Provided by: dbacl_1.12-3_amd64 bug


       dbacl - a digramic Bayesian classifier for text recognition.


       dbacl  [-01dvnirmwMNDXW]  [-T  type  ]  -l  category  [-h  size] [-H gsize] [-x decim] [-q
              quality] [-w  max_order]  [-e  deftok]  [-o  online]  [-L  measure]  [-g  regex]...

       dbacl [-vnimNRX] [-h size] [-T type] -c category [-c category]...  [-f keep]...  [FILE]...

       dbacl -V


       dbacl  is a Bayesian text and email classifier. When using the -l switch, it learns a body
       of text and produce a file named category which summarizes the text.  When  using  the  -c
       switch,  it  compares  an input text stream with any number of category files, and outputs
       the name of the closest match, or optionally various numerical scores explained below.

       Whereas this manual page is intended as a  reference,  there  are  several  tutorials  and
       documents  you  can read to get specialized information.  Specific documentation about the
       design of dbacl and the statistical models that it uses can be found in   For  a
       basic overview of text classification using dbacl, see tutorial.html. A companion tutorial
       geared towards email filtering is  email.html.  If  you  have  trouble  getting  dbacl  to
       classify  reliably,  read  is_it_working.html.  The USAGE section of this manual page also
       has some examples.





       dbacl uses a maximum entropy (minimum divergence) language model constructed with  respect
       to  a digramic reference measure (unknown tokens are predicted from digrams, i.e. pairs of
       letters). Practically, this means that a  category  is  constructed  from  tokens  in  the
       training  set,  while  previously  unseen tokens can be predicted automatically from their
       letters. A token here is either a word (fragment) or a combination of  words  (fragments),
       selected  according  to  various  switches.  Learning  roughly  works  by  tweaking  token
       probabilities until the training data is least surprising.


       The normal shell exit conventions aren't followed (sorry!).  When  using  the  -l  command
       form,  dbacl  returns zero on success, nonzero if an error occurs. When using the -c form,
       dbacl returns a positive integer corresponding to the category with the highest  posterior
       probability.  In  case  of  a tie, the first most probable category is chosen. If an error
       occurs, dbacl returns zero.


       When using the -l command form, dbacl learns a category when given one or more FILE names,
       which should contain readable ASCII text. If no FILE is given, dbacl learns from STDIN. If
       FILE is a directory, it is opened and all its files are read, but not its  subdirectories.
       The  result  is  saved  in  the  binary  file  named category, and completely replaces any
       previous contents. As a convenience, if the environment  variable  DBACL_PATH  contains  a
       directory, then that is prepended to the file path, unless category starts with a '/' or a

       The input text for learning is assumed to be unstructured plain text by default.  This  is
       not  suitable  for  learning email, because email contains various transport encodings and
       formatting instructions which can reduce classification effectiveness. You must use the -T
       switch  in  that case so that dbacl knows it should perform decoding and filtering of MIME
       and HTML as appropriate.  Apropriate switch values are "-T email" for RFC2822 email input,
       "-T  html"  for  HTML  input,  "-T  xml"  for generic XML style input and "-T text" is the
       default plain text format. There are other values of the -T switch that  also  allow  fine
       tuning of the decoding capabilities.

       When  using  the  -c  command  form, dbacl attempts to classify the text found in FILE, or
       STDIN if no FILE is given. Each possible category must be given separately, and should  be
       the  file  name  of  a  previously  learned text corpus. As a convenience, if the variable
       DBACL_PATH contains a directory, it is prepended to each file  path  which  doesn't  start
       with  a  '/' or a '.'. The visible output of the classification depends on the combination
       of extra switches used. If no switch is used, then no output is shown on STDOUT.  However,
       dbacl always produces an exit code which can be tested.

       To  see an output for a classification, you must use at least one of the -v,-U,-n,-N,-D,-d
       switches. Sometimes, they can be used in combination to produce  a  natural  variation  of
       their individual outputs. Sometimes, dbacl also produces warnings on STDERR if applicable.

       The -v switch outputs the name of the best category among all the choices given.

       The  -U  switch outputs the name of the best category followed by a confidence percentage.
       Normally, this is the switch that you want to use. A percentage of 100% means  that  dbacl
       is  sure of its choice, while a percentage of 0% means that some other category is equally
       likely. This is not the model probability, but measures how unambiguous the classification
       is, and can be used to tag unsure classifications (e.g. if the confidence is 25% or less).

