Python代做编程辅导:ECM1414 Data Structures and Algorithms

Insert Sort和Merge Sort是排序算法中两个最基础的算法,虽然实际中很难用到,但是作为排序的启蒙还是不错的。

此次作业要求写出Insert Sort和Merge Sort,并根据随机输入对比两个算法的时间复杂度。分别在最好和最坏以及平均的情况下,通过不同数据量的输入进行对比实验。

LE PHUONG撰写

Requirement

In this exercise you will implement two sorting algorithms and compare their computational complexities, as measured by the number of comparisons, expressed as a function of the length of the list being sorted.

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现在提到了代写服务,肯定很多人都不会觉得陌生,就算是国内也是有着专业代写作业的服务行业的,能够为有需求的学生提供很多的帮助,不过其实代写机构在国外会更获得学生的支持,这是因为国外的学校对于平时的作业要求比较严格,为了获得更高的分数顺利毕业,不少留学生就会让代写机构帮忙完成作业,比较常见的作业代写类型,就是计算机专业了,因为对于留学生来说这个技术对于Machine Learning或者AI的代码编程要求更高,所以找代写机构完成作业会简单轻松很多,那么代写机构的水平,要怎么选择才会比较高?

1、代写机构正规专业

不论是在什么情况下,选择正规合法经营的机构肯定是首要的操作,这也是为了避免自己在找机构的时候,出现上当受骗的现象,造成自己的经济出现损失,带来的影响还是非常大的,所以需要注意很多细节才可以,所以在这样的情况下,代写机构的选择,也要选择在经营方面属于正规合法的类型,这样才可以保证服务进行的时候,不会出现各种问题,也可以减少损失的出现,而且正规合法也是代写机构的合格基础。

2、代写机构编程能力

作业的难度相信很多人都很熟悉,特别是对于AI深度学习或者是人工神经网络这种算法来说,因为要对SVM、Design Tree、线性回归以及编程有很高的要求,可以说作业的完成要求非常高,因此才会带动代写机构的发展,找专业的代写机构,一般都是会有专业的人员帮忙进行作业的完成,因为这类型的作业对专业要求比较高,因此代写机构也要具备专业能力才可以,否则很容易导致作业的完成出现问题,出现低分的评价。

3、代写机构收费情况

现在有非常多的留学生,都很在意作业的完成度,为了保证作业可以顺利的被完成,要进行的相关操作可是非常多的,代写机构也是因为如此才会延伸出来的,在现在发展也很迅速,现在选择代写机构的时候,一定要重视收费情况的合理性,因为代写作业还是比较费精力的,而且对于专业能力要求也高,所以价格方面一般会收取几千元至万元左右的价格,但是比较简单的也只需要几百元价格。

4、代写机构完成速度

大部分人都很在意代写机构的专业能力,也会很关心要具备什么能力,才可以展现出稳定的代写能力,其实专业的代写机构,对于作业完成度、作业完成时间、作业专业性等方面,都是要有一定的能力的,特别是在完成的时间上,一定要做到可以根据客户规定的时间内完成的操作,才可以作为合格专业的代写机构存在,大众在选择的时候,也可以重视完成时间这一点来。

现在找专业的CS代写机构帮忙完成作业的代写,完全不是奇怪的事情了,而且专业性越强的作业,需要代写机构帮忙的几率就会越高,代写就发展很好,需求量还是非常高的,这也可以很好的说明了,这个专业的难度以及专业性要求,才可以增加代写机构的存在。



Implement the following (see lecture notes for guidance):
lessThan(x, y), which performs a standard comparison operation (using “x < y”) and increases the global number of comparisons by 1. You should use this function instead of x < y in the following functions so that you have an easy way of counting comparison operations for the complexity analyses.

  1. insert(item, list), which uses the straight (linear) insertion method to insert an element item into a sorted list list.
    insertSort(list), which sorts a list list by insertion, using the insert function.
    split(list, list, list), which splits a list in the middle into two (see lecture notes for the detailed description).
    merge(list, list), which merges two sorted lists, and return the merged list.
    mergeSort(list), which sorts a list by recursive merging.
    randomList(n), which generates a list of random integers, of length n specified by the user (the integers should be in the range [-10n, +10n]).
  2. Use these procedures to write a function listdemo(n), which demonstrates the above functions by generating a random list of length n, sorting it by both insertSort and mergeSort, and printing the original unsorted list and the number of comparisons made in each case.
    In your hard-copy submission you should include print-outs of four runs of listdemo(n), with n = 25, 50, 75, 100.

Now for n = 200, 400, 600, 800, 1000 generate 5 random lists of length n and tabulate the number of comparisons made in sorting each list both by insertSort and by mergeSort. Plot these results in a graph, clearly distinguishing between data for insertSort and data for mergeSort (e.g., using different symbols or colours). Your graph should have n, the length of the list, plotted along the horizontal axis, and the number of comparisons plotted along the vertical axis.


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In the same graph, for each n, plot the number of comparisons for sorting (a) the already sorted list


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  1. [1, 2, 3, … , n], and (b) the reverse-sorted list [n, n – 1, … , 2, 1], using both sorting methods.
  2. Assuming that the average-case and worst-case complexity functions of the two sorting algorithms are of the form an2 (linear insert sort) and bn log2 n (merge sort) respectively, use your data to form empirical estimates of the coefficients a and b. To this end, you may and it helpful to tabulate the values of cn=n2 and cn=(n log2 n), where cn is the number of comparisons.
    Also use your data to form a hypothesis about the best-case complexities for the two algorithms.

Comment on your results, with reference to the theoretical complexity analyses of the algorithms used, best and worst cases, and the choice of complexity measure. To what extent is the theory borne out in practice? What have you learnt from this exercise that will be useful in future?


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在此对LE PHUONG对本文所作的贡献表示诚挚感谢,她在山东大学完成了计算机科学与技术专业的硕士学位,专注数据分析、数据可视化、数据采集等。擅长Python、SQL、C/C++、HTML、CSS、VSCode、Linux、Jupyter Notebook。

 
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