Making choices from a probability distribution
Project description
The PDist is designed to make it easy to make it easy to make things happen in a probablistic manner. Provide keys with an associated probability attached, and then call the resulting object when you need a new value.
>>> mood = PDist({'happy': 0.3, 'neutral': 0.6, 'sad': 0.1}) >>> mood() 'happy' >>> mood() 'neutral'
You can retrieve the distribution of how those values are applied by accessing the PDist.distribution attribute:
>>> mood.distribution [('happy', 0.3), ('neutral', 0.6), ('sad', 0.1)]
As well as providing a dict to assign probabilities, you can send in a list of lists/tuples. This allows for for non-hashable types to be used. This means that you provide objects such as functions to be used as keys:
>>> def grumpy(news): ... return ':/' >>> def happy(news): ... return ':)' >>> react = PDist([(grumpy, 0.7), (happy, 0.3)]) >>> reaction = react() >>> reaction("We're getting married!") ':/'
You are not restricted to adding your input variables up to 1. For example, if you only have a tally chart, and wish to calculate a probability distribution from that sample, you can provide that too:
>>> spotted = {'geese': 0, 'ducks': 12, 'sparrows': 4, 'other': 39} >>> bird_pdist = PDist(spotted) >>> bird_pdist.distribution [('geese', 0.0), ('sparrows', 0.07272727272727272), ('ducks', 0.21818181818181817), ('other', 0.7090909090909091)]
Retrieving the probability of a particular key is supported. However, the search strategy is very inefficient and probably shouldn’t be used outside of the interactive Python shell.
>>> bird_pdist['geese'] 0.0
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