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COM S 228 Programming
创建日期：2024-06-19 16:25:29
标签： COM S 228
Programming

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COM S 228 Programming

As always, before every assignment, make sure you read the preamble.

Monitor the OFFICIAL A10 Piazza Post for any corrections, updates, or frequently asked questions.

The assignment files are available here (they are also provided in seashell). You do not have to complete this assignment sequentially.

The content for question 1 is covered in section 12 of the notes.

The content for questions 2 and 3a are covered at the end of section 11 (trees). The content for question 3b, 3c and 4 has already been covered.

BLACK QUESTION (moderate collaboration allowed)

IMPORTANT: For this question, most of the code can be “borrowed” from the course notes. You only need to modify the provided code to meet the ADT specifications. For practice, you may want to implement the code from scratch, but you MUST ensure that the add/remove behaviour is the same as discussed in the notes. When inserting a duplicate key, do NOT do a remove followed by an insert — replace (free) the existing key/value pair with the new key/value.

For this question, you must complete the Dictionary ADT implementation (dictionary.c). There are only two primary differences between this ADT and the version in the notes:

  1. The data type of the keys and values are both void pointers (void *).
  2. The BST uses a node augmentation to store at each node the count of the number of nodes in the subtree rooted at that node. In other words, the count for a node is: 1 + count of the left child + count of right

Because of the first point above, you will need to provide functions to the dict_create function.

The dict_print function requires you to print out the underlying BST structure in one of the following orders:

INORDER

POSTORDER

Pre-order: F, B, A, D, C, E, G, I, H.

In-order: A, B, C, D, E, F, G, H, I.

Post-order: A, C, E, D, B, H, I, G, F.

 

These images are blatantly stolen from Tree_traversal page on Wikipedia, which can be consulted if you are not familiar with tree traversal terminology.

For this question, the Marmoset basic tests (“public” tests) will be identical to the simple

(assertion) test provided.

We have ALSO provided an additional advanced test, which demonstrates how you can truly have ANYTHING as a key or value. For this test, we have nested dictionaries: the values of the master dictionary are themselves a dictionary.


GOLD QUESTION (no collaboration allowed)

In the notes (here), we discuss how it can be confusing because there is section of memory and a data structure known as a “heap”. In this question you will implement a (min) heap data structure.


A minheap, like a binary search tree, is a form of binary tree. To be considered a minheap, the tree must satisfy two requirements:

 

  1. Structural Property: The binary tree is complete. In other words, a binary tree in which every level, except possibly the last, is completely filled, and all nodes are filled in as far left as
  2. Order Property: The value of every node in the tree is greater than or equal to its parent. Note the “or equal” terminology: a minheap may contain

The following are examples of a minheap:

The following violate at least one of the two requirements for a minheap:

 

Of course, in addition to the minheap data structure there is also a maxheap, where the order property specifies that the largest value is at the root of the tree.


Pseudocode for Adding an Element:

 

  1. Place the new node in the minheap in the first available location. This keeps the structure as a complete binary tree, but it might no longer be a minheap since the new node might have a smaller value than its
  2. While (the new node has a smaller value than its parent) swap the new node with its parent.

Notice that Step 2 will stop when the new node reaches the root or when the new node’s parent has a value smaller than or equal to the new node’s value.

Psuedocode for Removing the Root:

 

  1. “Backup” the value of the node at the root of the
  2. Copy the node that is farthest right on the deepest level of the tree to the root. This node is called the “out-of-place” node. Remove the original out-of-place node from the
  3. While (the out-of-place node has a value that is greater than one of its children) swap the out-of-place node with its smallest value child. If both children have the same value, swap with the left
  4. Return the answer that was saved in Step

Notice that Step 3 will stop when the out-of-place node reaches a leaf or it has a value that is smaller than or equal to all its children.

Instead of storing a minheap as a set of linked nodes, it is far more common to encode the tree in an array. This is space efficient (there is no need to store pointers at every node) and makes it substantially easier to access the node that is farthest right on the deepest

level of the tree. Encoding the tree into an array works as follows:

The root node’s value is stored at index 0.

The left child of the node at index i is stored at index i*2+1. The right child of the node at index i is stored at index i*2+2.

The parent of the node at index i is stored at index (i-1)/2 [using int division].

Using this encoding, a complete binary tree will leave no “holes” in the array. In other words, if there are n nodes in the tree, then the node that is farthest right on the deepest level of the tree is stored at index n-1. Additionally, the next available spot in the tree that retains the completeness of the tree is index n.

A minheap can be easily used to sort numbers. Simply add all of the numbers to a heap, and then when they are removed they will be removed in ascending order.

 

For your implementation, you must use the data structure we have provided. You should use the array doubling scheme, but you do not have to shrink the array. Instead of doubling the length of the array, increase the array by (2 * maxlen + 1), as this will always ensure your array can store a completely full tree (e.g., 1, 3, 7, 15, …)

You MUST follow the procedures for adding and removing from a minheap described above. In addition, your heapsort function must use a heap as described above.

