puterleat

sudo apt-get install vagrant
git clone https://bitbucket.org/puffnfresh/vagrant-haskell-heroku.git
cd vagrant-haskell-heroku
vagrant up
vagrant ssh
cabal update
cabal install cabal-install
cabal --version
> cabal-install version 0.14.0
> using version 1.14.0 of the Cabal library

puterleat

---
finish_on_last_page: false
name: Lung Information Needs Questionnaire
redirect_url: /accounts/profile/
show_progress: true
slug: linq
step_navigation: true
steps_are_sequential: true
success_message: Questionnaire complete.
---

puterleat

---
finish_on_last_page: false
name: Tantrums daily phone measure
redirect_url: /profile/
show_progress: false
slug: tantrums-daily-phone-measure
step_navigation: true
steps_are_sequential: true
success_message: Questionnaire complete.
---

puterleat

# [PackageDev] target_format: plist, ext: tmLanguage
---
name: Pandoc2
scopeName: text.pandoc
uuid: 8763771e-a0e5-44ea-bd14-fb3b7ee1b23d
patterns:
- include: text.html.markdown
- include: text.tex.latex

puterleat

---
finish_on_last_page: true
name: The Dose Index
redirect_url: /accounts/profile/
show_progress: false
slug: dose
step_navigation: false
steps_are_sequential: true
success_message: ''
---

puterleat

# An associative mapping of desired outcomes to actions
# sometimes conditional (|) on a particular environment
goal_to_action_mapping = {

    "Attract a mate": {
        "Disco": ["Demonstrate virility", "Be funny", "Be a nice person"],
        "Bedroom": ["Demonstrate virility", "Be a nice person"],
    }

    "Demonstrate virility": {

puterleat

# An associative mapping of desired outcomes to actions which allows
# the nesting of goals.

goal_to_habit_mapping = {

    "Attract a mate": {
        "Disco": ["Demonstrate virility", "Be funny", "Be a nice person"],
        "Bedroom": ["Demonstrate virility", "Be a nice person"],
    }

puterleat

def plan_goal(desired_outcome, environment, movie_of_my_plan=[]):

    possibilities = filter(outcome_to_action_mapping, desired_outcome, environment)
    sort(possibilities, key=magic_inverse_inference_function_which_includes_affordances)

    best_bet = possibilities.pop()

    if is_an_an_actionable_thing(best_bet):
        movie_of_my_plan.append(best_bet)
        

puterleat

#!python
import random
import csv
from itertools import combinations

def chunks(l, n):
    """ Yield successive n-sized chunks from l.
    """
    for i in xrange(0, len(l), n):
        yield l[i:i+n]

puterleat

. binreg compnouc c.wingage , or n(20)

Iteration 1:   deviance =  1118.075
Iteration 2:   deviance =  1079.917
Iteration 3:   deviance =  1079.575
Iteration 4:   deviance =  1079.575
Iteration 5:   deviance =  1079.575

Generalized linear models                          No. of obs      =       376