def cseg_similarity_matrix_classes(card, prime_algorithm="prime_form_sampaio"):
"""Returns a matrix with CSIM between multiple csegs.
>>> cseg_similarity_matrix_classes(3)
[[< 0 1 2 >, < 0 2 1 >],
[1.0, 0.66666666666666663],
[0.66666666666666663, 1.0]]
"""
classes_lst = contour.build_classes_card(card, prime_algorithm)
classes = [contour.Contour(list(cseg)) for (a, b, cseg, c) in classes_lst]
return cseg_similarity_matrix(classes)
def cseg_from_class_number(card, class_number, prime_algorithm="prime_form_sampaio"):
"""Returns a cseg from a given cardinality and class
number. Sampaio Prime algorithm is default.
>>> cseg_from_class_number(4, 7)
< 1 0 3 2 >
"""
card_classes = contour.build_classes_card(card, prime_algorithm)
for classes in card_classes:
cc, cn, cs, ri = classes
if card == cc and class_number == cn:
return contour.Contour(cs)
def cseg_similarity_continuum(cseg, prime_algorithm="prime_form_marvin_laprade"):
"""Returns all csegs with the same cardinality of the given one
sorted by cseg similarity.
>>> cseg_similarity_continuum(Contour([1, 0, 3, 2]))
[[0.5, [< 0 2 1 3 >, < 0 3 2 1 >]],
[0.66666666666666663, [< 0 1 2 3 >, < 0 2 3 1 >, < 0 3 1 2 >]],
[0.83333333333333337, [< 0 1 3 2 >, < 1 3 0 2 >]],
[1.0, [< 1 0 3 2 >]]]
"""
size = len(cseg)
built_classes = contour.build_classes_card(size, prime_algorithm)
def cseg_similarity_lists(built_classes):
"""Returns a tuple with two lists:
1. cseg similarity (CSIM) and csegclass, and
2. all cseg similarity in previous list.
Accepts built classes with one cardinality
"""
csegclasses = []
similarity = []
for (a, b, csegclass, d) in built_classes:
csegclass = contour.Contour(csegclass)
csim = cseg_similarity(cseg, csegclass)
csegclasses.append([csim, csegclass])
similarity.append(csim)
return csegclasses, sorted(list(set(similarity)))
def grouped_cseg_similarity_lists((csegclasses, similarity)):
"""Returns csegclasses grouped by cseg similarity. Accepts
lists provided by cseg_similarity_lists function.
"""
result = []
for similarity_index in similarity:
simil_index = []
for csegclass in sorted(csegclasses):
if csegclass[0] == similarity_index:
simil_index.append(csegclass[1])
result.append([similarity_index, simil_index])
return result
return grouped_cseg_similarity_lists(cseg_similarity_lists(built_classes))