       The -N switch prints each category name followed by its (posterior) probability, expressed
       as a percentage. The percentages always sum to 100%. This is intuitive, but only  valuable
       if the document being classified contains a handful of tokens (ten or less). In the common
       case with many more tokens, the probabilities are always extremely close to 100% and 0%.

       The -n switch prints each  category  name  followed  by  the  negative  logarithm  of  its
       probability. This is equivalent to using the -N switch, but much more useful. The smallest
       number gives the best category. A more convenient form is to use  both  -n  and  -v  which
       prints each category name followed by the cross entropy and the number of tokens analyzed.
       The cross entropy measures (in bits) the average compression  rate  which  is  achievable,
       under  the given category model, per token of input text. If you use all three of -n,-v,-X
       then an extra value is output for each category, representing a kind of p-value  for  each
       category  score.  This  indicates  how  typical  the  score  is  compared  to the training
       documents, but only works if the -X switch was used during learning,  and  only  for  some
       types  of  models  (e.g. email).  These p-values are uniformly distributed and independent
       (if the categories are independent), so can be combined using Fisher's chi squared test to
       obtain composite p-values for groupings of categories.

       The  -v  and  -X  switches  together  print  each  category  name  followed  by a detailed
       decomposition of the category score, factored into ( divergence  rate  +  shannon  entropy
       rate )* token count @ p-value. Again, this only works in some types of models.

       The  -v  and  -U  switches  print  each  category  name followed by a decomposition of the
       category score into ( divergence rate + shannon entropy rate #  score  variance  )*  token

       The  -D  switch  prints  out  the  input  text  as  modified  internally by dbacl prior to
       tokenization. For example, if a MIME encoded  email  document  is  classified,  then  this
       prints  the  decoded  text  that will be actually tokenized and classified. This switch is
       mainly useful for debugging.

       The -d switch dumps tokens and scores  while  they  are  being  read.  It  is  useful  for
       debugging,  or  if  you  want to create graphical representations of the classification. A
       detailed explanation of the output is beyond  the  scope  of  this  manual  page,  but  is
       straightforward  if you've read  Possible variations include -d together with -n
       or -N.

       Classification can be done with one or several categories in principle. When two  or  more
       categories  are  used,  the  Bayesian posterior probability is used, given the input text,
       with a uniform prior distribution on categories. For  other  choices  of  prior,  see  the
       companion  utility bayesol(1).  When a single category is used, classification can be done
       by comparing the score with a treshold. In  practice  however,  much  better  results  are
       obtained with several categories.

       Learning and classifying cannot be mixed on the same command invocation, however there are
       no locking issues and separate dbacl processes can  operate  simultaneously  with  obvious
       results, because file operations are designed to be atomic.

       Finally,  note  that  dbacl  does  not  manage  your  document  corpora  or  your computed
       categories, and in particular it does not allow you to extend an  existing  category  file
       with  new  documents.   This  is  unlike various current spam filters, which can learn new
       emails incrementally. This limitation of dbacl is partially due to the nonlinear procedure
       used in the learning algorithm, and partially a desire for increased flexibility.

       You can simulate the effect of incremental learning by saving your training documents into
       archives and adding to these archives over time,  relearning  from  scratch  periodically.
       Learning  is  actually faster if these archives are compressed and decompressed on the fly
       when needed. By keeping control of your archives, you can never lose  the  information  in
       your categories, and you can easily experiment with different switches or tokenizations or
       sets of training documents if you like.


       By default, dbacl classifies the input text as a whole. However, when using the -f option,
       dbacl  can  be  used to filter each input line separately, printing only those lines which
       match one or more models identified by keep (use the category name or number to refer to a
       category). This is useful if you want to filter out some lines, but note that if the lines
       are short, then the error rate can be high.

       The -e,-w,-g,-j switches are used for selecting  an  appropriate  tokenization  scheme.  A
       token is a word or word fragment or combination of words or fragments. The shape of tokens
       is important because it forms the basis of the language models  used  by  dbacl.   The  -e
       switch  selects  a  predefined  tokenization  scheme, which is speedy but limited.  The -w
       switch specifies composite tokens derived from the -e switch. For example, "-e alnum -w 2"
       means  that  tokens  should be alphanumeric word fragments combined into overlapping pairs
       (bigrams). When the -j switch is used,  all  tokens  are  converted  to  lowercase,  which
       reduces the number of possible tokens and therefore memory consumption.

