Google Code Jam 2011 Qualification Round ProblemA

本文介绍了一个有趣的编程挑战问题,即两个机器人如何最高效地按下一组特定顺序的按钮。通过分解任务并采用并行处理策略,文章提供了一种解决方法,并分享了实现这一算法的Java代码。

Problem A. Bot Trust

Problem

Blue and Orange are friendly robots. An evil computer mastermind has locked them up in separate hallways to test them, and then possibly give them cake.

Each hallway contains 100 buttons labeled with the positive integers {1, 2, ..., 100}. Button k is always k meters from the start of the hallway, and the robots both begin at button 1. Over the period of one second, a robot can walk one meter in either direction, or it can press the button at its position once, or it can stay at its position and not press the button. To complete the test, the robots need to push a certain sequence of buttons in a certain order. Both robots know the full sequence in advance. How fast can they complete it?

For example, let's consider the following button sequence:

   O 2, B 1, B 2, O 4

Here, O 2 means button 2 in Orange's hallway, B 1 means button 1 in Blue's hallway, and so on. The robots can push this sequence of buttons in 6 seconds using the strategy shown below:

Time | Orange           | Blue
-----+------------------+-----------------
  1  | Move to button 2 | Stay at button 1
  2  | Push button 2    | Stay at button 1
  3  | Move to button 3 | Push button 1
  4  | Move to button 4 | Move to button 2
  5  | Stay at button 4 | Push button 2
  6  | Push button 4    | Stay at button 2
Note that Blue has to wait until Orange has completely finished pushing O 2 before it can start pushing B 1.

 

Input

The first line of the input gives the number of test cases, T. T test cases follow.

Each test case consists of a single line beginning with a positive integer N, representing the number of buttons that need to be pressed. This is followed by N terms of the form "Ri Pi" where Ri is a robot color (always 'O' or 'B'), and Pi is a button position.

Output

For each test case, output one line containing "Case #x: y", where x is the case number (starting from 1) and y is the minimum number of seconds required for the robots to push the given buttons, in order.

Limits

1 ≤ Pi ≤ 100 for all i.

Small dataset

1 ≤ T ≤ 20.
1 ≤ N ≤ 10.

Large dataset

1 ≤ T ≤ 100.
1 ≤ N ≤ 100.

Sample


Input
 

Output
 
3
4 O 2 B 1 B 2 O 4
3 O 5 O 8 B 100
2 B 2 B 1
Case #1: 6
Case #2: 100
Case #3: 4

我的代码如下:

复杂几何的多球近似MATLAB类及多球模型的比较 MATLAB类Approxi提供了一个框架,用于使用具有迭代缩放的聚集球体模型来近似解剖体积模型,以适应目标体积和模型比较。专为骨科、生物力学和计算几何应用而开发。 MATLAB class for multi-sphere approximation of complex geometries and comparison of multi-sphere models 主要特点: 球体模型生成 1.多球体模型生成:与Sihaeri的聚集球体算法的接口 2.音量缩放 基于体素的球体模型和参考几何体的交集。 迭代缩放球体模型以匹配目标体积。 3.模型比较:不同模型体素占用率的频率分析(多个评分指标) 4.几何分析:原始曲面模型和球体模型之间的顶点到最近邻距离映射(带颜色编码结果)。 如何使用: 1.代码结构:Approxi类可以集成到相应的主脚本中。代码的关键部分被提取到单独的函数中以供重用。 2.导入:将STL(或网格)导入MATLAB,并确保所需的函数,如DEM clusteredSphere(populateSpheres)和inpolyhedron,已添加到MATLAB路径中 3.生成多球体模型:使用DEM clusteredSphere方法从输入网格创建多球体模型 4.运行体积交点:计算多球体模型和参考几何体之间的基于体素的交点,并调整多球体模型以匹配目标体积 5.比较和可视化模型:比较多个多球体模型的体素频率,并计算多球体模型与原始表面模型之间的距离,以进行2D/3D可视化 使用案例: 骨科和生物力学体积建模 复杂结构的多球模型形状近似 基于体素拟合度量的模型选择 基于距离的患者特定几何形状和近似值分析 优点: 复杂几何的多球体模型 可扩展模型(基于体素)-自动调整到目标体积 可视化就绪输出(距离图)
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