--- autoclass-3.3.6.dfsg.1.orig/debian/simple.r-params
+++ autoclass-3.3.6.dfsg.1/debian/simple.r-params
@@ -0,0 +1,24 @@
+# AutoClass C report parameter file -- simple.r-params
+
+;   -- Creator/Donor: Jim Van Zandt <jrv@debian.org>
+;   -- Date: 23 January 2000
+
+report_mode = "data"
+
+comment_data_headers_p = true
+!       the default value does not insert # in column 1 of most 
+!       report_mode = "data" header lines.  If specified as true, the comment 
+!       character will be inserted in most header lines.
+
+free_storage_p = false
+!       The default value tells AutoClass to free the majority of its allocated
+!       storage.  This is not required, and in the case of DEC Alpha's causes
+!       core dump.  If specified as false, AutoClass will not attempt to free
+!       storage.
+
+# sigma_contours_att_list = 
+!       If specified, a list of real valued attribute indices (from .hd2 file) 
+!       will be to compute sigma class contour values, when generating 
+!       influence values report with the data option (report_mode = "data"). 
+!       If not specified, there will be no sigma class contour output.
+!       (e.g. sigma_contours_att_list = 3, 4, 5, 8, 15)
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.model
+++ autoclass-3.3.6.dfsg.1/debian/simple.model
@@ -0,0 +1,7 @@
+# AutoClass C model file -- simple.model
+
+;   -- Creator/Donor: Jim Van Zandt <jrv@debian.org>
+;   -- Date: 23 January 2000
+
+model_index 0 1
+single_normal_cn 0 1
--- autoclass-3.3.6.dfsg.1.orig/debian/docpatch
+++ autoclass-3.3.6.dfsg.1/debian/docpatch
@@ -0,0 +1,43 @@
+diff -rauN ../orig/autoclass-3.3.4/debian/autoclass.1 ./autoclass-3.3.4/debian/autoclass.1
+--- ../orig/autoclass-3.3.4/debian/autoclass.1	2006-01-25 15:00:34.000000000 +0100
++++ ./autoclass-3.3.4/debian/autoclass.1	2006-01-25 15:03:50.000000000 +0100
+@@ -764,7 +764,7 @@
+ restart run.  This list should bracket your expected number of
+ classes, and by a wide margin!
+ "start_j_list = -999" specifies an empty list (allowed only on restarts)
+-.IP "\fBn_classes_fn_type\fP = "random_ln_normal""
++.IP "\fBn_classes_fn_type\fP = ""random_ln_normal"""
+ Once \fBstart_j_list\fP is exhausted, \fBAutoClass\fP will call this
+ function to decide how many classes to start with on the next try,
+ based on the 10 best classifications found so far.  Currently only
+@@ -808,7 +808,7 @@
+ Specifies the maximum number of classifications stored internally.
+ .IP "\fBn_final_summary\fP = 10"
+ Specifies the number of trials to be printed out after search ends.
+-.IP "\fBstart_fn_type\fP = "random""
++.IP "\fBstart_fn_type\fP = ""random"""
+ One of {"random", "block"}.  This specifies the type of class
+ initialization.  For normal search, use "random", which randomly
+ selects instances to be initial class means, and adds appropriate
+@@ -816,7 +816,7 @@
+ partitions the database into successive blocks of near equal size.
+ For repeatable results, also see \fBforce_new_search_p\fP,
+ \fBmin_report_period\fP, and \fBrandomize_random_p\fP.
+-.IP "\fBtry_fn_type\fP = "converge_search_3""
++.IP "\fBtry_fn_type\fP = ""converge_search_3"""
+ One of {"converge_search_3", "converge_search_4", "converge"}. 
+ These specify alternate search stopping criteria.  
+ "converge" merely tests the rate of change of the log_marginal
+@@ -912,7 +912,7 @@
+ .IP "\fBcheckpoint_p\fP = false"
+ If true, checkpoints of the current classification will be written
+ every "min_checkpoint_period" seconds, with file extension
+-.chkpt[-bin]. This is only useful for very large classifications
++\&.chkpt[-bin]. This is only useful for very large classifications
+ .IP "\fBmin_checkpoint_period\fP = 10800"
+ If checkpoint_p = true, the checkpointed classification will be 
+ written this often - in seconds (default = 3 hours)
+
+--k1lZvvs/B4yU6o8G--
+
+
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.README
+++ autoclass-3.3.6.dfsg.1/debian/simple.README
@@ -0,0 +1,69 @@
+#!/bin/sh -e
+# use autoclass to separate observations into two groups
+
+if [ ! -f simple.db2 ]; then gunzip *.gz; fi
+
+# is this directory writable?
+if [ ! -w . ]; then
+    echo "permission denied."
+    echo 'please copy simple* to a directory where you have write permission'
+    exit 1
+fi
+
+# to see how the data file simple.db2 was created, see simple.c
+
+# output files may not already exist
+rm -f simple.results *data-1 *text-1
+
+# use autoclass to find the groups
+autoclass -search simple.db2 simple.hd2 simple.model simple.s-params
+
+# ask autoclass to generate reports
+autoclass -reports simple.results simple.search simple.r-params
+
+# copy the oservations into two files,
+# depending on the group assigned by autoclass
+awk '
+/^[0-9]/{
+  group_number=$2;
+  getline data < "simple.db2";
+  print data > "group" group_number;
+}' simple.case-data-1
+
+# display the original data (under X windows)
+gnuplot -persist <<EOF
+set key bottom
+set title "original data"
+plot "simple.db2"
+EOF
+
+# Display the two groups in different colors (under X windows).
+# For each class found, the data file has a section like this, from which we extract the mean
+# and standard deviation of latitude and longitude:
+# numb  t mtt  description           I-jk      Mean     StDev    Mean-*k|/     Mean      StDev
+# t   a                                        -jk      -jk       StDev-jk     -*k       -*k
+#
+# 000 000 R SNcn longitude
+#                                      1.266 ( 2.28e-02  9.50e-01)  3.40e+00 ( 3.25e+00  3.36e+00)
+# 001 001 R SNcn latitude
+#                                      1.266 ( 3.98e+00  9.71e-01)  3.40e+00 ( 7.28e+00  3.41e+00)
+cat  simple.influ-o-data-1 | awk '
+BEGIN{
+  FS="[ ()]+";
+  printf("set key bottom\n");
+  printf("set title \"groups found by AutoClass\"\n");
+  printf("set parametric; set trange [0:6.28]\n");
+  printf("plot \\\n");
+}
+/^[0-9][0-9]/{
+  if ($5~/latitude/) {getline; latmean=$3; latsd=$4; ++data}
+  if ($5~/longitude/) {getline; lonmean=$3; lonsd=$4; ++data}
+  if (data == 2) {
+    if (group) printf(",\\\n");
+    printf("%s+2.45*%s*sin(t),%s+2.45*%s*cos(t) title \"group %d 95%% contour\",\\\n",
+        lonmean, lonsd, latmean, latsd, group);
+    printf("  \"group%d\" title \"group %d members\"", group, group);
+    data=0; group++;
+  }
+}
+END {  printf("\n");}' | gnuplot -persist
--- autoclass-3.3.6.dfsg.1.orig/debian/dirs
+++ autoclass-3.3.6.dfsg.1/debian/dirs
@@ -0,0 +1 @@
+usr/bin
--- autoclass-3.3.6.dfsg.1.orig/debian/tests
+++ autoclass-3.3.6.dfsg.1/debian/tests
@@ -0,0 +1,110 @@
+#!/bin/sh
+#
+# This is a simple script that tests autoclass
+#
+# install as: /usr/lib/debian-test/tests/autoclass
+# or else as: /usr/lib/debian-test/tests/autoclass/test-1
+# In the latter case, /usr/lib/debian-test/tests/autoclass/ can contain
+# other file with test data and/or other scripts named test-2, test-3, etc.
