Huffman encoding came up on Rosetta Code.
Huffman encoding is a way to assign binary codes to symbols that reduces the overall number of bits used to encode a typical string of of those symbols.
For example, if you use letters as symbols and have details of the frequency of occurence of those letters in typical strings, then you could just encode each letter with a fixed number of bits, such as in ASCII codes. You can do better than this by encoding more frequently occurring letters such as e and a, with smaller bit strings; and less frequently occurring letters such as q and x with longer bit strings.
Any string of letters will be encoded as a string of bits that are nolonger of the same length per letter. To successfully decode such as string, the smaller codes assigned to letters such as 'e' cannot occur as a prefix in the larger codes such as that for 'x'.
A Huffman encoding can be computed by first creating a tree of nodes:
My 'true' Huffman code creator assembles each symbol and its weight into the following structure initially (the leaf structure):
The empty list is used to accumulate the Huffman code for the symbol as we manipulate the heap, without having to walk a constructed tree structure.
There are two types of input to the program that I am running examples with:
The tutor argument to the encode function shows what is happening in the loop around the heap pops
A run, with the tutor enabled gives the following output:
In the codecreate function, the leaf structure is modified:
After the outer expression is accumulated, it is turned into a lambda expression, the string eval'd, and assigned to a global variable (see line 47)
I use function probchoice (from earlier RC work), to create an arbitrary sequence of symbols to in the given weighting then encode and decode it as well as giving some stats on space saving.
This short run is in tutor mode, so you can track the accumulation of the decode function:
And if I change line 54 to be False, I get the following:
Note: The purpose of the program is to teach me more about Huffman coding and is not an exercise in speed!
I am the author of all the Python on this page. The diagram is from Wikipedia. Please refrain from passingoff my code as your own (that one is mainly for students).
Huffman encoding is a way to assign binary codes to symbols that reduces the overall number of bits used to encode a typical string of of those symbols.
For example, if you use letters as symbols and have details of the frequency of occurence of those letters in typical strings, then you could just encode each letter with a fixed number of bits, such as in ASCII codes. You can do better than this by encoding more frequently occurring letters such as e and a, with smaller bit strings; and less frequently occurring letters such as q and x with longer bit strings.
Any string of letters will be encoded as a string of bits that are nolonger of the same length per letter. To successfully decode such as string, the smaller codes assigned to letters such as 'e' cannot occur as a prefix in the larger codes such as that for 'x'.
 If you were to assign a code 01 for 'e' and code 011 for 'x', then if the bits to decode started as 011... then you would not know iif you should decode an 'e' or an 'x'.
A Huffman encoding can be computed by first creating a tree of nodes:

In Python
I orginally gave an an example that matched a definition that was later found to be insufficient, so substituted my own definition above.. My first Python solution on RC to the wrong definition, did have the advantage, (as I saw it), of not having to traverse a tree.My 'true' Huffman code creator assembles each symbol and its weight into the following structure initially (the leaf structure):
[ weight, [ symbol, []]]
The weight applies to every (in this case only one), of the [symbol, []] pairs after it in the same list.The empty list is used to accumulate the Huffman code for the symbol as we manipulate the heap, without having to walk a constructed tree structure.
There are two types of input to the program that I am running examples with:
 A string of space separated symbol, weight pairs, as used in small examples.
 A sample of text for which letters and letter frequencies are extracted.
