python实现哈夫曼编码

这里是用python实现的哈夫曼编码，拿出来跟大家一起分享

# -*- coding: utf-8 -*-
'''
Created on 2015-12-6

@author: zhouheng
'''

'''
This is the Huffman coding that write by zhouheng.
'''

import copy
import numpy as np
import math

# This class is the process of the haffman
class createHuffmanTree():
def __init__(self, source):
self.queue = source

def haf(self):
queue = self.queue[:]#复制原来的队列

newqueue = [] #构造一个存放新队列的列表

#构建哈夫曼树
while len(queue)>1:
queue.sort(key=lambda item :item.probability)
node_left = queue.pop(0)
node_right = queue.pop(0)
if node_left.symbol is not None:
newqueue.append(node_left)
if node_right.symbol is not None:
newqueue.append(node_right)
if node_left.probability>node_right.probability:
node_left.codeWord = '0'
node_right.codeWord = '1'
else:
node_left.codeWord = '1'
node_right.codeWord = '0'
node_fagher = HuffmanObjext( probability = node_left\
.probability+node_right.probability )
node_fagher.lChild = node_left
node_fagher.rChild = node_right
node_left.father = node_fagher
node_right.father = node_fagher
queue.append(node_fagher)

queue[0].father = None

return newqueue, queue[0]
# 哈夫曼编码
def huffmanEncoding(self):
queue, root = result.haf()
probability = []
codes = ['']*len(queue)
symbol = []

for i in range(len(queue)):
tmp_node = queue[i]
while tmp_node.father != None:
codes[i] = tmp_node.codeWord + codes[i]
tmp_node = tmp_node.father

queue[i].codeWord = codes[i]
symbol.append(queue[i].symbol)
probability.append(queue[i].probability)

return symbol, codes, probability

# This is the Objext the Huffman

class HuffmanObjext():

# get the Object of huffman
def __init__(self, symbol = None, probability = None,\
codeWord = '', procode = None ):

self.symbol = symbol
self.probability = probability
self.codeWord = codeWord
self.procode = procode
self.father = None
self.lChild = None
self.rChild = None

if __name__ == '__main__':
x1 = HuffmanObjext(symbol = "X1", probability = 0.375,\
codeWord = '' )
x2 = HuffmanObjext(symbol = "X2", probability = 0.125,\
codeWord = '' )

x3 = HuffmanObjext(symbol = "X3", probability = 0.25,\
codeWord = '')
x4 = HuffmanObjext(symbol = "X4", probability = 0.25,\
codeWord = '')
x5 = HuffmanObjext(symbol = "X5", probability = 0.125,\
codeWord = '')
# x6 = HuffmanObjext(symbol = "X6", probability = 0.1,\
# codeWord = '')

result = createHuffmanTree([ x1,x2, x3, x4, x5])
# a, b = result.haf()
symbol, code, probability = result.huffmanEncoding()
H = []
K = []
for item in zip(code, probability):
print item[1]
H_X = item[1] * math.log(item[1],2)
K_X = item[1] *len(item[0])
H.append(H_X)
K.append(K_X)

H_X = -np.sum(H)
K_X = np.sum(K)
n = H_X /K_X

print "Encoding"
print symbol, "\n"
print code
# print info
print H_X
print "average of procode", K_X
print "aberage of n", n