IterableProfileDiscreteGrid

class poisson_approval.IterableProfileDiscreteGrid(denominator, types, d_type_fixed_share=None, standardized=False, test=None, **kwargs)[source]

Iterate over discrete profiles (ProfileDiscrete) defined on a grid.

Parameters

denominator (int or iterable) – The grain(s) of the grid.
types (iterable) – These types will have a variable share. They can be discrete types, e.g. ('abc', 0.9), or weak orders, e.g. 'a~b>c'.
d_type_fixed_share (dict, optional) – A dictionary. For each entry type: fixed_share, this type will have at least this fixed share. The total must be lower or equal to 1.
standardized (bool, optional) – If True, then only standardized profiles are given. Cf. Profile.is_standardized(). You should probably use this option if the arguments types, d_type_fixed_share and test treat the candidates symmetrically.
test (callable, optional) – A function ProfileDiscrete -> bool. Only profiles meeting this test are given.
kwargs – Additional parameters are passed to ProfileDiscrete when creating the profile.

Examples

Basic usage:

>>> for profile in IterableProfileDiscreteGrid(denominator=3, types=[('abc', 0.9), ('bca', 0.4), 'a~b>c']):
...     print(profile)
<abc 0.9: 1> (Condorcet winner: a)
<abc 0.9: 2/3, bca 0.4: 1/3> (Condorcet winner: a)
<abc 0.9: 2/3, a~b>c: 1/3> (Condorcet winner: a)
<abc 0.9: 1/3, bca 0.4: 2/3> (Condorcet winner: b)
<abc 0.9: 1/3, bca 0.4: 1/3, a~b>c: 1/3> (Condorcet winner: a, b)
<abc 0.9: 1/3, a~b>c: 2/3> (Condorcet winner: a)
<bca 0.4: 1> (Condorcet winner: b)
<bca 0.4: 2/3, a~b>c: 1/3> (Condorcet winner: b)
<bca 0.4: 1/3, a~b>c: 2/3> (Condorcet winner: b)
<a~b>c: 1> (Condorcet winner: a, b)

For more examples, cf. IterableSimplexGrid.