utilities.py 文件源码

def find_matches(D_s, print_assignment=False):

    matches=[]
    costs=[]
    t_start=time.time()
    for ii,D in enumerate(D_s):
        DD=D.copy()    
        if np.sum(np.where(np.isnan(DD)))>0:
            raise Exception('Distance Matrix contains NaN, not allowed!')


    #    indexes = m.compute(DD)
#        indexes = linear_assignment(DD)
        indexes = linear_sum_assignment(DD)
        indexes2=[(ind1,ind2) for ind1,ind2 in zip(indexes[0],indexes[1])]
        matches.append(indexes)
        DD=D.copy()   
        total = []
        for row, column in indexes2:
            value = DD[row,column]
            if print_assignment:
                print '(%d, %d) -> %f' % (row, column, value)
            total.append(value)      
        print  'FOV: %d, shape: %d,%d total cost: %f' % (ii, DD.shape[0],DD.shape[1], np.sum(total))
        print time.time()-t_start
        costs.append(total)      

    return matches,costs

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