Showing posts with label Ruby. Show all posts
Showing posts with label Ruby. Show all posts

2009-05-05

Horspool's Algorithm

Today, another algorithm. Admittedly this one is not likely to be of much use to many programmers as an actual implementation. Horspool's algorithm, the subject of today's post, is nothing more than a pretty efficient way to search a string for a pattern. Obviously unless your project is to write a new programming language API then you already have such facilities ready at hand. In fact I'd be willing to bet that whatever API you're using has an algorithm similar to this to do the job.

The point here is that the purpose of these exercises are not to reinvent the wheel. They are to understand how things work, under the hood so to speak. I, for one, like that. I feel that it is the sign of a good programmer to have that sort of curiosity, to not just know how to do things but how things work. For my own selfish reasons, I find that I never really understand something until I write about it. So, acknowledging that this post might not have a huge audience, at least I'll get something out of it.

Anyway, about Horspool's algorithm. Obviously the simplest way to do a string pattern search is just to start at the beginning of both string and pattern and do a character by character comparison. Then, if a mismatch is found in any character, shift the pattern one position forward in the string and do the comparison again. Really, this is probably efficient enough for most purposes, should a programmer actually need to implement such an algorithm for some reason. For large searches through many large strings something better might be worth it, though.

Obviously (hopefully) I'm not going to be so foolish as to post such a trivial algorithm as the simple version described above, so there is an optimization, Horspool's that is. (One day I'll have an algorithm named after me.) Firstly, Horspool's starts by aligning the string and pattern as described above. Then it does it's comparison starting at the end of the pattern instead of the start. Now, the key insight is that if a mismatch is found the pattern can be shifted more than a single character. For instance if the final character, the first checked, does not match and also is not contained anywhere in the pattern then the pattern can be shifted the whole length of the pattern forward in the string. If, on the other hand, there is a mismatch but the character does exist elsewhere in the pattern then the pattern can be shifted forward the distance necessary to align that character in the pattern with the string character just checked. Furthermore this is true of any character mismatch anywhere in the string. The exact same shifting rule applies.

The algorithm will continue in this way until either all characters are found to match or the start position of the pattern within the string is such that the end of the pattern would be beyond the end of the string. in that case a "no match" indicator is returned (-1 in my version).

Note that an algorithm like this would not be worth it in code that just does low frequency single character comparisons. The simple algorithm is plenty good enough for that. In fact the computation of the shift table might erase any potential efficiency gain. For large comparisons, though, say a grep on a set of large files this algorithm could make a substantial difference. Note that the shift table is calculated only once and so can be amortized across multiple searches. Of course, as I mentioned before, in the real world we already have grep at our disposal but I'm sure whatever greppers one uses have a similar implementation to this (or maybe even more efficient versions, which exist, although those are quite a bit more complex and might be considered not worth it even for api vendors. Evidence of this is that Horpool published his algorithm after the more efficient, but similar, version. Apparently he saw a need for a simplification).

Anyway, here is my Ruby implementation. I tried to include a good many comments to make the logic easy to follow. Hopefully I succeeded. Enjoy.
class Horspool
def findIn(str, pattern)
@shiftTable=Hash.new(nil) #will contain shift amounts for all valid chars
@pattern=pattern
calcShift #buld the shift table
i=0 #pos in str
j=pattern.size-1 #pos in pattern
shift=pattern.size #initialize shift to maximum posible
while i+pattern.size-1<str.size #Pattern doesn't extend past end of str
#Use shift table lookup to shift the precomputed amount minus dist from
#end of pattern (which is (pattern.size-1)-j), if that's less then
#previously computed shift make it the current shift
newShift=@shiftTable[str[i+j,1]]-((pattern.size-1)-j)
(newShift<shift)?shift=newShift:nil
if str[i+j]==pattern[j] #str/pattern char match
j-=1 #moving backward through pattern
if j<0 #Made it all the way through pattern, must match
return i
end
else
i+=shift #Start comparing at new post in str
j=pattern.size-1 #reset j to end of pattern
shift=pattern.size
end
end
return -1 #No match
end

