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AR_ranker.py
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AR_ranker.py
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import numpy
def group_rank(matrix, wg, reviews):
group_scores = numpy.zeros(matrix.shape[1])
for group_index in xrange(matrix.shape[1]):
curr_score = 0
fg = []
volume = calc_volume(matrix, group_index)
fg.append(volume)
fg.append(calc_average_rating(matrix, group_index, reviews, volume))
for i in xrange(len(fg)):
curr_score += wg[i] * fg[i]
group_scores[group_index] = curr_score
return sorted(group_scores, reverse=True), sorted(range(len(group_scores)), key=lambda k: group_scores[k], reverse=True)
def calc_volume(matrix, group_index):
result = 0
for review_index in xrange(matrix.shape[0]):
result += matrix[review_index][group_index]
return result
def calc_average_rating(matrix, group_index, reviews, volume):
denominator = 0
for review_index in xrange(matrix.shape[0]):
denominator += matrix[review_index][group_index] * reviews[review_index].rating * 1.0
return volume / denominator