一、暴力枚举
1、实现代码
def fib(n): f = [1,1] for i in range(2,n+1): f.append(f[-1]+f[-2]) print(f) return f(n) fib(5)
2、输出
"C:Program FilesPython35python.exe" E:/工作目录/python/test/DP.py [1, 1, 2, 3, 5, 8] Process finished with exit code 1
二、动态规划定义
1、什么是动态规划?
动态规划的英文名,是一种分阶段求解决策略的数学思想,它不止用于编程领域,也用于管理学,经济学、生物学
2、初始为1
实现代码
def LTS(x): F = [0 for _ in range(len(x))] F[0] = 1 for k in range(1,len(F)): max_loc = None max_num = 0 for i in range(1,k): if x[i] < x[k]: if F[i] > max_num: max_loc = i max_num = F[i] F[k] = max_num + 1 return F print(LTS([1,7,2,8,3,5,2]))
输出
"C:Program FilesPython35python.exe" E:/工作目录/python/test/DP.py [1, 1, 1, 2, 2, 3, 1] Process finished with exit code 0
2、初始为0
1、实现代码
def LIS(x): F = [0 for _ in range(len(x))] #初始化 F[0] = 1 for k in range(1,len(F)): max_loc = None max_num = 0 #内层循环表示[0:R] 里所有小于x[k]的对应位置的F[i]最大值 for i in range(0,k): if x[i] < x[k]: if F[i] > max_num: max_loc = i max_num = F[i] F[k] = max_num + 1 return F print(LIS([1,7,2,8,3,5,2]))
2、输出
"C:Program FilesPython35python.exe" E:/工作目录/python/test/DP.py [1, 2, 2, 3, 3, 4, 2] Process finished with exit code 0
三、动态规划最长上升子序列
1、实现代码
def fib(n): f = [1,1] for i in range(2,n+1): f.append(f[-1]+f[-2]) print(f) return f(n) # fib(5) def LIS(x): F = [0 for _ in range(len(x))] p = [-1 for _ in range(len(x))] #初始化 F[0] = 1 p[0] = -1 for k in range(1,len(F)): max_loc = -1 max_num = 0 #内层循环表示[0:R] 里所有小于x[k]的对应位置的F[i]最大值 for i in range(0,k): if x[i] < x[k]: if F[i] > max_num: max_loc = i max_num = F[i] F[k] = max_num + 1 p[k] = max_loc max_i = 0 for i in range(1,len(F)): if F[i] > F[max_i]: max_i = i lis = [] i = max_i while i >= 0: lis.append(x[i]) i = p[i] lis.reverse() return lis print(LIS([1,7,2,8,3,5,2]))
2、输出结果
"C:Program FilesPython35python.exe" E:/工作目录/python/test/DP.py [1, 2, 3, 5] Process finished with exit code 0
最长公共子序列2
最长公共子序列1
动态规划最优子结构