The uploaded Java file implements the Huffman Coding algorithm, which is a lossless data compression technique.

Core Components and Functionality

The code defines the Huffman Coding process, including building the compression tree, generating the binary codes, and performing both encoding and decoding.

HuffmanNode Class

This inner class represents a node in the Huffman Tree.

• Attributes: Stores the frequency (how many times the character appears), the character itself (only for leaf nodes), and pointers to the left and right child nodes.
• compareTo Method: Implements the Comparable interface to allow a PriorityQueue to order nodes based on their frequency. Lower frequency nodes have higher priority for extraction.

HuffmanCoding Class

1. Building the Tree (buildTree method)

The main goal of this method is to construct the optimal binary tree for encoding.

1. Calculate Frequencies: It first iterates through the input text to count the occurrences of every character, storing them in a Map<Character, Integer>.
2. Create Leaf Nodes: Each character and its frequency are turned into a HuffmanNode (a leaf node), and all are added to a PriorityQueue (pq).
3. Merge Nodes: It repeatedly extracts the two nodes with the lowest frequency from the priority queue. A new internal node is created, whose frequency is the sum of the two extracted nodes, and the two extracted nodes become its left and right children. This new internal node is then re-inserted into the priority queue.
   • This process continues until only one node remains in the queue, which is the root of the final Huffman Tree.
   • This greedy strategy ensures that less frequent characters end up deeper in the tree, receiving longer codes, while more frequent characters stay higher up, receiving shorter codes.
4. Generate Codes: The final tree is passed to the generateCodes helper method.

2. Generating Codes (generateCodes method)

This is a recursive method that performs a traversal (typically a Depth First Search) of the Huffman Tree:

• It assigns '0' for a step to the left child and '1' for a step to the right child.
• When a leaf node (a character) is reached, the path taken from the root (the accumulated binary string) is stored as the character's unique Huffman code in the huffmanCodes map.

3. Encoding (encode method)

This method iterates through the input text and replaces each character with its corresponding binary code retrieved from the huffmanCodes map.

4. Decoding (decode method)

This method reconstructs the original text from the binary string:

• It starts at the root of the Huffman Tree.
• For each bit in the encoded text:
  • If the bit is '0', it moves to the left child.
  • If the bit is '1', it moves to the right child.
• When a leaf node is reached, the character is appended to the decoded text, and the traversal restarts from the root for the next character.

Example Execution (from main method)

The example uses the string: "huffman coding is a lossless data compression algorithm"

1. Frequencies are calculated for each character.
2. The Huffman Tree is built based on these frequencies.
3. Huffman Codes are generated (e.g., 'f' might get a short code, while 'z' if present, would get a long code).
4. The original text is Encoded into a binary string.
5. The binary string is Decoded back to the original text.
6. A Verification check confirms that Original Text == Decoded Text.