       If  the  -g  switch  is  used, you can completely specify what the tokens should look like
       using a regular  expression.  Several  -g  switches  can  be  used  to  construct  complex
       tokenization  schemes,  and  parentheses  within  each  expression  can  be used to select
       fragments and combine  them  into  n-grams.  The  cost  of  such  flexibility  is  reduced
       classification and learning speed. When experimenting with tokenization schemes, try using
       the -d or -D switches while learning  or  classifying,  as  they  will  print  the  tokens
       explicitly  so  you  can  see what text fragments are picked up or missed out. For regular
       exression syntax, see regex(7).

       The -h and -H switches  regulate  how  much  memory  dbacl  may  use  for  learning.  Text
       classification  can  use  a  lot of memory, and by default dbacl limits itself even at the
       expense of learning accuracy. In many cases if a limit is reached, a warning message  will
       be printed on STDERR with some advice.

       When  relearning the same category several times, a significant speedup can be obtained by
       using the -1 switch, as this allows the previously learned probabilities to be  read  from
       the category and reused.

       Note  that  classification  accuracy  depends  foremost  on  the amount and quality of the
       training samples, and then only on amount of tweaking.


       When using the -l command form, dbacl returns zero on success. When  using  the  -c  form,
       dbacl returns a positive integer (1,2,3...) corresponding to the category with the highest
       posterior probability. In case of a tie, the first most probable category is chosen. If an
       error occurs, dbacl returns zero.


       -0     When  learning,  prevents weight preloading. Normally, dbacl checks if the category
              file already exists, and if so, tries to use the existing  weights  as  a  starting
              point.  This  can  dramatically speed up learning.  If the -0 (zero) switch is set,
              then dbacl behaves as if no category file already exists. This is mainly useful for
              testing.   This  switch  is now enabled by default, to protect against weight drift
              which  can  reduce  accuracy  over  many  learning  iterations.  Use  -1  to  force

       -1     Force  weight preloading if the category file already exists. See discussion of the
              -0 switch.

       -a     Append scores. Every input line is written to  STDOUT  and  the  dbacl  scores  are
              appended.  This  is  useful  for  postprocessing  with  bayesol(1).   For  ease  of
              processing, every original input line is indented by a single space (to distinguish
              them  from  the  appended  scores), and the line with the scores (if -n is used) is
              prefixed with the string "scores ". If a second copy of dbacl needs  to  read  this
              output later, it should be invoked with the -A switch.

       -d     Dump  the  model  parameters  to  STDOUT.  In  conjunction with the -l option, this
              produces a human-readable summary of the maximum entropy model. In conjunction with
              the  -c  option,  displays  the  contribution  of  each  token  to the final score.
              Suppresses all other normal output.

       -e     Select character class for default  (not  regex-based)  tokenization.  By  default,
              tokens  are  alphabetic  strings  only. This corresponds to the case when deftok is
              "alpha". Possible values for deftok are "alpha", "alnum",  "graph",  "char",  "cef"
              and  "adp".   The  last two are custom tokenizers intended for email messages.  See
              also isalpha(3).  The "char" tokenizer picks up single printable characters  rather
              than bigger tokens, and is intended for testing only.

       -f     Filter  each line of input separately, passing to STDOUT only lines which match the
              category identified as keep.  This  option  should  be  used  repeatedly  for  each
              category  which  must  be  kept.   keep  can be either the category file name, or a
              positive integer representing the required category in the same order it appears on
              the command line.

              Output  lines  are flushed as soon as they are written. If the input file is a pipe
              or character device, then an attempt is made to use line buffering mode,  otherwise
              the more efficient block buffering is used.

       -g     Learn  only  features  described  by  the  extended regular expression regex.  This
              overrides the default feature selection method (see -w option) and learns, for each
              line  of  input,  only  tokens  constructed from the concatenation of strings which
              match the tagged subexpressions within the supplied regex.   All  substrings  which
              match  regex  within  a  suffix of each input line are treated as features, even if
              they overlap on the input line.

