# Using the cache¶

Whilst for stochastic strategies, every repetition of a Match will give a different result, for deterministic strategies, when there is no noise there is no need to re run the match. The library has a DeterministicCache class that allows us to quickly replay matches.

## Caching a Match¶

To illustrate this, let us time the play of a match without a cache:

>>> import axelrod as axl
>>> import timeit
>>> def run_match():
...     p1, p2 = axl.GoByMajority(), axl.Alternator()
...     match = axl.Match((p1, p2), turns=200)
...     return match.play()
>>> time_with_no_cache = timeit.timeit(run_match, number=500)
>>> time_with_no_cache
2.2295279502868652


Here is how to create a new empty cache:

>>> cache = axl.DeterministicCache()
>>> len(cache)
0


Let us rerun the above match but using the cache:

>>> p1, p2 = axl.GoByMajority(), axl.Alternator()
>>> match = axl.Match((p1, p2), turns=200, deterministic_cache=cache)
>>> match.play()
[(C, C), ..., (C, D)]


We can take a look at the cache:

>>> cache
{('Soft Go By Majority', 'Alternator'): [(C, C), ..., (C, D)]}
>>> len(cache)
1
>>> len(cache[(axl.GoByMajority(), axl.Alternator())])
200


This maps a triplet of 2 player names and the match length to the resulting interactions. We can rerun the code and compare the timing:

>>> def run_match_with_cache():
...     p1, p2 = axl.GoByMajority(), axl.Alternator()
...     match = axl.Match((p1, p2), turns=200, deterministic_cache=cache)
...     return match.play()
>>> time_with_cache = timeit.timeit(run_match_with_cache, number=500)
>>> time_with_cache
0.04215192794799805
>>> time_with_cache < time_with_no_cache
True


We can write the cache to file:

>>> cache.save("cache.txt")
True


## Caching a Tournament¶

Tournaments will automatically create caches as needed on a match by match basis.

## Caching a Moran Process¶

A prebuilt cache can also be used in a Moran process (by default a new cache is used):

>>> cache = axl.DeterministicCache("cache.txt")
>>> players = [axl.GoByMajority(), axl.Alternator(),
...            axl.Cooperator(), axl.Grudger()]
>>> mp = axl.MoranProcess(players, deterministic_cache=cache)
>>> populations = mp.play()
>>> mp.winning_strategy_name
Defector


We see that the cache has been augmented, although note that this particular number will depend on the stochastic behaviour of the Moran process:

>>> len(cache)
18