The following trie stores the words “a”, “ace” and “zoo”.

To see how the word “ace” is stored, consider that the first letter of the word is ‘a’, which is also the first letter of the alphabet. From the root node, we follow the first child link to the next node. This node is part of words that start with the letter ‘a’. Because the string “a” is contained in the trie, the value of end_word at this node is TRUE (filled in). You can think of this as corresponding to the null terminator for the word “a”. The next letter in the word “ace” is ‘c’ (third letter of the alphabet) and so we follow the third child to the next node. The word “ac” is not a valid word, so the value of end_word here is false.

Because the next letter ‘e’ is the fifth letter of the alphabet, we follow the fifth child.

There are no additional letters in the word “ace”, so the end_word at this node is TRUE to indicate that word “ace” ends there.

As an exercise, review how “zoo” is similarly stored.

We can now make a few observations about the structure of the tree. Each leaf in the tree must have end_word be true, and all of its children must be NULL, otherwise it would not be a valid leaf. Also, if the value of end_word is true at the root, then the empty string “” is contained in the trie. If there are no words in the trie, then the value of root would be NULL.

For this question, the Marmoset basic tests (“public” tests) will be identical to the simple

(assertion) test provided.

  1. Next, you must create an ADT wrapper for a trie data structure known as a wordlist ADT. A wordlist, as the name suggests, stores a collection of words and provides operations to add, remove and lookup For the most part, your implementation should be very straightforward with very little code required.

The only new wrinkle is that the wordlist ADT provides a wordlist_count operation which is O(1) (whereas, trie_count is O(n)).

We have provided an I/O-based client that will allow you to test your wordlist ADT.

  1. Finally, you will complete a suggest module that provides a suggest The suggest function is passed a keyword and a function (e.g., spellcheck) that determines if a word is spelled correctly. suggest returns a wordlist containing correctly spelled words that are “close” to the keyword (have one change), as defined below. In other words, suggest generates all words with an “edit distance” of one (including transpositions/swaps). If you wish, you can read more about edit distances online (but you are not required to do so).

For each possible change, we have shown what words would be generated for the word

“keyword”.

single deletions: each letter removed from the word. For a keyword of length n, this will be n suggestions.

“eyword”, “kyword”, … “keywor”

single insertions: every possible letter (a..z) inserted before each letter in the keyword (or at the end of the keyword). For a keyword of length n, this will be 26 * (n + 1) suggestions.

“akeyword”, “bkeyword”, … “zkeyword”, “kaeyword”, “kbeyword”, …

“kzeyword”, … “keyworzd”, “keyworda”, … “keywordz”

single substitutions: each letter replaced with every possible letter. For a keyword of length n, this will be 26 * n suggestions.

“aeyword”, “beyword”, … “zeyword”, “kayword”, … “keyworz”

adjacent swaps (transpositions): each adjacent pair of letters in the word swapped. For a keyword of length n, this will be n-1 suggestions. “ekyword”, “kyeword”, … “keywodr”

As described in suggest.h, suggest will not return a wordlist that contains the original keyword or the empty string (“”). You can simply remove those two words from the wordlist before returning the wordlist. Furthermore, if the wordlist to be returned is empty, no wordlist is returned and NULL is returned instead.

While hypothetically it might be possible to accommodate a pool_realloc (e.g., there is enough space before the original allocation but not after), pool_realloc is unsuccessful (returns NULL) if a new contiguous block of the required size cannot be accommodated.

freed by a single free within pool_destroy (and no reallocs). All addresses returned from successful calls to pool_alloc (and pool_realloc) must be addressess from within that pool.

In addition to the large pool itself, the ADT must use a data structure (e.g., a linked list or an array or a tree — your choice!) to maintain the pool and keep track of which areas of the pool have been allocated and which areas are still free. To maintain this data structure, you may use malloc, realloc, and free.

The following rules must be followed for allocating memory from within the pool:

 

pool_alloc returns the lowest possible address from within the pool that can accommodate the allocation size.

If the size argument of pool_realloc is less than or equal to the original allocation, the return address is the same as the address of the original allocation (addr).

If the size argument of pool_realloc is larger than the original allocation, and there is enough available (unallocated) space after the original allocation for the increase, the return address is the same as the address of the original allocation (addr).

Otherwise, the return address for pool_realloc is the same as what would be returned from a pool_alloc for the new size. Note that pool_realloc behaves the same as the “real” realloc where it does a new allocation, moves the data, and then frees the old allocation.

 

Note that while hypothetically you could use your pool to provide arrays of other types (e.g.,

ints) you should only use char arrays in your testing to avoid hardware alignment errors.

For this question, the MARMOSET basic tests (“public” tests) will be the same as the simple I/O test client provided.

Note that the test client can only have at most 26 active allocations at once (a…z), but your implementation must support an arbitrary number of allocations.

We have also provided a “DEMO” that you can use to further test your ADT, which may inspire you to create your own tests.


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MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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