def get_max_keyword_similarity(costfunction: CostFunction,
query: KeywordCoordinate, subsets):
"""
This function gets executed inside every maximum keyword similarity process.
:param costfunction: The CostFunction
:param query: The Query
:param subsets: The subsets for the process
:return: A list with tuples of the costs and their corresponding subset
"""
results = []
for subset in subsets:
current_cost = costfunction.get_maximum_keyword_distance(query, subset)
results.append((current_cost, subset))
return results
def test_get_maximum_keyword_distance4(self):
keywords_query = ['food', 'fun', 'outdoor']
keywords_kwc1 = ['food', 'fun', 'outdoor']
keywords_kwc2 = ['food', 'fun', 'outdoor']
keywords_kwc3 = ['food', 'fun', 'outdoor']
coordinates_dont_matter_here = 0
query = KeywordCoordinate(coordinates_dont_matter_here,
coordinates_dont_matter_here, keywords_query)
kwc1 = KeywordCoordinate(coordinates_dont_matter_here,
coordinates_dont_matter_here, keywords_kwc1)
kwc2 = KeywordCoordinate(coordinates_dont_matter_here,
coordinates_dont_matter_here, keywords_kwc2)
kwc3 = KeywordCoordinate(coordinates_dont_matter_here,
coordinates_dont_matter_here, keywords_kwc3)
dataset: dataset_type = [kwc1, kwc2, kwc3]
cf = CostFunction(euclidean_distance, separated_cosine_similarity, 0.3,
0.3, 0.4)
result = cf.get_maximum_keyword_distance(query, dataset)
self.assertAlmostEqual(result, 0.0, delta=0.01)