+#
+# You can run this script with the command
+#       sh debian/tests
+# After installation, you can run it with the commands
+#       /usr/lib/debian-test/tests/autoclass
+# or
+#       debian-test -v autoclass
+# see debian-test(1)
+
+. ${DEBIANTEST_LIB:-/usr/lib/debian-test/lib}/functions.sh
+
+if [ -f my-data-file ]; then
+    TESTDIR=`pwd`;
+else
+    TESTDIR=/usr/lib/debian-test/tests/autoclass;
+fi
+
+
+## we need a scratch directory in which to execute
+
+TMP=/tmp/autoclass-test.$$
+test -e $TMP && rm -rf $TMP
+mkdir $TMP
+cd $TMP
+trap "rm -rf $TMP" EXIT
+
+test1(){
+    RESULT=0
+    cp /usr/share/doc/autoclass/examples/simple* .
+
+    # use autoclass to separate observations into two groups
+
+    if [ ! -f simple.db2 ]; then gunzip *.gz; fi
+
+    # use autoclass to find the groups
+    autoclass -search simple.db2 simple.hd2 simple.model simple.s-params
+
+    # ask autoclass to generate reports
+    autoclass -reports simple.results simple.search simple.r-params
+## FIXME
+
+# copy the oservations into two files,
+# depending on the group assigned by autoclass
+awk '
+/^[0-9]/{
+  group_number=$2;
+  getline data < "simple.db2";
+  print data > "group" group_number;
+}' simple.case-data-1
+
+# display the original data (under X windows)
+gnuplot -persist <<EOF
+set key bottom
+set title "original data"
+plot "simple.db2"
+EOF
+
+# display the two groups in different colors (under X windows)
+cat  simple.influ-o-data-1 | awk '
+BEGIN{
+  FS="[ ()]+";
+  printf("set key bottom\n");
+  printf("set title \"groups found by AutoClass\"\n");
+  printf("set parametric; set trange [0:6.28]\n");
+  printf("plot \\\n");
+}
+/^[0-9][0-9]/{
+  if ($5~/latitude/) {latmean=$7; latsd=$8; ++data}
+  if ($5~/longitude/) {lonmean=$7; lonsd=$8; ++data}
+  if (data == 2) {
+    if (group) printf(",\\\n");
+    printf("%s+2.45*%s*sin(t),%s+2.45*%s*cos(t) title \"group %d 95%% contour\",\\\n",
+        lonmean, lonsd, latmean, latsd, group);
+    printf("  \"group%d\" title \"group %d members\"", group, group);
+    data=0; group++;
+  }
+}
+END {  printf("\n");}' | gnuplot -persist
+    
+    
+# execute  autoclass  against first test case
+# in case of failure, set the shell variable RESULT like this:
+    RESULT=1
+
+    return $RESULT
+}
+
+test2(){
+    RESULT=0
+
+# execute  autoclass  against second test case,  
+# OR test for a different bug which has been reported in autoclass
+# OR test a different binary supplied by autoclass.
+# in case of failure, set the shell variable RESULT like this:
+    RESULT=1
+
+    return $RESULT
+}
+
+runtest "first test case" test1
+runtest "second test case" test2
+# extend as necessary...
--- autoclass-3.3.6.dfsg.1.orig/debian/copyright
+++ autoclass-3.3.6.dfsg.1/debian/copyright
@@ -0,0 +1,36 @@
+This package was debianized by James R. Van Zandt <jrv@debian.org> on
+Fri, 10 Sep 1999 22:07:41 -0400.
+
+It was received by email from the upstream maintainer:
+	Will Taylor <taylor@ptolemy.arc.nasa.gov> 
+
+A slightly earlier version is available from:
+	http://ic-www.arc.nasa.gov/ic/projects/bayes-group/autoclass/
+
+The following files where removed from the tarball, as their prefered form
+of modification is missing:
+doc/tr-fia-90-12-7-01.ps
+doc/kdd-95.ps
+
+
+Upstream Authors:
+
+   Dr. Peter Cheeseman
+   Principal Investigator - NASA Ames, Computational Sciences Division
+   cheesem@ptolemy.arc.nasa.gov
+
+   John Stutz
+   Research Programmer - NASA Ames, Computational Sciences Division
+   stutz@ptolemy.arc.nasa.gov
+
+   Will Taylor
+   Support Programmer - NASA Ames, Computational Sciences Division
+   taylor@ptolemy.arc.nasa.gov
+
+Copyright:
+
+         AutoClass has been placed in the public domain.
+
+One file in the upstream sources, prog/fcntlcom-ac.h, contained an
+AT&T copyright statement.  That file is not necessary for Linux, and
+has been omitted from the Debian sources.
--- autoclass-3.3.6.dfsg.1.orig/debian/changelog
+++ autoclass-3.3.6.dfsg.1/debian/changelog
@@ -0,0 +1,198 @@
+autoclass (3.3.6.dfsg.1-1.4.201403132110) ucs4-0-0-0; urgency=low
+
+  * UCS auto build. No patches were applied to the original source package
+
+ -- Univention builddaemon <buildd@univention.de>  Thu, 13 Mar 2014 21:10:17 +0100
+
+autoclass (3.3.6.dfsg.1-1) unstable; urgency=low
+
+  * QA upload.
+  * Set maintainer to QA group
+  * Remove sourceless PostScript files
+    * Add comment to debian/copyright
+    * Drop debian/autoclass.doc-base.autoclass-theory and
+      debian/autoclass.doc-base.autoclass-results
+  * Drop build-depends on gs-common, as we don't build the PDFs anymore
+    (Closes: #614525)
+  * Introduce recommended build-arch: and build-indep: targets in
+    debian/rules
+  * Bump standards to 3.9.2 (no further changes needed)
+
+ -- Alexander Reichle-Schmehl <tolimar@debian.org>  Wed, 07 Dec 2011 10:36:07 +0100
+
+autoclass (3.3.6-1) unstable; urgency=low
+
+  * New upstream release
+  
+  * debian/control: Add dependency on ${misc:Depends}. Bump policy level
+    to 3.8.4 (no changes needed).  Update link to home page.