private

def calcShift
#Precompute to shift whole pattern length for chars not in pattern
for i in 'a'..'z'
@shiftTable[i]=@pattern.size
end
#Shift distance from end of pattern-1 for chars in pattern
for i in 0... @pattern.size-1
@shiftTable[@pattern[i,1]]=(@pattern.size-i)-1
end
end
end

if __FILE__ == $0
print Horspool.new.findIn("ericzetterbaum","zett")
end

2009-04-25

Floyd's Algorithm (Ruby)

I've got another algorithm post today. My latest is Floyd's algorithm, again in Ruby. My ultimate goal is to publish a large library of algorithms in Java, Ruby and C. Gonna have to really get moving on that project. I'll have the Java version of this guy shortly.

Floyd's algorithm is very closely related to Warshall's (as I previously presented here and here). Indeed it's sometimes called the Floyd-Warshall algorithm since Floyd apparently got the idea for it from Warshall's algorithm. It's also often called the all-pairs shortest-paths problem which is the descriptive version.

Floyd's algorithm is more commonly seen in the literature (a Google on Warshall is in fact brings this algorithm higher that Warshall's own). The reason for this is that it's useful in more applications. One often wants to know how far it is to something, say a network resource, rather than just whether or not one can get there.

The way Floyd's algorithm works is so. We start with a weight matrix of a directed graph, which is like the adjacency matrix except that it contains "weights" of edges representing distances between nodes. As with Warshall's algorithm one then iterates through intermediate nodes, call them k, to find the distance of the path between each node i,j through k. If it is smaller than the distance already calculated through a previous k, and smaller than the weight (distance) of any edge connected i to j, then the current distance in the distance matrix is replaced with the new, shorter, distance. Note that in my version I initialize the distance matrix with the weight matrix so we always compare the distance paths with, at least, the direct distance between the nodes. We might call this k=0 (or maybe k=-1 would be better given my 0 indexed arrays).

One thing I didn't note in my discussion of Warshall's algorithm is that both these algorithms are examples of dynamic programming. In dynamic programming one builds a final result from intermediate results. I this case each intermediate result is the intermediate node, k. Since we iterate through each k we build up successively all possible paths from all nodes using all possible intermediate nodes thus comparing all paths between all nodes to find the smallest.

OK, so here's the algorithm. Enjoy. (Oh, I included a randomized again, so its easy to generate lots of examples.)
class Floyd #aka all-pairs shortest paths algorithm
def initialize(weightMatrix=nil,oo=256) #256?
@weightMatrix=weightMatrix
@oo=oo #needed to set a weight considered "infinite"
end

attr_accessor :oo

attr_accessor :weightMatrix

def getDistanceMatrix
numNodes=@weightMatrix[0].length
distanceMatrix=Array.new(@weightMatrix)

for k in 0...numNodes
for i in 0...numNodes
#Optimization: if no path from i to k, no need to test k to j
if distanceMatrix[i][k]<@oo then
for j in 0...numNodes
distance = distanceMatrix[i][k] + distanceMatrix[k][j]
if distance<distanceMatrix[i][j] then
distanceMatrix[i][j]=distance
end
end
end
end
end
distanceMatrix
end

def showDistanceMatrix
puts "weight"
@weightMatrix.each{|c| print c.to_s.gsub(@oo.to_s,"oo"),"\n"}
puts "distance"
getDistanceMatrix.each{|c| print c.to_s.gsub(@oo.to_s,"oo"),"\n"}
end

end

#tests:
if __FILE__ == $0
floyd=Floyd.new
oo=floyd.oo
floyd.weightMatrix=[[0,oo,3,oo],[2,0,oo,oo],[oo,7,0,1],[6,oo,oo,0]]
floyd.showDistanceMatrix
dim=10 #randomize out a big graph
weightMatrix=Array.new(dim){Array.new(dim)}
for i in 0...dim do
for j in 0...dim do
if i==j then
weightMatrix[i][j]=0
elsif rand(dim/2)==0 then
weightMatrix[i][j]=rand(9)+1
else
weightMatrix[i][j]=oo
end
end
end
floyd.weightMatrix=weightMatrix
floyd.showDistanceMatrix
end

