devmacrile

# Wrapper function for the Sentiment140 API
# An API for a maximum entropy model trained on ~1.5M tweets
# The server will timeout if the job takes > 60 seconds,
# so if the tweet count is relatively high, the function
# will split the data into chunks of 2500 (fairly arbitrary choice)
# http://help.sentiment140.com/api
Sentiment140 <- function(sentences){
  
  # Load required packages
  library(plyr)

devmacrile

# Example of a common cross-validation mistake
# Described in The Elements of Statistical Learning, 7.10.2
# http://statweb.stanford.edu/~tibs/ElemStatLearn/
#
# Consider a scenario with
# N = 50 samples in two equal-sized classes, and p = 5000 quantitative
# predictors (standard Gaussian) that are independent of the class labels.
# The true (test) error rate of any classifier is 50%. We carried out the above
# recipe, choosing in step (1) the 100 predictors having highest correlation
# with the class labels, and then using a 1-nearest neighbor classifier, based

devmacrile

#!/usr/bin/python
import sys
import re
import nltk
from nltk.corpus import stopwords

stop_words = stopwords.words('english')
#input comes from standard input
for line in sys.stdin:
	#separate incident id from text

devmacrile

# Write R data.frame to a Tableau data extract file (.tde) by building and executing 
# a python script which utilizes the Tableau data extract API (a hack, yes).
#
# This, naturally, has a hard dependency on the TDE API, so is only available for 
# Windows and Linux systems (unfortunately)
#
# Devin Riley
# October, 2014

devmacrile

Keybase proof

I hereby claim:

• I am devmacrile on github.
• I am devmacrile (https://keybase.io/devmacrile) on keybase.
• I have a public key whose fingerprint is 08D8 CCCC 5D01 1F96 285C 2606 7709 364D 9CCA 6F14

To claim this, I am signing this object:

devmacrile

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

di1 = np.random.random_integers(1, 20, 100000)
di2 = np.random.random_integers(1, 20, 100000)

values = (di1 + di2) - (np.absolute(di1 - di2))
sample_mean = np.nanmean(values)

devmacrile

#(let ((a 1))
#    (define (f x)
#        (define b (+ a x))
#        (define a 5)
#        (+ a b))
#    (f 10))

a = 1

def f(x):