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(defn lazy-inc [x] | |
(lazy-seq | |
(print ".") | |
(cons x (lazy-inc (inc x))))) | |
(print "\n take 2: ") | |
(take 2 | |
(lazy-inc 1)) | |
(print "\n first: ") |
# File basics: | |
############################ | |
# list files, sorted by update date (recent last) | |
ls -rclt | |
# list files, sorted by update date (recent first) | |
ls -clt | |
# (recursive) folder sizes | |
du -h -d1 # human-readable sizes + dir depth of 1 |
out = new File("C:/tmp/output.txt") | |
out.write "-- Dummy file header\n" | |
for (index in 1..5) { | |
out.append "line${index}\n" | |
} | |
println "Generated file ${out}." |
# install '.deb' package (local file) | |
dpkg -i my-package | |
# list installed packages | |
dpkg-query -W | |
# list installed ('ii' marker) and not installed ('un') packages | |
dpkg -l | |
# list available packages |
(ns nightclazz.gameOfLife | |
(:require [quil.core :as q])) | |
;; generation: | |
(def world (atom | |
#{[0 1] [1 2] [1 1] [1 3]})) | |
(defn setup [] | |
(q/smooth) | |
(q/background 30)) |
Nicolas Kosinski's notes from Coursera course "Computing for Data Analysis" that teaches R basics for statistics: https://www.coursera.org/course/compdata (session #4: https://class.coursera.org/compdata-004) | |
✔ R installation @done (14-01-21 10:59) | |
✔ Week 1 @done (14-01-22 07:55) | |
✔ What Makes R Different? (4:20) @done (14-01-21 12:08) | |
R mixes: | |
interactive (command-oriented) tool | |
programming lang | |
✔ How to Get Help (13:53) @done (14-01-21 12:37) | |
✔ Background and Overview (16:38) @done (14-01-21 15:39) |
IntelliJ IDEA shortcuts
search action: Ctrl + Shift + A
Navigation: open class by name: Ctrl + N open file by name: Ctrl + Shift + N goto anything: Ctrl + Alt + Shift + N recent files: Ctrl + E switch file: Ctrl + Tab
# R code that generates graph for collinear detection, week 3 assigment from Coursera MOOC "Algoritms, part I" (https://www.coursera.org/course/algs4partI). N.B.: input CSV file contains this kind of data: "n,time,algo\n10,0.497,brute\n10,0.006,fast\n# etc...". | |
library(ggplot2) | |
d <- read.csv("collinear-timings-brute-vs-fast.csv") | |
d$algo <- factor(d$algo) | |
levels(d$algo)[levels(d$algo)=="brute"] <- "Brute force" | |
levels(d$algo)[levels(d$algo)=="fast"] <- "Fast" |
Notes from Coursera course 'Functional Programming Principles in Scala": | |
https://class.coursera.org/progfun-004 | |
✔ Week 1: Functions & Evaluations @done (14-05-01 17:20) | |
✔ Lecture 1.1 - Programming Paradigms (14:32) @done (14-04-27 17:54) | |
3 paradigms: imperative, functional, logic | |
OO: orthogonal | |
imperative: |