Cyril Auburtin caub

The positions of green nodes are initially set, other nodes are guessed incrementally from neighbors distance. A noise is added to distances, up to N(0, 1.7^2) to simulate measurement error, greater values can make the nodes positions diverge importantly
To fix this stability problem, it's possible to avoid 'flat' triangles (where the 3 points are almost collinear)
Other perspective: make it work in 3D, or more, with the intersection of (n+1) n-spheres, compare with more robust optimizations in litterature
Another perspective, average the result of multiple different estimations for a node positions, to eliminate the noise better

X =[-1 -1; -2 -1;-3 -2;1 1;2 1; 3 2];
y = [1 1 1 2 2 2];
m = lda(X,y);
m.predict([-0.8 -1]) %1
gscatter(X(:,1),X(:,2),y','rb','v^',[],'off');
hold on
subtract = @(X) X(:,2) - X(:,1);

ezplot(@(x,y) subtract(m.decision_function([x y])))

	const babel = require('babel-standalone');
	const fs = require('fs');
	const express = require('express');
	const app = express();

	app.get('/*.jsx', function(req, res){
	fs.readFile('.'+req.path, (err,data)=>{
	res.send(babel.transform(data, {presets:['react']}).code);
	});
	});

	(function(){
	let scopedRule = (scope, {selectorText: selector='', cssText:css, style: {cssText}={}, styleSheet, cssRules, media}) =>
	styleSheet? // @import rules
	scopedRules(scope, styleSheet.cssRules):
	cssRules && media? // @media rules
	`@media ${Array.from(media).join(',\n')} {${scopedRules(scope, cssRules)}}`:
	!selector\|\|selector.startsWith(':root')\|\|selector.startsWith('body')?
	css:
	`${selector.split(',').map(s=>`${scope} ${s}`).join(', ')} {${cssText}}`;
	//`${selector.replace(/([^,]+)/g, (_, s) => `${scope} ${s}`)} {${cssText}}`;

	var cheerio = require('cheerio');
	var Promise = require("bluebird");
	var request = Promise.promisifyAll(require('request'));
	var WebSocket = require('ws');

	var email = "SE email",
	password = "SE pw",
	roomid = 17;

	var j = request.jar()

	package ws;

	import java.lang.reflect.Type;
	import java.net.CookieManager;
	import java.net.CookieStore;
	import java.net.HttpCookie;
	import java.net.URI;
	import java.util.LinkedHashMap;
	import java.util.concurrent.Future;

	function conv(a, b, mode){
	var dl = b.length-1
	var aa=a.concat(repeat(0, dl)) //0-padding

	var c=[]
	for(var i=0;i<aa.length;i++){
	c[i]=0;
	for(var m=0;m<aa.length;m++)
	if (0<=i-m && i-m<b.length)
	c[i]+=aa[m]*b[i-m];

	from scipy import *
	def sma(v, k):
	weights = repeat(1.0,k)/k
	return convolve(v, weights, 'valid')

	def ema(v, k):
	weights = exp(linspace(-1,0,k))
	weights /= weights.sum()
	#print 'we: %s' % weights
	#print 'c: %s' % convolve(v, weights)

	% largely inspired from http://www.mathworks.fr/matlabcentral/fileexchange/37932-automated-trading-with-matlab-2012
	% small script to generate signals from rsi indicator, and calculate the return

	function [s,r,sh,mar] = rsi(price,M,N,thresh,scaling,cost)
	% returns the signals array, the returns array (profit and loss), the
	% sharpe ratio, the MAR ratio
	if nargin==0
	% fill params for you..
	M = 22; % Moving Average for rsi
	N = 14; % rsi loopback

	from selenium import webdriver
	from selenium.webdriver.common.keys import Keys
	from selenium.common.exceptions import NoSuchElementException,ElementNotVisibleException
	import time
	import requests
	import json
	import urlparse


	browser = webdriver.Firefox()