              As an optional convenience, regex can include  the  suffix  ||xyz  which  indicates
              which  parenthesized  subexpressions  should  be  tagged.  In this case, xyz should
              consist exclusively of digits 1 to 9, numbering exactly those subexpressions  which
              should  be  tagged. Alternatively, if no parentheses exist within regex, then it is
              assumed that the whole expression must be captured.

       -h     Set the size of the hash table to 2^size elements. When using the -l  option,  this
              refers  to  the total number of features allowed in the maximum entropy model being
              learned. When using the -c option toghether with the -M switch and multinomial type
              categories, this refers to the maximum number of features taken into account during
              classification.  Without the -M switch, this option has no effect.

       -i     Fully internationalized mode. Forces the use of wide characters  internally,  which
              is necessary in some locales. This incurs a noticeable performance penalty.

       -j     Make  features  case  sensitive. Normally, all features are converted to lower case
              during processing, which reduces  storage  requirements  and  improves  statistical
              estimates for small datasets. With this option, the original capitalization is used
              for each feature. This can improve classification accuracy.

       -m     Aggressively maps categories into  memory  and  locks  them  into  RAM  to  prevent
              swapping,  if  possible.  This  is  useful  when  speed  is paramount and memory is
              plentiful, for example when testing the classifier on large datasets.

              Locking  may  require  relaxing  user  limits  with  ulimit(1).   Ask  your  system
              administrator. Beware when using the -m switch together with the -o switch, as only
              one dbacl process must learn or classify at a time to prevent file  corruption.  If
              no  learning takes place, then the -m switch for classifying is always safe to use.
              See also the discussion for the -o switch.

       -n     Print scores for each category.  Each score is the  product  of  two  numbers,  the
              cross  entropy  and  the  complexity of the input text under each model. Multiplied
              together, they represent the log probability that the input resembles the model. To
              see  these  numbers  separately, use also the -v option. In conjunction with the -f
              option, stops filtering but prints each input line prepended with a list of  scores
              for that line.

       -q     Select quality of learning, where quality can be 1,2,3,4. Higher values take longer
              to learn, and should be slightly more accurate. The default quality  is  1  if  the
              category file doesn't exist or weights cannot be preloaded, and 2 otherwise.

       -o     When  learning,  reads/writes partial token counts so they can be reused. Normally,
              category files are learned from exactly the input data  given,  and  don't  contain
              extraneous  information.  When  this option is in effect, some extra information is
              saved in the file online, after all input was read. This information can be  reread
              the  next  time  that  learning occurs, to continue where the previous dataset left
              off. If online doesn't exist, it is created. If online exists, it  is  read  before
              learning,  and  updated  afterwards.  The  file is approximately 3 times bigger (at
              least) than the learned category.

              In dbacl, file updates are atomic,  but  if  using  the  -o  switch,  two  or  more
              processes should not learn simultaneously, as only one process will write a lasting
              category and memory dump. The -m switch can also  speed  up  online  learning,  but
              beware  of possible corruption.  Only one process should read or write a file. This
              option is intended primarily for controlled test runs.

       -r     Learn the digramic reference model only. Skips the learning of  extra  features  in
              the text corpus.

       -v     Verbose   mode.   When  learning,  print  out  details  of  the  computation,  when
              classifying, print out the name of the most probable category.  In conjunction with
              the  -n  option,  prints the scores as an explicit product of the cross entropy and
              the complexity.

       -w     Select default features to be n-grams up to max_order.  This is  incompatible  with
              the  -g  option,  which  always takes precedence. If no -w or -g options are given,
              dbacl assumes -w 1. Note that n-grams for n greater than 1  do  not  straddle  line
              breaks by default.  The -S switch enables line straddling.

       -x     Set  decimation  probability to 1 - 2^(-decim).  To reduce memory requirements when
              learning, some inputs are randomly skipped, and only a few are added to the  model.
              Exact  behaviour  depends on the applicable -T option (default is -T "text").  When
              the type is not "email" (eg "text"), then individual input features are added  with
              probability  2^(-decim).  When  the  type  is "email", then full input messages are
              added with probability 2^(-decim).  Within each  such  message,  all  features  are

       -A     Expect indented input and scores. With this switch, dbacl expects input lines to be
              indented by a single space character (which is then skipped).  Lines starting  with
              any  other  character  are ignored. This is the counterpart to the -a switch above.
              When used together with the -a switch, dbacl outputs the skipped lines as they are,
              and reinserts the space at the front of each processed input line.

