TheDataLeek

#!/usr/bin/env python3

import sys
import os
import argparse

import re
from pprint import pprint as pp
import pytz
import dateutil

TheDataLeek

#!/usr/bin/env python3.6

"""
Simulated Annealing and Genetic Algorithm solutions to districting problem.

Notes on implementation:
    * I like properties and I use them in a couple spots...
    * Use `pip install --user -r requirements.txt` on the requirements file
    available in the root of this git repository.
    * If by some strange happenstance you only have this file, go to the

TheDataLeek

#!/usr/bin/env python3.6

import time
import math
import numpy as np
import random

def distance(coord1, coord2):
    return math.sqrt(sum((coord1[i] - coord2[i])**2
                         for i in range(len(coord1))))

TheDataLeek

#!/usr/bin/env python3.6

import sys
import time
import random
import multiprocessing as mp
from multiprocessing import Queue
from tqdm import tqdm

import numpy as np

TheDataLeek

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

TheDataLeek

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

TheDataLeek

#!/usr/bin/env python2

import numpy as np

import matplotlib.pyplot as plt
import matplotlib.patches as ptch
import matplotlib.lines as lne
import matplotlib.animation as ani

TheDataLeek

TheDataLeek

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Initialize Vundle
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
set nocompatible
filetype off

" set the runtime path to include Vundle and initialize
set rtp+=~/.config/nvim/bundle/Vundle.vim
call vundle#begin('~/.config/nvim/bundle')

TheDataLeek

# Python                                                                    # MATLAB
def bastard_svd(A, k, p):                                                   # function [U,D,V] = LOCAL_rsft(A,k,p)
    m, n = A.shape                                                          # n          = size(A,2);
    # Create diagonal matrix of random numbers                              #
    dd = np.exp(1j * 2 * np.pi * np.random.random(size=n))                  # dd         = exp(1i*2*pi*rand(1,n));
    Y = A @ np.diag(dd)                                                     # Y          = A*diag(dd);
    # Apply full FFT to rows of AD, extract k + p columns                   #
    Y = np.fft.fft(Y, axis=1)  # Axis0 is columns, Axis1 is Rows            # Y          = fft(Y,[],2);
    # Create vector J of length k + p from the set {1, 2, ..., n}           #
    J = np.random.choice(np.arange(n),                                      # [~,ind]    = sort(rand(1,n));