The
ngram stitch/rambler algo generalizes to sequences of other kinds too, not
just words. For example, music. In this post some examples with letter rambling.
We use
this code to find our letter ngrams:
def create_ngram_letter_pairs(s):
return [["".join(s[i:i+3]),"".join(s[i+3:i+5])] for i in range(len(s) - 4)]
# learn ngram letter pairs:
def learn_ngram_letter_pairs(context,filename):
with open(filename,'r') as f:
text = f.read()
clean_text = re.sub('[<|>=\r\n]',' ',text)
for ngram_pairs in create_ngram_letter_pairs(list(clean_text)):
try:
head,tail = ngram_pairs
context.add_learn("next-2-letters",head,tail)
except:
continue
learn_ngram_letter_pairs(C,filename)
dest = "sw-examples/ngram-letter-pairs--sherlock-holmes.sw"
save_sw(C,dest)
Some example learn rules in
that sw are:
next-2-letters |e R> => |ed> + |oa> + |ep> + |oy> + |eg> + |oc> + |uc>
next-2-letters | Re> => |d-> + |ti> + |ge> + |st> + |ad> + |pu> + |me> + |ce> + |di> + |pl> + |fu> + |ve>
next-2-letters |Red> => |-h> + |is>
next-2-letters |ed-> => |he> + |su> + |-e> + |in> + |-i> + |gi> + |-h> + |-w> + |lo> + |ta> + |ye> + |-s> + |co> + |up> + |-t>
next-2-letters |d-h> => |ea> + |um>
next-2-letters |-he> => |ad> + |re> + |ar> + | w> + | s> + | j> + |r > + | g>
next-2-letters |hea> => |de> + |rd> + |d > + |r > + |vy> + |d,> + |d.> + |rt> + |ds> + |vi> + |d;> + |d?> + |p.> + |ri> + |di> + |lt> + |r!> + |rs> + |ti> + |ve> + |p > + |l > + |da> + |te> + |dg> + |th> + |sa> + |pe> + |r:>
next-2-letters |ead> => |ed> + |er> + | s> + |fu> + | u> + | i> + | o> + |, > + |y > + |. > + |s > + |,"> + | t> + | w> + | a> + |; > + |?"> + |y,> + | f> + |y.> + |."> + |y-> + |in> + |en> + | b> + | h> + |ly> + |ow> + | m> + |li> + |il> + | D> + |ne> + | c> + | H> + |--> + | r> + | l> + |th> + |ac> + |ge> + |st> + | n> + | p> + | g> + |s?> + |ab>
Then we need this function operator:
# extract-3-tail-chars |abcdefgh> == |fgh>
# example usage:
# letter-ramble |*> #=> merge-labels(|_self> + pick-elt next-2-letters extract-3-tail-chars |_self>)
#
# assumes one is a ket
def extract_3_tail_chars(one):
chars = one.label[-3:]
return ket(chars)
Now some examples:
sa: load ngram-letter-pairs--sherlock-holmes.sw
sa: letter-ramble |*> #=> merge-labels(|_self> + pick-elt next-2-letters extract-3-tail-chars |_self>)
sa: letter-ramble^1000 |The>
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sa: letter-ramble^1000 |Here>
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sa: letter-ramble^1000 |Here>
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So a little bit of fun. A couple of things to note. Here we are just working at one level, the letter level. And last time just at the
word level. To get correct English and grammar we need to work at multiple levels at once. Not yet sure the best way to do that. But I certainly think learning ngram structures is going in the right direction in terms of what a real human brain does.
The next thing to wonder is what if we counted frequencies too? Would that give better results or worse? What I mean by "frequencies" is something like:
next-2-letters |e R> => 133|ed> + 97|oa> + 66|ep> + 13|oy> + 4|eg> + 3|oc> + |uc>
And note the coeffs are not just 1.
I think that is it for today.