The tutor argument to the encode function shows what is happening in the loop around the heap pops
1 2 from heapq import heappush, heappop, heapify 3 4 def codecreate(symbol2weights, tutor= False): 5 ''' Huffman encode the given dict mapping symbols to weights ''' 6 heap = [ [float(wt), [sym, []]] for sym, wt in symbol2weights.iteritems() ] 7 heapify(heap) 8 if tutor: print "ENCODING:", sorted(symbol2weights.iteritems()) 9 while len(heap) >1: 10 lo = heappop(heap) 11 hi = heappop(heap) 12 if tutor: print " COMBINING:", lo, '\n AND:', hi 13 for i in lo[1:]: i[1].insert(0, '0') 14 for i in hi[1:]: i[1].insert(0, '1') 15 lohi = [ lo[0] + hi[0] ] + lo[1:] + hi[1:] 16 if tutor: print " PRODUCING:", lohi, '\n' 17 heappush(heap, lohi) 18 codes = heappop(heap)[1:] 19 for i in codes: i[1] = ''.join(i[1]) 20 return sorted(codes, key=lambda x: (len(x[1]), x)) 21 22 # Input types 23 if 1: 24 readin = "B 25 C 2.5 D 12.5 A 5 \n" 25 #readin = "a .1 b .15 c .3 d .16 e .29" # Wikipedia sample 26 #readin = "a1 .4 a2 .35 a3 .2 a4 .05" # Wikipedia sample 27 #readin = "A 50 B 25 C 12.5 D 12.5" # RC example 28 29 cleaned = readin.strip().split() 30 symbol2weights = dict((symbol, wt) 31 for symbol, wt in zip(cleaned[0::2], cleaned[1::2]) ) 32 else: 33 astring = "this is an example for huffman encoding" 34 symbol2weights = dict((ch, astring.count(ch)) for ch in set(astring)) # for astring 35 36 huff = codecreate(symbol2weights, True) 37 print "\nSYMBOL\tWEIGHT\tHUFFMAN CODE" 38 for h in huff: 39 print "%s\t%s\t%s" % (h[0], symbol2weights[h[0]], h[1])
A run, with the tutor enabled gives the following output:
ENCODING: [('A', '5'), ('B', '25'), ('C', '2.5'), ('D', '12.5')]
COMBINING: [2.5, ['C', []]]
AND: [5.0, ['A', []]]
PRODUCING: [7.5, ['C', ['0']], ['A', ['1']]]
COMBINING: [7.5, ['C', ['0']], ['A', ['1']]]
AND: [12.5, ['D', []]]
PRODUCING: [20.0, ['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]]
COMBINING: [20.0, ['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]]
AND: [25.0, ['B', []]]
PRODUCING: [45.0, ['C', ['0', '0', '0']], ['A', ['0', '0', '1']], ['D', ['0', '1']], ['B', ['1']]]
SYMBOL WEIGHT HUFFMAN CODE
B 25 1
D 12.5 01
A 5 001
C 2.5 000
COMBINING: [2.5, ['C', []]]
AND: [5.0, ['A', []]]
PRODUCING: [7.5, ['C', ['0']], ['A', ['1']]]
COMBINING: [7.5, ['C', ['0']], ['A', ['1']]]
AND: [12.5, ['D', []]]
PRODUCING: [20.0, ['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]]
COMBINING: [20.0, ['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]]
AND: [25.0, ['B', []]]
PRODUCING: [45.0, ['C', ['0', '0', '0']], ['A', ['0', '0', '1']], ['D', ['0', '1']], ['B', ['1']]]
SYMBOL WEIGHT HUFFMAN CODE
B 25 1
D 12.5 01
A 5 001
C 2.5 000
Encode/Decode Roundtripping
I realised that I could use a method similar to how I accumulate the codes in the heap loop, to generate a single function that can recognise a single symbol from the beginning of the encoded symbols. By using the function in a loop, I could regenerate the symbol list.In the codecreate function, the leaf structure is modified:
[weight, [ [symbol, []] ], repr(sym)]
Their is an extra level of list around the [symbol, code accumulation list pair] as well as a new item: 'repr(sym)' it is the third item in the outer list and will always be the function to generate the item as accumulated so far.. This function starts off by returning just the symbol and an outer if/then/else expression is added as we go around the heap loop (see line 43)After the outer expression is accumulated, it is turned into a lambda expression, the string eval'd, and assigned to a global variable (see line 47)
I use function probchoice (from earlier RC work), to create an arbitrary sequence of symbols to in the given weighting then encode and decode it as well as giving some stats on space saving.