2009-04-19

Warshall's Algorithm in Ruby

Among my current projects is to learn Ruby. Although my background is almost entirely in statically typed languages I feel that to be a complete programmer I also need to be comfortable in a dynamically typed language. Ruby is definitely interesting. I'm not sure I'm sold on the value of dynamic typing. It seems to deny one the opportunity of letting the language itself help with the programming task, not to mention being much less amenable to static analysis and the such. Still, one might make the case that most of the problems static typing are designed to prevent are one's good programmers shouldn't make very often anyway and that one can simply code faster in statically typed languages since one doesn't need to worry about such minor issues as... knowing the type at design time. Ruby definitely allows one to be very terse with the syntax, do complex things simply and hence program fast. And we all want to program fast, I'm sure.

Anyway this post wasn't really meant to be a discussion of the relative merits of static vs. dynamic typing, or even on why Ruby is cool (although my example below does illustrate the terseness of Ruby relative to Java). It was meant to present my first program in Ruby, a translation of my Java Warshall's algorithm implementation. Actually it's not a direct translation since I added a small optimization. I realized that one doesn't need to bother with the j loop if (i,k)=0. If you can't get from i to k at all you definitely can't get form i to j through k. This needs to be added to the Java implementation, which I'll get around to eventually. Note that this improvement changes the algorithm's best case (a completely unconnected graph) time complexity to Θ(n)=n2. Average and worst cases remain Θ(n)=n3.

I don't have a test case for this since I haven't yet learned how to do test cases in Ruby. OK, I'm sure I could rig one up myself, but I don't see the point. Instead I just have a script at the bottom that displays the path matrix for various adjacency matrix. I also added a little random adjacency matrix generator so that one can see more examples of the algorithm at work. One of the interesting parts of that was to choose reasonable edge densities to get a nice number of paths (using n/2 as the randomizer's cutoff seems to work well).

So, here's the algorithm:

class Warshall
def initialize(adjacencyMatrix)
@adjacencyMatrix=adjacencyMatrix
end

def getPathMatrix
numNodes=@adjacencyMatrix[0].length
pathMatrix=Array.new(@adjacencyMatrix)
for k in 0...numNodes
for i in 0...numNodes
#Optimization: if no path from i to k, no need to test k to j
if pathMatrix[i][k]==1 then
for j in 0...numNodes
if pathMatrix[k][j]==1 then
pathMatrix[i][j]=1
end
end
end
end
end
return pathMatrix
end

def showPathMatrix
puts "adjacency"
@adjacencyMatrix.each{|c| print c,"\n"}
puts "path"
getPathMatrix.each{|c| print c,"\n"}
end

end
#tests:
if __FILE__ == $0
adjMatrix = [[0,1],[1,0]]
Warshall.new(adjMatrix).showPathMatrix
adjMatrix = [[0,1,0],[0,0,1],[0,0,0]]
Warshall.new(adjMatrix).showPathMatrix
adjMatrix = [[0,0,0,1],[1,0,1,1],[1,0,0,1],[1,0,1,0]]
Warshall.new(adjMatrix).showPathMatrix
dim=10 #randomize out a big graph
adjMatrix=Array.new(dim){Array.new(dim)}
for i in 0...dim do
for j in 0...dim do
if rand(dim/2)==0 then
adjMatrix[i][j]=1
else
adjMatrix[i][j]=0
end
end
end
Warshall.new(adjMatrix).showPathMatrix
end #tests