CSC3022H/CSC3023F Huffman Tree Encoding solved

Description

You are asked to write a compression algorithm to compress English language text files.
You decide to use a Huffman encoder, which is a fairly quick and easy way of turning a
sequence of ASCII characters into a compressed bit stream. For this tutorial, you need
only implement the compression side, and you do not need to pack the output into a stream
of bytes. You can simply use the ASCII characters 0 and 1 to represent the compressed
bit representation. Of course, your compression factor will be 8x less as a consequence!
So for extra credit, you can do some bit operations to pack the 0s and 1s into an array of

1 Constraints
The constraints for this tutorial are as follows:
1. Your implementation must use at least a HuffmanTree class (which manages the tree
and has methods to compress data, build a Huffman tree etc) and a HuffmanN-ode
class, which represent the tree nodes in the underlying Huffman tree (which is a
simple binary tree). These must be constructed and destructed appropriately. Your
constructor and destructor will need to be defined to use the specified smart pointers
to manage memory see point 2) below.
2. You must use std::shared ptr to represent the left/right links for
your tree nodes as well as the root of the tree in your HuffmanTree class.
3. your destructors must *not* explicitly delete the tree node representation: this should
happen automatically when the root node is set to “nullptr” in the Huff-manTree
destructor. You should not call delete at all in the HuffmanNode destruc-tor. That is
the benefit of using a managed pointer. You may, however, allocated your
HuffmanTree instance on the heap using new, in which case you would call delete to
free that up and the end of your program.
4. You will need to implement: a default constructor, a move constructor, a copy
constructor, a destructor, an assignment operator and a move assignment operator
more details are provided below.
5. Your program will be driven from the command line, and should be invoked as follows:
huffencode

