOSITORY https://github.com/boost-ext/ut.git
        GIT_TAG v2.3.1)
FetchContent_MakeAvailable(boost_ut)


# link this object to your test executable, then call discover_boost_ut_test() on it
add_library(boost_ut_runner OBJECT test_runner.cpp)
target_link_libraries(boost_ut_runner PUBLIC Boost::ut)

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{
  "D": 2,
  "r": 2.5,
  "p": 0.6666666666666666
}

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# based on tutorial from https://www.cnx-software.com/2017/03/12/how-to-control-your-air-conditioner-with-raspberry-pi-board-and-anavi-infrared-phat/

begin remote

  name   ge_ac
  flags  RAW_CODES
  eps     30
  aeps   100

  ptrail   0

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let style_of_string : string -> Fmt.style option = function
  | "b" | "bold" -> Some `Bold
  | "i" -> Some `Italic
  | "u" -> Some `Underline
  | "f" | "faint" -> Some `Faint
  | "r" | "reset" -> Some `None
  | "reverse" -> Some `Reverse
  | "black" -> Some (`Fg `Black)
  | "cyan" -> Some (`Fg `Cyan)
  | "green" -> Some (`Fg `Green)

WardBrian

# code adapted from https://github.com/stan-dev/example-models/blob/master/knitr/lotka-volterra/lotka-volterra-predator-prey.Rmd

library(posterior)
library(reshape2)
library(ggplot2)

lynx_hare_df <-
  read.csv("https://raw.githubusercontent.com/stan-dev/example-models/refs/heads/master/knitr/lotka-volterra/hudson-bay-lynx-hare.csv",
           comment.char="#")

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from jax import random, jit
import jax.numpy as jnp
from jax.scipy import stats

from util import ravelize_function, make_log_density

__all__ = ["log_density", "log_density_vec", "init_draw_zero"]


def constrain_parameters(sigma_unc, alpha, beta):

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# posterior predictive check using the pred_cases generated quantity

install.packages("bayesplot")
library(posterior)
library(ggplot2)

# load from data
d <- jsonlite::read_json('./data.json')
cases <- unlist(d$cases)
n_days <- d$n_days