# Classification of strategies¶

Due to the large number of strategies, every class and instance of the class has a classifier attribute which classifies that strategy according to various dimensions.

Here is the classifier for the Cooperator strategy:

>>> import axelrod as axl
>>> expected_dictionary = {
...    'manipulates_state': False,
...    'long_run_time': False,
...    'stochastic': False,
...    'manipulates_source': False,
...    'inspects_source': False,
...    'memory_depth': 0
... }  # Order of this dictionary might be different on your machine
>>> axl.Cooperator.classifier == expected_dictionary
True


Note that instances of the class also have this classifier:

>>> s = axl.Cooperator()
>>> s.classifier == expected_dictionary
True


The instance starts with a copy of the class’s classifier dictionary, but is allowed to change this classifier dictionary at any point, and many strategies do so upon initialization.

In addition to the classifier dictionary, each classifier is defined with some logic that maps classifier definitions to values. To learn the classification of a strategy, we first look in the strategy’s classifier dictionary, then if the key is not present, then we refer to this logic. This logic must be defined for a class, and not specific instances.

To lookup the classifier of a strategy, using the classifier dict, or the strategy’s logic as default, we use Classifiers[<classifier>]( <strategy>):

>>> from axelrod import Classifiers
>>> Classifiers['memory_depth'](axl.TitForTat())
1
>>> Classifiers['stochastic'](axl.Random())
True


We can use this classification to generate sets of strategies according to filters which we define in a ‘filterset’ dictionary and then pass to the ‘filtered_strategies’ function. For example, to identify all the stochastic strategies:

>>> filterset = {
...     'stochastic': True
... }
>>> strategies = axl.filtered_strategies(filterset)
>>> len(strategies)
88


Or, to find out how many strategies only use 1 turn worth of memory to make a decision:

>>> filterset = {
...     'memory_depth': 1
... }
>>> strategies = axl.filtered_strategies(filterset)
>>> len(strategies)
32


Multiple filters can be specified within the filterset dictionary. To specify a range of memory_depth values, we can use the ‘min_memory_depth’ and ‘max_memory_depth’ filters:

>>> filterset = {
...     'min_memory_depth': 1,
...     'max_memory_depth': 4
... }
>>> strategies = axl.filtered_strategies(filterset)
>>> len(strategies)
55


We can also identify strategies that make use of particular properties of the tournament. For example, here is the number of strategies that make use of the length of each match of the tournament:

>>> filterset = {
...     'makes_use_of': ['length']
... }
>>> strategies = axl.filtered_strategies(filterset)
>>> len(strategies)
22


Note that in the filterset dictionary, the value for the ‘makes_use_of’ key must be a list. Here is how we might identify the number of strategies that use both the length of the tournament and the game being played:

>>> filterset = {
...     'makes_use_of': ['length', 'game']
... }
>>> strategies = axl.filtered_strategies(filterset)
>>> len(strategies)
15


Some strategies have been classified as having a particularly long run time:

>>> filterset = {
...     'long_run_time': True
... }
>>> strategies = axl.filtered_strategies(filterset)
>>> len(strategies)
18


Strategies that manipulate_source, manipulate_state and/or inspect_source return False for the Classifier.obey_axelrod function:

>>> s = axl.MindBender()
>>> axl.Classifiers.obey_axelrod(s)
False
>>> s = axl.TitForTat()
>>> axl.Classifiers.obey_axelrod(s)
True