While there are examples of Python-based apps in mobile App stores, the knowledge of how to actually create a mobile app in Python hasn't been well documented, or simplified for mass use - until now.
In this talk, Dr Russell Keith-Magee will demonstrate a collection of tools from the BeeWare Project that enable you to build a cross-platform mobile app using Python in a matter of minutes.
Around 2006-2007, it was a bit of a fashion to hook lava lamps up to the build server. Normally, the green lava lamp would be on, but if the build failed, it would turn off and the red lava lamp would turn on.
By coincidence, I've actually met, about that time, (probably) the first person to hook up a lava lamp to a build server. It was Alberto Savoia, who'd founded a testing tools company (that did some very interesting things around generative testing that have basically never been noticed). Alberto had noticed that people did not react with any urgency when the build broke. They'd check in broken code and go off to something else, only reacting to the breakage they'd caused when some other programmer pulled the change and had problems.
This is a study of interpersonal closeness, and your task, which we think will be quite enjoyable, is simply to get close to your partner. We believe that the best way for you to get close to your partner is for you to share with them and for them to share with you. Of course, when we advise you about getting close to your partner, we are giving advice regarding your behavior in this demonstration only, we are not advising you about your behavior outside of this demonstration.
In order to help you get close we've arranged for the two of you to engage in a kind of sharing game. You're sharing time will be for about one hour, after which time we ask you to fill out a questionnaire concerning your experience of getting close to your partner.
You have been given three sets of slips. Each slip has a question or a task written on it. As soon as you both finish reading these instructions, you should
| from sklearn.datasets import fetch_20newsgroups, load_digits | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.cross_validation import train_test_split | |
| import numpy as np | |
| from sklearn.naive_bayes import MultinomialNB, BernoulliNB | |
| from sklearn.linear_model import LogisticRegression, SGDClassifier | |
| from sklearn import metrics | |
| newsgroups_train = fetch_20newsgroups(subset='train') | |
| vectorizer = TfidfVectorizer(encoding='latin-1', max_features=10000) |
| """ Non-negative matrix factorization for I divergence | |
| This code was implements Lee and Seung's multiplicative updates algorithm | |
| for NMF with I divergence cost. | |
| Lee D. D., Seung H. S., Learning the parts of objects by non-negative | |
| matrix factorization. Nature, 1999 | |
| """ | |
| # Author: Olivier Mangin <[email protected]> |
| #!/usr/bin/env python | |
| # encoding: utf-8 | |
| """ | |
| Created by Preston Holmes on 2010-11-25. | |
| [email protected] | |
| Copyright (c) 2010 | |
| Permission is hereby granted, free of charge, to any person obtaining | |
| a copy of this software and associated documentation files (the |
