[Spambayes-checkins] spambayes/spambayes compatheapq.py,NONE,1.1.2.1
compatsets.py,NONE,1.1.2.1 Options.py,1.1.2.1,1.1.2.2
TestDriver.py,1.1.2.1,1.1.2.2 classifier.py,1.1.2.1,1.1.2.2
tokenizer.py,1.1.2.1,1.1.2.2
Anthony Baxter
anthonybaxter at users.sourceforge.net
Sun Jan 12 22:45:42 EST 2003
Update of /cvsroot/spambayes/spambayes/spambayes
In directory sc8-pr-cvs1:/tmp/cvs-serv2675/spambayes
Modified Files:
Tag: reorg-branch
Options.py TestDriver.py classifier.py tokenizer.py
Added Files:
Tag: reorg-branch
compatheapq.py compatsets.py
Log Message:
distutils ate my brain. Rather than continue down the horrible horrible
path of distutils madness, take a less nuanced approach to the sets.py and
heapq.py modules.
--- NEW FILE: compatheapq.py ---
# -*- coding: Latin-1 -*-
"""Heap queue algorithm (a.k.a. priority queue).
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0. For the sake of comparison,
non-existing elements are considered to be infinite. The interesting
property of a heap is that a[0] is always its smallest element.
Usage:
heap = [] # creates an empty heap
heappush(heap, item) # pushes a new item on the heap
item = heappop(heap) # pops the smallest item from the heap
item = heap[0] # smallest item on the heap without popping it
heapify(x) # transforms list into a heap, in-place, in linear time
item = heapreplace(heap, item) # pops and returns smallest item, and adds
# new item; the heap size is unchanged
Our API differs from textbook heap algorithms as follows:
- We use 0-based indexing. This makes the relationship between the
index for a node and the indexes for its children slightly less
obvious, but is more suitable since Python uses 0-based indexing.
- Our heappop() method returns the smallest item, not the largest.
These two make it possible to view the heap as a regular Python list
without surprises: heap[0] is the smallest item, and heap.sort()
maintains the heap invariant!
"""
# Original code by Kevin O'Connor, augmented by Tim Peters
__about__ = """Heap queues
[explanation by François Pinard]
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0. For the sake of comparison,
non-existing elements are considered to be infinite. The interesting
property of a heap is that a[0] is always its smallest element.
The strange invariant above is meant to be an efficient memory
representation for a tournament. The numbers below are `k', not a[k]:
0
1 2
3 4 5 6
7 8 9 10 11 12 13 14
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In
an usual binary tournament we see in sports, each cell is the winner
over the two cells it tops, and we can trace the winner down the tree
to see all opponents s/he had. However, in many computer applications
of such tournaments, we do not need to trace the history of a winner.
To be more memory efficient, when a winner is promoted, we try to
replace it by something else at a lower level, and the rule becomes
that a cell and the two cells it tops contain three different items,
but the top cell "wins" over the two topped cells.
If this heap invariant is protected at all time, index 0 is clearly
the overall winner. The simplest algorithmic way to remove it and
find the "next" winner is to move some loser (let's say cell 30 in the
diagram above) into the 0 position, and then percolate this new 0 down
the tree, exchanging values, until the invariant is re-established.
This is clearly logarithmic on the total number of items in the tree.
By iterating over all items, you get an O(n ln n) sort.
A nice feature of this sort is that you can efficiently insert new
items while the sort is going on, provided that the inserted items are
not "better" than the last 0'th element you extracted. This is
especially useful in simulation contexts, where the tree holds all
incoming events, and the "win" condition means the smallest scheduled
time. When an event schedule other events for execution, they are
scheduled into the future, so they can easily go into the heap. So, a
heap is a good structure for implementing schedulers (this is what I
used for my MIDI sequencer :-).
Various structures for implementing schedulers have been extensively
studied, and heaps are good for this, as they are reasonably speedy,
the speed is almost constant, and the worst case is not much different
than the average case. However, there are other representations which
are more efficient overall, yet the worst cases might be terrible.
