ko-lem

/**
 * @param {number[][]} buildings
 * @return {number[][]}
 */
var getSkyline = function(buildings) {
    var edges = calcSortedEdges(buildings);
    var skyline = [];
    var heights = new SlowPriorityQueue();
    var currHeight = 0;
    heights.add(0);

ko-lem

git clone https://github.com/lemonkoala/utils.git
echo "source ~/utils/_all.bash" > ~/.bashrc
source ~/.bashrc

mkdir jupyter
docker pull kaggle/python
docker run \
  -it \
  -d  \
  -p 8888:8888 \

ko-lem

setwd('~/kaggle/criteo')

library(ggplot2)
library(scales)

probs <- read.csv('submit/submit-8.csv')

num_less <- length(which(probs$Predicted < 0.5))
num_more <- length(which(probs$Predicted > 0.5))
num_less / (num_less + num_more)

ko-lem

require(ggplot2)
require(reshape)
require(scales)

setwd("~/Dropbox/Projects/deped-enrollment")

which.data <- 'elementary' # elementary or secondary

num.enrollees <- read.csv(paste('data/num-enrollees-', which.data, '.csv', sep=''))

ko-lem

setwd("~/Dropbox/Projects/suertres-analysis/")

data <- read.csv("data/processed/suertres.csv", header=TRUE,
                 colClasses=c(rep("factor", 6), "integer"))
data$date <- as.Date(data$date, format= "%m/%d/%Y")


length(data)
length(data[,1])

ko-lem

{
  "word_wrap": false,
  "fade_fold_buttons": false,
  "highlight_line": true,

  "font_size": 9,
  "line_padding_bottom": 1,
  "line_padding_top": 1,

  "tab_size": 2,