[Numpy-discussion] Optimizing similarity matrix algorithm
Wed Sep 12 05:43:56 CDT 2007
Hi to all!
For reverse engineering purposes, I need to find where every possible chunk
of bytes in file A is contained in file B. Obviously, if a chunk of length n
is contained in B, I dont' want my script to recognize also all the
subchunks of size < n contained in the chunk.
I coded a naive implementation of a similarity algorithm: scan the
similarity algorithm and find all the diagonals. Here's the code:
file_a = open(argv)
file_b = open(argv)
a = numpy.fromstring(file_a.read(),'c')
b = numpy.fromstring(file_b.read(),'c')
tolerance = int(argv)
chunks = 
valid = True
count_xcent = 0
for x in xrange(len(a)):
for y in xrange(len(b)):
count = 0
if (a[x] == b[y]):
x_cnt, y_cnt = x,y
if (a[x_cnt] == b[y_cnt]):
while (a[x_cnt+1] == b[y_cnt+1]):
count += 1
x_cnt += 1
y_cnt += 1
if ((count > tolerance) or (count == tolerance)):
for tuple in chunks:
if (((x >= tuple) and (x_cnt <= tuple)) and
((y >= tuple) and (y_cnt <= tuple))):
valid = False
print "Found an already processed subchunk"
chunks.append ((x, x_cnt, y, y_cnt))
print "Corresponding chunk found. List 1: from " +
str(x) + " to " + str(x_cnt) +". List 2: from " + str(y) + " to " +
print "with length of " + str (x_cnt + 1 - x)
valid = True
It simply scans the similarity matrix, finds the diagonals in wich a[x] ==
a[y] and interprets the diagonal as a chunk. Then, it stores the chunk in a
list and determines if it's a subchunk of another greater chunk already
The problem is: this implementation is very slow, imho because of three
1. I use a nested for loop to scan the matrix
2. When the start of a diagonal is found, the program scans the diagonal
with another additional loop. Maybe it would be faster to use a function
such as diagonal() (but I can't actually _create_ the boolean similarity
matrix, as it gets way too big for files big enough (we're talking about ~
100 Kb files) - I am forced to compute it "on the way".
3. When I find a chunk, I compute all its subchunks with this approach, and
compare them with the "big" chunk I stored in the list. Maybe there's a
better algorithmical way.
What do you think about these issues? Is there a way to optimize them? And
are there other issues I didn't take in account?
Thanks in advance,
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Numpy-discussion