Heaps are also very useful in big disk sorts. You most probably all
know that a big sort implies producing "runs" (which are pre-sorted
sequences, which size is usually related to the amount of CPU memory),
followed by a merging passes for these runs, which merging is often
very cleverly organised[1]. It is very important that the initial
sort produces the longest runs possible. Tournaments are a good way
to that. If, using all the memory available to hold a tournament, you
replace and percolate items that happen to fit the current run, you'll
produce runs which are twice the size of the memory for random input,
and much better for input fuzzily ordered.
Moreover, if you output the 0'th item on disk and get an input which
may not fit in the current tournament (because the value "wins" over
the last output value), it cannot fit in the heap, so the size of the
heap decreases. The freed memory could be cleverly reused immediately
for progressively building a second heap, which grows at exactly the
same rate the first heap is melting. When the first heap completely
vanishes, you switch heaps and start a new run. Clever and quite
effective!
In a word, heaps are useful memory structures to know. I use them in
a few applications, and I think it is good to keep a `heap' module
around. :-)
--------------------
[1] The disk balancing algorithms which are current, nowadays, are
more annoying than clever, and this is a consequence of the seeking
capabilities of the disks. On devices which cannot seek, like big
tape drives, the story was quite different, and one had to be very
clever to ensure (far in advance) that each tape movement will be the
most effective possible (that is, will best participate at
"progressing" the merge). Some tapes were even able to read
backwards, and this was also used to avoid the rewinding time.
Believe me, real good tape sorts were quite spectacular to watch!
>From all times, sorting has always been a Great Art! :-)
"""
def heappush(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
_siftdown(heap, 0, len(heap)-1)
def heappop(heap):
"""Pop the smallest item off the heap, maintaining the heap invariant."""
lastelt = heap.pop() # raises appropriate IndexError if heap is empty
if heap:
returnitem = heap[0]
heap[0] = lastelt
_siftup(heap, 0)
else:
returnitem = lastelt
return returnitem
def heapreplace(heap, item):
"""Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable uses of
this routine.
"""
returnitem = heap[0] # raises appropriate IndexError if heap is empty
heap[0] = item
_siftup(heap, 0)
return returnitem
def heapify(x):
"""Transform list into a heap, in-place, in O(len(heap)) time."""
n = len(x)
# Transform bottom-up. The largest index there's any point to looking at
# is the largest with a child index in-range, so must have 2*i + 1 < n,
# or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
# j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is
# (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
for i in xrange(n//2 - 1, -1, -1):
_siftup(x, i)
# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos
# is the index of a leaf with a possibly out-of-order value. Restore the
# heap invariant.
def _siftdown(heap, startpos, pos):
newitem = heap[pos]
# Follow the path to the root, moving parents down until finding a place
# newitem fits.
while pos > startpos:
parentpos = (pos - 1) >> 1
parent = heap[parentpos]
if parent <= newitem:
break
heap[pos] = parent
pos = parentpos
heap[pos] = newitem
# The child indices of heap index pos are already heaps, and we want to make
# a heap at index pos too. We do this by bubbling the smaller child of
# pos up (and so on with that child's children, etc) until hitting a leaf,
# then using _siftdown to move the oddball originally at index pos into place.
#
# We *could* break out of the loop as soon as we find a pos where newitem <=
# both its children, but turns out that's not a good idea, and despite that
# many books write the algorithm that way. During a heap pop, the last array
# element is sifted in, and that tends to be large, so that comparing it
# against values starting from the root usually doesn't pay (= usually doesn't
# get us out of the loop early). See Knuth, Volume 3, where this is
# explained and quantified in an exercise.
#
# Cutting the # of comparisons is important, since these routines have no
# way to extract "the priority" from an array element, so that intelligence
# is likely to be hiding in custom __cmp__ methods, or in array elements
# storing (priority, record) tuples. Comparisons are thus potentially
# expensive.
#
# On random arrays of length 1000, making this change cut the number of
# comparisons made by heapify() a little, and those made by exhaustive
# heappop() a lot, in accord with theory. Here are typical results from 3
# runs (3 just to demonstrate how small the variance is):
#
# Compares needed by heapify Compares needed by 1000 heapppops
# -------------------------- ---------------------------------
# 1837 cut to 1663 14996 cut to 8680
# 1855 cut to 1659 14966 cut to 8678
# 1847 cut to 1660 15024 cut to 8703
#
# Building the heap by using heappush() 1000 times instead required
# 2198, 2148, and 2219 compares: heapify() is more efficient, when
# you can use it.
#
# The total compares needed by list.sort() on the same lists were 8627,
# 8627, and 8632 (this should be compared to the sum of heapify() and
# heappop() compares): list.sort() is (unsurprisingly!) more efficient
# for sorting.
def _siftup(heap, pos):
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the smaller child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos < endpos:
# Set childpos to index of smaller child.
rightpos = childpos + 1
if rightpos < endpos and heap[rightpos] <= heap[childpos]:
childpos = rightpos
# Move the smaller child up.
heap[pos] = heap[childpos]
pos = childpos
childpos = 2*pos + 1
# The leaf at pos is empty now. Put newitem there, and and bubble it up
# to its final resting place (by sifting its parents down).
heap[pos] = newitem
_siftdown(heap, startpos, pos)
if __name__ == "__main__":
# Simple sanity test
heap = []
data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
for item in data:
heappush(heap, item)
sort = []
while heap:
sort.append(heappop(heap))
print sort
--- NEW FILE: compatsets.py ---
"""Classes to represent arbitrary sets (including sets of sets).
This module implements sets using dictionaries whose values are
ignored. The usual operations (union, intersection, deletion, etc.)
are provided as both methods and operators.
Important: sets are not sequences! While they support 'x in s',
'len(s)', and 'for x in s', none of those operations are unique for
sequences; for example, mappings support all three as well. The
characteristic operation for sequences is subscripting with small
integers: s[i], for i in range(len(s)). Sets don't support
subscripting at all. Also, sequences allow multiple occurrences and
their elements have a definite order; sets on the other hand don't
record multiple occurrences and don't remember the order of element
insertion (which is why they don't support s[i]).
The following classes are provided:
BaseSet -- All the operations common to both mutable and immutable
sets. This is an abstract class, not meant to be directly
instantiated.
Set -- Mutable sets, subclass of BaseSet; not hashable.
ImmutableSet -- Immutable sets, subclass of BaseSet; hashable.
An iterable argument is mandatory to create an ImmutableSet.
_TemporarilyImmutableSet -- Not a subclass of BaseSet: just a wrapper
around a Set, hashable, giving the same hash value as the
immutable set equivalent would have. Do not use this class
directly.
Only hashable objects can be added to a Set. In particular, you cannot
really add a Set as an element to another Set; if you try, what is
actually added is an ImmutableSet built from it (it compares equal to
the one you tried adding).
When you ask if `x in y' where x is a Set and y is a Set or
ImmutableSet, x is wrapped into a _TemporarilyImmutableSet z, and
what's tested is actually `z in y'.
"""
# Code history:
#
# - Greg V. Wilson wrote the first version, using a different approach
# to the mutable/immutable problem, and inheriting from dict.
#
# - Alex Martelli modified Greg's version to implement the current
# Set/ImmutableSet approach, and make the data an attribute.
#
# - Guido van Rossum rewrote much of the code, made some API changes,
# and cleaned up the docstrings.
#
# - Raymond Hettinger added a number of speedups and other
# improvements.
__all__ = ['BaseSet', 'Set', 'ImmutableSet']
try:
True, False
except NameError:
# Maintain compatibility with Python 2.2
True, False = 1, 0
class BaseSet(object):
"""Common base class for mutable and immutable sets."""
__slots__ = ['_data']
# Constructor
def __init__(self):
"""This is an abstract class."""
# Don't call this from a concrete subclass!
if self.__class__ is BaseSet:
raise TypeError, ("BaseSet is an abstract class. "
"Use Set or ImmutableSet.")
# Standard protocols: __len__, __repr__, __str__, __iter__
def __len__(self):
"""Return the number of elements of a set."""
return len(self._data)
def __repr__(self):
"""Return string representation of a set.
This looks like 'Set([<list of elements>])'.
"""
return self._repr()
# __str__ is the same as __repr__
__str__ = __repr__
def _repr(self, sorted=False):
elements = self._data.keys()
if sorted:
elements.sort()
return '%s(%r)' % (self.__class__.__name__, elements)
def __iter__(self):
"""Return an iterator over the elements or a set.
