gavinwhyte

add the PPA to the local index:
>sudo add-apt-repository ppa:git-core/ppa

update the local repository index
>sudo apt-get update

and lastly install the git package.
>sudo apt-get install git

gavinwhyte

beautifulsoup4==4.4.0
blosc==1.2.7
Bottleneck==1.0.0
funcsigs==0.4
google-api-python-client==1.4.1
html5lib==0.999999
httplib2==0.9.1
lxml==3.4.4
matplotlib==1.4.3
mock==1.1.3

gavinwhyte

Installing Homebrew

First, we need to install Homebrew. Homebrew allows us to install and compile software packages easily from source.

Homebrew comes with a very simple install script. When it asks you to install XCode CommandLine Tools, say yes.

Open Terminal and run the following command:

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Installing Ruby

gavinwhyte

Installing Homebrew

First, we need to install Homebrew. Homebrew allows us to install and compile software packages easily from source.

Homebrew comes with a very simple install script. 

When it asks you to install XCode CommandLine Tools, say yes.

Open Terminal and run the following command:

gavinwhyte

__author__ = 'gavinwhyte'
from numpy import *
import operator

import matplotlib
import matplotlib.pyplot as plt

def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A', 'A', 'B', 'B']

gavinwhyte

# After determining with attributes are categorical and which
# are numeric , you'll want descriptive stat for the numeric
# variables and a count of the unique categories in each
# categorical attribute


import urllib2
import sys
import numpy as np

gavinwhyte

from scipy.stats import poisson
import matplotlib.pyplot as plt


import numpy as np


fig, ax = plt.subplots(1, 1)

x = np.fromfile('tripcountssample.txt',

gavinwhyte

#!/usr/bin/env python
import os, sys
import math
import boto

AWS_ACCESS_KEY_ID = ''
AWS_SECRET_ACCESS_KEY = ''

def upload_file(s3, bucketname, file_path):

gavinwhyte

Principal component analysis (PCA) is a dimensionality reduction technique that is widely used in data analysis. 

Reducing the dimensionality of a dataset can be useful in different ways. For example, our ability to visualize data is limited to 2 or 3 dimensions. 

Lower dimension can sometimes significantly reduce the computational time of some numerical algorithms. 

Besides, many statistical models suffer from high correlation between covariates, and PCA can be used to produce linear combinations of the covariates that are uncorrelated between each other.


Computing PCA

gavinwhyte

import java.util

import org.apache.kafka.clients.consumer.KafkaConsumer

import scala.collection.JavaConverters._

object ConsumerExample extends App {

  import java.util.Properties