+
+  * debian/rules: call dh_prep instead of deprecated dh_clean -k.  Copy
+    all the import-* files to the /usr/share/doc/autoclass/examples.  
+
+  * debian/compat: Bump debhelper compatibility version to 7.
+
+  * debian/README.sample: Also run with -reports.
+  
+  * debian/simple.README: adjust awk script for new output format, which
+    spreads info on each variable across two lines.
+
+ -- James R. Van Zandt <jrv@debian.org>  Sun, 07 Feb 2010 10:52:08 -0500
+
+autoclass (3.3.4-7.1) unstable; urgency=low
+
+  * eliminate segfault in import example (Closes:Bug#554785)
+
+ -- Tim Retout <tim@retout.co.uk>  Thu, 31 Dec 2009 18:33:11 +0000
+
+autoclass (3.3.4-7) unstable; urgency=low
+
+  * *.doc-base*: Move documentation to section Science/Data Analysis to
+    conform with revised Debian Menu Policy.
+  
+  * debian/dirs: don't package empty /usr/sbin
+  
+  * debian/rules: handle DEB_BUILD_OPTIONS flag "parallel"
+  
+  * debian/autoclass.1: fix capitalization of bogus macro ".pp" and change
+    line breaks so file names ".search" and ".s-params" are not
+    interpreted as roff macros.
+
+  * debian/control: Add Homepage field.  Bump policy level to 3.8.0.
+
+ -- James R. Van Zandt <jrv@debian.org>  Thu, 14 Aug 2008 22:07:47 -0400
+
+autoclass (3.3.4-6) unstable; urgency=low
+
+  * prog/utils.c: use O_NONBLOCK rather than O_NDELAY to enable building
+    on GNU/kFreeBSD (thanks to Cyril Brulebois
+    <cyril.brulebois@enst-bretagne.fr>, closes:Bug#413860)
+  * debian/autoclass.1: escape quotes (thanks to "Nicolas François"
+    <nicolas.francois@centraliens.net>, closes:Bug#349817)
+  * debian/control: bump Debian policy level to 3.7.2 (no changes needed)
+
+ -- James R. Van Zandt <jrv@debian.org>  Wed,  4 Apr 2007 19:56:58 -0400
+
+autoclass (3.3.4-5) unstable; urgency=low
+
+  * debian/autoclass.1: fix manpage formatting errors, thanks to Nicolas
+    =?UTF-8?Q?Fran=C3=A7ois?= <nicolas.francois@centraliens.net>
+    (fixes:Bug#349817)
+
+ -- James R. Van Zandt <jrv@debian.org>  Fri, 27 Jan 2006 20:15:56 -0500
+
+autoclass (3.3.4-4) unstable; urgency=low
+
+  * debian/control: add gs-common to Build-Depends
+
+ -- James R. Van Zandt <jrv@debian.org>  Sat, 24 Sep 2005 22:40:20 -0400
+
+autoclass (3.3.4-3) unstable; urgency=low
+
+  * debian/control: put in math-optional, to match overrides
+
+ -- James R. Van Zandt <jrv@debian.org>  Sat, 24 Sep 2005 20:08:06 -0400
+
+autoclass (3.3.4-2) unstable; urgency=low
+
+  * debian/control: policy version 3.6.2
+  * build and install PDF rather than PostScript documentation.  Register
+    documentation in section Apps/Math rather than Math
+    (closes:Bug#329961).
+  * intf-reports.c: split writes to influence_report_fp into more calls to
+    fprintf, so we don't exceed the ISO C89 guaranteed supported string
+    length.
+  * debian/compat, debian/control: debhelper compat level 4
+  * debian/rules: call dh_installman rather than dh_installmanpages,
+    install files into debian/autoclass rather than debian/tmp.
+
+ -- James R. Van Zandt <jrv@debian.org>  Sat, 24 Sep 2005 19:29:55 -0400
+
+autoclass (3.3.4-1) unstable; urgency=low
+
+  * New upstream release (closes:Bug#262242)
+
+  * debian/rules: don't copy sample/imports-85c.search into
+    /usr/share/doc/autoclass/examples, because it's no longer included in
+    the upstream sources.  (It's generated when the example is executed.)
+    Don't bother running obsolete program dh_suidregister.  Update to
+    policy 3.6.1.
+
+  * autoclass.doc-base.autoclass-{results,theory}: PostScript
+    documentation given clearer document IDs and moved from
+    Apps/Programming to Math.
+  
+  * debian/{{pre,post}{inst,rm},watch}.ex: delete unused examples
+
+ -- James R. Van Zandt <jrv@debian.org>  Sun, 19 Sep 2004 20:42:29 -0400
+
+autoclass (3.3.3-5) unstable; urgency=low
+
+  * debian/autoclass.1: Fix typo "were" -> "where".
+  * new maintainer email
+  * io-results-bin.c:597: update first_clsf->models (thanks to Nick
+    Mitchell <mitchell@cs.ucsd.edu>, fixes:Bug#65522)
+
+ -- James R. Van Zandt <jrv@debian.org>  Sun,  9 Dec 2001 14:31:46 -0500
+
+autoclass (3.3.3-4) unstable; urgency=low
+
+  * debian/control: move build-depends line to source section.  Update to
+    policy version 3.5.2.
+  * debian/rules: support DEB_BUILD_OPTIONS
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Tue, 22 May 2001 21:43:17 -0400
+
+autoclass (3.3.3-3) unstable; urgency=low
+
+  * simple.README: Check for writable directory.  Display 95% contours of
+    classes found by AutoClass.
+  * simple.c, simple.db2: increase y values by 4 to remove the symmetry.
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Wed, 10 Jan 2001 22:54:56 -0500
+
+autoclass (3.3.3-2) unstable; urgency=low
+
+  * build-depends on debhelper (Closes:Bug#70323)
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Sun,  3 Sep 2000 13:48:58 -0400
+
+autoclass (3.3.3-1) unstable; urgency=low
+
+  * New upstream release
+  * debian/rules: fix commands to assemble single changelog from separate
+    upstream release notes.
+  * simple.README: Uncompress data files if necessary.  Stop if any
+    command fails.
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Mon, 19 Jun 2000 17:15:53 -0400
+
+autoclass (3.3.2-3) unstable; urgency=low
+
+  * Add "see also: multimix" reference to man page.
+  * In man page, fix pointer to documentation files.
+  * Extend man page to include input file formats.
+  * Add data files and script for simple example.
+  * Update policy to 3.1.1
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Mon, 24 Jan 2000 21:15:22 -0500
+
+autoclass (3.3.2-2) unstable; urgency=low
+
+  * Amplify man page: searching, checkpoints, predictions.
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Sat, 30 Oct 1999 12:31:37 -0400
+
+autoclass (3.3.2-1) unstable; urgency=low
+
+  * Initial Release.
