cgDdZddlZejeZdZdZdZdS)zKThis module contains functions to perform segmentation on a list of topics.Ncg}|D]]}g}t|ddD],\}}|d|dzD]}|||f-||^|S)aPerforms segmentation on a list of topics. Notes ----- Segmentation is defined as :math:`s_{pre} = {(W', W^{*}) | W' = w_{i}; W^{*} = {w_j}; w_{i}, w_{j} \in W; i > j}`. Parameters ---------- topics : list of np.array list of topics obtained from an algorithm such as LDA. Returns ------- list of list of (int, int) :math:`(W', W^{*})` for all unique topic ids. Examples -------- .. sourcecode:: pycon >>> import numpy as np >>> from gensim.topic_coherence import segmentation >>> >>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])] >>> segmentation.s_one_pre(topics) [[(2, 1), (3, 1), (3, 2)], [(5, 4), (6, 4), (6, 5)]] N enumerateappend)topics s_one_pre_res top_words s_one_pre_t w_prime_indexw_primew_stars Clib/python3.11/site-packages/gensim/topic_coherence/segmentation.py s_one_prers<M**  &/ !"" &>&> 6 6 "M7#$6]Q%6$67 6 6""GV#45555 6[)))) cg}|D]a}g}t|D]8\}}t|D]#\}}||kr |||f$9||b|S)aPerform segmentation on a list of topics. Segmentation is defined as :math:`s_{one} = {(W', W^{*}) | W' = {w_i}; W^{*} = {w_j}; w_{i}, w_{j} \in W; i \neq j}`. Parameters ---------- topics : list of `numpy.ndarray` List of topics obtained from an algorithm such as LDA. Returns ------- list of list of (int, int). :math:`(W', W^{*})` for all unique topic ids. Examples ------- .. sourcecode:: pycon >>> import numpy as np >>> from gensim.topic_coherence import segmentation >>> >>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])] >>> segmentation.s_one_one(topics) [[(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)], [(4, 5), (4, 6), (5, 4), (5, 6), (6, 4), (6, 5)]] r)r s_one_one_resr s_one_one_tr r w_star_indexrs r s_one_oner8s6M**  &/ &:&: : : "M7(1)(<(< : :$ f L0:&&'89999  : [)))) rczg}|D]5}g}|D]}|||f||6|S)aPerform s_one_set segmentation on a list of topics. Segmentation is defined as :math:`s_{set} = {(W', W^{*}) | W' = {w_i}; w_{i} \in W; W^{*} = W}` Parameters ---------- topics : list of `numpy.ndarray` List of topics obtained from an algorithm such as LDA. Returns ------- list of list of (int, int). :math:`(W', W^{*})` for all unique topic ids. Examples -------- .. sourcecode:: pycon >>> import numpy as np >>> from gensim.topic_coherence import segmentation >>> >>> topics = [np.array([9, 10, 7])] >>> segmentation.s_one_set(topics) [[(9, array([ 9, 10, 7])), (10, array([ 9, 10, 7])), (7, array([ 9, 10, 7]))]] )r)r s_one_set_resr s_one_set_tr s r s_one_setrbse6M**    5 5G   3 4 4 4 4[)))) r)__doc__logging getLogger__name__loggerrrrrrr!sbRQ  8 $ $'''T'''T#####r