This is the keys iterator for the underlying dict.
"""
return self._data.iterkeys()
# Equality comparisons using the underlying dicts
def __eq__(self, other):
self._binary_sanity_check(other)
return self._data == other._data
def __ne__(self, other):
self._binary_sanity_check(other)
return self._data != other._data
# Copying operations
def copy(self):
"""Return a shallow copy of a set."""
result = self.__class__()
result._data.update(self._data)
return result
__copy__ = copy # For the copy module
def __deepcopy__(self, memo):
"""Return a deep copy of a set; used by copy module."""
# This pre-creates the result and inserts it in the memo
# early, in case the deep copy recurses into another reference
# to this same set. A set can't be an element of itself, but
# it can certainly contain an object that has a reference to
# itself.
from copy import deepcopy
result = self.__class__()
memo[id(self)] = result
data = result._data
value = True
for elt in self:
data[deepcopy(elt, memo)] = value
return result
# Standard set operations: union, intersection, both differences.
# Each has an operator version (e.g. __or__, invoked with |) and a
# method version (e.g. union).
# Subtle: Each pair requires distinct code so that the outcome is
# correct when the type of other isn't suitable. For example, if
# we did "union = __or__" instead, then Set().union(3) would return
# NotImplemented instead of raising TypeError (albeit that *why* it
# raises TypeError as-is is also a bit subtle).
def __or__(self, other):
"""Return the union of two sets as a new set.
(I.e. all elements that are in either set.)
"""
if not isinstance(other, BaseSet):
return NotImplemented
result = self.__class__()
result._data = self._data.copy()
result._data.update(other._data)
return result
def union(self, other):
"""Return the union of two sets as a new set.
(I.e. all elements that are in either set.)
"""
return self | other
def __and__(self, other):
"""Return the intersection of two sets as a new set.
(I.e. all elements that are in both sets.)
"""
if not isinstance(other, BaseSet):
return NotImplemented
if len(self) <= len(other):
little, big = self, other
else:
little, big = other, self
common = filter(big._data.has_key, little._data.iterkeys())
return self.__class__(common)
def intersection(self, other):
"""Return the intersection of two sets as a new set.
(I.e. all elements that are in both sets.)
"""
return self & other
def __xor__(self, other):
"""Return the symmetric difference of two sets as a new set.
(I.e. all elements that are in exactly one of the sets.)
"""
if not isinstance(other, BaseSet):
return NotImplemented
result = self.__class__()
data = result._data
value = True
selfdata = self._data
otherdata = other._data
for elt in selfdata:
if elt not in otherdata:
data[elt] = value
for elt in otherdata:
if elt not in selfdata:
data[elt] = value
return result
def symmetric_difference(self, other):
"""Return the symmetric difference of two sets as a new set.
(I.e. all elements that are in exactly one of the sets.)
"""
return self ^ other
def __sub__(self, other):
"""Return the difference of two sets as a new Set.
(I.e. all elements that are in this set and not in the other.)
"""
if not isinstance(other, BaseSet):
return NotImplemented
result = self.__class__()
data = result._data
otherdata = other._data
value = True
for elt in self:
if elt not in otherdata:
data[elt] = value
return result
def difference(self, other):
"""Return the difference of two sets as a new Set.
(I.e. all elements that are in this set and not in the other.)
"""
return self - other
# Membership test
def __contains__(self, element):
"""Report whether an element is a member of a set.
(Called in response to the expression `element in self'.)
"""
try:
return element in self._data
except TypeError:
transform = getattr(element, "_as_temporarily_immutable", None)
if transform is None:
raise # re-raise the TypeError exception we caught
return transform() in self._data
# Subset and superset test
def issubset(self, other):
"""Report whether another set contains this set."""
self._binary_sanity_check(other)
if len(self) > len(other): # Fast check for obvious cases
return False
otherdata = other._data
for elt in self:
if elt not in otherdata:
return False
return True
def issuperset(self, other):
"""Report whether this set contains another set."""
