def sumMeasure(M_lil, M_csc, queryString):
"""
"""
### TEMP ###
# (Lav precomputed RL-hash for labelmatrices hvis noedvendigt)
#vectorLength=SearchTermDoc.createRLHash(M_lil, None,False)
############
# Extract the relevant indices of the row-vectors (pmid-hashes)
searchIndices,hashedSearchTerms = SearchTermDoc.extractRowIndices(M_csc, queryString)
# Union the arrays to avoid searching each row more than once
searchIndices = reduce(set.union,map(set,searchIndices))
results=[]
for pmidHash in searchIndices:
Sum=0
for termHash in hashedSearchTerms:
Sum+=M_lil[pmidHash,termHash] #/ vectorLength[pmidHash]
results.append((Sum,pmidHash))
return results
def cosineMeasure(M_lil, M_csc, queryString):
"""
This function calculates the square-root of the cosine score for each
document containing one or more of the query-terms in the query string
(thereby the implicit 'or' between each query-term).
It returns a scored list of all the documents mentioned above.
"""
# Extract the relevant indices of the row-vectors (pmid-hashes)
searchIndices,hashedSearchTerms = SearchTermDoc.extractRowIndices(M_csc, queryString)
# Union the arrays to avoid searching each row more than once
searchIndices = reduce(set.union,map(set,searchIndices))
results=[]
for pmidHash in searchIndices:
Sum=0
for termHash in hashedSearchTerms:
Sum+=M_lil[pmidHash,termHash]
results.append((Sum,pmidHash))
return results
