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COMP26120 Python code
Creation date：2024-05-16 16:47:26
Belonging category：计算机computer science
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COMP26120

Lab Exercise 3: Spellchecking (Better Trade-offs)
Duration: (almost) 4 weeks
You should do all your work in the lab3 directory of the COMP26120 2022 repository - see Blackboard
for further details. You will need to make use of the existing code in the branch as a starting point.
Important: You submit this lab via a quiz on Blackboard. This will:
1. Ask you some questions about your implementation, including the hash and tag of the commit
you want us to mark (see below for details of this).
2. Ask you to upload a zip file of the python, c or java folder you have been working in.
3. Ask you to upload a PDF report of the experiments you will run in Part C of this lab.
You can save your answers and submit later so we recommend filling in the questions for each part as
you complete it, rather than entering everything at once at the end.
Code Submission Details
You have the choice to complete the lab in C, Java or Python. Program stubs for this exercise exist
for each language. Only one language solution will be marked.
Because people had a number of issues with GitLab last year we are going to take a multiple
redundancy approach to submission of code. This involves both pushing and tagging a commit to
GitLab and uploading a zip of your code to Blackboard. By preference we will mark the code you
submitted to GitLab but if we can’t find it or it doesn’t check out properly then we will look in the
zip file on Blackboard. Please do both to maximise the chance that one of them will work.
When you submit the assignment through Blackboard you will be asked for the hash and tag of
the commit you want marked. This is to make sure the TAs can identify exactly which GitLab commit
you want marked. You tag a commit lab3 solution (we recommend you use this tag, but you do not
have to) by typing the following at the command line:
git tag lab3_solution
git push
git push origin lab3_solution
You can find the hash of your most recent commit by looking in your repository on GitLab as
shown in figure 1.
You can also find the hash for a previous commit by clicking on the “commits” link and then iden-
tifying the commit you are interested in. This is shown in figure 2.
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Figure 1: Identifying the hash of your most recent commit in GitLab
Figure 2: Identifying the hashes of previous commits in GitLab
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For this assignment, you can only get full credit if you do not use code from outside
sources. Built-in libraries in your chosen language are allowed but bear in mind that we
ask you to implement hash sets - simply using the Java HashMap class (for instance)
will not get you the marks for implementation.
Note on extension activities: The marking scheme for this lab is organised so that you can get
over 75% without attempting any of the extension activities. These activities are directed at those
wanting a challenge and may take considerable extra time or effort. This lab is designed to take
up 8 hours of student time spread over four weeks – if you have not achieved the extension activi-
ties after you have put 8 hours of work into this lab then we recommend that you do not attempt them.
Reminder: It is bad practice to include automatically generated files in source control (e.g. your git
repositories). This applies to object files (C), class files (Java), and compiled bytecode files (Python).
This is a general poor practice but for this course it can also cause issues with the online tests. Please
also do not include files containing dictionaries and queries that you have generated – the size of
these tends to cause issues with marking.
While it is fine to discuss this coursework with your friends and compare notes, the work submitted
should be your own. In particular this means you should not have copied any of the source code,
or the report. We will be using the turnitin tool to compare reports for similarities.
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Learning Objectives
By the end of this lab you will be able to:
• Describe the role of the hash function in the implementation of hash tables and describe and
compare various hash collision strategies
• Describe the basic structure of a binary search tree as well as the basic operations and their
complexities
• Write C, Java, or Python code to implement the above concepts
• Evaluate experimentally the behaviour of hash sets and binary search trees.
Introduction
The aim of this exercise is to use the context of a simple spell-checking program to explore the binary
search trees and hash tables data structures.
This exercise builds on work in Labs 1 and 2. Even if you have not done these labs we recommend
you take a look at them and their model solutions in order to familiarise yourself with the spell-checking
program and our expectations for creating and writing up experiments.
Getting the Support Code
You should then be able to see a lab3 folder in your Gitlab that contains all the support files for this
lab.
Data Structures
The spell-checking program stores a dictionary of words using a Set datatype. There are three data
structures used to implement this Set datatype in this lab. In Lab 1 we introduced this datatype but
we repeat that information here but you may also need to look online (e.g. the Wikipedia page) to
complete your knowledge.
