rpick is a command line tool that helps you to pick items from a list, using
configurable algorithms.
An example use case for this is picking a restaurant. You might want to generally go to restaurants you haven't visited in a while, but you also might not want to use a strict least recently used model and spice things up with some element of chance, with restaurants you've least recently visited getting a boost in their chances.
rpick keeps its state in a YAML file in your home config directory called
rpick.yml. For now, users must create this file by hand, and rpick will manage it from
there. To get started with some examples, create ~/.config/rpick.yml like this:
prs: model: even choices: - paper - rock - scissors restaurant: model: gaussian choices: - Spirits - Lucky 32 - Centro - Sitti - Cookout ```
Then you can ask rpick to pick a game of paper rock scissors for you:
$ rpick prs
Choice is scissors. Accept? (Y/n)
Note that it would be bad to use the Gaussian model for paper rock scissors, because you have a statistical advantage of guessing that model's results. Let's take a look at the Gaussian model:
$ rpick restaurant
Choice is Lucky 32. Accept? (Y/n)
If you say yes, it will rewrite the yaml file like this since we used the Gaussian model:
prs: model: even choices: - paper - rock - scissors restaurant: model: gaussian stddevscalingfactor: 3.0 choices: - Spirits - Centro - Sitti - Cookout - Lucky 32 ```
Note that we passed prs and then restaurant as arguments when we called rpick -
this told rpick to look for those objects in rpick.yml to find out which models to use
and which choices were available. This parameter is required, but its possible values are defined by
you in your config file.
The model field in the config file defines which mathematical
model to use to pick from the given choices. See the Models section below for more information about
which models are available and how you can configure them.
It added one setting to your restaurant object that wasn't there originally:
stddev_scaling_factor. You can read more about this setting in the Gaussian model
documentation below.
This project is available on crates.io.
rpick is capable of a few different algorithms for picking choices: even, gaussian, lottery,
and weighted.
The even distribution model is the simplest available choice model. It will give an even
chance to each item in the list of strings to be chosen. It requires two keys:
model: This must be set to the string "even", in order to select this model.choices: This is a list of strings that are the options for the model to choose from.Example:
convertible_top:
model: even
choices:
- up
- down
You might want to consult the weather before using rpick for this use case…
The gaussian distribution model is more complex. It uses the
Gaussian distribution to prefer choices that
have been less recently chosen. Things near the top of the list of choices have the highest
probability of being chosen, while things at the end of the list have the lowest chance. Once an
item has been picked and the user has accepted the choice, the list is saved to disk with the picked
item moved to the end of the list. This model accepts three keys:
model: This must be set to the string "gaussian", in order to select this model.stddev_scaling_factor is used to derive the standard deviation; the standard deviation is
the length of the list of choices, divided by this scaling factor. Thus, a larger scaling factor
will result in a stronger preference for items near the top of the list, and a smaller scaling
factor will result in a more even distribution among the choices. Note that the smaller the
scaling factor is, the longer rpick will take to make a decision, on average. The default is
3.0, which is chosen because it places the last item on the list at three standard
deviations, giving it a 0.03% chance of being chosen. This key is optional, and defaults to 3.0.choices: This is a list of strings that are the options for the model to choose from.Example:
album:
model: gaussian
stddev_scaling_factor: 5.0
choices:
- Fountains of Wayne/Fountains Of Wayne
- Beck/Odelay
- "Townes Van Zandt/High, Low and In Between"
- Tori Amos/From The Choirgirl Hotel
- Zao/Parade Of Chaos
The lottery distribution model is a dynamic version of the weighted model. Each of the
choices has a certain number of lottery tickets that influence how likely they are to be picked that
round. Once an item is picked, it loses all of its lottery tickets and every choice that wasn't
picked gains more lottery tickets. It accepts three keys:
model: This must be set to the string "lottery", in order to select this model.choices: This must be a list of objects. Each object accepts three keys:
name: This is required, and is the name of the choice.tickets: The current number of lottery tickets that this choice has. This is optional, an
integer, and defaults to 1.weight: This is an integer expressing how many lottery tickets are given to this choice
when it is not chosen. You can use this to influence how often this item gets favored relative
to the other choices. It is optional, and defaults to 1.Example:
activity:
model: lottery
choices:
- name: exercise
- name: read documentation
- name: watch tv
weight: 1000
The weighted distribution model is a more general version of the even model that allows
you to express different weights for each of the choices. It accepts two keys:
model: This must be set to the string "weighted", in order to select this model.choices: This must be a list of objects. Each object accepts two keys:
name: This is required, and is the name of the choice.weight: This is an integer expressing the weight for the choice. It is optional, and
defaults to 1.Example:
cereal:
model: weighted
choices:
- name: generic bran flakes
- name: cracklin oat bran
weight: 1000
even distribution
model, which does a nice flat random pick.weighted
distribution model, which does a weighted random pick.lottery distribution model, which gives lottery tickets to unpicked items and
resets the picked item's lottery tickets to 0.