austinogilvie

img_clf = ImageClassifier(pca=pca, knn=knn, STANDARD_SIZE=STANDARD_SIZE)

# authenticate
yh = Yhat("YOUR USERNAME", "YOUR API KEY")

# upload model to yhat
yh.upload("imageClassifier", img_clf)

austinogilvie

class ImageClassifier(BaseModel):
    def require(self):
        from StringIO import StringIO
        from PIL import Image
        import base64

    def transform(self, image_string):
        #we need to decode the image from base64
    	image_string = base64.decodestring(image_string)
        #since we're seing this as a JSON string, we use StringIO so it acts like a file

austinogilvie

{
    "color_scheme": "Packages/Color Scheme - Default/Idle.tmTheme"
    , "font_size": 10
    , "line_padding_bottom": 1
    , "line_padding_top": 1
    , "wide_caret": true
    , "draw_white_space": false
    , "fold_buttons": true
    , "highlight_line": true
    , "auto_complete": true

austinogilvie

var walk = function () {
  
  return {walk_it_out: walk_it_out}
  
  function randint () {
   var r = Math.random () 
   return Math.round(r*10)
  };
  
  function get_steps (n) {

austinogilvie

• https://code.google.com/p/julialang/downloads/list
• julia0.1.2-WINNT-i686+Git.zip
• Save that .zip and uncompress it wherever you'd like Julia to be stored on your computer (program files, C:, or wherever you have your other programming languages installed)
• Edit Environment Variables and add this to your path
• you need to add a few things to your path: root julia directory, julia\lib, and julia\lib\julia
• I saved mine in C:\julia-c4b3649af6 so this is what i added to my path
• C:\julia-c4b3649af6;C:\julia-c4b3649af6\lib;C:\julia-c4b3649af6\lib\julia;
• With that on your path, you can open cmd and type julia to launch the julia shell

austinogilvie

from matplotlib.dates import HourLocator, DateFormatter, date2num
from datetime import timedelta

start = df.index.min()
end = df.index.max()

delta = timedelta(hours=1)
dates = matplotlib.dates.drange(start, end, delta)

lines = df.pageviews, df.visitors

austinogilvie

import numpy as np
import pandas as pd
import pandas.io.ga as ga

hosts = ['blog.example.com', 'www.example.com']
account_id = "12345678"

# construct a list of filters
# following the regex =~ contains syntax
# documented in the google API docs

austinogilvie

# To source this file into an environment to avoid cluttering the global workspace, put this in Rprofile:
# my.env <- new.env(); sys.source("C:/PathTo/THIS_FILE.r", my.env); attach(my.env)

#-----------------------------------------------------------------------
#     Load packages, set options and cwd, set up database connection
#-----------------------------------------------------------------------

## Load packages
library(RPostgreSQL)
library(grid)

austinogilvie

import pandas as pd
import pandas.io.ga as ga
from IPython.display import display

pd.set_option('display.notebook_repr_html', True)
pd.set_option('display.precision', 4)

pd.set_option('display.max_rows', 50)
pd.set_option('display.max_columns', 10)

austinogilvie

from datetime import datetime, timedelta
from pandas import *

s  = Series(date_range('2012-1-1', periods=3, freq='D'))
s
# Out[52]: 
# 0   2012-01-01 00:00:00
# 1   2012-01-02 00:00:00
# 2   2012-01-03 00:00:00