+  * Remove fcntlcom.h, because it's copyright AT&T.
+  * In prog/utils.c, include fcntl.h rather than fcntlcom.h.
+  * Add man page.
+  * In sample/imports-85c.s-params, comment out the line
+    "force_new_search_p = false".  The .results-bin file is not included,
+    so we have to start a new search.
+
+ -- James R. Van Zandt <jrv@vanzandt.mv.com>  Sat, 11 Sep 1999 15:43:11 -0400
+
+
--- autoclass-3.3.6.dfsg.1.orig/debian/compat
+++ autoclass-3.3.6.dfsg.1/debian/compat
@@ -0,0 +1 @@
+7
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.s-params
+++ autoclass-3.3.6.dfsg.1/debian/simple.s-params
@@ -0,0 +1,15 @@
+# AutoClass C search parameters -- simple.s-params
+
+;   -- Creator/Donor: Jim Van Zandt <jrv@debian.org>
+;   -- Date: 23 January 2000
+
+max_duration = 20
+# max_n_tries = 0
+
+save_compact_p = false
+!       true saves classifications as machine dependent binary (.results-bin & 
+!       .chkpt-bin); false saves as ascii text (.results & .chkpt)
+
+# max_cycles = 200
+!       passed to all try functions.  They will end a trial if this many cycles
+!       have been done and the convergence criterion has not been satisfied.
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.c
+++ autoclass-3.3.6.dfsg.1/debian/simple.c
@@ -0,0 +1,39 @@
+/*
+  simple - manufacture a simple test data set for multimix
+*/
+
+#include <stdio.h>
+#include <math.h>
+#include <stdlib.h>
+
+double gauss(void);
+
+/*      return normally distributed random number (zero mean, unit variance) */
+double gauss()
+{       int i;
+ double sum=0;
+ for (i = 0; i < 12; i++) sum += rand();
+ return sum/RAND_MAX - 6.;
+}
+
+int main()
+{
+  int threshold = RAND_MAX/3;
+  int i;
+  double x, y;
+
+  for (i = 0; i < 1000; i++)
+    {
+      if (rand() < threshold)
+	{
+	  x = gauss();
+	  y = gauss() + 4;
+	}
+      else
+	{
+	  x = gauss()*3 + 5;
+	  y = gauss()*3 + 9;
+	}
+      printf("%6.5f %6.5f\n", x, y);
+    }
+}
--- autoclass-3.3.6.dfsg.1.orig/debian/rules
+++ autoclass-3.3.6.dfsg.1/debian/rules
@@ -0,0 +1,141 @@
+#!/usr/bin/make -f
+#-*- makefile -*-
+# Made with the aid of dh_make, by Craig Small
+# Sample debian/rules that uses debhelper. GNU copyright 1997 by Joey Hess.
+# Some lines taken from debmake, by Christoph Lameter.
+
+# build without debugging symbols and strip executables when installing,
+# unless DEB_BUILD_OPTIONS specifies otherwise
+
+# Note: when changing the debhelper compatibility level, also update
+# the dependency in debian/control (e.g. "debhelper (>= 7)")
+
+CFLAGS = -Wall -g
+INSTALL = install
+INSTALL_FILE    = $(INSTALL) -p    -o root -g root  -m  644
+INSTALL_PROGRAM = $(INSTALL) -p    -o root -g root  -m  755
+INSTALL_SCRIPT  = $(INSTALL) -p    -o root -g root  -m  755
+INSTALL_DIR     = $(INSTALL) -p -d -o root -g root  -m  755
+          
+ifneq (,$(findstring noopt,$(DEB_BUILD_OPTIONS)))
+CFLAGS += -O0
+else
+CFLAGS += -O2
+endif
+ifeq (,$(findstring nostrip,$(DEB_BUILD_OPTIONS)))
+INSTALL_PROGRAM += -s
+endif
+ifneq (,$(filter parallel=%,$(DEB_BUILD_OPTIONS)))
+    NUMJOBS = $(patsubst parallel=%,%,$(filter parallel=%,$(DEB_BUILD_OPTIONS)))
+    MAKEFLAGS += -j$(NUMJOBS)
+endif
+
+DOC= doc/checkpoint-c.text			\
+  doc/classes-c.text				\
+  doc/interpretation-c.text			\
+  doc/introduction-c.text			\
+  doc/models-c.text				\
+  doc/prediction-c.text				\
+  doc/preparation-c.text			\
+  doc/reports-c.text				\
+  doc/search-c.text				\
+  read-me.text
+
+EXAMPLES= sample/imports-*			\
+  sample/read.me.c				\
+  sample/screenc.text 				\
+  sample/scriptc.text				\
+  debian/README.sample				\
+  debian/simple.README				\
+  debian/simple.c				\
+  debian/simple.db2				\
+  debian/simple.hd2				\
+  debian/simple.model				\
+  debian/simple.r-params			\
+  debian/simple.s-params
+
+# Uncomment this to turn on verbose mode.
+#export DH_VERBOSE=1
+
+
+build: build-arch build-indep
+build-arch: build-stamp
+build-indep: build-stamp
+build-stamp:
+	dh_testdir
+
+	# Add here commands to compile the package.
+	(cd prog; $(MAKE) $(MAKEFLAGS))
+
+	# assemble changelog in reverse chronological order
+	-rm changelog
+	for a in 9 8 7 6 5 4 3 2 1; do				\
+	  for b in 9 8 7 6 5 4 3 2 1 0; do			\
+	    for c in 9 8 7 6 5 4 3 2 1; do			\
+	      if [ -f version-$$a-$$b-$$c.text ]; then		\
+		cat version-$$a-$$b-$$c.text >>changelog; fi;	\
+	    done;						\
+	  if [ -f version-$$a-$$b.text ]; then			\
+	    cat version-$$a-$$b.text >>changelog; fi;		\
+	  done;							\
+	done
+
+	touch build-stamp
+
+clean:
+	dh_testdir
+	dh_testroot
+	rm -f build-stamp install-stamp
+
+	# Add here commands to clean up after the build process.
+	rm -f  doc/*.pdf  prog/autoclass prog/*.o
+
+	dh_clean
+
+install: install-stamp
+install-stamp: build-stamp
+	dh_testdir
+	dh_testroot
+	dh_prep
+	dh_installdirs
+
+	# Add here commands to install the package into debian/autoclass.
+	$(INSTALL_PROGRAM) prog/autoclass debian/autoclass/usr/bin/autoclass
+
+	touch install-stamp
+
+# Build architecture-independent files here.
+binary-indep: build install
+# We have nothing to do by default.
+
+# Build architecture-dependent files here.
