rasmusab

Inlämningsuppgifter SM 1 & 2
========================================================
Rasmus Bååth, Sumsar Htååb

Detta är ett exempeldokumen skrivet med R markdown. 
R markdown-dokumentet måste vara fristående från din nuvarande R session. 
T.ex. så måste du i R markdown dokumentet alltid ladda in de paket du vill 
använda även om de redan är inladdade i din nuvarande R-session:

```{r message=FALSE}

rasmusab

# The code below will probably only work on Linux and requires VLC on the path.
library(beepr)
library(stringr)

# Plays a file with vlc and returns the vlc instance's PID
play_vlc_pid <- function(fname) {
  system(paste("vlc -Idummy --no-loop --no-repeat --playlist-autostart --no-media-library --play-and-exit", fname), 
         ignore.stdout = TRUE, ignore.stderr=TRUE,wait = FALSE)
  ps_out <- system("ps -eo pid,comm,etime| grep vlc", intern=TRUE)
  

rasmusab

### Function for generating the posterior of Robo's position ###

landscape <- c(rep("plain", 50), rep("mountain", 25), rep("forest", 45))
landscape_color <- c(mountain = "black", forest = "green", plain = "yellow")
cover_cost <- c(mountain = 10, forest = 5, plain = 1)


posterior_sample <- function(n) {
  dist_i <- sample(3, n, replace = TRUE, c(18, 17, 65))
  mu <- c(15, 40, 90)

rasmusab

library(microbenchmark)
library(arm)
library(rstan)
library(bbmle)

log_post <- function(par, y, x) {
  sigma <- exp(par[1])
  intercept <- par[2]
  beta <- as.matrix(par[-c(1,2)])
  mu <- intercept + x %*% beta

rasmusab

# Takes the matrix created by this script: https://gist.github.com/rasmusab/fb98cced046d4c675d74
# and calculates some statistics and fits some models to it. All this is pretty memory heavy so saving
# the interesting parts using saveRDS so the script only need to be run once.

set.seed(123)
games <- as.data.frame(readRDS("milionbase_matrix.rds"))

# Saving some info
n_games <- max(games[,"game_id"])
fullmoves_white <- games[ games[, "fullmoves"] < 100 & games[,"active_player"] == 1, "fullmoves"]

rasmusab

library(ggplot2)
library(plyr)

n_games <- readRDS("n_games.Rdata")
n_positions <- readRDS("n_positions.Rdata")
fullmoves_white <- readRDS("fullmoves_white.Rdata")

fits <- readRDS("chess_fits.Rdata")
fit <- fits$fit
fit <- fit 

rasmusab

# From https://aschinchon.wordpress.com/2015/06/23/the-2d-harmonograph-in-shiny/
# All code by Antonio S. Chinchón (@aschinchon), just rearranged so that you can directly
# run it by copy and pasting it into an R console.

library(shiny)
CreateDS = function () {
  f=jitter(sample(c(2,3),4, replace = TRUE))
  d=runif(4,0,1e-02)
  p=runif(4,0,pi)
  xt = function(t) exp(-d[1]*t)*sin(t*f[1]+p[1])+exp(-d[2]*t)*sin(t*f[2]+p[2])

rasmusab

# Prior to the tutorial make sure that the script below runs without error on your python installation.
# What you need is a working installation of Stan: http://mc-stan.org/ .
# For installation instructions, see here: 
# http://mc-stan.org/interfaces/pystan.html

# After installation you should be able to run this script which should output
# some summary statistics and some pretty plots, :)

# Fitting a simple binomial model using Stan

rasmusab

# A Bayesian model that calculates a probability that a couple is fertile 
# and pregnant. Please use this for fun only, not for any serious purpose 
# like *actually* trying to figure out whether you are pregnant.

# Enter your own period onsets here:
period_onset <- as.Date(c("2014-07-02", "2014-08-02", "2014-08-29", "2014-09-25", 
                          "2014-10-24", "2014-11-20", "2014-12-22", "2015-01-19"))
# If you have no dates you can just set days_between_periods to c() instead like:
# days_between_periods <- c()
days_between_periods <- as.numeric(diff(period_onset))

rasmusab

---
title: "A Bayesian Model to Calculate Whether My Wife is Pregnant or Not"
author: "Rasmus Bååth"
output: html_document
---

```{r echo = FALSE}
library(knitr)
options(digits=2)
```