Nifty, Tim Peters responded to my e-mail. I must've said something
interesting. Cool, a PyCelebrity!
[barnesc at engr.orst.edu]
...
I've gotten bored and went back to one of my other projects:
reimplementing the Python builtin classes list(), set(), dict(),
and frozenset() with balanced
Hi again,
Since my linear algebra library appears not to serve any practical
need (I found cgkit, and that works better for me), I've gotten bored
and went back to one of my other projects: reimplementing the Python
builtin classes list(), set(), dict(), and frozenset() with balanced
trees
[EMAIL PROTECTED] wrote:
Hi again,
Since my linear algebra library appears not to serve any practical
need (I found cgkit, and that works better for me), I've gotten bored
and went back to one of my other projects: reimplementing the Python
builtin classes list(), set(), dict(), and
barnesc at engr.orst.edu wrote:
So my question is: are there any other *practical* applications of a
B-tree based list/set/dict ? In other words, is this module totally
worth coding, or is it just academic wankery and theoretical flim
flam ? :)
B-trees are specifically designed for disk
[EMAIL PROTECTED] wrote in message
news:[EMAIL PROTECTED]
The bset() and bdict() classes improve upon the builtin set and
dictionary types by maintaining the elements in sorted order.
Generally, the b* classes are a factor of O(log(N)) slower[2] than the
corresponding builtin classes,
IMO sorted dict implementation can be useful, eg. one can get an
interval:
L = D['A' : 'K']
other useful data types:
linkedlist
queue, stack (well deque can do it efficiently in py 2.4)
prioritydict (for graph algorithms)
multimap, multiset (i've never used it but it's in the c++ stl)
mutable
[EMAIL PROTECTED] wrote:
Here overhead is compared to a C array of 1 million PyObject *s.
Thus, on average, a 1 million element B-tree uses 25% less memory
than any other balanced data structure which I am aware of, and 50%
more memory than a raw C array.
That's overhead of indexing;
