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# 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)) |
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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, |
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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, |
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# 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) |
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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) |
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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) { |
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# 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", |
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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) |
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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") | |
} |
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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) |