+binary-arch: build install
+	rm -f debian/postinst.debhelper debian/prerm.debhelper
+#	dh_testversion
+	dh_testdir
+	dh_testroot
+	dh_installdocs $(DOC)
+
+	dh_installexamples $(EXAMPLES)
+
+#	dh_installmenu
+#	dh_installemacsen
+#	dh_installinit
+#	dh_installcron
+	dh_installman debian/autoclass.1
+#	dh_undocumented
+	dh_installchangelogs  changelog
+	dh_link
+	dh_strip
+	dh_compress -X.pdf
+	dh_fixperms
+#	dh_makeshlibs
+	dh_installdeb
+#	dh_perl 
+	dh_shlibdeps
+	dh_gencontrol
+	dh_md5sums
+	dh_builddeb
+
+binary: binary-indep binary-arch
+.PHONY: build clean binary-indep binary-arch binary
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.db2
+++ autoclass-3.3.6.dfsg.1/debian/simple.db2
@@ -0,0 +1,1000 @@
+6.47411 8.52204
+6.03408 12.10169
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+3.01608 11.41887
--- autoclass-3.3.6.dfsg.1.orig/debian/control
+++ autoclass-3.3.6.dfsg.1/debian/control
@@ -0,0 +1,22 @@
+Source: autoclass
+Section: math
+Priority: optional
+Maintainer: Debian QA Group <packages@qa.debian.org>
+Build-Depends: debhelper (>= 7)
+Standards-Version: 3.9.2
+Homepage: http://ti.arc.nasa.gov/project/autoclass/
+
+Package: autoclass
+Architecture: any
+Depends: ${shlibs:Depends},${misc:Depends}
+Description: automatic classification or clustering
+ AutoClass solves the problem of automatic discovery of classes in data
+ (sometimes called clustering, or unsupervised learning), as distinct
+ from the generation of class descriptions from labeled examples
+ (called supervised learning).  It aims to discover the "natural"
+ classes in the data.  AutoClass is applicable to observations of
+ things that can be described by a set of attributes, without referring
+ to other things.  The data values corresponding to each attribute are
+ limited to be either numbers or the elements of a fixed set of
+ symbols.  With numeric data, a measurement error must be provided.  
+
--- autoclass-3.3.6.dfsg.1.orig/debian/README.Debian
+++ autoclass-3.3.6.dfsg.1/debian/README.Debian
@@ -0,0 +1,57 @@
+autoclass for Debian
+----------------------
+
+AutoClass and multimix are both clustering programs which have been
+packaged for Debian.  Here is a comparison written by the Multimix
+authors:
+
+"AutoClass is a Bayesian clustering program developed by Peter
+Cheeseman and colleagues at NASA Ames Research Center.  The models
+fitted by AutoClass are very similar to those fitted by Multimix,
+although both programs were developed independently.  Two obvious
+differences are
+
+  "1. AutoClass has automated the process of model selection as well as
+  that of parameter estimation but Multimix leaves model-specification
+  to the user;
+  
+  "2. AutoClass uses Maximum Posterior estimation in place of Maximum
+  Likelihood estimation.
+
+"In fact the first is the more crucial difference, because the EM
+algorithm at the basis of both programs accommodates both ML and MAP
+estimation.  AutoClass compares different models by calculating an
+approximation to the marginal density of the observed data after the
+model parameters have been integrated out.  In usual EM language the
+approximation used is analogous to taking observed data likelihood to
+be proportional to complete data likelihood with the constant of
+proportionality to be evaluated at the maximum likelihood estimates.
+
+"The models currently available in AutoClass for attributes within a
+component are as follows.  Categorical attributes are modelled by
+general discrete distributions (multi-category Bernoulli) as in
+Multimix.  Continuous attributes may be taken to have uniform or
+normal distributions, possibly after transformation.  Poisson
+distributions are available for count attributes.  Cheeseman and Stutz
+report that von Mises-Fisher distributions for circular and spherical
+attributes are under development.  At present it appears that
+AutoClass does not offer facilities for modelling within cluster
+dependencies, the is, all models assume within-cluster independence of
+attributes.  Missing values are treated as a special kind of value in
+some attribute models, but there has been no implementation of the
+Little and Rubin methodology for data missing at random."
+
+For more details, including references and comparison with Snob and
+Mclust, see these articles:
+  "Mixture Model Clustering with the Multimix Program" by Jorgensen
+and Hunt, in /usr/share/doc/PPAPER.ps.gz.
+  "Mixture Model Clustering using the Multimix Program" by Hunt and
+Jorgensen, in /usr/share/doc/paper.ps.
+
+For an example problem solved by both programs, see
+
+	/usr/share/doc/multimix/examples/simple.*
+	/usr/share/doc/autoclass/examples/simple.*
+
+ -- James R. Van Zandt <jrv@debian.org>, Sun Dec  9 15:19:50 EST 2001
+
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.hd2
+++ autoclass-3.3.6.dfsg.1/debian/simple.hd2
@@ -0,0 +1,12 @@
+# AutoClass C header file -- simple.hd2
+
+;   -- Creator/Donor: Jim Van Zandt <jrv@debian.org>
+;   -- Date: 23 January 2000
+
+num_db2_format_defs 2
+number_of_attributes 2
+separator_char  ' '
+
+
+0 real location "longitude" error 0.1
+1 real location "latitude" error 0.1
--- autoclass-3.3.6.dfsg.1.orig/debian/README.sample
+++ autoclass-3.3.6.dfsg.1/debian/README.sample
@@ -0,0 +1,6 @@
+# to run the example, do this:
+gunzip *.gz
+
+autoclass -search imports-85c.db2 imports-85c.hd2 imports-85c.model imports-85c.s-params
+
+autoclass -reports imports-85c.results-bin imports-85c.search imports-85c.r-params
--- autoclass-3.3.6.dfsg.1.orig/debian/simple.script
+++ autoclass-3.3.6.dfsg.1/debian/simple.script
@@ -0,0 +1,19 @@
+BEGIN{
+  FS="[ ()]+";
+  printf("set key bottom\n");
+  printf("set title \"groups found by AutoClass\"\n");
+  printf("set parametric; set trange [0:6.28]\n");
+  printf("plot \\\n");
+}
+/^[0-9][0-9]/{
+  if ($5~/latitude/) {next; latmean=$3; latsd=$4; ++data}
+  if ($5~/longitude/) {next; lonmean=$3; lonsd=$4; ++data}
+  if (data == 2) {
+    if (group) printf(",\\\n");
+    printf("%s+2.45*%s*sin(t),%s+2.45*%s*cos(t) title \"group %d 95%% contour\",\\\n",
+        lonmean, lonsd, latmean, latsd, group);
+    printf("  \"group%d\" title \"group %d members\"", group, group);
+    data=0; group++;
+  }
+}
+END {  printf("\n");}
--- autoclass-3.3.6.dfsg.1.orig/debian/autoclass.1
+++ autoclass-3.3.6.dfsg.1/debian/autoclass.1
@@ -0,0 +1,1588 @@
+.\" -*-nroff-*-
+.TH AUTOCLASS 1 "December 9, 2001"
+.SH NAME
+autoclass \- automatically discover classes in data
+.SH SYNOPSIS
+.ad l
+.B autoclass "-search "
+.I data_file header_file model_file s_param_file
+.br
+.B autoclass "-report "
+.I results_file search_file r_params_file
+.\" .br
+.\" .B autoclass "-predict "
+.\" .I data_file
+.br
+.B autoclass "-predict "
+.I results_file search_file results_file
+.ad b
+.br
+.SH "DESCRIPTION"
+\fBAutoClass\fP solves the problem of automatic discovery of classes in data
+(sometimes called clustering, or unsupervised learning), as distinct
+from the generation of class descriptions from labeled examples
+(called supervised learning).  It aims to discover the "natural"
+classes in the data.  \fBAutoClass\fP is applicable to observations of
+things that can be described by a set of attributes, without referring
+to other things.  The data values corresponding to each attribute are
+limited to be either numbers or the elements of a fixed set of
+symbols.  With numeric data, a measurement error must be provided.
