# Let's imagine you need to assign N people to N jobs. Additionally, each # person will make your company a certain amount of money at each job, but each # person has different skills so they are better at some jobs and worse at # others. You would like to find the best way to assign people to these jobs. # In particular, you would like to maximize the amount of money the group makes # as a whole. This is an example of an assignment problem and is what is solved # by the dlib.max_cost_assignment() routine. # So in this example, let's imagine we have 3 people and 3 jobs. We represent # the amount of money each person will produce at each job with a cost matrix. # Each row corresponds to a person and each column corresponds to a job. So for # example, below we are saying that person 0 will make $1 at job 0, $2 at job 1, # and $6 at job 2. cost = dlib.matrix([[1, 2, 6], [5, 3, 6], [4, 5, 0]]) # To find out the best assignment of people to jobs we just need to call this # function. assignment = dlib.max_cost_assignment(cost) # This prints optimal assignments: [2, 0, 1] # which indicates that we should assign the person from the first row of the # cost matrix to job 2, the middle row person to job 0, and the bottom row # person to job 1. print("Optimal assignments: {}".format(assignment)) # This prints optimal cost: 16.0 # which is correct since our optimal assignment is 6+5+5. print("Optimal cost: {}".format(dlib.assignment_cost(cost, assignment)))
import dlib # Let's imagine you need to assign N people to N jobs. Additionally, each # person will make your company a certain amount of money at each job, but each # person has different skills so they are better at some jobs and worse at # others. You would like to find the best way to assign people to these jobs. # In particular, you would like to maximize the amount of money the group makes # as a whole. This is an example of an assignment problem and is what is solved # by the dlib.max_cost_assignment() routine. # So in this example, let's imagine we have 3 people and 3 jobs. We represent # the amount of money each person will produce at each job with a cost matrix. # Each row corresponds to a person and each column corresponds to a job. So for # example, below we are saying that person 0 will make $1 at job 0, $2 at job 1, # and $6 at job 2. cost = dlib.matrix([[1, 2, 6], [5, 3, 6], [4, 5, 0]]) # To find out the best assignment of people to jobs we just need to call this # function. assignment = dlib.max_cost_assignment(cost) # This prints optimal assignments: [2, 0, 1] # which indicates that we should assign the person from the first row of the # cost matrix to job 2, the middle row person to job 0, and the bottom row # person to job 1. print("Optimal assignments: {}".format(assignment)) # This prints optimal cost: 16.0 # which is correct since our optimal assignment is 6+5+5. print("Optimal cost: {}".format(dlib.assignment_cost(cost, assignment)))
# your company a certain amount of money at each job, but each person has different skills # so they are better at some jobs and worse at others. You would like to find the best way # to assign people to these jobs. In particular, you would like to maximize the amount of # money the group makes as a whole. This is an example of an assignment problem and is # what is solved by the dlib.max_cost_assignment() routine. # So in this example, let's imagine we have 3 people and 3 jobs. We represent the amount of # money each person will produce at each job with a cost matrix. Each row corresponds to a # person and each column corresponds to a job. So for example, below we are saying that # person 0 will make $1 at job 0, $2 at job 1, and $6 at job 2. cost = dlib.matrix([[1, 2, 6], [5, 3, 6], [4, 5, 0]]) # To find out the best assignment of people to jobs we just need to call this function. assignment = dlib.max_cost_assignment(cost) # This prints optimal assignments: [2, 0, 1] # which indicates that we should assign the person from the first row of the cost matrix to # job 2, the middle row person to job 0, and the bottom row person to job 1. print "optimal assignments: ", assignment # This prints optimal cost: 16.0 # which is correct since our optimal assignment is 6+5+5. print "optimal cost: ", dlib.assignment_cost(cost, assignment)