在《算法图解》里面有一个蛮有意思的小案例，背景是一个广播节目，要让全美的50个周的听众都能够听到，但是每个电台可能覆盖多个州，每在一个电台播出就需要一笔费用，所以就是从成本的角度来看，怎么尽可能在所有的州都播出，这是一个典型的集合覆盖的问题，而且在我们的生活中算是比较典型。
比如我们先缩小范围，指定5个州，那么50个州也是同样的算法。
states_need = set(["mt", "wa", "or", "id", "nv", "ut", "ca", "az"]) # 传入一个数组， 它被转换为集合
有的同学可能对这些州没概念，这个简称就跟京代表北京，鲁代表山东，甘代表甘肃一样，细细一看，都是西部的一些州。
如何使用贪心算法呢，就是选择覆盖尽可能多的州的电台，然后逐步缩小范围。那么覆盖面广的州所对应的电台就优先被选中，依次类推。
程序的实现是指定了一个集合states_need，里面包含所有的州，每个电台对应的州是通过初始化的数组元素来实现的，按照一二三四五的顺序来命名，当然实际上这种元素的排列set不是按照数组名的顺序，在这个场景里是kfive,ktwo,kthree,kone,kfour
然后逐步缩小范围来收敛，里面比较特别的一点就是集合的运算，使用了 & ,得到的是交集，如果是并集是 |，差集是 -，
程序代码如下：
#!/usr/bin/env
python# coding:utf-8states_need = set(["mt", "wa", "or", "id", "nv",
"ut", "ca", "az"]) # 传入一个数组， 它被转换为集合# 可供选择的广播台清单stations =
{}stations["kone"] = set(["id", "nv", "ut"])stations["ktwo"] =
set(["wa", "id", "mt"])stations["kthree"] = set(["or", "nv",
"ca"])stations["kfour"] = set(["nv", "ut"])stations["kfive"] =
set(["ca", "az"])print(stations)# 最终选择的广播台集合final_stations = set()while
states_need:best_station = Nonestates_covered = set()for station,
states_for_station in stations.items():covered = states_need &
states_for_station #
求交集print("states_need:",states_need,"states_for_station:",states_for_station,"covered:",covered)if
len(covered) > len(states_covered):best_station =
stationstates_covered = coveredstates_need -=
states_coveredfinal_stations.add(best_station)print("states_needed:",states_need,"best_station:",best_station,"final_stations:",final_stations)print("---")print("Final_stations:",final_stations)
为了方便调试和得到一个迭代的结果，我加了几处输出日志，工参考。
{'kfive':
set(['ca', 'az']), 'ktwo': set(['mt', 'id', 'wa']), 'kthree':
set(['ca', 'or', 'nv']), 'kone': set(['ut', 'id', 'nv']), 'kfour':
set(['ut', 'nv'])}('states_need:', set(['wa', 'ut', 'ca', 'id', 'mt',
'az', 'or', 'nv']), 'states_for_station:', set(['ca', 'az']),
'covered:', set(['ca', 'az']))('states_needed:', set(['wa', 'ut', 'id',
'mt', 'or', 'nv']), 'best_station:', 'kfive', 'final_stations:',
set(['kfive']))---('states_need:', set(['wa', 'ut', 'id', 'mt', 'or',
'nv']), 'states_for_station:', set(['mt', 'id', 'wa']), 'covered:',
set(['mt', 'id', 'wa']))('states_needed:', set(['ut', 'or', 'nv']),
'best_station:', 'ktwo', 'final_stations:', set(['ktwo',
'kfive']))---('states_need:', set(['ut', 'or', 'nv']),
'states_for_station:', set(['ca', 'or', 'nv']), 'covered:', set(['or',
'nv']))('states_needed:', set(['ut', 'or', 'nv']), 'best_station:',
'ktwo', 'final_stations:', set(['ktwo', 'kfive']))---('states_need:',
set(['ut', 'or', 'nv']), 'states_for_station:', set(['ut', 'id', 'nv']),
'covered:', set(['ut', 'nv']))('states_needed:', set(['ut', 'or',
'nv']), 'best_station:', 'ktwo', 'final_stations:', set(['ktwo',
'kfive']))---('states_need:', set(['ut', 'or', 'nv']),
'states_for_station:', set(['ut', 'nv']), 'covered:', set(['ut',
'nv']))('states_needed:', set(['ut', 'or', 'nv']), 'best_station:',
'ktwo', 'final_stations:', set(['ktwo', 'kfive']))---('states_need:',
set(['ut', 'or', 'nv']), 'states_for_station:', set(['ca', 'az']),
'covered:', set([]))('states_needed:', set(['ut', 'or', 'nv']),
'best_station:', None, 'final_stations:', set(['ktwo', None,
'kfive']))---('states_need:', set(['ut', 'or', 'nv']),
'states_for_station:', set(['mt', 'id', 'wa']), 'covered:',
set([]))('states_needed:', set(['ut', 'or', 'nv']), 'best_station:',
None, 'final_stations:', set(['ktwo', None,
'kfive']))---('states_need:', set(['ut', 'or', 'nv']),
'states_for_station:', set(['ca', 'or', 'nv']), 'covered:', set(['or',
'nv']))('states_needed:', set(['ut']), 'best_station:', 'kthree',
'final_stations:', set(['ktwo', 'kthree', None,
'kfive']))---('states_need:', set(['ut']), 'states_for_station:',
set(['ut', 'id', 'nv']), 'covered:', set(['ut']))('states_needed:',
set(['ut']), 'best_station:', 'kthree', 'final_stations:', set(['ktwo',
'kthree', None, 'kfive']))---('states_need:', set(['ut']),
'states_for_station:', set(['ut', 'nv']), 'covered:',
set(['ut']))('states_needed:', set(['ut']), 'best_station:', 'kthree',
'final_stations:', set(['ktwo', 'kthree', None,
'kfive']))---('states_need:', set(['ut']), 'states_for_station:',
set(['ca', 'az']), 'covered:', set([]))('states_needed:', set(['ut']),
'best_station:', None, 'final_stations:', set(['ktwo', 'kthree', None,
'kfive']))---('states_need:', set(['ut']), 'states_for_station:',
set(['mt', 'id', 'wa']), 'covered:', set([]))('states_needed:',
set(['ut']), 'best_station:', None, 'final_stations:', set(['ktwo',
'kthree', None, 'kfive']))---('states_need:', set(['ut']),
'states_for_station:', set(['ca', 'or', 'nv']), 'covered:',
set([]))('states_needed:', set(['ut']), 'best_station:', None,
'final_stations:', set(['ktwo', 'kthree', None,
'kfive']))---('states_need:', set(['ut']), 'states_for_station:',
set(['ut', 'id', 'nv']), 'covered:', set(['ut']))('states_needed:',
set([]), 'best_station:', 'kone', 'final_stations:', set(['ktwo',
'kthree', None, 'kfive', 'kone']))---('states_need:', set([]),
'states_for_station:', set(['ut', 'nv']), 'covered:',
set([]))('states_needed:', set([]), 'best_station:', 'kone',
'final_stations:', set(['ktwo', 'kthree', None, 'kfive',
'kone']))---('Final_stations:', set(['ktwo', 'kthree', None, 'kfive',
'kone']))
最后的结果是：ktwo,kthree,kfive,kone这四个电台。
当然贪心算法得到的不是精确的结果，即可能不是最优解，算是一种近似算法，能够基本得到的最优解，而且效率很高。