       -D     Print  debug output. Do not use normally, but can be very useful for displaying the
              list features picked up while learning.

       -H     Allow hash table to grow up to a  maximum  of  2^gsize  elements  during  learning.
              Initial size is given by -h option.

       -L     Select the digramic reference measure for character transitions. The measure can be
              one of "uniform", "dirichlet" or "maxent". Default is "uniform".

       -M     Force multinomial calculations. When learning, forces  the  model  features  to  be
              treated  multinomially.  When  classifying,  corrects  entropy  scores  to  reflect
              multinomial probabilities (only applicable to multinomial type models, if present).
              Scores will always be lower, because the ordering of features is lost.

       -N     Print  posterior  probabilities  for  each  category.   This  assumes  the supplied
              categories form an exhaustive list of possibilities.  In conjunction  with  the  -f
              option,  stops filtering but prints each input line prepended with a summary of the
              posterior distribution for that line.

       -R     Include an extra category for purely random text. The category is called  "random".
              Only makes sense when using the -c option.

       -S     Enable line straddling. This is useful together with the -w option to allow n-grams
              for n > 1 to ignore line breaks, so a complex token can continue past  the  end  of
              the line. This is not recommended for email.

       -T     Specify nonstandard text format. By default, dbacl assumes that the input text is a
              purely ASCII text file. This corresponds to the case when type is "text".

              There are several types and subtypes which can be used to clean the input  text  of
              extraneous tokens before actual learning or classifying takes place. Each (sub)type
              you wish to use must be indicated with a separate -T option on  the  command  line,
              and automatically implies the corresponding type.

              The  "text"  type  is for unstructured plain text. No cleanup is performed. This is
              the default if no types are given on the command line.

              The "email" type is for mbox format input files or single RFC822  emails.   Headers
              are  recognized  and  most  are  skipped.  To include extra RFC822 standard headers
              (except for trace headers), use the  "email:headers"  subtype.   To  include  trace
              headers, use the "email:theaders" subtype. To include all headers in the email, use
              the "email:xheaders"  subtype.  To  skip  all  headers,  except  the  subject,  use
              "email:noheaders".  To  scan  binary  attachments for strings, use the "email:atts"

              When the "email" type is in effect, HTML markup is automatically removed from  text
              attachments  except  text/plain  attachments. To also remove HTML markup from plain
              text attachments, use "email:noplain". To prevent HTML markup removal in  all  text
              attachments, use "email:plain".

              The  "html"  type is for removing HTML markup (between <html> and </html> tags) and
              surrounding text. Note that  if  the  "email"  type  is  enabled,  then  "html"  is
              automatically enabled for compatible message attachments only.

              The  "xml"  type is like "html", but doesn't honour <html> and </html>, and doesn't
              interpret tags (so this should be more properly called "angle markup" removal,  and
              has nothing to do with actual XML semantics).

              When "html" is enabled, most markup attributes are lost (for values of 'most' close
              to 'all').  The "html:links" subtype forces link urls to  be  parsed  and  learned,
              which  would  otherwise  be  ignored.  The  "html:alt"  subtype  forces  parsing of
              alternative text in ALT attributes  and  various  other  tags.  The  "html:scripts"
              subtype  forces  parsing  of  scripts,  "html:styles"  forces  parsing  of  styles,
              "html:forms" forces parsing of form values, while "html:comments" forces parsing of
              HTML comments.

       -U     Print  (U)nambiguity.   When  used in conjunction with the -v switch, prints scores
              followed by their empirical standard deviations. When used alone, prints  the  best
              category,  followed  by  an  estimated  probability  that  this  category choice is
              unambiguous. More precisely, the  probability  measures  lack  of  overlap  of  CLT
              confidence  intervals  for  each category score (If there is overlap, then there is

              This estimated probability can be used as an "unsure" flag, e.g. if  the  estimated
              probability  is  lower  than 50%. Formally, a score of 0% means another category is
              equally likely to apply to the input, and a score of 100% means no  other  category
              is  likely to apply to the input. Note that this type of confidence is unrelated to
              the -X switch. Also, the probability estimate is usually low  if  the  document  is
              short,  or  if  the  message  contains many tokens that have never been seen before
              (only applies to uniform digramic measure).

