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TreeHuffmanDecoding.java
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175 lines (127 loc) · 4.8 KB
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/*
Huffman Coding Decoder
You are tasked with implementing a decoder for Huffman coding. Huffman coding is a lossless data compression algorithm
that assigns variable-length codes to input characters based on their frequencies. More frequent characters are assigned shorter codes,
while less frequent characters receive longer codes. Your goal is to decode a given binary-encoded string using a provided Huffman tree.
Input
A string s representing the Huffman-encoded binary string (composed of '0's and '1's).
A reference to the root node of a Huffman tree that has been constructed based on character frequencies.
Output
A single line containing the decoded string.
Constraints
The input string s will only contain characters '0' and '1'.
The Huffman tree will be valid and contain characters in its leaf nodes.
*/
import java.util.*;
abstract class Node implements Comparable<Node> {
public int frequency; // the frequency of this tree
public char data;
public Node left, right;
public Node(int freq) {
frequency = freq;
}
// compares on the frequency
public int compareTo(Node tree) {
return frequency - tree.frequency;
}
}
class HuffmanLeaf extends Node {
public HuffmanLeaf(int freq, char val) {
super(freq);
data = val;
}
}
class HuffmanNode extends Node {
public HuffmanNode(Node l, Node r) {
super(l.frequency + r.frequency);
left = l;
right = r;
}
}
class Decoding {
/*
class Node
public int frequency; // the frequency of this tree
public char data;
public Node left, right;
*/
void decode(String s, Node root)
{
int n = s.length();
Node currentNode = root;
StringBuilder decodedHuffString = new StringBuilder();
for(int i = 0; i < n; i++)
{
currentNode = s.charAt(i) == '0'? currentNode.left : currentNode.right;
if(currentNode.left == null && currentNode.right == null)
{
decodedHuffString = decodedHuffString.append(currentNode.data);
currentNode = root;
}
}
System.out.println(decodedHuffString.toString());
}
}
public class Solution {
// input is an array of frequencies, indexed by character code
public static Node buildTree(int[] charFreqs) {
PriorityQueue<Node> trees = new PriorityQueue<Node>();
// initially, we have a forest of leaves
// one for each non-empty character
for (int i = 0; i < charFreqs.length; i++)
if (charFreqs[i] > 0)
trees.offer(new HuffmanLeaf(charFreqs[i], (char)i));
assert trees.size() > 0;
// loop until there is only one tree left
while (trees.size() > 1) {
// two trees with least frequency
Node a = trees.poll();
Node b = trees.poll();
// put into new node and re-insert into queue
trees.offer(new HuffmanNode(a, b));
}
return trees.poll();
}
public static Map<Character,String> mapA=new HashMap<Character ,String>();
public static void printCodes(Node tree, StringBuffer prefix) {
assert tree != null;
if (tree instanceof HuffmanLeaf) {
HuffmanLeaf leaf = (HuffmanLeaf)tree;
// print out character, frequency, and code for this leaf (which is just the prefix)
//System.out.println(leaf.data + "\t" + leaf.frequency + "\t" + prefix);
mapA.put(leaf.data,prefix.toString());
} else if (tree instanceof HuffmanNode) {
HuffmanNode node = (HuffmanNode)tree;
// traverse left
prefix.append('0');
printCodes(node.left, prefix);
prefix.deleteCharAt(prefix.length()-1);
// traverse right
prefix.append('1');
printCodes(node.right, prefix);
prefix.deleteCharAt(prefix.length()-1);
}
}
public static void main(String[] args) {
Scanner input = new Scanner(System.in);
String test= input.next();
// we will assume that all our characters will have
// code less than 256, for simplicity
int[] charFreqs = new int[256];
// read each character and record the frequencies
for (char c : test.toCharArray())
charFreqs[c]++;
// build tree
Node tree = buildTree(charFreqs);
// print out results
printCodes(tree, new StringBuffer());
StringBuffer s = new StringBuffer();
for(int i = 0; i < test.length(); i++) {
char c = test.charAt(i);
s.append(mapA.get(c));
}
//System.out.println(s);
Decoding d = new Decoding();
d.decode(s.toString(), tree);
}
}