Alexander RANUX

🏠

👉JavaScript dev. Open for job offerings

RANUX / iterm2-solarized.md

Created December 14, 2018 11:38 — forked from kevin-smets/iterm2-solarized.md

iTerm2 + Oh My Zsh + Solarized color scheme + Meslo powerline font + [Powerlevel9k] - (macOS)

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RANUX / draw-grid.js

Last active September 3, 2018 21:13

Draw squares grid with p5.js and Sketch

	var canvasWidth = 1200;
	var canvasHeight = 1024;

	function setup() {
	createCanvas(canvasWidth, canvasHeight);
	}

	function draw() {
	stroke("#DCA6A6");
	strokeWeight(0.1);

RANUX / cached-val-func.js

Created March 29, 2018 14:53

Computed (cached) function value JS pattern

	function isPrime(value) {
	// create cache
	if (!isPrime.answers) {
	isPrime.answers = {};
	}

	// check was value cached
	if (isPrime.answers[value] !== undefined) {
	return isPrime.answers[value];
	}

RANUX / observer.js

Created March 29, 2018 09:55

Simple JS Observer pattern

RANUX / 18-challange-styles.css

Created March 16, 2018 19:27

Styles from task

RANUX / range.js

Created March 16, 2018 14:14

Range function for JavaScript

	function *range(start, end, step=1) {
	let i = start;
	while ( i < end ) {
	yield i;
	i += step;
	}
	}

	[...range(0,5)].map(x => x*2);

RANUX / fib-fact-iterator.js

Created March 16, 2018 08:45

Fibonaci and factorial iterators on JavaScript

	var Fib = {
	[Symbol.iterator]() {
	var n1 = 1, n2 = 1;

	return {
	// make the iterator an iterable
	[Symbol.iterator]() { return this; },

	next() {
	var current = n2;

RANUX / md2pdf.spy

Created February 24, 2018 21:01

Compile *.md files to single or multiple pdf files with Python

	import sys
	import os
	import re
	from shellpython.helpers import Dir

	'''
	Compile *.md files to single or multiple pdf files

	Requirments:
	Python 3

RANUX / cloudSettings

Last active January 10, 2019 08:38

Visual Studio Code Settings Sync Gist

{"lastUpload":"2019-01-10T08:38:13.438Z","extensionVersion":"v3.2.4"}

RANUX / 3-layer-nn.py

Created November 23, 2017 14:10

Simple neural network with 3 layers

	import numpy as np
	import math


	inp = np.matrix([[0.9], [0.1], [0.8]])
	wInp = np.matrix([[0.9, 0.3, 0.4],[0.2,0.8, 0.2],[0.1,0.5,0.6]])
	xHid = wInp.dot(inp)

	sigmoid = np.vectorize(lambda x: 1 / (1 + math.exp(-x)))
	outHid = sigmoid(xHid)