+.PP
+\fBAutoClass\fP is looking for the best classification(s) of the data it can find.
+A classification is composed of: 
+.IP 1) 
+A set of classes, each of which is described by a set of class 
+parameters, which specify how the class is distributed along the 
+various attributes.  For example, "height normally distributed with 
+mean 4.67 ft and standard deviation .32 ft", 
+.IP 2) 
+A set of class weights, describing what percentage of cases are 
+likely to be in each class. 
+.IP 3) 
+A probabilistic assignment of cases in the data to these classes.  
+I.e. for each case, the relative probability that it is a member of
+each class.  
+.PP
+As a strictly Bayesian system (accept no substitutes!), the quality
+measure \fBAutoClass\fP uses is the total probability that, had you known
+nothing about your data or its domain, you would have found this set of
+data generated by this underlying model.  This includes the prior
+probability that the "world" would have chosen this number of classes,
+this set of relative class weights, and this set of parameters for each
+class, and the likelihood that such a set of classes would have generated
+this set of values for the attributes in the data cases.
+.PP
+These probabilities are typically very small, in the range of e^-30000,
+and so are usually expressed in exponential notation.
+.PP
+When run with the \fB-search\fP command, \fBAutoClass\fP searches for
+a classification.  The required arguments are the paths to the four
+input files, which supply the data, the data format, the desired
+classification model, and the search parameters, respectively.
+.PP
+By default, \fBAutoClass\fP writes intermediate results in a binary file.
+With the \fB-report\fP command, \fBAutoClass\fP generates an ASCII
+report.  The arguments are the full path names of the .results, .search, and .r-params files.
+.PP
+When run with the \fB-predict\fP command, \fBAutoClass\fP predicts the
+class membership of a "test" data set based on classes found in a
+"training" data set (see "PREDICTIONS" below).
+.SH "INPUT FILES"
+An AutoClass data set resides in two files.  There is a header file
+(file type "hd2") that describes the specific data format and attribute
+definitions.  The actual data values are in a data file (file type "db2").
+We use two files to allow editing of data descriptions without having to
+deal with the entire data set.  This makes it easy to experiment with 
+different descriptions of the database without having to reproduce the data
+set.  Internally, an AutoClass database structure is identified by its
+header and data files, and the number of data loaded.
+.PP
+For more detailed information on the formats of these files, see
+\fI/usr/share/doc/autoclass/preparation-c.text\fP.
+.SS "DATA FILE"
+The data file contains a sequence of data objects (datum or case)
+terminated by the end of the file. The number of values for each data
+object must be equal to the number of attributes defined in the header
+file.  Data objects must be groups of tokens delimited by "new-line".
+Attributes are typed as REAL, DISCRETE, or DUMMY.  Real attribute
+values are numbers, either integer or floating point.  Discrete
+attribute values can be strings, symbols, or integers.  A dummy
+attribute value can be any of these types.  Dummys are read in but
+otherwise ignored -- they will be set to zeros in the the internal
+database.  Thus the actual values will not be available for use in
+report output.  To have these attribute values available, use either
+type REAL or type DISCRETE, and define their model type as IGNORE in
+the .model file.  Missing values for any attribute type may be
+represented by either "?", or other token specified in the header
+file.  All are translated to a special unique value after being read,
+so this symbol is effectively reserved for unknown/missing values.
+.PP
+For example:
+.nf
+      white       38.991306 0.54248405  2 2 1
+      red         25.254923 0.5010235   9 2 1
+      yellow      32.407973 ?           8 2 1
+      all_white   28.953982 0.5267696   0 1 1
+.fi
+.SS "HEADER FILE"
+The header file specifies the data file format, and the definitions of
+the data attributes.  The header file functional specifications
+consists of two parts -- the data set format definition
+specifications, and the attribute descriptors. ";" in column 1
+identifies a comment.
+.PP
+A header file follows this general format:
+.nf
+
+    ;; num_db2_format_defs value (number of format def lines
+    ;; that follow), range of n is 1 -> 5
+    num_db2_format_defs n
+    ;; number_of_attributes token and value required
+    number_of_attributes <as required>
+    ;; following are optional - default values are specified
+    separator_char  ' '
+    comment_char    ';'
+    unknown_token   '?'
+    separator_char  ','
+    
+    ;; attribute descriptors
+    ;; <zero-based att#>  <att_type>  <att_sub_type>  <att_description>
+    ;; <att_param_pairs>
+
+.fi
+Each attribute descriptor is a line of:
+.nf
+
+      Attribute index (zero based, beginning in column 1)
+      Attribute type.  See below.
+      Attribute subtype.  See below
+      Attribute description: symbol (no embedded blanks) or
+            string; <= 40 characters
+      Specific property and value pairs.
+            Currently available combinations:
+
+         type           subtype         property type(s)
+         ----           --------        ---------------
+         dummy          none/nil        --
+         discrete       nominal         range
+         real           location        error
+         real           scalar          zero_point rel_error
+
+.fi
+The ERROR property should represent your best estimate of the
+average error expected in the measurement and recording of that real
+attribute.  Lacking better information, the error can be taken as 1/2
+the minimum possible difference between measured values.  It can be
+argued that real values are often truncated, so that smaller errors
+may be justified, particularly for generated data.  But AutoClass only
+sees the recorded values.  So it needs the error in the recorded
+values, rather than the actual measurement error.  Setting this error
+much smaller than the minimum expressible difference implies the
+possibility of values that cannot be expressed in the data.  Worse, it
+implies that two identical values must represent measurements that
+were much closer than they might actually have been.  This leads to
+over-fitting of the classification.
+
+The REL_ERROR property is used for SCALAR reals when the error is
+proportional to the measured value.  The ERROR property is not
+supported.
+
+AutoClass uses the error as a lower bound on the width of the
+normal distribution.  So small error estimates tend to give narrower
+peaks and to increase both the number of classes and the
+classification probability.  Broad error estimates tend to limit the
+number of classes.
+
+The scalar ZERO_POINT property is the smallest value that the
+measurement process could have produced.  This is often 0.0, or less
+by some error range.  Similarly, the bounded real's min and max
+properties are exclusive bounds on the attributes generating process.