       -V     Print the program version number and exit.

       -W     Like -w, but prevents features from straddling newlines. See the description of -w.

       -X     Print the confidence in the score calculated for each category, when used  together
              with  the -n or -N switch. Prepares the model for confidence scores, when used with
              the -l switch.  The confidence is an estimate  of  the  typicality  of  the  score,
              assuming the null hypothesis that the given category is correct. When used with the
              -v switch alone, factorizes the score as the empirical divergence plus the  shannon
              entropy, multiplied by complexity, in that order. The -X switch is not supported in
              all possible models, and displays a percentage of "0.0" if it can't be  calculated.
              Note  that  for  unknown documents, it is quite common to have confidences close to


       To create two category files in the current directory from  two  ASCII  text  files  named
       Mark_Twain.txt and William_Shakespeare.txt respectively, type:

       % dbacl -l twain Mark_Twain.txt
       % dbacl -l shake William_Shakespeare.txt

       Now you can classify input text, for example:

       % echo "howdy" | dbacl -v -c twain -c shake
       % echo "to be or not to be" | dbacl -v -c twain -c shake

       Note  that  the  -v option at least is necessary, otherwise dbacl does not print anything.
       The return value is 1 in the first case, 2 in the second.

       % echo "to be or not to be" | dbacl -v -N -c twain -c shake
       twain 22.63% shake 77.37%
       % echo "to be or not to be" | dbacl -v -n -c twain -c shake
       twain  7.04 * 6.0 shake  6.74 * 6.0

       These invocations are equivalent. The numbers  6.74  and  7.04  represent  how  close  the
       average  token  is to each category, and 6.0 is the number of tokens observed. If you want
       to print a simple confidence value together with the best category, replace -v with -U.

       % echo "to be or not to be" | dbacl -U -c twain -c shake
       shake # 34%

       Note that the true probability of category shake versus category twain is 77.37%, but  the
       calculation  is  somewhat  ambiguous,  and  34%  is  the  confidence  out of 100% that the
       calculation is qualitatively correct.

       Suppose a file document.txt contains English text lines interspersed with noise lines.  To
       filter  out the noise lines from the English lines, assuming you have an existing category
       shake say, type:

       % dbacl -c shake -f shake -R document.txt > document.txt_eng
       % dbacl -c shake -f random -R document.txt > document.txt_rnd

       Note that the quality of the results will vary depending on how well the categories  shake
       and  random  represent  each  input  line.   It  is  sometimes useful to see the posterior
       probabilities for each line without filtering:

       % dbacl -c shake -f shake -RN document.txt > document.txt_probs

       You can now postprocess the posterior probabilities for each line  of  text  with  another
       script, to replicate an arbitrary Bayesian decision rule of your choice.

       In the special case of exactly two categories, the optimal Bayesian decision procedure can
       be implemented for documents as follows: let p1 be the prior probability  that  the  input
       text  is  classified  as category1.  Consequently, the prior probability of classifying as
       category2 is 1 - p1.  Let u12 be the cost of misclassifying  a  category1  input  text  as
       belonging to category2 and vice versa for u21.  We assume there is no cost for classifying
       correctly.  Then the following command implements the optimal Bayesian decision:

       % dbacl -n -c category1 -c category2 | awk '{ if($2 * p1 * u12 > $4 * (1 - p1) * u21) {
              print $1; } else { print $3; } }'

       dbacl  can  also  be  used  in conjunction with procmail(1) to implement a simple Bayesian
       email classification system. Assume that incoming mail should be  automatically  delivered
       to  one  of  three  mail  folders  located in $MAILDIR and named work, personal, and spam.
       Initially, these must be created and filled with appropriate sample emails.  A  crontab(1)
       file can be used to learn the three categories once a day, e.g.

