# Information Analysis¶

Out of the box, Neet provides facilities for computing a few common information-theoretic quantities from networks. All of these methods rely on constructing time series, from which a collection of probabilities distributions are built. The Information class provides a simple mechanism for automating this process, and caching results for relatively efficient computation.

## Initialization¶

Constructing an instance of Information, you simply provide a network, a history length (used to compute measures such as active information or transfer entropy), and the length of time series to compute.

>>> Information(s_pombe, k=5, timesteps=20)
<neet.information.Information object at 0x...>


At initialization, a time series is computed based on the parameters provided. This is cached and used whenever you request an information measure.

Of course, you can override the parameters after initialization, and the time series will be recomputed.

>>> arch = Information(s_pombe, k=5, timesteps=20)
>>> arch.net = s_cerevisiae
>>> arch.k = 2
>>> arch.timesteps = 100


## Information Measures¶

Once you have an Information instance, you can request an informormation measure. This will compute and cache the value.

>>> arch = Information(s_pombe, k=5, timesteps=20)
>>> arch.active_information()  # computed and cached
array([0.        , 0.4083436 , 0.62956679, 0.62956679, 0.37915718,
0.40046165, 0.67019615, 0.67019615, 0.39189127])
>>> arch.active_information()  # cached value is returned
array([0.        , 0.4083436 , 0.62956679, 0.62956679, 0.37915718,
0.40046165, 0.67019615, 0.67019615, 0.39189127])


Each information measure is only computed and cached when you request it. In the event that you change some aspect of the information architecture, e.g. the network, the cache of information measures is also cleared.

>>> arch = Information(s_pombe, k=5, timesteps=20)
>>> arch.active_information()
array([0.        , 0.4083436 , 0.62956679, 0.62956679, 0.37915718,
0.40046165, 0.67019615, 0.67019615, 0.39189127])
>>> arch.net = s_cerevisiae
>>> arch.active_information()
array([0.        , 0.35677758, 0.410884  , 0.44191249, 0.54392362,
0.42523414, 0.35820287, 0.13355861, 0.42823889, 0.22613507,
0.28059538])