+For a calculated percentage these would be 0-e and 100+e, where e is
+an error value.  The discrete attribute's range is the number of
+possible values the attribute can take on.  This range must include
+unknown as a value when such values occur.
+
+Header File Example:
+.nf
+
+!#; AutoClass C header file -- extension .hd2
+!#; the following chars in column 1 make the line a comment:
+!#; '!', '#', ';', ' ', and '\\n' (empty line)
+
+;#! num_db2_format_defs <num of def lines -- min 1, max 4>
+num_db2_format_defs 2
+;; required
+number_of_attributes 7
+;; optional - default values are specified
+;; separator_char  ' '
+;; comment_char    ';'
+;; unknown_token   '?'
+separator_char     ','
+
+;; <zero-based att#>  <att_type>  <att_sub_type>  <att_description>
+<att_param_pairs>
+0 dummy nil       "True class, range = 1 - 3"
+1 real location "X location, m. in range of 25.0 - 40.0" error .25
+2 real location "Y location, m. in range of 0.5 - 0.7" error .05
+3 real scalar   "Weight, kg. in range of 5.0 - 10.0" zero_point 0.0
+rel_error .001
+4 discrete nominal  "Truth value, range = 1 - 2" range 2
+5 discrete nominal  "Color of foobar, 10 values" range 10
+6 discrete nominal  Spectral_color_group range 6
+.fi
+.SS "MODEL FILE"
+A classification of a data set is made with respect to a model which
+specifies the form of the probability distribution function for classes in that
+data set.  Normally the model structure is defined in a model file (file
+type "model"), containing one or more models.  Internally, a model is defined
+relative to a particular database.  Thus it is identified by the corresponding
+database, the model's model file and its sequential position in the
+file.
+.PP
+Each model is specified by one or more model group definition lines.
+Each model group line associates attribute indices with a
+model term type.
+.PP
+Here is an example model file:
+.nf
+
+# AutoClass C model file -- extension .model
+model_index 0 7
+ignore 0
+single_normal_cn 3
+single_normal_cn 17 18 21
+multi_normal_cn 1 2
+multi_normal_cn 8 9 10
+multi_normal_cn 11 12 13
+single_multinomial default
+.fi
+.PP
+Here, the first line is a comment.  The following characters in column
+1 make the line a comment: `!', `#', ` ', `;', and `\\n' (empty line).
+.PP
+The tokens "model_index \fIn m\fP" must appear on the first non-comment
+line, and precede the model term definition lines. \fIn\fP is the
+zero-based model index, typically 0 where there is only one model --
+the majority of search situations.  \fIm\fP is the number of model term
+definition lines that follow.
+
+The last seven lines are model group lines.  Each model group line
+consists of:
+.br
+.ad l
+.nh
+.HP 4
+A model term type (one of 
+.BR single_multinomial ,
+.BR single_normal_cm , 
+.BR single_normal_cn ,
+.BR multi_normal_cn ", or"
+.BR ignore ).
+.HP 4
+A list of attribute indices (the attribute set list), or the symbol
+\fBdefault\fP.  Attribute indices are zero-based.  Single model terms
+may have one or more attribute indices on each line, while multi model
+terms require two or more attribute indices per line.  An attribute
+index must not appear more than once in a model list.
+.ad b
+.hy
+.PP
+Notes:
+.IP 1)
+At least one model definition is required (model_index token).
+.IP 2)
+There may be multiple entries in a model for any model term type.
+.IP 3)
+Model term types currently consist of:
+.RS
+.TP
+.B single_multinomial
+models discrete attributes as multinomials, with missing values.
+.TP
+.B single_normal_cn
+models real valued attributes as normals; no missing values.
+.TP
+.B single_normal_cm
+models real valued attributes with missing values.
+.TP
+.B multi_normal_cn
+is a covariant normal model without missing values.
+.TP
+.B ignore
+allows the model to ignore one or more attributes.
+\fBignore\fP is not a valid default model term type.
+.PP
+See the documentation in models-c.text for further information about
+specific model terms.
+.RE
+.IP 4)
+\fBSingle_normal_cn\fP, \fBsingle_normal_cm\fP, and
+\fBmulti_normal_cn\fP modeled data, whose subtype is \fBscalar\fP
+(value distribution is away from 0.0, and is thus not a "normal"
+distribution) will be log transformed and modeled with the log-normal
+model.  For data whose subtype is \fBlocation\fP (value distribution
+is around 0.0), no transform is done, and the normal model is used.
+.SH SEARCHING
+AutoClass, when invoked in the "search" mode will check the validity
+of the set of data, header, model, and search parameter files.  Errors
+will stop the search from starting, and warnings will ask the user
+whether to continue.  A history of the error and warning messages is
+saved, by default, in the log file.
+.PP
+Once you have succeeded in describing your data with a header file
+and model file that passes the AUTOCLASS -SEARCH <...> input checks,
+you will have entered the search domain where \fBAutoClass\fP classifies
+your data.  (At last!)
+.PP
+The main function to use in finding a good classification of your data
+is AUTOCLASS -SEARCH, and using it will take most of the computation
+time.  Searches are invoked with:
+.nf
+
+autoclass -search <.db2 file path> <.hd2 file path> 
+	<.model file path> <.s-params file path>
+
+.fi
+All files must be specified as fully qualified relative or absolute
+pathnames.  File name extensions (file types) for all files are forced
+to canonical values required by the AutoClass program:
+.nf
+
+        data file   ("ascii")   db2
+        data file   ("binary")  db2-bin
+        header file             hd2
+        model file              model
+        search params file      s-params
+.fi
+.PP
+The sample-run (\fI/usr/share/doc/autoclass/examples/\fP) that comes
+with \fBAutoClass\fP shows some sample searches, and browsing these is
+probably the fastest way to get familiar with how to do searches.  The
+test data sets located under \fI/usr/share/doc/autoclass/examples/\fP
+will show you some other header (.hd2), model (.model), and search
+params (.s-params) file setups.  The remainder of this section
+describes how to do searches in somewhat more detail.
+.PP
+The \fBbold faced\fP tokens below are generally search params file
+parameters.  For more information on the s-params file, see \fBSEARCH
+PARAMETERS\fP below, or
+\fI/usr/share/doc/autoclass/search-c.text.gz\fP.
+.SS "WHAT RESULTS ARE"
+\fBAutoClass\fP is looking for the best classification(s) of the data
+it can find.  A classification is composed of:
+.IP 1) 
+a set of classes, each of which is described by a set of class 
+parameters, which specify how the class is distributed along the 
+various attributes.  For example, "height normally distributed with 
+mean 4.67 ft and standard deviation .32 ft", 
+.IP 2)
+a set of class weights, describing what percentage of cases are 
+likely to be in each class. 
+.IP 3)
+a probabilistic assignment of cases in the data to these classes.  
+I.e. for each case, the relative probability that it is a member of
+each class.  
