see matpalm.com/resemblance for a proper walkthrough
instructions here are more for replicating the results on the above project page

measuring exact resemblance (jaccard coeff against shingles)

ruby

run ruby version and output all with resemblance > 0.5

> cat test.data | ./ruby/shingle.rb coeff 0.5 > result

cpp

run cpp version outputting resemblances above 0 (ie all of them)

outputs to N files generated from N cores so need to collate

> cat test.data | cpp/bin/Release/resemblance 0
> cat resemblance.*.out > result

munging

examine a result file

ie see phrases used for comparison rather than just raw result (eg 1 3 0.88)

> cat test.data | ./examine_result.rb result

generate a histogram of resemblance values

> cat result | ./histo.rb | sort -n > histo.dat
> ./plot_histo.sh histo.dat histo.100.png

measuring resemblance (distance)

ruby

> cat test.data | ./ruby/shingle.rb distance 0 > distances.original

cpp

change the code, cause i havent yet (ie wip!)

converting from distances to points

project distances into 2 dimensional space

then check mean square error for projected distances versus original distances

(similiarly change 2 to whatever dimensionality)

> cat distances.original | ./fastmap.rb 2 > points.2d
> cat points.2d | ./convert_points_to_distances.rb > distances.projected.2d
> ./mean_square_error.rb distances.original distances.projected.2d

plot 2d and 3d data with gnuplot

gnuplot> plot 'points.2d' with dots, 'points.2d' with labels
gnuplot> splot 'points.3d' with dots, 'points.3d' with labels
gnuplot> splot 'points.11d' with dots, 'points.11d' with labels # good luck with this one! ;)

simhash heuristic

compare simhash to brute force order n squared compare all (considering only values above resemblance 0.5)

> cat test.data | ./ruby/shingle.rb coeff 0.5 | sort -nr -k3 > shingling.result
> cat test.data | ./ruby/simhash.rb | sort -nr -k3 > simhash.result
> ./compare.rb 0.5 shingle.result simhash.result

sketching heuristic

compare sketching to brute force order n squared compare all (considering only values above resemblance 0.5)
use 64bit hash, calculate 10 shingles and cutoff at MAX/2

> cat test.data | ./ruby/shingle.rb coeff 0.5 | sort -nr -k3 > shingling.result
> cat test.data | ./ruby/sketch.rb 64 10 2 | sort -nr -k3 > sketch.result
> ./compare.rb 0.5 shingle.result simhash.result

sketching in erlang

> cd erl && rake
> ln -s ../test.data . # todo: make path configurable
> erl -noshell -pa erl/ebin -s main main > erl.result