Set. This is an abstract datatype that is used to store the dictionary of words. The operations
required for this application are:
• find whether a given value (i.e. string, alphabetic word) appears in the set.
• insert a given value in the set. Note that there is no notion of multiplicity in sets - a value
either appears or it does not. Therefore, if insert is called with a duplicate value (i.e. it is
already in the set) it can be ignored.
There would usually also be a remove function but this is not required for this application. We also
include two further utility functions, which we ask you to implement, that are useful for printing what
is happening :
• print set to list the contents of a Set.
• print stats to output statistical information to show how well your code is working.
You should have already used a dynamic array to implement this dataype in Lab 1.
In this exercise we use hash tables and binary search trees to implement the Set datatype. These
have been introduced in lectures and we describe these briefly below. You may want to look at the
recommended textbooks or online to complete your knowledge.
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Hash Table. The underlying memory structure for a hash table is an array. A hash function is
then used to map from a large ‘key’ domain to the (much smaller) set of indices into the array, where
a value can be stored. When two values in the input domain map to the same index in the array this
is called a collision and there are multiple ways to resolve these collisions. To use a hash table to
represent a set we make the key and value the same - the result is usually called a hash set.
Binary Search Tree. You can think of a tree as a linked list where every node has two ‘children’.
This allows us to differentiate between what happens in each sub-tree. In a binary search tree the
general idea is that the ‘left’ sub-tree holds values smaller than that found in the current node, and
the ‘right’ sub-tree holds larger values.
Lab 3: Better Storage
In Lab 1 we achieved a faster find function by first sorting the input. In this part we explore two data
structures that organise the data in a way that should make it faster to perform the find operation.
Part a: Hash Table Lookup
This part of the lab should take you between one and two weeks to complete (i.e., 3-4 hours).
So far our solution to a fast find function has been sorting the input and in Part b we will look
at storing it in a sorted order. In this part you will take a different approach that relies on a hash
function to distribute the values to store into a fixed size array. We have only provided you with very
basic program stubs. You need to decide what internal data structure you need etc.
In this part you need to edit the program stub in your chosen language for hash sets. You should:
1. Complete the insert function for hash sets. What should you do if the value to be inserted
already exists? Hint: this is representing a set.
2. Complete the find function. Importantly, it should follow the same logic as insertion.
3. Implement the print set function – This should print out a sensible representation of the hash
set when called (for instance when running the program with the −vv flag)
4. Implement the print stats function – This should print out a sensible set of statistics, such as
average collisions per insert, that can be used to understand how your program is performing.
This will require some extra fields in the class (Java, Python) or node struct (C).
You can find a discussion of how to test your code in the Section of these instructions on Testing
(after Part b).
The hash-value(s) needed for inserting a string into your hash-table should be derived from the
string. For example, you can consider a simple summation key based on ASCII values of the individ-
ual characters, or a more sophisticated polynomial hash code, in which different letter positions are
associated with different weights. (Warning: if your algorithm is too simple, you may find
that your program is very slow when using a realistic dictionary.) You should experiment
with the various hash functions described in the lectures and textbook and implement at least two
for comparison. One can be terrible, but one should be reasonable otherwise you are likely to have
issues when performing an experimental comparison in Part c. These can be accessed by modes al-
ready given in the program stubs and more details of these are contained in the language specific
instructions, do not change these since they are used in our testing processes. Write your code so that
you can use the −m parameter, which sets the mode.
Initially, you should use an open addressing hashing strategy so that collisions are dealt with
by using a collision resolution function. As a first step you should use linear probing, the most
5
straightforward strategy. To understand what is going on you should add code to count the num-
ber of collisions so you can calculate the average per access in print stats.
An issue with open addressing is what to do when it is not possible to insert a value. To make things
simple, to begin with you can simply fail in such cases. Your code should keep the num entries field
of the table up to date. Your insert function should check for a full table, and exit the program
cleanly should this occur. Once this is working you should increase (double?) the hash table size
when the table is getting full and then rehash into a larger table. Implement this resize and
rehash functionality. When the program is called using the −vvv flag it should print out
a message when it rehashes and then call the print stats function to show the new state
of the hash set once rehashing completes. In C, beware of memory leaks!1
Mod of negative numbers:
Most of you are likely to use the mod operator in your hash functions by doing something
like
h = x % size
to try to get a value of h that is between 0 and size - 1.
