GarrettMooney

# venv
# usage:
#   $ venv .recsys
function venv {
    default_envdir=".env"
    envdir=${1:-$default_envdir}

    if [ ! -d $envdir ]; then
        python3.7 -m virtualenv -p python3.7 $envdir
        echo -e "\x1b[38;5;2m✔ Created virtualenv $envdir\x1b[0m"

GarrettMooney

#!/bin/bash
flag=$1
if [[ $flag == 'DL' ]]; then
	# -------------
	# Deep Learning
	# -------------
	echo "Configuring GPU for Deep Learning..."
	sudo apt-get install -y libnvidia-common-440
	sudo apt-get install -y cuda
	sudo apt-get install -y cuda-drivers

GarrettMooney

#pragma once

#include <algorithm>
#include <functional>
#include <future>
#include <thread>
#include <vector>

// adapted from https://stackoverflow.com/a/49188371/2055486

GarrettMooney

library(ggplot2)
theme_set(theme_classic())

# Visualizing entropy ---------------------------------------------------------
entropy <- function(p) -sum(p * log(p))
n <- 1e6
p <- runif(n)
q <- 1 - p
z <- matrix(c(p, q), ncol = 2)
ent <- apply(z, 1, entropy)

GarrettMooney

li(ggplot2)
theme_set(theme_classic())

entropy <- function(p) -sum(p * log(p))
n <- 1e6
p <- runif(n)
q <- 1 - p
z <- matrix(c(p, q), ncol = 2)
ent <- apply(z, 1, entropy)
qplot(q, ent, geom = "line") +

GarrettMooney

li(ggplot2)
theme_set(theme_classic())

kld <- function(p, q) sum(p * log(p / q))
n <- 1e6
p <- c(0.6, 0.4)
q1 <- runif(n)
q2 <- 1 - q1
z <- matrix(c(q1, q2), ncol = 2)
kl <- apply(z, 1, kld, p = p)

GarrettMooney

.First <- function() {

  # set TZ if unset
  if (is.na(Sys.getenv("TZ", unset = NA)))
    Sys.setenv(TZ = "America/New_York")

  # bail if revo R
  if (exists("Revo.version"))
    return()

GarrettMooney

library(tidyverse)

y <- as.matrix(mtcars$mpg)
X <- model.matrix(mpg ~ ., mtcars)

# linear regression
solve(crossprod(X)) %*% t(X) %*% y
solve(crossprod(X) + 0 * diag(rep(1, ncol(X)))) %*% t(X) %*% y

# ridge regression

GarrettMooney

library(gistr)

# create
gist_create(files='../create_gist.R', 
            description='Create gist via R.')

# update
gists(what = "minepublic")[[1]] %>%
  update_files('../create_gist.R') %>%
  update()

GarrettMooney

library(purrr)

# two regressions
lm_fit <- lm(mpg ~ wt + cyl, data = mtcars)
bayes_fit <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)

# design matrix [32 x 3]
X <- model.matrix(lm_fit)

# coefficient matrix [3 x 2]