Eric Bakan ebakan

ebakan / gist:721822c03ce9ad1b7d30

Created August 18, 2014 17:23

Verifying that +ebakan is my Bitcoin username. You can send me #bitcoin here: https://onename.io/ebakan

ebakan / gist:a2f6a3a55a874fb56f63

Created October 23, 2014 08:42

keybase.md

	### Keybase proof

	I hereby claim:

	* I am ebakan on github.
	* I am ebakan (https://keybase.io/ebakan) on keybase.
	* I have a public key whose fingerprint is 3419 FDB4 1E03 8E08 7DF1 F16E 0B9A C1B4 E0BF F2F9

	To claim this, I am signing this object:

ebakan / Popper.js

Last active January 24, 2018 19:25

React Component for Popper.js that takes the reference as its first child and the popper as its second child (a la react-tether)

	import React, { Component, PropTypes } from 'react';
	import popperJS from 'popper.js';

	export default class Popper extends Component {
	constructor(props) {
	super(props);
	this.state = {};
	this.update = this.update.bind(this);
	}

ebakan / Rakefile

Created December 21, 2016 14:44 — forked from schickling/Rakefile

Activerecord without Rails

	require "active_record"

	namespace :db do

	db_config = YAML::load(File.open('config/database.yml'))
	db_config_admin = db_config.merge({'database' => 'postgres', 'schema_search_path' => 'public'})

	desc "Create the database"
	task :create do
	ActiveRecord::Base.establish_connection(db_config_admin)

ebakan / includes_test.rb

Last active March 2, 2017 01:28

If I have an n-element array and I want to test element inclusion n times, is it faster to use Array#includes? or create a set and use Set#includes?

	require 'benchmark'
	require 'set'


	[1, 10, 100, 110, 125, 150, 200, 250, 500, 1000, 10000].each do \|i\|
	arr = (0..i).to_a.shuffle
	set_time = Benchmark.realtime do
	set = Set.new arr
	i.times do \|j\|
	set.include? j

ebakan / ridge_bias_variance.py

Created May 24, 2019 23:30 — forked from oliverangelil/ridge_bias_variance.py

generate dummy data and apply ridge with varying levels of regularisation. Plots train and test results. This code goes with my blog post: http://snoek.ddns.net/~oliver/mysite/the-bias-variance-tradeoff.html

	from sklearn.datasets import make_regression
	from sklearn.model_selection import train_test_split
	from sklearn.linear_model import Ridge
	from sklearn.metrics import mean_squared_error
	import numpy as np
	import matplotlib.pyplot as plt

	# generate data
	X, y, w = make_regression(n_samples=1000, n_features=200, coef=True,
	random_state=1, bias=0, noise=3, tail_strength=0.9, effective_rank=10)