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library(mnormt)
mycols <- topo.colors(100,0.5)
xhat <- c(0.2, -0.2)
Sigma <- matrix(c(0.4, 0.3, 
                  0.3, 0.45), ncol=2)
x1 <- seq(-2, 4,length=151)
x2 <- seq(-4, 2,length=151)
f <- function(x1,x2, mean=xhat, varcov=Sigma) 
  dmnorm(cbind(x1,x2), mean,varcov)
z <- outer(x1,x2, f)

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// Open the wallet console: Menu -> Develop -> Toggle console ...
// Run the following script

_.each(Wallets.find().fetch(), function(item){
    if(item.address)
        Wallets.update(item._id, {$set: {address: item.address.toLowerCase()}}); 
});
_.each(CustomContracts.find().fetch(), function(item){
    if(item.address)
        CustomContracts.update(item._id, {$set: {address: item.address.toLowerCase()}}); 

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/*
Basically "web3" comes from Mist,
but "Web3" CAN come from  the dapp.

A Dapp has 3 ways to use web3.
2. and 3. would work when in Mist and outside.
*/

// 1. simply use, web3 comes already defined

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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# Singular Value Decomposition Example

import numpy as np

# example taken from Video Tutorials - All in One
# https://www.youtube.com/watch?v=P5mlg91as1c
a = np.array([[1, 1, 1, 0, 0],
              [3, 3, 3, 0, 0],
              [4, 4, 4, 0, 0],
              [5, 5, 5, 0, 0],

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