psifr.maskers.sequences_masker#

psifr.maskers.sequences_masker(n_transitions, pool_items, recall_items, pool_output, recall_output, pool_test=None, recall_test=None, test=None)#

Yield sequences of adjacent included transitions.

Parameters:

  • n_transitions (int) – Number of transitions to include in yielded sequences.

  • pool_items (list) – Items available for recall. Order does not matter. May contain repeated values. Item identifiers must be unique within pool.

  • recall_items (list) – Recalled items in output position order.

  • pool_output (list) – Output values for pool items. Must be the same order as pool.

  • recall_output (list) – Output values in output position order.

  • pool_test (list, optional) – Test values for items available for recall. Must be the same order as pool.

  • recall_test (list, optional) – Test values for items in output position order.

  • test (callable, optional) –

    Used to test whether individual transitions should be included, based on test values.

    test(prev, curr) - test for included transition

    test(prev, poss) - test for included possible transition

Yields:

  • output (int) – Output positions of included transitions. The first transition is 1.

  • prev (list) – Output values for the “from” item in included transitions.

  • curr (list) – Output values for the “to” item in included transitions.

  • poss (list of numpy.ndarray) – Output values for all possible valid “to” items in included transitions.

See also

transitions_masker

Yield included transitions.

Examples

>>> from psifr import maskers
>>> pool = [1, 2, 3, 4, 5, 6]
>>> recs = [6, 2, 3, 6, 1, 4, 5]
>>> masker = maskers.sequences_masker(
...     2, pool_items=pool, recall_items=recs, pool_output=pool, recall_output=recs
... )
>>> for output, prev, curr, poss in masker:
...     print(output, prev, curr, poss)
[1, 2] [6, 2] [2, 3] [array([1, 2, 3, 4, 5]), array([1, 3, 4, 5])]
[5, 6] [1, 4] [4, 5] [array([4, 5]), array([5])]

>>> pool = [1, 2, 3, 4]
>>> recs = [4, 3, 1, 2]
>>> masker = maskers.sequences_masker(
...     3, pool_items=pool, recall_items=recs, pool_output=pool, recall_output=recs
... )
>>> for output, prev, curr, poss in masker:
...     print(output, prev, curr, poss)
[1, 2, 3] [4, 3, 1] [3, 1, 2] [array([1, 2, 3]), array([1, 2]), array([2])]