where huffencode is the name

of your application, inputFile is an input English (ASCII) text file and output file
is the names of your output compressed “bitstream”.
Note: if you do not do any bit packing, this file may be larger than the input (!).
To compute the actual packed file size in bytes (approximately) you can compute:
(Nbits/8) + (Nbits%8 ? 1 : 0) using integer arithmetic. This accommodates
extra padding bits needed to deal with output bit stream sizes that are not a power
of 2.
6. Prior to constructing the huffman encoding tree you have to count the number of
occurrences of each letter in the file. To store these frequencies you can use a
std::unordered map with keys of type character and values of integer type (the
unordered map is very similar to Java’s HashMap). When you build the Huffman tree,
you will need this information to decide how to insert nodes for each letter. The
relevant header file is . More information is available at http://
en.cppreference.com/w/cpp/container/unordered_map.
7. You should use a std::priority queue data structure to assist you
in the building the tree. The relevant header file is
A priority queue returns the next largest or smallest item, depending on how you
have set it up. This is required to decide which tree nodes to aggregate as you build
your tree. To ensure it works, you will need to provide a binary function predicate
bool compare(const HuffmanNode& a, const HuffmanNode& b)
{
If (a < b) return true; // or > if the algorithm requires that ordering
else return false;
}
You will need to ensure the “<” operation is evaluated sensibly for the type HuffmanNode , as required by the tree construction algorithm. This function (which
can be a free function) will be used by the priority queue to decide how to order its
HuffmanNode elements.
A priority queue supports a “push()” method (to insert elements), a “top()” method
to copy the next element to be returned, and a “pop()” method to discard the next
element to be returned. You can call “empty()” to see if the queue is empty or
“size()” to see how many elements remain.
To create an instance of the priority queue you would have code like the following: std::priority queue<HuffmanNode, compare>myQueue; See http://www.
cplusplus.com/reference/queue/priority_queue/pop/ for a simple usage with
plain old ints.
2 Huffman compression algorithm
You can build a Huffman tree as follows:
1. allocate a shared pointer for each letter, which contains the letter
and frequency;
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2. create a std::priority queue<HuffmanNode, compare>, making sure that the
right ordering (i.e. return smallest item) has been set in the compare function;
Then, repeat until the queue has only one node, which will be the tree root:
3. choose the two nodes with the smallest letter frequency and create a new internal
parent node which has these two nodes as its left and right children. The smaller
nodes are inserted as the left child. These nodes should be popped out of the queue
-you do not need to examine them again;
4. the new node (an internal node) has no letter associated with it, only a frequency.
This frequency is the sum of the frequencies of its two children.
5. insert this new node in to the priority queue
At the end of this process you will have built your Huffman tree. If you have done this
correctly the smart pointers will be sharing resources correctly and will auto destroy when
the HuffmanTree goes out of scope (or is deleted) at the end of your program.
3 Code table
Next, you need to build the “code table”: this is a mapping that gives a bit string code for
each letter in your alphabet. The easiest way to do this (perhaps) is to recurse down the
tree from the root building up a string as you go. When you hit a leaf node with a letter,
you then output that letter with the corresponding bit string you have accumulated. When
traversing the tree, add a “0” if branching left and add “1” if branching right. You can
use an inorder walk and output when you get to a leaf node. It is up to you to decide how
to store this code book. Ideally, finding symbol and its corresponding bit string should be
fast.
4 Compression
To compress your ASCII text file, take each character, in turn, finding its bit string code and
write this into the output buffer. This “buffer” can just be a std::string which you
append characters/strings to. At the end, you can extract the c str() (a pointer to a byte
array) and write this out to file as a block of bytes. You should write out your code table to a
separate file named “output file”.hdr where output file is passed using command line
argument. The header should have a field count at the top and a field for every character
(consisting of the character and its code representation).
5 Mark allocation
Doing everything required above will get you 90%.
Extra credit:
• Convert to bit stream (5%): Convert your bits into an actual bit stream and store
this in a byte array that is large enough; this should be written to disk as a binary
file with a single int as header which lists the number of bits in the file. (you can
work out number of bytes to read from this).
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• Now write a method to read in and unpack the bitstream (5%). You should check
that your unpacked bitstream matches the original encoded data.
• You may find the discussion at http://www.cprogramming.com/tutorial/bitwise_
operators.html useful if you have never done this in Java. Take care to observe the
behaviour of the shift operators for signed and unsigned integral types.
6 Unit tests
You must provide unit tests for each of the sections of your code. The unit tests should
be specified in a separate file and compiled using a separate rule in your Makefile. Unit
testing will require that you separate out the different steps into separate units of work,
so that they can be tested individually. Therefore you should aim not write all of your
code in one very long driver file for instance.
The unit tests will account up to 10% of your final mark (depending on whether each of
the steps (frequency counting, tree construction, code table construction (tree traversal),
encoding, etc.) is tested to satisfaction. The marks awarded for bit packing and unpacking
includes the unit tests to show that these work as expected.
You should use the catch.hpp unit testing framework to test your code. A full tutorial is
available at https://github.com/philsquared/Catch/blob/master/docs/tutorial.md.
7 Suggested work flow
• Week 1:
– Define classes, default constructor/destructor
– Build tree
– Construct frequency table
– Write unit tests
• Week 2:
– Add in all extra constructors /move ops for tree and Node
– create code book table
– Encode string
– Output bit string to file
– Compute compression ratio (on “bit string” vs input chars)
– extra: pack bit string, unpack
– More unit tests
Please Note:
1. A working makefile must be submitted. If the tutor cannot compile your program
in the lab by typing make, you will receive 50% of your final mark.
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2. You must use version control from the get-go. This means that there must be a .git
folder alongside the code in your project folder. A 10% penalty apply should you
fail to include a local repository.
3. You must provide a README file explaining what each file submitted does and how
it fits into the program as a whole. The README file should not explain any theory
that you have used. These will be used by the tutors if they encounter any problems.
4. Do not hand in any binary files. Do not add binaries (.o files and your executable) to
your local repository.
5. Please ensure that your tarball works and is not corrupt (you can check this by trying
to extract the contents of your tarball – make this a habit!). Corrupt or non-working
tarballs will not be marked – no exceptions!
6. A 10% penalty per day will be incurred for all late submissions. No hand-ins will be
accepted if later than 5 days.
7. DO NOT COPY. All code submitted must be your own. Copying is
punishable by 0 and can cause a blotch on your academic record.
Scripts will be used to check that code submitted is unique.
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