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2.3 KiB
Python

# Code-A2 (Huffman Coding)
import heapq
# Node class for Huffman Tree
class Node:
def __init__(self, char, freq):
self.char = char
self.freq = freq
self.left = None
self.right = None
# Comparison function for priority queue
def __lt__(self, other):
return self.freq < other.freq
# Function to build Huffman Tree
def build_huffman_tree(char_freq):
heap = [Node(ch, freq) for ch, freq in char_freq.items()]
heapq.heapify(heap)
while len(heap) > 1:
# Pick two smallest nodes (greedy choice)
left = heapq.heappop(heap)
right = heapq.heappop(heap)
# Merge them into a new node
merged = Node(None, left.freq + right.freq)
merged.left = left
merged.right = right
heapq.heappush(heap, merged)
return heap[0]
# Function to generate Huffman codes
def generate_codes(root, current_code="", codes={}):
if root is None:
return
if root.char is not None:
codes[root.char] = current_code
generate_codes(root.left, current_code + "0", codes)
generate_codes(root.right, current_code + "1", codes)
return codes
# Main program
text = input("Enter text to encode: ")
# Step 1: Calculate frequency of each character
freq = {}
for ch in text:
freq[ch] = freq.get(ch, 0) + 1
# Step 2: Build Huffman Tree using greedy approach
root = build_huffman_tree(freq)
# Step 3: Generate Huffman Codes
codes = generate_codes(root)
# Step 4: Encode the text
encoded_text = "".join(codes[ch] for ch in text)
# Step 5: Display results
print("\nCharacter | Frequency | Huffman Code")
print("------------------------------------")
for ch in freq:
print(f" {ch!r} | {freq[ch]} | {codes[ch]}")
print("\nEncoded Text:", encoded_text)
# SAMPLE OUTPUT
"""
Enter text to encode: lord kska git
Character | Frequency | Huffman Code
------------------------------------
'l' | 1 | 1100
'o' | 1 | 1101
'r' | 1 | 001
'd' | 1 | 1010
' ' | 2 | 011
'k' | 2 | 100
's' | 1 | 000
'a' | 1 | 010
'g' | 1 | 1011
'i' | 1 | 1111
't' | 1 | 1110
Encoded Text: 110011010011010011100000100010011101111111110
"""