       5  0 * * * dbacl -T email -l $CATS/work $MAILDIR/work
       10 0 * * * dbacl -T email -l $CATS/personal $MAILDIR/personal
       15 0 * * * dbacl -T email -l $CATS/spam $MAILDIR/spam

       To  automatically  deliver  each incoming email into the appropriate folder, the following
       procmailrc(5) recipe fragment could be used:


       # run the spam classifier
       :0 c
       YAY=| dbacl -vT email -c $CATS/work -c $CATS/personal -c $CATS/spam

       # send to the appropriate mailbox
       * ? test -n "$YAY"


       Sometimes, dbacl will send the email to the wrong mailbox. In that case, the misclassified
       message  should  be  removed from its wrong destination and placed in the correct mailbox.
       The error will be corrected the next time your messages are learned.  If it is left in the
       wrong category, dbacl will learn the wrong corpus statistics.

       The  default  text  features  (tokens)  read by dbacl are purely alphabetic strings, which
       minimizes memory requirements but can be unrealistic in some cases.  To  construct  models
       based  on alphanumeric tokens, use the -e switch. The example below also uses the optional
       -D switch, which prints a list of actual tokens found in the document:

       % dbacl -e alnum -D -l twain Mark_Twain.txt | less

       It is also possible to override the default feature selection method  used  to  learn  the
       category  model by means of regular expressions. For example, the following duplicates the
       default feature selection method in the C locale, while being much slower:

       % dbacl -l twain -g '^([[:alpha:]]+)' -g '[^[:alpha:]]([[:alpha:]]+)' Mark_Twain.txt

       The category twain which is obtained depends only on single alphabetic words in  the  text
       file  Mark_Twain.txt  (and  computed  digram  statistics  for  prediction).   For a second
       example, the following command builds a  smoothed  Markovian  (word  bigram)  model  which
       depends  on  pairs of consecutive words within each line (but pairs cannot straddle a line

       % dbacl -l twain2 -g '(^|[^[:alpha:]])([[:alpha:]]+)||2' -g
              '(^|[^[:alpha:]])([[:alpha:]]+)[^[:alpha:]]+([[:alpha:]]+)||23' Mark_Twain.txt

       More  general, line based, n-gram models of all orders (up to 7) can be built in a similar
       way.  To construct paragraph based models, you should  reformat  the  input  corpora  with
       awk(1)  or  sed(1)  to  obtain  one  paragraph per line. Line size is limited by available
       memory, but note that regex performance will degrade quickly for long lines.


       The underlying assumption of statistical learning is that a  relatively  small  number  of
       training  documents  can  represent a much larger set of input documents. Thus in the long
       run, learning can grind to a halt without serious impact on classification accuracy. While
       not  true  in reality, this assumption is surprisingly accurate for problems such as email
       filtering.  In practice, this means that a well chosen corpus on the order of ten thousand
       documents  is  sufficient for highly accurate results for years.  Continual learning after
       such a critical mass results in diminishing returns.  Of course,  when  real  world  input
       document  patterns change dramatically, the predictive power of the models can be lost. At
       the other end, a few hundred documents already give acceptable results in most cases.

       dbacl is heavily optimized for the case of frequent classifications but  infrequent  batch
       learning.  This is the long run optimum described above. Under ideal conditions, dbacl can
       classify a hundred emails per second on low end hardware (500Mhz  Pentium  III).  Learning
       speed  is  not  very  much  slower,  but  takes effectively much longer for large document
       collections  for  various  reasons.   When  using  the  -m  switch,  data  structures  are
       aggressively  mapped  into  memory if possible, reducing overheads for both I/O and memory

       dbacl throws away its input as soon as possible, and has no limits on the  input  document
       size.  Both  classification  and learning speed are directly proportional to the number of
       tokens in the input, but learning also needs a nonlinear  optimization  step  which  takes
       time proportional to the number of unique tokens discovered.  At time of writing, dbacl is
       one of the fastest open source mail filters given its optimal  usage  scenario,  but  uses
       more memory for learning than other filters.


       When  saving category files, dbacl first writes out a temporary file in the same location,
       and renames it afterwards. If a problem or crash occurs during learning, the old  category
       file  is therefore left untouched. This ensures that categories can never be corrupted, no
       matter how many processes try to simultaneously learn or classify, and  means  that  valid
       categories are available for classification at any time.

       When  using the -m switch, file contents are memory mapped for speedy reading and writing.
       This, together with the -o switch, is intended mainly for testing purposes, when  tens  of
       thousands  of  messages  must  be  learned  and  scored in a laboratory to measure dbacl's
       accuracy. Because no file locking is attempted for performance  reasons,  corruptions  are
       possible, unless you make sure that only one dbacl process reads or writes any file at any
       given time. This is the only case (-m and -o together) when corruption is possible.


