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47
report.tex
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report.tex
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@ -254,42 +254,21 @@ stops是规划得到的停靠点集合，min\_sdis(stops,p)用来计算p到stops
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\section{实验总结}
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\begin{table}[!h]
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\renewcommand\arraystretch{1.1}
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\centering
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\begin{tabular}{c c c}
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\hline
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在本次实验中，我设计了交通规划算法， 以科大校园为基础创建了地图模型，分别完成了地图模型渲染、最短路径求解、考虑人群量因素影响的环校公交线路规划以及校车站点停靠这几个问题，并取得了较好的实验结果。
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K值 & 预测错误数 & 预测错误率 \\
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\hline
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1 & 13 & 0.013742 \\
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通过这次实验，我加深了对搜索算法的理解，也增强了动手能力，收获了很多。
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\begin{itemize}
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\item 之前对算法的理解只是局限于从书本上了解到的内容，没有具体实现过。自己动手实现才知道有很多细节需要处理，有很多地方需要进行效率的优化。本以为很简单的程序，具体实现起来可能需要上百行代码，就拿这次实验为例，总的加起来400多行，虽然其中很大原因是因为自己编程水平有限造成的，但是仍然说明了从抽象层面去理解算法和具体去实现算法有很大的区别的。
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\item 模型一开始考虑的很难得话，会给实现造成困难，因此不要一开始就把问题考虑的很复杂，要由浅入深，先把模型简单化，得到初步的思路和解决方案，然后慢慢的给模型加东西，这样一步步来才更有助于解决问题。
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\item 通过这次实验能够把之前学到的很多东西都融会贯通了，比如当我看到计算速度太慢的时候，我就分析瓶颈在哪里，然后用代码调试来验证，当知道原因之后，借鉴了之前本科学过的动态规划缓存记忆历史的思路进行了优化，结果大大提高了计算速度。这种实践活动很好地把我的知识体系串了起来。
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\end{itemize}
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2 & 12 & 0.012685 \\
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3 & 11 & 0.011628 \\
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4 & 12 & 0.012685 \\
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5 & 17 & 0.017970 \\
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6 & 17 & 0.017970 \\
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7 & 20 & 0.021142 \\
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8 & 19 & 0.020085 \\
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9 & 22 & 0.023256 \\
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10 & 20 & 0.021142 \\
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\hline
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\end{tabular}
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\caption{利用欧几里得距离得到的结果}
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\end{table}
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\section{实验总结}
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通过本次实验，我基于Spark实现了KNN算法，Spark提供了非常丰富的API来支持分布式计算，同时KNN算法的计算过程又很简洁，所以本次的实验过程没有遇到太大的问题。在实现KNN算法的过程中，为了搞懂Spark的原理，需要翻阅很多的资料，虽然在完成报告2的过程中已经对Spark有了一定的了解，但是那还都是停留在基本的使用层次，大部分工作是学习官网提供的教程和网络相关介绍，只是对一些基本的概念和MLlib的API的使用有了个大概的认识。但是对于如何使用Spark 进行分布式编程还是了解的太少，这个时候我就找了些论文和书籍来看看，由于时间比较紧，虽然只看了部分内容，但是对于编程实现KNN我感觉还是起到了很大的帮助。这次实验让我感受到了Spark编程的强大以及简洁，往往几行代码就能够实现很复杂的功能。不过在实验过程中也还是遇到了很多的问题：
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但是在实验过程中仍有很多地方值得改进和完善。
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\begin{itemize}
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\item 为了简化问题，我把要处理的建筑物都当成是一个矩阵点来看待的，但这是不现实的，实际情况中，一个建筑物占地往往是不规则的。后续的实验中可以丰富建筑物的形态，并且能够正确分辨出其实际方位和格局等。
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\item 一些试验参数设置的过于简单，比如在第一章路线规划图中，当各个地点的人群量分布是一样的时候，规划得到的线路并不是太好，因为她为了避免过长的路径，而放弃了把路径延伸到更远的地点去。这应该是代价函数和启发函数的参数没有设置好，如果能够根据一些现实数据来设置参数的话效果应该会更好一些。
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\end{itemize}
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最后，要感谢祝老师的教导，通过这门课我学到了很多有用也很有趣的知识，对于我以后的研究都有很大的启发和帮助。尤其是这个实验作业对我改变最大，我一直以来对算法问题都有很大的抵触心态，感觉很枯燥，因此以前都是尽力避开这方面的研究。但是通过这次试验，我发现任何的算法都有其具体的应用场景，而任何的现实问题，都可以通过建模然后应用相应的算法来求解。并且在不断尝试和不断优化问题的解决方案的过程中，能够学到很多有用的东西。
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\end{spacing}
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\end{document}
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