psifr.stats.count_distance#

psifr.stats.count_distance(distances, edges, pool_items, recall_items, pool_index, recall_index, pool_test=None, recall_test=None, test=None, count_unique=False)#

Count transitions within distance bins.

Parameters:

  • distances (numpy.array) – Items x items matrix of pairwise distances or similarities.

  • edges (array-like) – Edges of bins to apply to distances.

  • pool_items (list of list) – Unique item codes for each item in the pool available for recall.

  • recall_items (list of list) – Unique item codes of recalled items.

  • pool_index (list of list) – Index of each item in the distances matrix.

  • recall_index (list of list) – Index of each recalled item.

  • pool_test (list of list, optional) – Test value for each item in the pool.

  • recall_test (list of list, optional) – Test value for each recalled item.

  • test (callable) – Called as test(prev, curr) or test(prev, poss) to screen actual and possible transitions, respectively.

  • count_unique (bool, optional) – If true, only unique values will be counted toward the possible transitions. If multiple items are avilable for recall for a given transition and a given bin, that bin will only be incremented once. If false, all possible transitions will add to the count.

Returns:

  • actual (pandas.Series) – Count of actual transitions made for each bin.

  • possible (pandas.Series) – Count of possible transitions for each bin.

See also

rank_distance

Calculate percentile rank of transition distances.

Examples

>>> import numpy as np
>>> from psifr import stats
>>> distances = np.array([[0, 1, 2, 2], [1, 0, 2, 2], [2, 2, 0, 3], [2, 2, 3, 0]])
>>> edges = np.array([0.5, 1.5, 2.5, 3.5])
>>> pool_items = [[1, 2, 3, 4]]
>>> recall_items = [[4, 2, 3, 1]]
>>> pool_index = [[0, 1, 2, 3]]
>>> recall_index = [[3, 1, 2, 0]]
>>> actual, possible = stats.count_distance(
...     distances, edges, pool_items, recall_items, pool_index, recall_index
... )
>>> actual
(0.5, 1.5]    0
(1.5, 2.5]    3
(2.5, 3.5]    0
Name: count, dtype: int64
>>> possible
(0.5, 1.5]    1
(1.5, 2.5]    4
(2.5, 3.5]    1
Name: count, dtype: int64