Will Geary willgeary

Messing with Maps and ML quickstart

Installation instructions

By far the most annoying part of getting started with messing with ML is installing researcher-made code and turning it into something fun to play with.

Before doing any of these, please install Miniconda. If you don't have it installed already, here's how:

For OSX, this is:

Messing with Maps and ML quickstart

This document: https://goo.gl/AqGoE8

Installation instructions

By far the most annoying part of getting started with messing with ML is installing researcher-made code and turning it into something fun to play with.

Before doing any of these, please install Miniconda. If you don't have it installed already, here's how:

For OSX, this is:

	from geopy import distance
	import pandas as pd

	def make_distance_matrix(coords, addresses):
	from_list = []
	to_list = []
	distance_list = []

	for a in list(range(len(coords))):
	for b in list(range(len(coords))):

	{'name': 'DAY_ODD',
	'type': 'esriFieldTypeString',
	'alias': 'DAY_ODD',
	'length': 25,
	'domain': {'type': 'codedValue',
	'name': 'StreetSweepingD',
	'codedValues': [{'name': 'Everyday', 'code': 'E'},
	{'name': 'No Signage', 'code': 'N-S'},
	{'name': 'No Sweeping (Exempt)', 'code': 'N'},
	{'name': 'No Odd Addresses', 'code': 'N-O'},

	// Processing 3.0x code to demonstrate seamless loop of 1D noise
	// Inspired by, and created in support of:
	// "Drawing from noise, and then making animated loopy GIFs from there" by Etienne Jacob (@n_disorder)
	// https://necessarydisorder.wordpress.com/2017/11/15/drawing-from-noise-and-then-making-animated-loopy-gifs-from-there/
	// Note: this program has no dependencies, and does not require SimplexNoise.
	// Demo GIF: https://media.giphy.com/media/xUOxeU2ELSPeTbevle/giphy.gif or http://gph.is/2Ah5kqG

	PGraphics offscreenImg;
	float myScale = 0.01;
	float radius = 100.0;

	#!/usr/bin/python
	# GoogleMapDownloader.py
	# Created by Hayden Eskriett [http://eskriett.com]
	#
	# A script which when given a longitude, latitude and zoom level downloads a
	# high resolution google map
	# Find the associated blog post at: http://blog.eskriett.com/2013/07/19/downloading-google-maps/

	import urllib.request
	from PIL import Image

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats