COMP20003代写、代做c/c++，Java语言编程
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0. General
You must read fully and carefully the assignment specification and instructions.
Course: COMP20003 Algorithms and Data Structures @ Semester 2, 2024
Deadline Submission: Friday 6th September 2024 @ 11:59 pm (end of Week 7)
Course Weight: 15%
Assignment type: individual
ILOs covered: 1, 2, 3, 4
Submission method: via ED
Purpose
The purpose of this assignment is for you to:
Improve your proficiency in C programming and your dexterity with dynamic memory
allocation.
Demonstrate understanding of a concrete data structure (radix tree) and implement a set of
algorithms.
Practice multi-file programming and improve your proficiency in using UNIX utilities.
Practice writing reports which systematically compare the efficiency of different data structures.
Walkthrough1. Background
In Assignment 1 you implemented a dictionary using a linked list. Many applications of dictionaries
cannot assume that the user will query in the correct format. Take Google search as an example. If
someone is so excited to check the recent Olympics updates, they may accidentally search something
similar to this:
Thankfully, there is enough context in the original query that the intended search result is
recommended back to the user. In Assignment 2 you will be extending your implementation to
account for misspelled keys when querying your dictionary.2. Your Task
Assignment:
Overall, you will create a partial error-handling dictionary (spellchecker) using a radix tree.
You will be using the same dataset as Assignment 1.
Users will be able to query the radix tree and will get either the expected key, or the closest
recommended key.
You will then write a report to analyse the time and memory complexity of your Assignment 1
linked list compared to your radix tree implementation.
C Implementation:
Your programs will build the dictionary by reading data from a file. They will insert each
suburb into the dictionary (either the linked list (Stage 3) or radix tree (Stage 4)).
Your programs will handle the search for keys. There are three situations that your programs
must handle:
Handle exact matches: output all records that match the key (Stage 3 and 4).
Handle similar matches: if there are no exact matches, find the most similar key and
output its associated records (Stage 4 only).
Handle no matches being found: if neither exact nor similar matches are found, indicate
that there is no match or recommendation (Stage 3 and 4).
Your programs should also be able to populate and query the dictionary with the Assignment
1 linked list approach and the radix tree approach.
Your programs should also provide enough information to compare the efficiency of the
linked list with the radix tree with a operation-based measure that ignores less important runtime
factors (e.g. comparison count).
It is recommended to use your own solution of Assignment 1, and extend it for Assignment 2. But, we will also
provide a solution for Assignment 1 if you prefer to work with our code.3. An Introduction to Tries and Radix Trees
First, it is important to establish the difference between a Trie and a Tree. This is best illustrated
with an example. One example of a tree is a binary search tree (BST), where each node in the tree
stores an entire string, as illustrated below. The nodes are ordered and allow easy searching. When
searching in the BST, we compare the query with the entire string at each node, deciding whether to
switch to the left or right subtree or stop (if the subtree is empty) based on the result of the
comparison.
A trie is slightly different. It has multiple names, such as retrieval tree or prefix tree. In a trie, the
traversal of the tree determines the corresponding key. For the same strings as above with one
letter per node, it would look like:Tries allow for quick lookup of strings. Querying this trie with the key "hey" would find no valid path
after the "e" node. Therefore, you can determine that the key "hey" does not exist in this trie.
A radix trie is a subtype of trie, sometimes called a compressed trie. Radix trees do not store a single
letter at each node, but compress multiple letters into a single node to save space. At the character
level, it would look like this:
Radix tries can again be broken down into different types depending on how many bits are used to
define the branching factor. In this case, we are using a radix (r) of 2, which means every node has 2
children. This is accomplished by using 1 bit of precision, so each branch would be either a 0 or 1.
This type of radix trie (with r=2) is called a Patricia trie. Another example of a radix trie with r=4
would have 4 children, determined by the binary numbers 00, 01, 10, 11. The radix is related to the
binary precision by r = 2x where x is the number of bits used for branching.
Radix trees benefit from less memory usage and quicker search times when compared to a regular
trie.
While these visual representations are at the character level, a Patricia trie is implemented using
the binary of each character. Each node in the trie stores the binary prefix at the current node,
rather than the character prefix. For example, we can insert 3 binary numbers into a PATRICIA trie:
0000, 0010 and 0011.Combining the previous worded example with the binary representation gives us a patricia tree of the
form:
You should trace along each path and validate that the stored strings are the same as the example
above. Each character is represented over 8 bits, and the last character is followed by an end of string
00000000 8 bit character, i.e. NULL .
It is important to note that these representations only show the prefix at each node. An actual
implementation will require more information within a node struct. To see this, look at the "extra
insertion example" slide.
Implementation TipsTo implement the Radix Tree, a recommended approach is to use the functions provided below.
1. Get a particular bit from a given character getBit(key, n) and
2. Extract a stem from a given key splitStem(key, start, length) which extracts a certain
number of bits from a given key.
If you want to understand the code provided, you need to understand the following bit operators
over numbers a and b:
Operator
a & b
a | b
a |= b
a << b
a >> b
Meaning
Where both corresponding bits in a and b are 1,
give a 1 in the output, else give a 0 in that position
Where either corresponding bit in a and b is 1,
give a 1 in the output, else give a 0 in that position.
Compute a | b and assign the result of the operation to a.
For all bits in a, move each bit b bits to the left.
For all bits in a, move each bit b bits to the right.
Use The Following Functions
The two recommended functions, getBit and splitStem are provided here as they can typically be
used without making major changes to their structure. Diagrams explaining their algorithmic
structure and example outputs are also provided.
getBit
This function works out the byte which the bit we're trying to extract lies in and then works out which
bit it will be in. The offset is not simply the remainder of the division because the bits are filled and
split from the left (highest value bit) rather than the right.
/* Number of bits in a single character. */
#define BITS_PER_BYTE 8
/* Helper function. Gets the bit at bitIndex from the string s. */
static int getBit(char *s, unsigned int bitIndex);
static int getBit(char *s, unsigned int bitIndex){
assert(s && bitIndex >= 0);
unsigned int byte = bitIndex / BITS_PER_BYTE;
unsigned int indexFromLeft = bitIndex % BITS_PER_BYTE;
/*
Since we split from the highest order bit first, the bit we are interested
will be the highest order bit, rather than a bit that occurs at the end of the
number.
*/
unsigned int offset = (BITS_PER_BYTE - (indexFromLeft) - 1) % BITS_PER_BYTE;
unsigned char byteOfInterest = s[byte]; unsigned int offsetMask = (1 << offset);
unsigned int maskedByte = (byteOfInterest & offsetMask);
/*
The masked byte will still have the bit in its original position, to return
either 0 or 1, we need to move the bit to the lowest order bit in the number.
*/
unsigned int bitOnly = maskedByte >> offset;
return bitOnly;
}
An example diagram of what the calculation of the offset does from each possible value of
indexFromLeft is shown here:
Offset
7 indexFromLeft
0
6
1
5
2
4
3
3
4
2
5
1
6
0
7 offset
An example for getting bit number 12 from the string "hi" could be called using getBit("hi", 12) .
An example of how the calculation occurs is shown below:getBit
Input
IndexFromLeft
Offset
byteOfInterest
offsetMask
maskedByte
bitOnly
s
bitIndex
0b01101000 01101001 00000000
h i \0
s[byte]
12
bitIndex % 8 bitIndex / 8
4
4 7 indexFromLeft
0
6
1
5
3 2
3
4
2
5
1
6
0
7 offset
1 << offset
maskedByte >> offset
byte = 1
0 1 1 0 1 0 0 1
byteOfInterest & offsetMask
0 0 0 0 0 0 0 1
0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1
createStem
This function creates a new collection of bytes from an existing collection of bytes. Conceptually this
is extracting a substring from a string and returning the new substring, but the returned value will not
necessarily be NULL terminated and may not start from the beginning of a character - so may not be
printable as a string for multiple reasons.
/* Allocates new memory to hold the numBits specified and fills the allocated
memory with the numBits specified starting from the startBit of the oldKey
array of bytes. */
char *createStem(char *oldKey, unsigned int startBit, unsigned int numBits);
char *createStem(char *oldKey, unsigned int startBit, unsigned int numBits){
assert(numBits > 0 && startBit >= 0 && oldKey);
int extraBytes = 0; if((numBits % BITS_PER_BYTE) > 0){
extraBytes = 1;
}
int totalBytes = (numBits / BITS_PER_BYTE) + extraBytes
char *newStem = malloc(sizeof(char) * totalBytes);
assert(newStem);
for(unsigned int i = 0; i < totalBytes; i++){
newStem[i] = 0;
}
for(unsigned int i = 0; i < numBits; i++){
unsigned int indexFromLeft = i % BITS_PER_BYTE;
unsigned int offset = (BITS_PER_BYTE - indexFromLeft - 1) % BITS_PER_BYTE;
unsigned int bitMaskForPosition = 1 << offset;
unsigned int bitValueAtPosition = getBit(oldKey, startBit + i);
unsigned int byteInNewStem = i / BITS_PER_BYTE;
newStem[byteInNewStem] |= bitMaskForPosition * bitValueAtPosition;
}
return newStem;
}
If the number of bits to extract matches exactly on a character border (i.e. if it is a multiple of 8) an
extra character is not needed, but otherwise, an extra character is needed - with the remainder of the
value being filled with 0's.
It works by fetching each bit from the given byte array and setting it in the new byte array.
An example of createStem("hello", 0, 12) is shown here:
Input
Processing
Output
oldKey
startBit
0b01101000 01100101 01101100 01101100 01101111 00000000
h e l l o \0
numBits
0
12
0b01101000 0110 0101 01101100 01101100 01101111 00000000
h e l l o \0
0b01101000 0110xxxx
h `-o
newStem
An example of createStem("hello", 12, 15) is shown here:Input
Processing
Output
oldKey
startBit
0b01101000 01100101 01101100 01101100 01101111 00000000
h e l l o \0
numBits
12
15
0b01101000 0110 0101 01101100 011 01100 01101111 00000000
h e l l o \0
0b0101 01101100 011x
e l `-[DEL]
newStem
Note: The result of createStem is not a string, strlen will not reliably count its length and printing it as a
string may lead to unexpected results.4. Extra Insertion Example and Insertion Process
This extra slide shows an additional example for inserting data into a prefix tree. This shows the
process of inserting the seven strings:
"a" 01100001 00000000
"b" 01100010 00000000
"c" 01100011 00000000
"d" 01100100 00000000
"e" 01100101 00000000
"f" 01100110 00000000
"g" 01100111 00000000
The first inserted string ("a" - 1100001 00000000) has no splits, so the prefix is the whole string:
Root Node
prefixBits 16
prefix 0b01100001 00000000
a \0
branchA NULL
branchB NULL
data ...
The second inserted string ("b" - 1100010 00000000) first differs in the second last bit of the first
character, so the tree splits at that point. The root node's prefix only determines the first 6 bits of the
string - narrowing all strings in the the tree to starting with a character which has the first six bits
011000. Therefore, the root node can lead to any character from ` to c. Check this ASCII-Binary table
(numbers 96-99).The third inserted string ("c" - 01100011 00000000) produces a new difference in the last bit (of the
first character) in the node completing the string "b". After the split, the right branch of the second
level node can lead the (first) character to be 'b' and 'c'.The fourth inserted string ("d" - 01100100 00000000) produces a difference in the root node, as the
(first) character differs in its third last bit - causing a split in the root node after 5 bits. After the split,
the root node only narrows the letters in the first character of all items in the tree from the character
'`' to the character 'g'.The fifth inserted string ("e"- 01100101 00000000) produces a new difference in the node completing
the string "d" in the last bit of the (first) character in the string. This causes a split, the third last and
second last bits for 'd' and 'e' are still the same, so the node retains these two bits.The sixth inserted string ("f" - 01100110 00000000) produces a split in the node deciding between
the (first) character being 'd' or 'e', with the second last bit of the character (the second bit in the node)
being different.Then the final inserted string ("g" - 01100111 00000000) produces a split in the node completing the
string "f", with the last bit of the first character differing.Insertion
In addition to the two functions provided to work over bit representations of strings, you will likely
have some kind of insertion operation to handle the example above.
The code logic details will likely vary depending on your data structure and implementation of
Assignment 1. Therefore, this diagram is provided as an overarching diagram to help you through the
task of inserting a new string.Insert into Tree
Check if in Tree Already
Not in Tree Already
Start
Start at Root
See if All Bits Match
Check Next Bit
all match
Not in Tree
Bit mismatch or
more bits in tree node
than remaining in string
Node Found
all exhausted in search string
Advance Down branchB
1
Advance Down branchA
0 Start at Root
Insert into
Found Node
See if All Bits Match
Check Next Bit
all match
Split Node
Insert Mismatched Stem
as New Branch Child
and Old Stem as Other
Branch, Leaving Common
Stem in Node
Bit mismatch or
more bits in tree node
than remaining in string
Advance Down branchB
1
Advance Down branchA
0Inserting into the found node simply adds to the list of data in the node and the split node step will
split at the common bit position, leaving the common stem in the node, placing the stem from the
inserted string in a newly created node down the appropriate branch (A if a 0 is the following bit in
the string, and B if it is a 1) and the stem from the existing string down the other branch in a newly
created branch. After this split - the insertion is complete.
You may find it useful to set the data in the existing node to NULL to avoid the same data existing in multiple
places in your tree.
By storing the \0 character, we do not need to worry about the case where the string continues after an
existing node is exhausted - we will always reach a mismatch or the string will match, simplifying both the
search and insertion logic.5. Dataset and Assumptions
Note: this slide is identical to the Assignment 1 dataset and assumptions.
The opendatasoft website provides numerous databases, enabling organisations and individuals to
seamlessly access and share data. The dataset used in this assignment is a simplified version derived
from the "State Suburbs - Australia" database on that website which originates from ABS data. You
can browse or visualize the original database using the link above. The whole simplified dataset, as
well as smaller samples, can be found in the Dataset Download slide.
The processed dataset can be found in the Dataset Download slide. You aren't expected to perform
this processing yourself.
The provided dataset has the following 10 fields:
COMP20003 Code - The record IDs added by the COMP20003 teaching team (integer)
Official Code Suburb - The suburb ID (integer)
Official Name Suburb - The name of the suburb (string)
Year - The year the information was recorded for (integer)
Official Code State - The IDs of the corresponding states (string)
Official Name State - The names of the corresponding states (string)
Official Code Local Government Area - The IDs of the corresponding local government areas (string)
Official Name Local Government Area - The names of the corresponding local government areas (string)Latitude - The latitude (y) of the suburb centre (double)
Longitude - The longitude (x) of the suburb centre (double)
The provided text gives a detailed description of the data format and assumptions for the
assignment. Here's a summary of the key points:
Fields and Data Types:
COMP20003 Code , Official Code Suburb , Year : integers
Longitude , Latitude : doubles
all other fields: strings (may contain spaces and commas)
Special Cases:
Official Code State , Official Code Local Government Area : comma-separated lists of
integers (treated as strings for this assignment).
comma-containing strings: enclosed in double quotes ("") within the CSV file, the quotes are
removed when stored in your program according to standard CSV rules.
CSV File Assumptions:
Note that normally a suburb lies entirely inside a single state, as well as a single local government
area, but this is not a universal rule. Suburbs might be in multiple local governments areas and
multiple states. This is why the fields Official Code State and Official Code Local Government
Area are not classified as integers. Each of these fields is actually a comma-separated list of integers.
For the purposes of this assignment, it is fully sufficient to consider this as a string.
This data is in CSV format, with each field separated by a comma. For the purposes of the
assignment, you may assume that:
the input data is well-formatted,
input files are not empty,
input files include a header line that contains the filled headers in the CSV format ,
any field containing a comma will begin with a double quotation mark ( " ) and end with a
quotation mark ( " ),
each field contains at least one character and at most 127 characters,
fields always occur in the order given above, and that
the maximum length of an input record (a single full line of the CSV) is 511 characters.
In this dataset, CSV conventions are followed for strings containing commas. These strings are
enclosed in double quotation marks (") at the beginning and end. However, when processing the data
and storing it in your dictionary, these quotation marks are removed. You can safely assume there
won't be any double quotes within the actual string values stored in the dictionary so your program
does not have to handle this case.Processing Assumptions:
The column headers (such as COMP20003 Code and Official Name Suburb ) can be assumed
to match the names expected and can be used directly in the output as the labels for each field.
The number of columns (10 in this assignment), as well as the order, text and data type of each
column are assumed to be known. While it's possible to automatically determine this
information, it's beyond the scope of this assignment.6. Implementation Details
Assignment 2 will involve roughly three new stages.
Stage 3 will extend your Assignment 1 solution to count the number of comparisons it
takes to find a given key, i.e. a search query.
Stage 4 will implement the lookup of data by a given key (Official Name Suburb) in a Patricia
tree.
Stage 5 is a report about the complexity of a linked list compared to the Patricia tree.
Stage 3: Linked List Search Comparisons
In this stage, you will extend your Assignment 1 solution to count the number of binary and string
comparisons and node accesses used when searching for a given key.
Your Makefile will produce an executable called dict3 . The command line arguments are identical
to assignment 1 but with the stage being 3 instead.
The first argument will be the stage, for this part, the value will always be 3 (for linked list
lookup with comparison count added).
The second argument will be the name of the input CSV data file.
The third argument will be the name of the output file.
Your dict3 program should function exactly the same as assignment 1's stage 1. You will add the
functionality to count comparisons when searching for a key which will be added to the stdout
output. For this stage, and this stage only, you may assume:
Each character compared adds exactly 8 bits to the bit comparison count.
The node access is incremented once per accessed linked list node.
Each string comparison, even if a mismatch occurs on the first character, is 1 string
comparison.
You should create the functionality to store comparisons with stage 4 in mind. You will also be
recording comparisons in the Patricia tree implementation, so your code should be easily applied to
both. For this reason, you may want to extend your search function to include a pointer to a struct
that holds information about the query and results.
Important Notes:
You do not have to implement any spellchecking in the linked list. Your only task at this stage is
to add functionality to count comparisons. You do not need to add this functionality to the deletion of nodes. Only the search will be
assessed. You should, however, be able to recognize how to implement this in your deletion
code.
Example Execution
make -B dict3
./dict3 3 tests/dataset_1.csv output.txt < tests/test1.in
Example Output
The expected output to the output file would look like:
Carlton -->
COMP20003 Code: 9773, Official Code Suburb: 20495, Official Name Suburb: Carlton, Year: 2021, Official Code State: 2,
South Melbourne -->
With the following printed to stdout:
Carlton --> 1 records - comparisons: b64 n1 s1
South Melbourne --> NOTFOUND
Note: the bit comparisons are 8 times the character comparisons here
Stage 4: Patricia Tree Spellchecker
In Stage 4, you will implement the another dictionary allowing the lookup of data by key ( Suburb
Name ).
Your Makefile should produce an executable program called dict4 . This program should take three
command line arguments:
The first argument will be the stage, for this part, the value will always be 4 (for Patricia tree
lookup and comparison counting).
The second argument will be the name of the input CSV data file.
The third argument will be the name of the output file.
Your dict4 program should:
Read the data in the same format as assignment 1 and stage 1, and save each entry in a Patricia
tree using the Official Name Suburb as the key.
The program should:Accept Official Name Suburb s from stdin , and search the tree for the matching key.
Since your program should act as a simple spellchecker, an exact match may not be
found. Follow the process "mismatch in key" as outlined below to implement
spellchecking logic.
You must output all matching records to the output file, where a matching record may be
an exact match, or the closest match determined by the mismatch key algorithm. If no
matches for the query are found (i.e. the trie is empty), your program should output
NOTFOUND . When outputting data records, you should follow the guidelines described in
the slide Dataset and Assumptions.
In addition to outputting the record(s) to the output file, the number of records found and
the comparisons and node accesses when querying should be output to stdout .
Mismatch in Key
Since a Patricia tree is a type of prefix tree, any mismatch that occurs will contain the same prefix. For
example, say we are searching for "trie", but our dictionary contains "tree". Here, a mismatch occurs
at some binary bit in the third character. This mismatch will only occur once we are past the node in
our tree that contains all prefixes with "tr". Therefore, when the mismatch occurs, we can get all
descendants at the mismatched node and calculate the most likely candidate using the edit distance
of the query and a key. This way, if we query "trie", we should return "tree". This is explained with the
following graphic:
The patricia tree contains 4 strings as listed. If we query "trie", the mismatch happens on the bit
highlighted in red. At this node, the only descendant data is "tree", and is therefore determined to be
the closest match.If we query "Tree", the mismatch happens on the third bit. This means that all descendant data is
taken to be possible candidates, so all 4 keys have their edit distance against the query calculated.
"tree" is determined to be the closest match with an edit distance of 1, and is returned.
Tips:
The results struct you created to hold comparisons can hold matching data
A node access count should be incremented each time a new node of the trie is looked at.
If two strings have an equal edit distance, return all results for the first suburb name
encountered with that edit distance (this will be the alphabetically earliest result).
Edit Distance
In Stage 4, you will need to calculate the edit distance of multiple strings to determine the closest
match. You are welcome to use the code here to calculate the edit distance - you do not have to worry
about its complexity:
int editDistance(char *str1, char *str2, int n, int m);
int int min(int a, int b, int c);
/* Returns min of 3 integers
reference: https://www.geeksforgeeks.org/edit-distance-in-c/ */
int min(int a, int b, int c) {
if (a < b) {
if(a < c) {
return a;
} else {
return c;
}
} else {
if(b < c) {
return b;
} else {
return c;
}
}
}
/* Returns the edit distance of two strings
reference: https://www.geeksforgeeks.org/edit-distance-in-c/ */
int editDistance(char *str1, char *str2, int n, int m){
assert(m >= 0 && n >= 0 && (str1 || m == 0) && (str2 || n == 0));
// Declare a 2D array to store the dynamic programming
// table
int dp[n + 1][m + 1];
// Initialize the dp table
for (int i = 0; i <= n; i++) {
for (int j = 0; j <= m; j++) {
// If the first string is empty, the only option // is to insert all characters of the second
// string
if (i == 0) {
dp[i][j] = j;
}
// If the second string is empty, the only
// option is to remove all characters of the
// first string
else if (j == 0) {
dp[i][j] = i;
}
// If the last characters are the same, no
// modification is necessary to the string.
else if (str1[i - 1] == str2[j - 1]) {
dp[i][j] = min(1 + dp[i - 1][j], 1 + dp[i][j - 1],
dp[i - 1][j - 1]);
}
// If the last characters are different,
// consider all three operations and find the
// minimum
else {
dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1],
dp[i - 1][j - 1]);
}
}
}
// Return the result from the dynamic programming table
return dp[n][m];
}
Example Execution
make -B dict4
./dict4 4 tests/dataset_15.csv output.txt < tests/test1.in
Example Output
Output file:
Carlton -->
COMP20003 Code: 9773, Official Code Suburb: 20495, Official Name Suburb: Carlton, Year: 2021, Official Code State: 2,
South Melbourne -->
COMP20003 Code: 390, Official Code Suburb: 22313, Official Name Suburb: South Wharf, Year: 2021, Official Code State:
stdout:
Carlton --> 1 records - comparisons: b58 n4 s1
South Melbourne --> 1 records - comparisons: b52 n5 s1Stage 5: Complexity Report
The final deliverable for this assignment is a small report analyzing the complexity of both
dictionaries. You will run various test cases through your program to test its correctness and also to
examine the number of key comparisons used when searching keys across different dictionaries. You
will report on the number of comparisons used by your linked list and patricia tree implementations
for different data file inputs and key prefixes. You will compare these results with what you expect
based on theory (Big-O).
Your approach should be systematic, varying the size of the files you use and their characteristics (e.g.
sorted/unsorted, duplicates, range of key space, length of prefix, etc.), and observing how the number
of key comparisons varies given different sizes of key prefixes. Looking at statistical measures about
the results of your tests may help.
UNIX programs such as sort (in particular -R ), head , tail , cat and awk may be valuable. You
may also find small C programs (or additional "Stages") useful, the test data you produce and use
does not have to be restricted to the dataset provided.
Additionally, though your C code should run on Ed, you may like to run offline tests or other work
outside of Ed and are welcome to do so.
You will write up your findings and submit them along with your Assignment Submission in the
Assignment Submission Tab.
Make sure to click the "Mark" button after uploading your report. It is often the case for results to be released
and more than a handful of students to find an unexpected 0 mark for the report due to not technically
submitting it for marking.
You are encouraged to present the information you've collected in appropriate tables and graphs.
Select informative data, more is not always better.
In particular, you should present your findings clearly, in light of what you know about the data
structures used in your programs and in light of their known computational complexity. You may find
that your results are what you expected, based on theory. Alternatively, you may find your results do
not agree with theory. In either case, you should state what you expected from the theory, and if
there is a discrepancy you should suggest possible reasons. You might want to discuss space-time
trade-offs, if this is appropriate to your code and data.
It is recommended to look at extreme cases where each option will do better or worse - e.g. think
about dense data where the Patricia tree will save many bit comparisons and character sparse data
where string comparisons would terminate quickly in both data structures. It is also highly
recommended to look at the theory covered so far and how you can match it - i.e. if a particular
complexity is expected, that it appears at large input sizes and represents the growth behaviour - not
just single point comparisons. Similarly, pay particular attention to the meaning of variables,
character comparisons, string comparisons and bit comparisons may all have different behaviours and certain investigations might expect different relations on these.
You are not constrained to any particular structure in this report, but a well laid out plan with a strong
structure tends to lead to the best results. A more general and helpful guiding structure is shown in
the Additional Support slide, but you
0. General
You must read fully and carefully the assignment specification and instructions.
Course: COMP20003 Algorithms and Data Structures @ Semester 2, 2024
Deadline Submission: Friday 6th September 2024 @ 11:59 pm (end of Week 7)
Course Weight: 15%
Assignment type: individual
ILOs covered: 1, 2, 3, 4
Submission method: via ED
Purpose
The purpose of this assignment is for you to:
Improve your proficiency in C programming and your dexterity with dynamic memory
allocation.
Demonstrate understanding of a concrete data structure (radix tree) and implement a set of
algorithms.
Practice multi-file programming and improve your proficiency in using UNIX utilities.
Practice writing reports which systematically compare the efficiency of different data structures.
Walkthrough1. Background
In Assignment 1 you implemented a dictionary using a linked list. Many applications of dictionaries
cannot assume that the user will query in the correct format. Take Google search as an example. If
someone is so excited to check the recent Olympics updates, they may accidentally search something
similar to this:
Thankfully, there is enough context in the original query that the intended search result is
recommended back to the user. In Assignment 2 you will be extending your implementation to
account for misspelled keys when querying your dictionary.2. Your Task
Assignment:
Overall, you will create a partial error-handling dictionary (spellchecker) using a radix tree.
You will be using the same dataset as Assignment 1.
Users will be able to query the radix tree and will get either the expected key, or the closest
recommended key.
You will then write a report to analyse the time and memory complexity of your Assignment 1
linked list compared to your radix tree implementation.
C Implementation:
Your programs will build the dictionary by reading data from a file. They will insert each
suburb into the dictionary (either the linked list (Stage 3) or radix tree (Stage 4)).
Your programs will handle the search for keys. There are three situations that your programs
must handle:
Handle exact matches: output all records that match the key (Stage 3 and 4).
Handle similar matches: if there are no exact matches, find the most similar key and
output its associated records (Stage 4 only).
Handle no matches being found: if neither exact nor similar matches are found, indicate
that there is no match or recommendation (Stage 3 and 4).
Your programs should also be able to populate and query the dictionary with the Assignment
1 linked list approach and the radix tree approach.
Your programs should also provide enough information to compare the efficiency of the
linked list with the radix tree with a operation-based measure that ignores less important runtime
factors (e.g. comparison count).
It is recommended to use your own solution of Assignment 1, and extend it for Assignment 2. But, we will also
provide a solution for Assignment 1 if you prefer to work with our code.3. An Introduction to Tries and Radix Trees
First, it is important to establish the difference between a Trie and a Tree. This is best illustrated
with an example. One example of a tree is a binary search tree (BST), where each node in the tree
stores an entire string, as illustrated below. The nodes are ordered and allow easy searching. When
searching in the BST, we compare the query with the entire string at each node, deciding whether to
switch to the left or right subtree or stop (if the subtree is empty) based on the result of the
comparison.
A trie is slightly different. It has multiple names, such as retrieval tree or prefix tree. In a trie, the
traversal of the tree determines the corresponding key. For the same strings as above with one
letter per node, it would look like:Tries allow for quick lookup of strings. Querying this trie with the key "hey" would find no valid path
after the "e" node. Therefore, you can determine that the key "hey" does not exist in this trie.
A radix trie is a subtype of trie, sometimes called a compressed trie. Radix trees do not store a single
letter at each node, but compress multiple letters into a single node to save space. At the character
level, it would look like this:
Radix tries can again be broken down into different types depending on how many bits are used to
define the branching factor. In this case, we are using a radix (r) of 2, which means every node has 2
children. This is accomplished by using 1 bit of precision, so each branch would be either a 0 or 1.
This type of radix trie (with r=2) is called a Patricia trie. Another example of a radix trie with r=4
would have 4 children, determined by the binary numbers 00, 01, 10, 11. The radix is related to the
binary precision by r = 2x where x is the number of bits used for branching.
Radix trees benefit from less memory usage and quicker search times when compared to a regular
trie.
While these visual representations are at the character level, a Patricia trie is implemented using
the binary of each character. Each node in the trie stores the binary prefix at the current node,
rather than the character prefix. For example, we can insert 3 binary numbers into a PATRICIA trie:
0000, 0010 and 0011.Combining the previous worded example with the binary representation gives us a patricia tree of the
form:
You should trace along each path and validate that the stored strings are the same as the example
above. Each character is represented over 8 bits, and the last character is followed by an end of string
00000000 8 bit character, i.e. NULL .
It is important to note that these representations only show the prefix at each node. An actual
implementation will require more information within a node struct. To see this, look at the "extra
insertion example" slide.
Implementation TipsTo implement the Radix Tree, a recommended approach is to use the functions provided below.
1. Get a particular bit from a given character getBit(key, n) and
2. Extract a stem from a given key splitStem(key, start, length) which extracts a certain
number of bits from a given key.
If you want to understand the code provided, you need to understand the following bit operators
over numbers a and b:
Operator
a & b
a | b
a |= b
a << b
a >> b
Meaning
Where both corresponding bits in a and b are 1,
give a 1 in the output, else give a 0 in that position
Where either corresponding bit in a and b is 1,
give a 1 in the output, else give a 0 in that position.
Compute a | b and assign the result of the operation to a.
For all bits in a, move each bit b bits to the left.
For all bits in a, move each bit b bits to the right.
Use The Following Functions
The two recommended functions, getBit and splitStem are provided here as they can typically be
used without making major changes to their structure. Diagrams explaining their algorithmic
structure and example outputs are also provided.
getBit
This function works out the byte which the bit we're trying to extract lies in and then works out which
bit it will be in. The offset is not simply the remainder of the division because the bits are filled and
split from the left (highest value bit) rather than the right.
/* Number of bits in a single character. */
#define BITS_PER_BYTE 8
/* Helper function. Gets the bit at bitIndex from the string s. */
static int getBit(char *s, unsigned int bitIndex);
static int getBit(char *s, unsigned int bitIndex){
assert(s && bitIndex >= 0);
unsigned int byte = bitIndex / BITS_PER_BYTE;
unsigned int indexFromLeft = bitIndex % BITS_PER_BYTE;
/*
Since we split from the highest order bit first, the bit we are interested
will be the highest order bit, rather than a bit that occurs at the end of the
number.
*/
unsigned int offset = (BITS_PER_BYTE - (indexFromLeft) - 1) % BITS_PER_BYTE;
unsigned char byteOfInterest = s[byte]; unsigned int offsetMask = (1 << offset);
unsigned int maskedByte = (byteOfInterest & offsetMask);
/*
The masked byte will still have the bit in its original position, to return
either 0 or 1, we need to move the bit to the lowest order bit in the number.
*/
unsigned int bitOnly = maskedByte >> offset;
return bitOnly;
}
An example diagram of what the calculation of the offset does from each possible value of
indexFromLeft is shown here:
Offset
7 indexFromLeft
0
6
1
5
2
4
3
3
4
2
5
1
6
0
7 offset
An example for getting bit number 12 from the string "hi" could be called using getBit("hi", 12) .
An example of how the calculation occurs is shown below:getBit
Input
IndexFromLeft
Offset
byteOfInterest
offsetMask
maskedByte
bitOnly
s
bitIndex
0b01101000 01101001 00000000
h i \0
s[byte]
12
bitIndex % 8 bitIndex / 8
4
4 7 indexFromLeft
0
6
1
5
3 2
3
4
2
5
1
6
0
7 offset
1 << offset
maskedByte >> offset
byte = 1
0 1 1 0 1 0 0 1
byteOfInterest & offsetMask
0 0 0 0 0 0 0 1
0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1
createStem
This function creates a new collection of bytes from an existing collection of bytes. Conceptually this
is extracting a substring from a string and returning the new substring, but the returned value will not
necessarily be NULL terminated and may not start from the beginning of a character - so may not be
printable as a string for multiple reasons.
/* Allocates new memory to hold the numBits specified and fills the allocated
memory with the numBits specified starting from the startBit of the oldKey
array of bytes. */
char *createStem(char *oldKey, unsigned int startBit, unsigned int numBits);
char *createStem(char *oldKey, unsigned int startBit, unsigned int numBits){
assert(numBits > 0 && startBit >= 0 && oldKey);
int extraBytes = 0; if((numBits % BITS_PER_BYTE) > 0){
extraBytes = 1;
}
int totalBytes = (numBits / BITS_PER_BYTE) + extraBytes
char *newStem = malloc(sizeof(char) * totalBytes);
assert(newStem);
for(unsigned int i = 0; i < totalBytes; i++){
newStem[i] = 0;
}
for(unsigned int i = 0; i < numBits; i++){
unsigned int indexFromLeft = i % BITS_PER_BYTE;
unsigned int offset = (BITS_PER_BYTE - indexFromLeft - 1) % BITS_PER_BYTE;
unsigned int bitMaskForPosition = 1 << offset;
unsigned int bitValueAtPosition = getBit(oldKey, startBit + i);
unsigned int byteInNewStem = i / BITS_PER_BYTE;
newStem[byteInNewStem] |= bitMaskForPosition * bitValueAtPosition;
}
return newStem;
}
If the number of bits to extract matches exactly on a character border (i.e. if it is a multiple of 8) an
extra character is not needed, but otherwise, an extra character is needed - with the remainder of the
value being filled with 0's.
It works by fetching each bit from the given byte array and setting it in the new byte array.
An example of createStem("hello", 0, 12) is shown here:
Input
Processing
Output
oldKey
startBit
0b01101000 01100101 01101100 01101100 01101111 00000000
h e l l o \0
numBits
0
12
0b01101000 0110 0101 01101100 01101100 01101111 00000000
h e l l o \0
0b01101000 0110xxxx
h `-o
newStem
An example of createStem("hello", 12, 15) is shown here:Input
Processing
Output
oldKey
startBit
0b01101000 01100101 01101100 01101100 01101111 00000000
h e l l o \0
numBits
12
15
0b01101000 0110 0101 01101100 011 01100 01101111 00000000
h e l l o \0
0b0101 01101100 011x
e l `-[DEL]
newStem
Note: The result of createStem is not a string, strlen will not reliably count its length and printing it as a
string may lead to unexpected results.4. Extra Insertion Example and Insertion Process
This extra slide shows an additional example for inserting data into a prefix tree. This shows the
process of inserting the seven strings:
"a" 01100001 00000000
"b" 01100010 00000000
"c" 01100011 00000000
"d" 01100100 00000000
"e" 01100101 00000000
"f" 01100110 00000000
"g" 01100111 00000000
The first inserted string ("a" - 1100001 00000000) has no splits, so the prefix is the whole string:
Root Node
prefixBits 16
prefix 0b01100001 00000000
a \0
branchA NULL
branchB NULL
data ...
The second inserted string ("b" - 1100010 00000000) first differs in the second last bit of the first
character, so the tree splits at that point. The root node's prefix only determines the first 6 bits of the
string - narrowing all strings in the the tree to starting with a character which has the first six bits
011000. Therefore, the root node can lead to any character from ` to c. Check this ASCII-Binary table
(numbers 96-99).The third inserted string ("c" - 01100011 00000000) produces a new difference in the last bit (of the
first character) in the node completing the string "b". After the split, the right branch of the second
level node can lead the (first) character to be 'b' and 'c'.The fourth inserted string ("d" - 01100100 00000000) produces a difference in the root node, as the
(first) character differs in its third last bit - causing a split in the root node after 5 bits. After the split,
the root node only narrows the letters in the first character of all items in the tree from the character
'`' to the character 'g'.The fifth inserted string ("e"- 01100101 00000000) produces a new difference in the node completing
the string "d" in the last bit of the (first) character in the string. This causes a split, the third last and
second last bits for 'd' and 'e' are still the same, so the node retains these two bits.The sixth inserted string ("f" - 01100110 00000000) produces a split in the node deciding between
the (first) character being 'd' or 'e', with the second last bit of the character (the second bit in the node)
being different.Then the final inserted string ("g" - 01100111 00000000) produces a split in the node completing the
string "f", with the last bit of the first character differing.Insertion
In addition to the two functions provided to work over bit representations of strings, you will likely
have some kind of insertion operation to handle the example above.
The code logic details will likely vary depending on your data structure and implementation of
Assignment 1. Therefore, this diagram is provided as an overarching diagram to help you through the
task of inserting a new string.Insert into Tree
Check if in Tree Already
Not in Tree Already
Start
Start at Root
See if All Bits Match
Check Next Bit
all match
Not in Tree
Bit mismatch or
more bits in tree node
than remaining in string
Node Found
all exhausted in search string
Advance Down branchB
1
Advance Down branchA
0 Start at Root
Insert into
Found Node
See if All Bits Match
Check Next Bit
all match
Split Node
Insert Mismatched Stem
as New Branch Child
and Old Stem as Other
Branch, Leaving Common
Stem in Node
Bit mismatch or
more bits in tree node
than remaining in string
Advance Down branchB
1
Advance Down branchA
0Inserting into the found node simply adds to the list of data in the node and the split node step will
split at the common bit position, leaving the common stem in the node, placing the stem from the
inserted string in a newly created node down the appropriate branch (A if a 0 is the following bit in
the string, and B if it is a 1) and the stem from the existing string down the other branch in a newly
created branch. After this split - the insertion is complete.
You may find it useful to set the data in the existing node to NULL to avoid the same data existing in multiple
places in your tree.
By storing the \0 character, we do not need to worry about the case where the string continues after an
existing node is exhausted - we will always reach a mismatch or the string will match, simplifying both the
search and insertion logic.5. Dataset and Assumptions
Note: this slide is identical to the Assignment 1 dataset and assumptions.
The opendatasoft website provides numerous databases, enabling organisations and individuals to
seamlessly access and share data. The dataset used in this assignment is a simplified version derived
from the "State Suburbs - Australia" database on that website which originates from ABS data. You
can browse or visualize the original database using the link above. The whole simplified dataset, as
well as smaller samples, can be found in the Dataset Download slide.
The processed dataset can be found in the Dataset Download slide. You aren't expected to perform
this processing yourself.
The provided dataset has the following 10 fields:
COMP20003 Code - The record IDs added by the COMP20003 teaching team (integer)
Official Code Suburb - The suburb ID (integer)
Official Name Suburb - The name of the suburb (string)
Year - The year the information was recorded for (integer)
Official Code State - The IDs of the corresponding states (string)
Official Name State - The names of the corresponding states (string)
Official Code Local Government Area - The IDs of the corresponding local government areas (string)
Official Name Local Government Area - The names of the corresponding local government areas (string)Latitude - The latitude (y) of the suburb centre (double)
Longitude - The longitude (x) of the suburb centre (double)
The provided text gives a detailed description of the data format and assumptions for the
assignment. Here's a summary of the key points:
Fields and Data Types:
COMP20003 Code , Official Code Suburb , Year : integers
Longitude , Latitude : doubles
all other fields: strings (may contain spaces and commas)
Special Cases:
Official Code State , Official Code Local Government Area : comma-separated lists of
integers (treated as strings for this assignment).
comma-containing strings: enclosed in double quotes ("") within the CSV file, the quotes are
removed when stored in your program according to standard CSV rules.
CSV File Assumptions:
Note that normally a suburb lies entirely inside a single state, as well as a single local government
area, but this is not a universal rule. Suburbs might be in multiple local governments areas and
multiple states. This is why the fields Official Code State and Official Code Local Government
Area are not classified as integers. Each of these fields is actually a comma-separated list of integers.
For the purposes of this assignment, it is fully sufficient to consider this as a string.
This data is in CSV format, with each field separated by a comma. For the purposes of the
assignment, you may assume that:
the input data is well-formatted,
input files are not empty,
input files include a header line that contains the filled headers in the CSV format ,
any field containing a comma will begin with a double quotation mark ( " ) and end with a
quotation mark ( " ),
each field contains at least one character and at most 127 characters,
fields always occur in the order given above, and that
the maximum length of an input record (a single full line of the CSV) is 511 characters.
In this dataset, CSV conventions are followed for strings containing commas. These strings are
enclosed in double quotation marks (") at the beginning and end. However, when processing the data
and storing it in your dictionary, these quotation marks are removed. You can safely assume there
won't be any double quotes within the actual string values stored in the dictionary so your program
does not have to handle this case.Processing Assumptions:
The column headers (such as COMP20003 Code and Official Name Suburb ) can be assumed
to match the names expected and can be used directly in the output as the labels for each field.
The number of columns (10 in this assignment), as well as the order, text and data type of each
column are assumed to be known. While it's possible to automatically determine this
information, it's beyond the scope of this assignment.6. Implementation Details
Assignment 2 will involve roughly three new stages.
Stage 3 will extend your Assignment 1 solution to count the number of comparisons it
takes to find a given key, i.e. a search query.
Stage 4 will implement the lookup of data by a given key (Official Name Suburb) in a Patricia
tree.
Stage 5 is a report about the complexity of a linked list compared to the Patricia tree.
Stage 3: Linked List Search Comparisons
In this stage, you will extend your Assignment 1 solution to count the number of binary and string
comparisons and node accesses used when searching for a given key.
Your Makefile will produce an executable called dict3 . The command line arguments are identical
to assignment 1 but with the stage being 3 instead.
The first argument will be the stage, for this part, the value will always be 3 (for linked list
lookup with comparison count added).
The second argument will be the name of the input CSV data file.
The third argument will be the name of the output file.
Your dict3 program should function exactly the same as assignment 1's stage 1. You will add the
functionality to count comparisons when searching for a key which will be added to the stdout
output. For this stage, and this stage only, you may assume:
Each character compared adds exactly 8 bits to the bit comparison count.
The node access is incremented once per accessed linked list node.
Each string comparison, even if a mismatch occurs on the first character, is 1 string
comparison.
You should create the functionality to store comparisons with stage 4 in mind. You will also be
recording comparisons in the Patricia tree implementation, so your code should be easily applied to
both. For this reason, you may want to extend your search function to include a pointer to a struct
that holds information about the query and results.
Important Notes:
You do not have to implement any spellchecking in the linked list. Your only task at this stage is
to add functionality to count comparisons. You do not need to add this functionality to the deletion of nodes. Only the search will be
assessed. You should, however, be able to recognize how to implement this in your deletion
code.
Example Execution
make -B dict3
./dict3 3 tests/dataset_1.csv output.txt < tests/test1.in
Example Output
The expected output to the output file would look like:
Carlton -->
COMP20003 Code: 9773, Official Code Suburb: 20495, Official Name Suburb: Carlton, Year: 2021, Official Code State: 2,
South Melbourne -->
With the following printed to stdout:
Carlton --> 1 records - comparisons: b64 n1 s1
South Melbourne --> NOTFOUND
Note: the bit comparisons are 8 times the character comparisons here
Stage 4: Patricia Tree Spellchecker
In Stage 4, you will implement the another dictionary allowing the lookup of data by key ( Suburb
Name ).
Your Makefile should produce an executable program called dict4 . This program should take three
command line arguments:
The first argument will be the stage, for this part, the value will always be 4 (for Patricia tree
lookup and comparison counting).
The second argument will be the name of the input CSV data file.
The third argument will be the name of the output file.
Your dict4 program should:
Read the data in the same format as assignment 1 and stage 1, and save each entry in a Patricia
tree using the Official Name Suburb as the key.
