[Spambayes-checkins] spambayes hammie.py,1.26,1.27

[Neale Pickett]

Update of /cvsroot/spambayes/spambayes
In directory usw-pr-cvs1:/tmp/cvs-serv29544

Modified Files:
	hammie.py 
Log Message:
* Uses options.spam_cutoff now, instead of hard-coded 0.9.  (Thanks
  to Richie Hindle for the heads up while I was dealing with a RL
  intrusion)
* Grammar fix :)


Index: hammie.py
===================================================================
RCS file: /cvsroot/spambayes/spambayes/hammie.py,v
retrieving revision 1.26
retrieving revision 1.27
diff -C2 -d -r1.26 -r1.27
*** hammie.py	27 Sep 2002 22:38:53 -0000	1.26
--- hammie.py	1 Oct 2002 15:07:45 -0000	1.27
***************
*** 44,47 ****
--- 44,48 ----
  import mboxutils
  import classifier
+ from Options import options
  
  program = sys.argv[0] # For usage(); referenced by docstring above
***************
*** 54,58 ****
  
  # Probability at which a message is considered spam
! SPAM_THRESHOLD = 0.9
  
  # Tim's tokenizer kicks far more booty than anything I would have
--- 55,59 ----
  
  # Probability at which a message is considered spam
! SPAM_THRESHOLD = options.spam_cutoff
  
  # Tim's tokenizer kicks far more booty than anything I would have
***************
*** 140,149 ****
      """A persistent Bayes classifier.
  
!     This is just like classifier.Bayes, except that the dictionary
!     is a database.  You take less disk this way, I think, and you can
!     pretend it's persistent.  It's much slower training, but much faster
!     checking, and takes less memory all around.
  
!     On destruction, an instantiation of this class will write it's state
      to a special key.  When you instantiate a new one, it will attempt
      to read these values out of that key again, so you can pick up where
--- 141,151 ----
      """A persistent Bayes classifier.
  
!     This is just like classifier.Bayes, except that the dictionary is a
!     database.  You take less disk this way and you can pretend it's
!     persistent.  The tradeoffs vs. a pickle are: 1. it's slower
!     training, but faster checking, and 2. it needs less memory to run,
!     but takes more space on the hard drive.
  
!     On destruction, an instantiation of this class will write its state
      to a special key.  When you instantiate a new one, it will attempt
      to read these values out of that key again, so you can pick up where