If x is negative then the behaviour of this operator varies by language: in par-
ticular in C and Java it will return a negative number. See the discussion at
https://torstencurdt.com/tech/posts/modulo-of-negative-numbers/
You might think that x cannot be negative. However, if you are adding or multiplying large
numbers together (e.g., when hashing a string) then you are likely to get integer overflow.
In hashing, we don’t care about such overflows as it is deterministic and just adds to the
‘chaos’ a bit but Java, for instance, may throw ArrayIndexOutOfBoundsException if a
negative number is passed to the hash data structure, if you have not accounted for this
possibility in your implementation.
In C you could use an unsigned integer to make sure that you only have positive numbers.
Once you have completed this implementation you should look at the Blackboard submission form
where you will be asked the following questions about Part a.
1. What did you implement as your first hash function? How does it work? (No more than 100
words)
2. How do find and insert with linear probing work? Illustrate your explanation with a sample of
your code for insert - this may be tidied up a bit for presentation but should clearly be the same
as the code you have submitted. (no more than 200 words, not counting the code snippet).
3. What did you implement as your second hash function? How does it work? (No more than 100
words)
4. Did you implement rehashing? If so briefly explain your implementation including explaining
when you choose to rehash. (No more than 200 words, you may include code snippets which do
not count towards the word count)
Part b: Binary Search Tree Lookup
This part of the lab should take you a week to complete (i.e., 2 hours).
1One can also get memory leaks in memory managed languages but these are more semantic e.g. preserving a
reference to something you will never use again.
6
In this part you need to edit the program stub in your chosen language for binary search trees.
You should:
1. Complete the insert function by comparing the value to insert with the existing value.
2. Complete the find function. Importantly, it should follow the same logic as insertion.
3. Implement the print set function – This should print out a sensible representation of the hash
set when called (for instance when running the program with the −vv flag)
4. Implement the print stats function – This should print out a sensible set of statistics, such as
the height and average number of comparisons per insert and find.
Once you have completed this implementation you should look at the Blackboard submission form
where you will be asked the following question about Part b.
1. How do find and insert for binary trees work? Include your code for insert and relate your
explanation to this code. (No more than 200 words, not including the code snippet)
In this lab exercise we will stop there with trees. However, it is worth noting that this implementation
is not optimal. What will happen if the input dictionary is already sorted? In the next lab we will be
exploring self-balancing trees.
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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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 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COMSM0093 COMP281 AFM 333 IBUS1001 CP2501 Math 105A VE320 TEC101 SOCY2166 COM240 EFIM20033 ENGI4467 EFIM20034 EC 979 BE432 7EJ502 CSSE6400 COMP90025 Econ 312 ENG2031 ECO0006 EXERSCI 207 CAN209 ECO 137W FT312 ECO 2199 MATH32052 BDW4213 APS 360 SAS 6 CHME0017 PO92Q Economics 01AV MATH39512 MATH 5486 COMP8620 ISE102 ECON235 ECON 206 FI 345 PHAY0020 MATH0099 MATH0085 EFB106 PHY 134 MAE115 circuits Chemistry CSE12 MATH-UA 233 PHAS0038 COMP2140 CONMGNT 7049 COMP151101 ECON 485 ACX5903 ENGN6536 ARCH1162 Law PHAS0042 CCF1C03 MATH 351 Phys 139 AG942 MGT001371 APH106 ECON1051 MA4AM MA3Z7 NUMBER THEORY REST0006 SESS0026 STAT0023 TABL 2741 CMP4008Y EFIM10015 Acct 560 PSYC735 GEOG 5 ACCT3104 CSEC5616 PHIL1012 ITLS6111 ECON8022 5CCM226A compX123 MA4XJ R001 EWB LLP120 COMP643 ENG 103 ECON 23950 ACCT 207 FINC 616 Bio99 Math 137A Psychology BCPM0054 MATH20212 EC3450 CENV6141 PSY 205 CE335 CS365 Calculus ACSD INFT6800 MCIT INFO6030 PHYS 111LV H63EEJ ECON2151 ENGI 2181 MTRX1701 PLIT08009 CS240 Math 270 CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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