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Comment:SQLiteStorage.find_courses(term) now also finds the non-hyphenated version of hyphenated words.
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User & Date: florian 2024-10-15 12:23:59
Context
2024-10-16
10:15
Merged changes from view. check-in: 5e71483a64 user: florian tags: development
2024-10-15
15:43
Merged changes from development. check-in: 250e22e3e6 user: florian tags: view
12:23
SQLiteStorage.find_courses(term) now also finds the non-hyphenated version of hyphenated words. check-in: 6a7c31f709 user: florian tags: development
2024-08-05
16:28
SQLiteStorage.find_courses(term) now returns the match ratio along with the matching courses. The course list is now sorted by match ration if a term was given. check-in: 127ed83d53 user: florian tags: development
Changes
Hide Diffs Unified Diffs Ignore Whitespace Patch

Changes to luna_lms/storage/sqlite_storage.py.

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							terms = terms.lower()

							# First, use the built-in all whitespace split.
							#
							terms = terms.split()

							# Then, add split versions of hyphenated words.

							# We want to be able to to a similarity search

							# both on the compound and on the parts of these.
							#
							split_hyphens = []

							for t in terms:
								if "-" in t:
									split_hyphens.extend(t.split("-"))


							if len(split_hyphens):
								LOGGER.debug("Added {} split hyphenated terms".format(len(split_hyphens)))

							terms.extend(split_hyphens)

							self.terms[identifier] = set(terms)

						# We use the built-in difflib for a fuzzy search.
						# We evaluate term by term, and decide by the highest
						# similarity.









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							terms = terms.lower()

							# First, use the built-in all whitespace split.
							#
							terms = terms.split()

							# Then, add split versions of hyphenated words, and
							# also a version with hyphens removed.
							# We want to be able to to a similarity search
							# on the compound, with and without hyphens, as well
							# as on the parts of these.
							#
							hyphen_alternatives = []

							for t in terms:
								if "-" in t:
									hyphen_alternatives.extend(t.split("-"))
									hyphen_alternatives.append(t.replace("-", ""))

							if len(hyphen_alternatives):
								LOGGER.debug("Added {} alternatives to hyphenated terms".format(len(hyphen_alternatives)))

							terms.extend(hyphen_alternatives)

							self.terms[identifier] = set(terms)

						# We use the built-in difflib for a fuzzy search.
						# We evaluate term by term, and decide by the highest
						# similarity.
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