self._binary_sanity_check(other)
if len(self) < len(other): # Fast check for obvious cases
return False
selfdata = self._data
for elt in other:
if elt not in selfdata:
return False
return True
# Inequality comparisons using the is-subset relation.
__le__ = issubset
__ge__ = issuperset
def __lt__(self, other):
self._binary_sanity_check(other)
return len(self) < len(other) and self.issubset(other)
def __gt__(self, other):
self._binary_sanity_check(other)
return len(self) > len(other) and self.issuperset(other)
# Assorted helpers
def _binary_sanity_check(self, other):
# Check that the other argument to a binary operation is also
# a set, raising a TypeError otherwise.
if not isinstance(other, BaseSet):
raise TypeError, "Binary operation only permitted between sets"
def _compute_hash(self):
# Calculate hash code for a set by xor'ing the hash codes of
# the elements. This ensures that the hash code does not depend
# on the order in which elements are added to the set. This is
# not called __hash__ because a BaseSet should not be hashable;
# only an ImmutableSet is hashable.
result = 0
for elt in self:
result ^= hash(elt)
return result
def _update(self, iterable):
# The main loop for update() and the subclass __init__() methods.
data = self._data
# Use the fast update() method when a dictionary is available.
if isinstance(iterable, BaseSet):
data.update(iterable._data)
return
if isinstance(iterable, dict):
data.update(iterable)
return
value = True
it = iter(iterable)
while True:
try:
for element in it:
data[element] = value
return
except TypeError:
transform = getattr(element, "_as_immutable", None)
if transform is None:
raise # re-raise the TypeError exception we caught
data[transform()] = value
class ImmutableSet(BaseSet):
"""Immutable set class."""
__slots__ = ['_hashcode']
# BaseSet + hashing
def __init__(self, iterable=None):
"""Construct an immutable set from an optional iterable."""
self._hashcode = None
self._data = {}
if iterable is not None:
self._update(iterable)
def __hash__(self):
if self._hashcode is None:
self._hashcode = self._compute_hash()
return self._hashcode
class Set(BaseSet):
""" Mutable set class."""
__slots__ = []
# BaseSet + operations requiring mutability; no hashing
def __init__(self, iterable=None):
"""Construct a set from an optional iterable."""
self._data = {}
if iterable is not None:
self._update(iterable)
def __hash__(self):
"""A Set cannot be hashed."""
# We inherit object.__hash__, so we must deny this explicitly
raise TypeError, "Can't hash a Set, only an ImmutableSet."
# In-place union, intersection, differences.
# Subtle: The xyz_update() functions deliberately return None,
# as do all mutating operations on built-in container types.
# The __xyz__ spellings have to return self, though.
def __ior__(self, other):
"""Update a set with the union of itself and another."""
self._binary_sanity_check(other)
self._data.update(other._data)
return self
def union_update(self, other):
"""Update a set with the union of itself and another."""
self |= other
def __iand__(self, other):
"""Update a set with the intersection of itself and another."""
self._binary_sanity_check(other)
self._data = (self & other)._data
return self
def intersection_update(self, other):
"""Update a set with the intersection of itself and another."""
self &= other
def __ixor__(self, other):
"""Update a set with the symmetric difference of itself and another."""
self._binary_sanity_check(other)
data = self._data
value = True
for elt in other:
if elt in data:
del data[elt]
else:
data[elt] = value
return self
def symmetric_difference_update(self, other):
"""Update a set with the symmetric difference of itself and another."""
self ^= other
def __isub__(self, other):
"""Remove all elements of another set from this set."""
self._binary_sanity_check(other)
data = self._data
for elt in other:
if elt in data:
del data[elt]
return self
def difference_update(self, other):
"""Remove all elements of another set from this set."""
self -= other
# Python dict-like mass mutations: update, clear
def update(self, iterable):
"""Add all values from an iterable (such as a list or file)."""
self._update(iterable)
def clear(self):
"""Remove all elements from this set."""
self._data.clear()
# Single-element mutations: add, remove, discard
def add(self, element):
"""Add an element to a set.
This has no effect if the element is already present.
"""
try:
self._data[element] = True
except TypeError:
transform = getattr(element, "_as_immutable", None)
if transform is None:
raise # re-raise the TypeError exception we caught
self._data[transform()] = True
def remove(self, element):
"""Remove an element from a set; it must be a member.
