Multinomial¶

Multinomial distribution for categorical data.

Parameters¶

• events (Union[dict, list]) – defaults to None

An optional list of events that already occurred.

• seed – defaults to None

Random number generator seed for reproducibility.

Attributes¶

• mode

The most likely value in the distribution.

• n_samples

The number of observed samples.

Examples¶

>>> from river import proba

>>> p = proba.Multinomial(['green'] * 3)
>>> p = p.update('red')

>>> p('red')
0.25

>>> p = p.update('red').update('red')
>>> p('green')
0.5

>>> p = p.revert('red').revert('red')
>>> p('red')
0.25


You can wrap this with a utils.Rolling to measure a distribution over a window:

>>> from river import utils

>>> X = ['red', 'green', 'green', 'blue', 'blue']

>>> dist = utils.Rolling(
...     proba.Multinomial(),
...     window_size=3
... )

>>> for x in X:
...     dist = dist.update(x)
...     print(dist)
...     print()
P(red) = 1.000
<BLANKLINE>
P(red) = 0.500
P(green) = 0.500
<BLANKLINE>
P(green) = 0.667
P(red) = 0.333
<BLANKLINE>
P(green) = 0.667
P(blue) = 0.333
P(red) = 0.000
<BLANKLINE>
P(blue) = 0.667
P(green) = 0.333
P(red) = 0.000
<BLANKLINE>


You can wrap this with a utils.Rolling to measure a distribution over a window of time:

>>> import datetime as dt

>>> X = ['red', 'green', 'green', 'blue']
>>> days = [1, 2, 3, 4]

>>> dist = utils.TimeRolling(
...     proba.Multinomial(),
...     period=dt.timedelta(days=2)
... )

>>> for x, day in zip(X, days):
...     dist = dist.update(x, t=dt.datetime(2019, 1, day))
...     print(dist)
...     print()
P(red) = 1.000
<BLANKLINE>
P(red) = 0.500
P(green) = 0.500
<BLANKLINE>
P(green) = 1.000
P(red) = 0.000
<BLANKLINE>
P(green) = 0.500
P(blue) = 0.500
P(red) = 0.000
<BLANKLINE>


Methods¶

call

Probability mass/density function.

Parameters

• x (Any)
revert

Reverts the parameters of the distribution for a given observation.

Parameters

• x (Hashable)
sample

Sample a random value from the distribution.

update

Updates the parameters of the distribution given a new observation.

Parameters

• x (Hashable)