Today we take a break from our discussions on Object Storage to review a few methods for topic detection in documents.
# Divides the region proposals on the Intersection-over-union value
def divideset(proposals,iou,value):
       # Make a function that tells us if a proposal is a candidate or not.
       split_function=None
       if isinstance(value,int) or isinstance(value,float):
              split_function=lambda proposal:proposal[iou]>=value
       else:
              split_function=lambda proposal:proposal[iou]==value
       # Divide the proposals into two sets
       set1 = [proposal for proposal in proposals if split_function(proposal)]
       set2 = [proposal for proposal in proposals if not split_function(proposal)]
       return (set1, set2)


Def gen_proposals(proposals, least_squares_estimates):
       # a proposal is origin, length, breadth written as say top-left and bottom-right corner of a bounding box
       # given many known topic vectors, the classifer helps detect the best match.
       # the bounding box is adjusted to maximize the intersection over union of this topic.
       # text is flowing so we can assume bounding boxes of sentences
       # fix origin and choose fixed step sizes to determine the adherence to the regression
       # repeat for different selections of origins.
       pass