1 2 from heapq import heappush, heappop, heapify 3 import random, bisect 4 5 6 # Helper routine for generating test sequences 7 def probchoice(items, probs): 8 '''\ 9 Splits the interval 0.01.0 in proportion to probs 10 then finds where each random.random() choice lies 11 (This routine, probchoice, was released under the 12 GNU Free Documentation License 1.2) 13 ''' 14 15 prob_accumulator = 0 16 accumulator = [] 17 for p in probs: 18 prob_accumulator += p 19 accumulator.append(prob_accumulator) 20 21 while True: 22 r = random.random() 23 yield items[bisect.bisect(accumulator, r)] 24 25 26 # placeholder 27 decode = lambda : None 28 29 def codecreate(symbol2weights, tutor= False): 30 ''' Huffman encode the given dict mapping symbols to weights ''' 31 global decode 32 33 heap = [ [float(wt), [[sym, []]], repr(sym)] for sym, wt in symbol2weights.iteritems() ] 34 heapify(heap) 35 if tutor: print "ENCODING:", sorted(symbol2weights.iteritems()) 36 while len(heap) >1: 37 lo = heappop(heap) 38 hi = heappop(heap) 39 if tutor: print " COMBINING:", lo, '\n AND:', hi 40 for i in lo[1]: i[1].insert(0, '0') 41 for i in hi[1]: i[1].insert(0, '1') 42 lohi = [ lo[0] + hi[0] ] + [lo[1] + hi[1]] 43 lohi.append('(%s if nextbit() else %s)' % (hi[2], lo[2])) 44 if tutor: print " PRODUCING:", lohi, '\n' 45 heappush(heap, lohi) 46 wt, codes, decoder = heappop(heap) 47 decode = eval('lambda :' + decoder, globals()) 48 decode.__doc__ = decoder 49 for i in codes: i[1] = ''.join(i[1]) 50 #for i in codes: i[::] = i[:2] 51 return sorted(codes, key=lambda x: (len(x[1]), x)) 52 53 # Input types 54 if 1: 55 tutor = True 56 sequencecount = 50 57 readin = "B 25 C 2.5 D 12.5 A 5 \n" 58 #readin = "a .1 b .15 c .3 d .16 e .29" # Wikipedia sample 59 #readin = "a1 .4 a2 .35 a3 .2 a4 .05" # Wikipedia sample 60 #readin = "A 50 B 25 C 12.5 D 12.5" # RC example 61 62 cleaned = readin.strip().split() 63 symbol2weights = dict((symbol, wt) 64 for symbol, wt in zip(cleaned[0::2], cleaned[1::2]) ) 65 else: 66 tutor = False 67 sequencecount = 500 68 astring = "this is an example for huffman encoding" 69 symbol2weights = dict((ch, astring.count(ch)) for ch in set(astring)) # for astring 70 71 huff = codecreate(symbol2weights, tutor= tutor) 72 print "\nSYMBOL\tWEIGHT\tHUFFMAN CODE" 73 for h in huff: 74 print "%s\t%s\t%s" % (h[0], symbol2weights[h[0]], h[1]) 75 76 ## 77 ## encodedecode check 78 ## 79 symbol2code = dict(huff) 80 symbols, weights = zip(*symbol2weights.iteritems()) 81 # normalize weights 82 weights = [float(wt) for wt in weights] 83 tot = sum(weights) 84 weights = [wt/tot for wt in weights] 85 # Generate a sequence 86 nxt = probchoice(symbols, weights).next 87 symbolsequence = [nxt() for i in range(sequencecount)] 88 # encode it 89 bitsequence = ''.join(symbol2code[sym] for sym in symbolsequence) 90 91 sslen, slen, blen = len(symbolsequence), len(symbols), len(bitsequence) 92 countlen = len(bin(slen1)[2:]) 93 print ''' 94 95 96 ROUNDTRIPPING 97 ============== 98 I have generated a random sequence of %i symbols to the given weights. 99 If I use a binary count to encode each of the %i symbols I would need 100 %i * %i = %i bits to encode the sequence. 101 Using the Huffman code, I need only %i bits. 102 ''' % (sslen, slen, sslen, countlen, sslen * countlen, blen ) 103 104 ## decoding 105 nextbit = (bit=='1' for bit in bitsequence).next 106 107 decoded = [] 108 try: 109 while 1: 110 decoded.append(decode()) 111 except StopIteration: 112 pass 113 114 print "Comparing the decoded sequence with the original I get:", decoded == symbolsequence
This short run is in tutor mode, so you can track the accumulation of the decode function:
ENCODING: [('A', '5'), ('B', '25'), ('C', '2.5'), ('D', '12.5')]
COMBINING: [2.5, [['C', []]], "'C'"]
AND: [5.0, [['A', []]], "'A'"]
PRODUCING: [7.5, [['C', ['0']], ['A', ['1']]], "('A' if nextbit() else 'C')"]
COMBINING: [7.5, [['C', ['0']], ['A', ['1']]], "('A' if nextbit() else 'C')"]
AND: [12.5, [['D', []]], "'D'"]
PRODUCING: [20.0, [['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]], "('D' if nextbit() else ('A' if nextbit() else 'C'))"]
COMBINING: [20.0, [['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]], "('D' if nextbit() else ('A' if nextbit() else 'C'))"]
AND: [25.0, [['B', []]], "'B'"]
PRODUCING: [45.0, [['C', ['0', '0', '0']], ['A', ['0', '0', '1']], ['D', ['0', '1']], ['B', ['1']]], "('B' if nextbit() else ('D' if nextbit() else ('A' if nextbit() else 'C')))"]
SYMBOL WEIGHT HUFFMAN CODE
B 25 1
D 12.5 01
A 5 001
C 2.5 000
ROUNDTRIPPING
==============
I have generated a random sequence of 50 symbols to the given weights.