       When classifying a document, dbacl loads all indicated categories into RAM, so  the  total
       memory  needed  is  approximately  the  sum  of the category file sizes plus a fixed small
       overhead.  The input document is consumed while being read, so its  size  doesn't  matter,
       but  very long lines can take up space.  When using the -m switch, the categories are read
       using mmap(2) as available.

       When learning, dbacl keeps a large structure in memory which contains many  objects  which
       won't be saved into the output category. The size of this structure is proportional to the
       number of unique tokens read, but not the size of the  input  documents,  since  they  are
       discarded  while  being  read.  As  a rough guide, this structure is 4x-5x the size of the
       final category file that is produced.

       To prevent unchecked memory growth, dbacl allocates by default a fixed smallish amount  of
       memory  for tokens. When this space is used up, further tokens are discarded which has the
       effect of skewing the learned category making it less usable as more tokens are dropped. A
       warning is printed on STDERR in such a case.

       The  -h switch lets you fix the initial size of the token space in powers of 2, ie "-h 17"
       means 2^17 = 131072 possible tokens. If you type "dbacl -V", you can  see  the  number  of
       bytes  needed  for each token when either learning or classifying. Multiply this number by
       the maximum number of possible tokens to estimate the memory needed for learning.  The  -H
       switch  lets  dbacl  grow  its  tables  automatically  if and when needed, up to a maximum
       specified. So if you type "-H 21", then the initial size will  be  doubled  repeatedly  if
       necessary, up to approximately two million unique tokens.

       When  learning  with  the  -X  switch,  a  handful of input documents are also kept in RAM


              When this variable is set, its value is prepended to every category filename  which
              doesn't start with a '/' or a '.'.


       INT    If  this  signal  is  caught, dbacl simply exits without doing any cleanup or other
              operations. This signal can often be sent by pressing Ctrl-C on the  keyboard.  See

       HUP, QUIT, TERM
              If  one  of  these  signals  is caught, dbacl stops reading input and continues its
              operation as if no more input was available. This is a way of quitting  gracefully,
              but  note  that  in  learning  mode,  a  category file will be written based on the
              incomplete input. The QUIT signal can  often  be  sent  by  pressing  Ctrl- on  the
              keyboard. See stty(1).

       USR1   If  this  signal  is  caught,  dbacl reloads the current categories at the earliest
              feasible opportunity. This is not normally useful at all, but might be  in  special
              cases,  such as if the -f switch is invoked together with input from a long running


       dbacl generated category files are in binary format, and may or may  not  be  portable  to
       systems  using  a  different  byte  order  architecture  (this  depends  on  how dbacl was
       compiled). The -V switch prints out whether categories are portable, or else you can  just

       dbacl  does  not  recognize  functionally equivalent regular expressions, and in this case
       duplicate features will be counted several times.

       With every learned category, the command line options that  were  used  are  saved.   When
       classifying,  make  sure  that  every  relevant  category was learned with the same set of
       options (regexes are allowed to differ), otherwise behaviour is  undefined.  There  is  no
       need to repeat all the switches when classifying.

       If you get many digitization warnings, then you are trying to learn too much data at once,
       or your model is too complex.  dbacl is  compiled  to  save  memory  by  digitizing  final
       weights, but you can disable digitization by editing dbacl.h and recompiling.

       dbacl  offers  several  built-in  tokenizers  (see  -e switch) with more to come in future
       versions, as the author  invents  them.   While  the  default  tokenizer  may  evolve,  no
       tokenizer should ever be removed, so that you can always simulate previous dbacl behaviour
       subject to bug fixes and architectural changes.

       The confidence estimates obtained through the -X switch are underestimates,  ie  are  more
       conservative than they should be.


       "Ya  know,  some  day  scientists are gonna invent something that will outsmart a rabbit."
       (Robot Rabbit, 1953)


       The source code for the latest version of this  program  is  available  at  the  following


       Laird A. Breyer <>


       awk(1),  bayesol(1),  crontab(1),  hmine(1), hypex(1), less(1), mailcross(1), mailfoot(1),
       mailinspect(1), mailtoe(1), procmailex(5), regex(7), stty(1), sed(1)