示例#1
0
    def s_find_nearest(instance, data, k, dist_meas, instance_tie_breaker=min, exclude_self=True):
        data = [(d, dist_meas(instance, d))  for d in data if not (d is instance and exclude_self)]
        
        if len(data) <= k:
            return [d[0] for d in data]
        
        cmp_key = functools.cmp_to_key(KNN._comparer)
        
        if len(data) == k+1:
            maxes = max_multiple(data, key=cmp_key)
            while len(maxes) > 1:
                best = instance_tie_breaker((m[0] for m in maxes))
                maxes = [m for m in maxes if m[0] is not best]
            m = maxes[0][0]
            return [d[0] for d in data if d[0] is not m]
        
        current_nearest_sorted = [] 
        for (other, other_dist) in data:
            if other is instance and exclude_self:
                continue
            other_dist = dist_meas(instance, other)
            
            if len(current_nearest_sorted) < k \
               or other_dist <= current_nearest_sorted[-1][1]:
                current_nearest_sorted.append((other, other_dist))
                current_nearest_sorted.sort(key=cmp_key)
                
                if len(current_nearest_sorted) <= k:
                    continue
                
                maxes = max_multiple(current_nearest_sorted, key=cmp_key)
                
                current_nearest_sorted = current_nearest_sorted[:-(len(maxes))]
                while len(current_nearest_sorted) < k:
                    max_instances = [m[0] for m in maxes]
                    best = instance_tie_breaker(max_instances)
                    best_index = max_instances.index(best)
                    current_nearest_sorted.append(maxes[best_index])
                    del(maxes[best_index])

        return [nnd[0] for nnd in current_nearest_sorted]
示例#2
0
 def classify_from_neighbours(self, instance, neighbours):
     class_probabilities = self.get_probabilities(instance, neighbours)
     max_class_probabilities = max_multiple(class_probabilities, key=lambda cp: cp[1]) or self.possible_classes
     return self.classification_tie_breaker((m[0] for m in max_class_probabilities))
示例#3
0
    def __suppose_nn(self, case):
        '''
        Determine the changes that would occur to all NN rNN sets if case were added.
        
        @param case: The case to suppose the addition of.
        '''
        assert(not self.case_info_lookup.has_key(case)) # Just don't want to deal with the hassle right now.
        
        add_removals_dict = SuppositionResults(case)
        
        def get_or_create(_case):
            if not add_removals_dict.has_key(_case):
                add_removals_dict[_case] = AddRemovalStore()
            return add_removals_dict[_case]
        
        case_changes = get_or_create(case)
        
        case_nns = self.nns_getter(self.case_base, case)
        
        case_changes.added.nearest_neighbours.update(case_nns)
        for nn in case_nns:
            nn_changes = get_or_create(nn)
            nn_changes.added.reverse_nearest_neighbours.add(case)
        
        for (other_case, other_case_profile) in self.case_info_lookup.items():
            if len(other_case_profile.nearest_neighbours) < self.__k: # Less than, as I'm going to be adding to (in which case if it was k-1, it's going to become k)
                other_case_new_nns = chain(other_case_profile.nearest_neighbours, (case,))
                shunted = None
            else:
                # TODO: There is the issue of comparability here. Change to pass distance_measurer to __init__ instead of distance_constructor
                dist_meas = self.__distance_constructor(other_case_profile.nearest_neighbours)
                maxes = max_multiple(((other_case_nn, dist_meas(other_case, other_case_nn))
                                for other_case_nn 
                                in other_case_profile.nearest_neighbours), key=lambda el: el[1])
                max_ex, max_dist = maxes[0]
                case_dist = dist_meas(other_case, case)
                
                if case_dist > max_dist:
                    continue
                elif case_dist == max_dist or len(maxes) > 1:
                    # Unsure - deferreing decision to nn finder for tie breaking
                    other_case_new_nns = self.nns_getter(chain(other_case_profile.nearest_neighbours, (case,)), other_case)
                    if case not in other_case_new_nns:
                        continue
                    
                    difference = set(other_case_profile.nearest_neighbours).difference(other_case_new_nns)
                    assert(len(difference) == 1)
                    shunted = difference.pop()
                else:
                    other_case_new_nns = [nn for nn in other_case_profile.nearest_neighbours 
                                          if nn is not max_ex]
                    other_case_new_nns.append(case)
                    shunted = max_ex
            
            other_case_changes = get_or_create(other_case)
            
            # Heuston - we have a reversed nearest neighbour - 
            case_changes.added.reverse_nearest_neighbours.add(other_case)
            other_case_changes.added.nearest_neighbours.add(case)
            
            # now, did it shunt out another one for the position
            if shunted is None:
                continue 

            shunted_changes = get_or_create(shunted)
            
            other_case_changes.removed.nearest_neighbours.add(shunted)
            shunted_changes.removed.reverse_nearest_neighbours.add(other_case)
        
        assert(all(ca not in add_removed.added.nearest_neighbours for (ca, add_removed) in add_removals_dict.items()))
        return add_removals_dict