If I use a binary count to encode each of the 4 symbols I would need
50 * 2 = 100 bits to encode the sequence.
Using the Huffman code, I need only 90 bits.
Comparing the decoded sequence with the original I get: True
COMBINING: [2.5, [['C', []]], "'C'"]
AND: [5.0, [['A', []]], "'A'"]
PRODUCING: [7.5, [['C', ['0']], ['A', ['1']]], "('A' if nextbit() else 'C')"]
COMBINING: [7.5, [['C', ['0']], ['A', ['1']]], "('A' if nextbit() else 'C')"]
AND: [12.5, [['D', []]], "'D'"]
PRODUCING: [20.0, [['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]], "('D' if nextbit() else ('A' if nextbit() else 'C'))"]
COMBINING: [20.0, [['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]], "('D' if nextbit() else ('A' if nextbit() else 'C'))"]
AND: [25.0, [['B', []]], "'B'"]
PRODUCING: [45.0, [['C', ['0', '0', '0']], ['A', ['0', '0', '1']], ['D', ['0', '1']], ['B', ['1']]], "('B' if nextbit() else ('D' if nextbit() else ('A' if nextbit() else 'C')))"]
SYMBOL WEIGHT HUFFMAN CODE
B 25 1
D 12.5 01
A 5 001
C 2.5 000
ROUNDTRIPPING
==============
I have generated a random sequence of 50 symbols to the given weights.
If I use a binary count to encode each of the 4 symbols I would need
50 * 2 = 100 bits to encode the sequence.
Using the Huffman code, I need only 90 bits.
Comparing the decoded sequence with the original I get: True
And if I change line 54 to be False, I get the following:
SYMBOL WEIGHT HUFFMAN CODE
6 101
n 4 010
a 3 1001
e 3 1100
f 3 1101
h 2 0001
i 3 1110
m 2 0010
o 2 0011
s 2 0111
g 1 00000
l 1 00001
p 1 01100
r 1 01101
t 1 10000
u 1 10001
x 1 11110
c 1 111110
d 1 111111
ROUNDTRIPPING
==============
I have generated a random sequence of 500 symbols to the given weights.
If I use a binary count to encode each of the 19 symbols I would need
500 * 5 = 2500 bits to encode the sequence.
Using the Huffman code, I need only 2012 bits.
Comparing the decoded sequence with the original I get: True
6 101
n 4 010
a 3 1001
e 3 1100
f 3 1101
h 2 0001
i 3 1110
m 2 0010
o 2 0011
s 2 0111
g 1 00000
l 1 00001
p 1 01100
r 1 01101
t 1 10000
u 1 10001
x 1 11110
c 1 111110
d 1 111111
ROUNDTRIPPING
==============
I have generated a random sequence of 500 symbols to the given weights.
If I use a binary count to encode each of the 19 symbols I would need
500 * 5 = 2500 bits to encode the sequence.
Using the Huffman code, I need only 2012 bits.
Comparing the decoded sequence with the original I get: True
Note: The purpose of the program is to teach me more about Huffman coding and is not an exercise in speed!
I am the author of all the Python on this page. The diagram is from Wikipedia. Please refrain from passingoff my code as your own (that one is mainly for students).
The bitarray module
ReplyDeletehttp://pypi.python.org/pypi/bitarray/
efficiently represents an array of booleans as bits. It also provides example code for performing Huffman encoding and decoding.
It takes doing my own program for me to truly appreciate what is being given in something like the bitarray module.
ReplyDelete Paddy.
Holy broken html, batman.
ReplyDelete