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* Add summary paragraph to howworks

This commit is contained in:
Matthew Honnibal 2015-01-25 01:58:48 +11:00
parent 94750819cd
commit 97a9bc6f61
1 changed files with 9 additions and 1 deletions
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docs/source/howworks.rst
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@ -203,7 +203,7 @@ loop:
weights = model[feat]
for i, weight in enumerate(weights):
scores[i] += weight
class_ = max(enumerate(scores), key=lambda item: item[1])
class_, score = max(enumerate(scores), key=lambda item: item[1])
transition(state, class_)
The parser makes 2N transitions for a sentence of length N. In order to select
@ -254,3 +254,11 @@ the classes. In the case of the parser, this means the hash table is accessed
also be careful to store the weights contiguously in memory --- you don't want
a linked list here. I use a block-sparse format, because my problems tend to
have a few dozen classes.
I guess if I had to summarize my experience, I'd say that the efficiency of
these models is really all about the data structures. We want to stay small,
and stay contiguous. Minimize redundancy and minimize pointer chasing.
That's why Cython is so well suited to this: we get to lay out our data
structures, and manage the memory ourselves, with full C-level control.