The program should:Accept Official Name Suburb s from stdin , and search the tree for the matching key.
Since your program should act as a simple spellchecker, an exact match may not be
found. Follow the process "mismatch in key" as outlined below to implement
spellchecking logic.
You must output all matching records to the output file, where a matching record may be
an exact match, or the closest match determined by the mismatch key algorithm. If no
matches for the query are found (i.e. the trie is empty), your program should output
NOTFOUND . When outputting data records, you should follow the guidelines described in
the slide Dataset and Assumptions.
In addition to outputting the record(s) to the output file, the number of records found and
the comparisons and node accesses when querying should be output to stdout .
Mismatch in Key
Since a Patricia tree is a type of prefix tree, any mismatch that occurs will contain the same prefix. For
example, say we are searching for "trie", but our dictionary contains "tree". Here, a mismatch occurs
at some binary bit in the third character. This mismatch will only occur once we are past the node in
our tree that contains all prefixes with "tr". Therefore, when the mismatch occurs, we can get all
descendants at the mismatched node and calculate the most likely candidate using the edit distance
of the query and a key. This way, if we query "trie", we should return "tree". This is explained with the
following graphic:
The patricia tree contains 4 strings as listed. If we query "trie", the mismatch happens on the bit
highlighted in red. At this node, the only descendant data is "tree", and is therefore determined to be
the closest match.If we query "Tree", the mismatch happens on the third bit. This means that all descendant data is
taken to be possible candidates, so all 4 keys have their edit distance against the query calculated.
"tree" is determined to be the closest match with an edit distance of 1, and is returned.
Tips:
The results struct you created to hold comparisons can hold matching data
A node access count should be incremented each time a new node of the trie is looked at.
If two strings have an equal edit distance, return all results for the first suburb name
encountered with that edit distance (this will be the alphabetically earliest result).
Edit Distance
In Stage 4, you will need to calculate the edit distance of multiple strings to determine the closest
match. You are welcome to use the code here to calculate the edit distance - you do not have to worry
about its complexity:
int editDistance(char *str1, char *str2, int n, int m);
int int min(int a, int b, int c);
/* Returns min of 3 integers
reference: https://www.geeksforgeeks.org/edit-distance-in-c/ */
int min(int a, int b, int c) {
if (a < b) {
if(a < c) {
return a;
} else {
return c;
}
} else {
if(b < c) {
return b;
} else {
return c;
}
}
}
/* Returns the edit distance of two strings
reference: https://www.geeksforgeeks.org/edit-distance-in-c/ */
int editDistance(char *str1, char *str2, int n, int m){
assert(m >= 0 && n >= 0 && (str1 || m == 0) && (str2 || n == 0));
// Declare a 2D array to store the dynamic programming
// table
int dp[n + 1][m + 1];
// Initialize the dp table
for (int i = 0; i <= n; i++) {
for (int j = 0; j <= m; j++) {
// If the first string is empty, the only option // is to insert all characters of the second
// string
if (i == 0) {
dp[i][j] = j;
}
// If the second string is empty, the only
// option is to remove all characters of the
// first string
else if (j == 0) {
dp[i][j] = i;
}
// If the last characters are the same, no
// modification is necessary to the string.
else if (str1[i - 1] == str2[j - 1]) {
dp[i][j] = min(1 + dp[i - 1][j], 1 + dp[i][j - 1],
dp[i - 1][j - 1]);
}
// If the last characters are different,
// consider all three operations and find the
// minimum
else {
dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1],
dp[i - 1][j - 1]);
}
}
}
// Return the result from the dynamic programming table
return dp[n][m];
}
Example Execution
make -B dict4
./dict4 4 tests/dataset_15.csv output.txt < tests/test1.in
Example Output
Output file:
Carlton -->
COMP20003 Code: 9773, Official Code Suburb: 20495, Official Name Suburb: Carlton, Year: 2021, Official Code State: 2,
South Melbourne -->
COMP20003 Code: 390, Official Code Suburb: 22313, Official Name Suburb: South Wharf, Year: 2021, Official Code State:
stdout:
Carlton --> 1 records - comparisons: b58 n4 s1
South Melbourne --> 1 records - comparisons: b52 n5 s1Stage 5: Complexity Report
The final deliverable for this assignment is a small report analyzing the complexity of both
dictionaries. You will run various test cases through your program to test its correctness and also to
examine the number of key comparisons used when searching keys across different dictionaries. You
will report on the number of comparisons used by your linked list and patricia tree implementations
for different data file inputs and key prefixes. You will compare these results with what you expect
based on theory (Big-O).
Your approach should be systematic, varying the size of the files you use and their characteristics (e.g.
sorted/unsorted, duplicates, range of key space, length of prefix, etc.), and observing how the number
of key comparisons varies given different sizes of key prefixes. Looking at statistical measures about
the results of your tests may help.
UNIX programs such as sort (in particular -R ), head , tail , cat and awk may be valuable. You
may also find small C programs (or additional "Stages") useful, the test data you produce and use
does not have to be restricted to the dataset provided.
Additionally, though your C code should run on Ed, you may like to run offline tests or other work
outside of Ed and are welcome to do so.
You will write up your findings and submit them along with your Assignment Submission in the
Assignment Submission Tab.
Make sure to click the "Mark" button after uploading your report. It is often the case for results to be released
and more than a handful of students to find an unexpected 0 mark for the report due to not technically
submitting it for marking.
You are encouraged to present the information you've collected in appropriate tables and graphs.
Select informative data, more is not always better.
In particular, you should present your findings clearly, in light of what you know about the data
structures used in your programs and in light of their known computational complexity. You may find
that your results are what you expected, based on theory. Alternatively, you may find your results do
not agree with theory. In either case, you should state what you expected from the theory, and if
there is a discrepancy you should suggest possible reasons. You might want to discuss space-time
trade-offs, if this is appropriate to your code and data.
It is recommended to look at extreme cases where each option will do better or worse - e.g. think
about dense data where the Patricia tree will save many bit comparisons and character sparse data
where string comparisons would terminate quickly in both data structures. It is also highly
recommended to look at the theory covered so far and how you can match it - i.e. if a particular
complexity is expected, that it appears at large input sizes and represents the growth behaviour - not
just single point comparisons. Similarly, pay particular attention to the meaning of variables,
character comparisons, string comparisons and bit comparisons may all have different behaviours and certain investigations might expect different relations on these.
You are not constrained to any particular structure in this report, but a well laid out plan with a strong
structure tends to lead to the best results. A more general and helpful guiding structure is shown in
the Additional Support slide, but you