WardBrian

The Genealogy of Programming Languages (30K Ft Edition)

Bob Muller — Jan. 2020

1600                                                   Symbolic Algebra

...                                                         |
                                                                           Set Theory
1879                                                   Symbolic Logic

WardBrian

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,

WardBrian

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)

WardBrian

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

WardBrian

# 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

WardBrian

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="#")

WardBrian

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):

WardBrian

# 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

WardBrian

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

# load data from data.json
data <- jsonlite::read_json("./data.json")
x <- unlist(data$x)
y <- unlist(data$y)

# posterior predictive check