josephmisiti

(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const l of document.querySelectorAll('link[rel="modulepreload"]'))r(l);new MutationObserver(l=>{for(const i of l)if(i.type==="childList")for(const o of i.addedNodes)o.tagName==="LINK"&&o.rel==="modulepreload"&&r(o)}).observe(document,{childList:!0,subtree:!0});function n(l){const i={};return l.integrity&&(i.integrity=l.integrity),l.referrerPolicy&&(i.referrerPolicy=l.referrerPolicy),l.crossOrigin==="use-credentials"?i.credentials="include":l.crossOrigin==="anonymous"?i.credentials="omit":i.credentials="same-origin",i}function r(l){if(l.ep)return;l.ep=!0;const i=n(l);fetch(l.href,i)}})();function Zu(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var Gu={exports:{}},nl={},Xu={exports:{}},I={};/**
 * @license React
 * react.production.min.js
 *
 * Copyright (c) Facebook, Inc. and its affiliates.
 *
 * This source code is licensed under the MIT license found

josephmisiti

#root{max-width:1280px;margin:0 auto;padding:2rem}#scrubbing-container{position:relative}.label-item{display:flex;align-items:center;border:1px dotted #888;justify-content:space-between;margin-top:5px}.label-item.selected{background:rgb(178,255,178)}.label-item button{margin:3px}.export{border:1px solid rgb(0,0,52);background:rgb(114,114,233)}.export:hover{background:rgb(30,30,236);color:#fff}.blue-hover{color:#7272e9;fill:#7272e9}.blue-hover:hover{color:#1e1eec;fill:#1e1eec;cursor:pointer}.missing{box-shadow:0 0 10px 5px #ff5959}.validation-error-modal{width:100%;height:100%;position:absolute;z-index:999;top:0;left:0;display:flex;justify-content:center;align-items:center;background-color:#f0f0f0e6;border:2px solid rgba(100,100,100,.5);pointer-events:all;background-image:url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTAwIiBoZWlnaHQ9IjEwMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxsaW5lIHgxPSIwIiB5MT0iMCIgeDI9IjEwMCIgeTI9IjEwMCIgc3Ryb2tlPSJyZ2JhKDEwMCwxMDAsMTAwLDAuNSkiIHN0cm9rZS13aWR0aD0iMiIvPgog

josephmisiti

<script>
// MULTIPLE CHOICE TEMPLATE v1.2 {{{
// https://gist.github.com/hgiesel/2e8361afccca5713414a6a4ee66b7ece
const query = 'div#thecard'
const colors = ['orange', 'olive', 'maroon', 'aqua', 'fuchsia', 'navy', 'lime']
const fieldPadding = '4px'

const syntax = {
    openDelim: '(^',
    closeDelim: '^)',

josephmisiti

1. update apt-get

cd /etc/apt/sources.list.d/
wget http://archive.cloudera.com/impala/ubuntu/precise/amd64/impala/cloudera.list
sudo apt-get update

2. Install Impala [found here [1]]

josephmisiti

Census Tracts of New York City

Here at PyData NYC, I heard a tutorial of how to use numpy and iPython notebooks. In a previous gist, I wrote drew all the zip codes of the Bronx in d3.js

This would be great for reproducing inforgraphics like Educational Attainment in New York City -- Brooklyn which looks a bit like a jigsaw puzzle:

Where to Obtain the Data

josephmisiti

"""
helloevolve.py implements a genetic algorithm that starts with a base
population of randomly generated strings, iterates over a certain number of
generations while implementing 'natural selection', and prints out the most fit
string.

The parameters of the simulation can be changed by modifying one of the many
global variables. To change the "most fit" string, modify OPTIMAL. POP_SIZE
controls the size of each generation, and GENERATIONS is the amount of 
generations that the simulation will loop through before returning the fittest

josephmisiti

/**
 * Chunkify
 * Google Chrome Speech Synthesis Chunking Pattern
 * Fixes inconsistencies with speaking long texts in speechUtterance objects 
 * Licensed under the MIT License
 *
 * Peter Woolley and Brett Zamir
 */
 
var speechUtteranceChunker = function (utt, settings, callback) {

josephmisiti

{'landmark_name': None, 'street': '857 ocean rd', 'zipcode': '11932', 'city': 'bridgehampton', 'state': 'ny', 'po_box': '', 'full_address': '857 Ocean Rd, Bridgehampton, NY 11932, USA', 'from_email': 'joseph.misiti@gmail.com', 'time_to_fire_station_units': 1.41, 'time_to_fire_station': 4, 'distance_to_coast': 4545, 'distance_to_fire_station': 1.41, 'elevation': 6.028863906860352, 'fema_flood_zone': 'X', 'number_of_buildings': 0, 'year_built': '2000', 'roof_type': None, 'roof_cover': None, 'building_area': 1544, 'land_use': 'Single Family Residential', 'basement': 'Full Basement', 'neptune_quote_response': {'error': False, 'data': {'userID': None, 'brokerId': '9C0CD433-CC15-4053-8579-581F51B11D50', 'agentNo': 'FL0001', 'producerName': None, 'producerLicense': None, 'isDirectToConsumer': False, 'quoteNumber': 'NY0197APZQ3R9', 'status': 'approved', 'isBound': False, 'dateBound': '0001-01-01T00:00:00', 'isLocationBound': False, 'link': 'https://uat.neptuneflood.com/agent-hub/#/quote/NY0197APZQ3R9/auth/eyJhbGciOiJ

josephmisiti

Text Classification

To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.

Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will be using supports multi-class classification, which opens up vast opportunities like author identification, support email routing, etc… However, in this example we'll just stick to two classes: SPAM and HAM.

For this exercise, we'll be using a combination of the Enron-Spam data sets and the SpamAssassin public corpus. Both are publicly available for download and are retreived from the internet during the setup phase of the example code that goes with this chapter.

Loading Examples

josephmisiti

    def get_quote(self, address, city, state, zipcode):
        """ """
        if not self.__token:
            self.get_token_bearer()

        assert len(state) == 2

        resp = requests.request(
            "POST",
            '%s/Services/API/v1/Quote' % (self.__base_url),