abehmiel

Twitter autoresponder bot

By Daniel15 (dan.cx) This is a very simple Twitter autoresponder bot. It requires PECL OAuth extension to be installed (run "pecl install oauth", or if on Windows, grab php-oauth.dll. If using cPanel you can install it via WHM). The authentication is designed for command-line usage, it won't work too well via a web browser. You'll have to sign up for an application on Twitter's site to get the consumer key and secret.

Could be modified to be more advanced (match regular expressions to answer questions, etc.)

Questions? See my blog post - http://dan.cx/blog/2011/06/twitter-autoreply-bot-dbznappa

Modified 2013-06-13 - Twitter API 1.0 discontinued, modified to use Twitter API 1.1

abehmiel

from datetime import datetime

import string

from nltk.stem.lancaster import LancasterStemmer
from nltk.corpus import stopwords

#Gets the tweet time.
def get_time(tweet):
    return datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")

abehmiel

--- ●●●●●●●
-- 7 successes / 0 failures / 0 errors / 0 pending : 48.994555 seconds


-- SOURCE: http://rosettacode.org/wiki/Roman_numerals/Decode#Lua
-- SOURCE: http://rosettacode.org/wiki/Roman_numerals/Encode#Lua
-- Tested with the Busted framework for lua (luarocks install busted)

local math = require  "math"
			function CiceroCiceroCiceroCiceroCiceroCiceroCiceroCiceroCiceroCicero()

abehmiel

#!/usr/bin/python3
# For the dataset (tweet ids), visit:
# http://www.romanklinger.de/ironysarcasm/
# this script lives in the same folder as the eight ID tweet identifier csv files.

import tweepy
import json
import math
import pandas as pd
import csv

abehmiel

[Unit]
Description=Potentially dangerous fix touchscreen after resume on the XPS 13 9350
After=suspend.target

[Service]
Type=simple
ExecStart=/home/anthony/path/to/xps-touchscreen-workaround.sh

[Install]
WantedBy=suspend.target

abehmiel

----
---- library code
----
_communic8_chars = "\n\32\33\34\35\36\37\38\39\40\41\42\43\44\45\46\47\48\49\50\51\52\53\54\55\56\57\58\59\60\61\62\63\64\65\66\67\68\69\70\71\72\73\74\75\76\77\78\79\80\81\82\83\84\85\86\87\88\89\90\91\92\93\94\95\96\97\98\99\100\101\102\103\104\105\106\107\108\109\110\111\112\113\114\115\116\117\118\119\120\121\122\123\124\125\126\127\128\129\130\131\132\133\134\135\136\137\138\139\140\141\142\143\144\145\146\147\148\149\150\151\152\153"
_communic8_charindices = {}
for i = 1, #_communic8_chars do
  _communic8_charindices[sub(_communic8_chars, i, i)] = 30 + i
end
arg_types = {
  byte={

abehmiel

# script to extract music from a pico-8
# requires exporting sounds from the pico-8 first!
# run as: python extract-music.py mygame.p8 sound%d.wav music%d.wav
# by eevee, do what you like with this code
from __future__ import print_function
import argparse
import struct
import wave

abehmiel

from sklearn.metrics import confusion_matrix

#type of classifier is not important

clf = GradientBoostingClassifier(loss='deviance', learning_rate=0.075, n_estimators=150, max_depth=3)
grbfit = clf.fit(X_train, y_train)
y_pred =  grbfit.predict(X_test)
print(clf.score(X_test, y_test))
class_names = [0,1]

abehmiel

# conversion rate exploratory analysis and ML algorithm hyperparameter optimization

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tpot import TPOTClassifier

# load the data
df = pd.read_csv("conversion_data.csv")

abehmiel

import json
import csv

# open a file for writing
data = open('trumptweets.csv', 'w')

# create the csv writer object
csvwriter = csv.writer(data)

#write the header