nicokosi

(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: ")

nicokosi

# 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

nicokosi

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}."

nicokosi

# 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

nicokosi

(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))

nicokosi

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)

nicokosi

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

nicokosi

# 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"

nicokosi

Varnish

Intro

• "HTTP reverse proxy" => server-side HTTP cache => HTTP accelerator

client 1    --- GET foo --->    Varnish    --- GET foo --->    web app    (cache miss)
client 2    --- GET foo --->    Varnish    (cache hit)

• Linux-only, OSS, *nix philosophy (command tools)

nicokosi

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: