coppelia machine learning and analytics coppeliaMLA

## bootstrap.ipynb

      
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                bootstrap.ipynb
              
          
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## binDiff.R
modBin<-function(k, n, p){

  if (k<=n) {
    return(dbinom(k, n, p))
  }
  else {
    return(0)
  }
}

## Readme.md

      
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                coppeliaMLA
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              October 5, 2017 17:22
            
              
                ROI Fan
              
          
    Return on investment is often measured as revenue divided by costs, sometimes expressed as a percentage. For example if a marketing campaign cost £10K but brought in £20K of additional revenue then the ROI is 200%. Now if you are just given the ROI you'll find you are missing any of idea of scale. The same ROI could be achieved with a revenue of £200 and with one of £200 million. So it would be nice to see cost, revenue and ROI visualised all in one go. There are a few ways to do this but after playing around I came up with the following representation which personally I like the best. It's a simple scatterplot of cost against revenue but since all points on straight lines radiating from the origin have the same ROI it's easy to overlay that information. If r is the ROI the the angle of the corresponding spoke is arctan(r).
Note you can drag about the labels. That's my preferred solution for messy scatterplot labelling.

  
## TestScript.py
#Test imports
import urllib2, ssl, json, time, re
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.support.ui import Select


#Test urllib2
import urllib2
response = urllib2.urlopen('https://en.wikipedia.org/wiki/Test', context=ssl.SSLContext(ssl.PROTOCOL_TLSv1))

## finSim.R
#First we are going to set up probaility distributions for our beliefs about the inputs
#We've been told ARPU is about £7 and it's very unlikely to be higher than £10 or lower than £4
#So we'll go for a normal distribution centred at 7 with 5% and 95% quantiles at 4 and 10

#Show how we get the variance

arpu.sd<-3/1.96
x<-seq(0, 15,by=0.5)
d<-dnorm(x, 7, arpu.sd)
plot(x, d, type='l')

## example6b.htm
<head>
    <script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min.js"></script>
    <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css"/>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
    <style> #mapid {
        height: 800px;
    } </style>
</head>
<body>

## arm.ino
#include <Servo.h>

Servo middle, left, right, claw ;  // creates 4 "servo objects"

//positions 30, 45, 60, 75, 90, 105, 120, 135, 150

void setup()
{
  middle.attach(8);
  left.attach(10);

## leafletShapes.htm
<head>
    <script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
    <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css" />
    <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
    <style> #mapid {
        height: 800px;
    } </style>
</head>
<body>

## leafletPoints.htm
<head>
    <script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
    <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css"/>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
    <style> #mapid {
        height: 800px;
    } </style>
</head>
<body>

## tree.json
{
    "name": "root",
    "visits": "202K",
    "children": [
        {
            "name": "purpledog.com",
            "visits": "50K"
        },
        {
            "name": "squishedfish.co.uk",
	modBin<-function(k, n, p){

	if (k<=n) {
	return(dbinom(k, n, p))
	}
	else {
	return(0)
	}
	}
	#Test imports
	import urllib2, ssl, json, time, re
	from bs4 import BeautifulSoup
	from selenium import webdriver
	from selenium.webdriver.support.ui import Select


	#Test urllib2
	import urllib2
	response = urllib2.urlopen('https://en.wikipedia.org/wiki/Test', context=ssl.SSLContext(ssl.PROTOCOL_TLSv1))
	#First we are going to set up probaility distributions for our beliefs about the inputs
	#We've been told ARPU is about £7 and it's very unlikely to be higher than £10 or lower than £4
	#So we'll go for a normal distribution centred at 7 with 5% and 95% quantiles at 4 and 10

	#Show how we get the variance

	arpu.sd<-3/1.96
	x<-seq(0, 15,by=0.5)
	d<-dnorm(x, 7, arpu.sd)
	plot(x, d, type='l')
	<head>
	<script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min.js"></script>
	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css"/>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
	<style> #mapid {
	height: 800px;
	} </style>
	</head>
	<body>
	#include <Servo.h>

	Servo middle, left, right, claw ; // creates 4 "servo objects"

	//positions 30, 45, 60, 75, 90, 105, 120, 135, 150

	void setup()
	{
	middle.attach(8);
	left.attach(10);
	{
	"name": "root",
	"visits": "202K",
	"children": [
	{
	"name": "purpledog.com",
	"visits": "50K"
	},
	{
	"name": "squishedfish.co.uk",