daviddalpiaz

# https://i.imgur.com/tqoTyK3.jpg
# TODO: add rest timings
# TODO: add weight based on previous?
# TODO: push / hold / find / extend
# TODO: 4.5 -> 4/3
gen_workout = function(type = "full") {
  
  tier_1_lift = sample(c("ossumo", "bp", "ohp"), size = 1, prob = c(2, 1, 1))
  tier_2_lift = sample(c("hbbs", "bp", "ohp"), size = 1, prob = c(2, 1, 1))
  tier_3_lift = sample(c("dbr", "sr", "hc", "bc"), size = 1, prob = c(4, 1, 1, 1))

daviddalpiaz

max = 210

data.frame(
  work_wt = c(
    max * 0.750,
    max * 0.800,
    max * 0.850,
    max * 0.900,
    max * 0.800,
    max * 0.850,

daviddalpiaz

y = c(2637,2495,2977,3062,3203,3860,3856,2920,1970,2920,3090,3941,3042,2836,
      1928,2977,4990,3983,3175,2240,3651,3544,2750,3416,3572,2353,2100,3132,
      1729,3643,2782,3005,2424,3799,3175,2778,3941,2211,2084,2353,3062,3770,
      3997,3374,4167,2414,2495,2920,2126,3487,2977,3572,1588,3614,3473,2523,
      3997,2557,2821,3637,3232,3969,3756,3460,3790,3651,2055,2877,2466,3100,
      2835,2906,3651,4238,3912,2084,2055,2410,2495,3225,2125,2733,3827,2948,
      2466,3770,2922,2920,1021,2296,4593,2438,3090,3203,3402,2187,3651,1818,
      1928,2600,1893,3317,1330,3062,3884,3080,3232,2442,2495,3884,2663,1899,
      3331,1790,2977,2225,3062,3860,3104,3629,3203,2769,2410,2450,2665,2466,
      1135,3225,3274,3317,4111,3234,3544,3444,3033,2187,2807,2296,3090,1928,

daviddalpiaz

# see here: https://keras.rstudio.com/
# code reproduced here for easy copy-pasting 

# install TF if necessary
devtools::install_github("rstudio/tensorflow")
tensorflow::install_tensorflow()

# load keras
library(keras)

daviddalpiaz

check_mem = function() {
  mem_lim = as.numeric(system("cat /sys/fs/cgroup/memory/memory.limit_in_bytes", intern = TRUE)) * 0.000001
  mem_use = as.numeric(system("cat /sys/fs/cgroup/memory/memory.usage_in_bytes", intern = TRUE)) * 0.000001
  prc_free = mem_use / mem_lim * 100, 1
  prc_used = 100 - prc_free
  memory_info = c("Used (MB)" = mem_use, 
                  "Free (MB)" = mem_lim - mem_use, 
                  "Used (%)" = prc_used, 
                  "Free (%)" = prc_free)
  round(memory_info, 1)

daviddalpiaz

plot_beta = function(a, b) {
  
  par(mfrow = c(1, 1))
  curve(dbeta(x, shape1 = a, shape2 = b), col = "darkorange",
        main = "beta distribution", xlab = "x", ylab = "density", lwd = 2)
  grid()

}

plot_prior_like_post = function(a, b, x, y, plot_est = FALSE) {

daviddalpiaz

# modification of https://gist.github.com/brendano/39760
# automatically obtains data from the web
# creates two data frames, test and train
# labels are stored in the y variables of each data frame
# can easily train many models using formula `y ~ .` syntax

# download data from http://yann.lecun.com/exdb/mnist/
download.file("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz",
              "train-images-idx3-ubyte.gz")
download.file("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz",

daviddalpiaz

obs = replicate(10000, sum(runif(n = 48, min = -0.5, max = 0.5)))
hist(obs, breaks = 25, col = "darkorange", border = "dodgerblue")
mean(abs(obs) < 1 * 2)
mean(abs(obs) < 2 * 2)
mean(abs(obs) < 3 * 2)

daviddalpiaz

plot_pois = function(lambda = 1.4) {
  stop_graph = max(15, ceiling(lambda + 3 * sqrt(lambda)))
  x  = 0:stop_graph
  fx = dpois(x, lambda = lambda)
  barplot(fx, names.arg = x, space = 0,
          xlab = "values", ylab = "probabilites",
          col  = "darkorange", border = "dodgerblue",
          main = "Poisson Probabilities")
}

daviddalpiaz

fit_test = lm(dist ~ ., data = cars)

n = length(resid(fit_test))
p = length(coef(fit_test))

-2 * logLik(fit_test) + 2 * p
AIC(fit_test)
extractAIC(fit_test) + n + n * log(2 * pi)