If the element is not a member, raise a KeyError.
"""
try:
del self._data[element]
except TypeError:
transform = getattr(element, "_as_temporarily_immutable", None)
if transform is None:
raise # re-raise the TypeError exception we caught
del self._data[transform()]
def discard(self, element):
"""Remove an element from a set if it is a member.
If the element is not a member, do nothing.
"""
try:
self.remove(element)
except KeyError:
pass
def pop(self):
"""Remove and return an arbitrary set element."""
return self._data.popitem()[0]
def _as_immutable(self):
# Return a copy of self as an immutable set
return ImmutableSet(self)
def _as_temporarily_immutable(self):
# Return self wrapped in a temporarily immutable set
return _TemporarilyImmutableSet(self)
class _TemporarilyImmutableSet(BaseSet):
# Wrap a mutable set as if it was temporarily immutable.
# This only supplies hashing and equality comparisons.
def __init__(self, set):
self._set = set
self._data = set._data # Needed by ImmutableSet.__eq__()
def __hash__(self):
return self._set._compute_hash()
Index: Options.py
===================================================================
RCS file: /cvsroot/spambayes/spambayes/spambayes/Attic/Options.py,v
retrieving revision 1.1.2.1
retrieving revision 1.1.2.2
diff -C2 -d -r1.1.2.1 -r1.1.2.2
*** Options.py 10 Jan 2003 10:41:08 -0000 1.1.2.1
--- Options.py 13 Jan 2003 06:45:40 -0000 1.1.2.2
***************
*** 7,11 ****
import StringIO
import ConfigParser
! from sets import Set
try:
--- 7,15 ----
import StringIO
import ConfigParser
! try:
! from sets import Set
! except ImportError:
! from spambayes.compatsets import Set
!
try:
Index: TestDriver.py
===================================================================
RCS file: /cvsroot/spambayes/spambayes/spambayes/Attic/TestDriver.py,v
retrieving revision 1.1.2.1
retrieving revision 1.1.2.2
diff -C2 -d -r1.1.2.1 -r1.1.2.2
*** TestDriver.py 10 Jan 2003 10:41:08 -0000 1.1.2.1
--- TestDriver.py 13 Jan 2003 06:45:40 -0000 1.1.2.2
***************
*** 22,28 ****
# alldone()
! from sets import Set
import cPickle as pickle
! from heapq import heapreplace
from spambayes.Options import options
--- 22,36 ----
# alldone()
! try:
! from sets import Set
! except ImportError:
! from spambayes.compatsets import Set
!
import cPickle as pickle
!
! try:
! from heapq import heapreplace
! except ImportError:
! from spambayes.compatheapq import heapreplace
from spambayes.Options import options
Index: classifier.py
===================================================================
RCS file: /cvsroot/spambayes/spambayes/spambayes/Attic/classifier.py,v
retrieving revision 1.1.2.1
retrieving revision 1.1.2.2
diff -C2 -d -r1.1.2.1 -r1.1.2.2
*** classifier.py 10 Jan 2003 10:41:08 -0000 1.1.2.1
--- classifier.py 13 Jan 2003 06:45:40 -0000 1.1.2.2
***************
*** 33,37 ****
import math
! from sets import Set
from spambayes.Options import options
--- 33,40 ----
import math
! try:
! from sets import Set
! except ImportError:
! from spambayes.compatsets import Set
from spambayes.Options import options
Index: tokenizer.py
===================================================================
RCS file: /cvsroot/spambayes/spambayes/spambayes/Attic/tokenizer.py,v
retrieving revision 1.1.2.1
retrieving revision 1.1.2.2
diff -C2 -d -r1.1.2.1 -r1.1.2.2
*** tokenizer.py 10 Jan 2003 10:41:08 -0000 1.1.2.1
--- tokenizer.py 13 Jan 2003 06:45:40 -0000 1.1.2.2
***************
*** 13,17 ****
import time
import os
! from sets import Set
from spambayes.Options import options
--- 13,21 ----
import time
import os
! try:
! from sets import Set
! except ImportError:
! from spambayes.compatsets import Set
!
from spambayes.Options import options
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