+.PP
+As a strictly Bayesian system (accept no substitutes!), the quality
+measure \fBAutoClass\fP uses is the total probability that, had you known
+nothing about your data or its domain, you would have found this set of
+data generated by this underlying model.  This includes the prior
+probability that the "world" would have chosen this number of classes,
+this set of relative class weights, and this set of parameters for each
+class, and the likelihood that such a set of classes would have generated
+this set of values for the attributes in the data cases.
+.PP
+These probabilities are typically very small, in the range of e^-30000,
+and so are usually expressed in exponential notation.
+.SS "WHAT RESULTS MEAN"
+It is important to remember that all of these probabilities are GIVEN
+that the real model is in the model family that \fBAutoClass\fP has
+restricted its attention to.  If \fBAutoClass\fP is looking for
+Gaussian classes and the real classes are Poisson, then the fact that
+\fBAutoClass\fP found 5 Gaussian classes may not say much about how
+many Poisson classes there really are.
+.PP
+The relative probability between different classifications found can
+be very large, like e^1000, so the very best classification found is
+usually overwhelmingly more probable than the rest (and overwhelmingly
+less probable than any better classifications as yet undiscovered).
+If \fBAutoClass\fP should manage to find two classifications that are
+within about exp(5-10) of each other (i.e. within 100 to 10,000 times
+more probable) then you should consider them to be about equally
+probable, as our computation is usually not more accurate than this
+(and sometimes much less).
+.SS "HOW IT WORKS"
+\fBAutoClass\fP repeatedly creates a random classification and then
+tries to massage this into a high probability classification though
+local changes, until it converges to some "local maximum".  It then
+remembers what it found and starts over again, continuing until you
+tell it to stop.  Each effort is called a "try", and the computed
+probability is intended to cover the whole volume in parameter space
+around this maximum, rather than just the peak.
+.PP
+The standard approach to massaging is to 
+.IP 1)
+Compute the probabilistic class memberships of cases using the class
+parameters and the implied relative likelihoods.
+.IP 2)
+Using the new class members, compute class statistics (like mean)
+and revise the class parameters.
+.PP
+and repeat till they stop changing.  There are three available
+convergence algorithms: "converge_search_3" (the default),
+"converge_search_4" and "converge".  Their specification is controlled
+by search params file parameter \fBtry_fn_type\fP.
+.SS "WHEN TO STOP"
+You can tell AUTOCLASS -SEARCH to stop by: 1) giving a
+\fBmax_duration\fP (in seconds) argument at the beginning; 2) giving a
+\fBmax_n_tries\fP (an integer) argument at the beginning; or 3) by
+typing a "q" and <return> after you have seen enough tries.  The
+\fBmax_duration\fP and \fBmax_n_tries\fP arguments are useful if you
+desire to run AUTOCLASS -SEARCH in batch mode.  If you are restarting
+AUTOCLASS -SEARCH from a previous search, the value of
+\fBmax_n_tries\fP you provide, for instance 3, will tell the program
+to compute 3 more tries in addition to however many it has already
+done.  The same incremental behavior is exhibited by
+\fBmax_duration\fP.
+.PP
+Deciding when to stop is a judgment call and it's up to you.  Since the
+search includes a random component, there's always the chance that if
+you let it keep going it will find something better.  So you need to
+trade off how much better it might be with how long it might take to
+find it.  The search status reports that are printed when a new best
+classification is found are intended to provide you information to help
+you make this tradeoff.
+.PP
+One clear sign that you should probably stop is if most of the
+classifications found are duplicates of previous ones (flagged by
+"dup" as they are found).  This should only happen for very small sets
+of data or when fixing a very small number of classes, like two.
+.PP
+Our experience is that for moderately large to extremely large data
+sets (~200 to ~10,000 datum), it is necessary to run \fBAutoClass\fP
+for at least 50 trials.
+.SS "WHAT GETS RETURNED"
+Just before returning, AUTOCLASS -SEARCH will give short descriptions
+of the best classifications found.  How many will be described can be
+controlled with \fBn_final_summary\fP.
+.PP
+By default AUTOCLASS -SEARCH will write out a number of files, both at
+the end and periodically during the search (in case your system
+crashes before it finishes).  These files will all have the same name
+(taken from the search params pathname [<name>.s-params]), and differ
+only in their file extensions.  If your search runs are very long and
+there is a possibility that your machine may crash, you can have
+intermediate "results" files written out.  These can be used to
+restart your search run with minimum loss of search effort.  See the
+documentation file \fI/usr/share/doc/autoclass/checkpoint-c.text\fP.
+.PP
+A ".log" file will hold a listing of most of what was printed to the
+screen during the run, unless you set \fBlog_file_p\fP to false to say
+you want no such foolishness.  Unless \fBresults_file_p\fP is false, a
+binary ".results-bin" file (the default) or an ASCII ".results" text
+file, will hold the best classifications that were returned, and
+unless \fBsearch_file_p\fP is false, a ".search" file will hold the
+record of the search tries. \fBsave_compact_p\fP controls whether the
+"results" files are saved as binary or ASCII text.
+.PP
+If the C global variable "G_safe_file_writing_p" is defined as TRUE in
+"autoclass-c/prog/globals.c", the names of "results" files (those that
+contain the saved classifications) are modified internally to account 
+for redundant file writing.  If the search params file name is
+"my_saved_clsfs" you will see the following "results" file names
+(ignoring directories and pathnames for this example)
+.sp
+.nf
+  \fBsave_compact_p\fP = true --
+  "my_saved_clsfs.results-bin"	- completely written file
+  "my_saved_clsfs.results-tmp-bin" - partially written file, renamed
+				  when complete
+
+  \fBsave_compact_p\fP = false --
+  "my_saved_clsfs.results"	- completely written file
+  "my_saved_clsfs.results-tmp"  - partially written file, renamed
+				  when complete
+.fi
+.sp
+If check pointing is being done, these additional names will appear
+.sp
+.nf
+  \fBsave_compact_p\fP = true --
+  "my_saved_clsfs.chkpt-bin"	- completely written checkpoint file
+  "my_saved_clsfs.chkpt-tmp-bin" - partially written checkpoint file, 
+				     renamed when complete
+  \fBsave_compact_p\fP = false --
+  "my_saved_clsfs.chkpt"	- completely written checkpoint file
+  "my_saved_clsfs.chkpt-tmp"    - partially written checkpoint file, 
+				     renamed when complete
+.fi
+.sp
+.SS "HOW TO GET STARTED"
+The way to invoke AUTOCLASS -SEARCH is:
+.nf
+
+autoclass -search <.db2 file path> <.hd2 file path> 
+	<.model file path> <.s-params file path>
+
+.fi
+To restart a previous search, specify that \fBforce_new_search_p\fP
+has the value false in the search params file, since its default is
+true.  Specifying false tells AUTOCLASS -SEARCH to try to find a
+previous compatible search (<...>.results[-bin] & <...>.search) to
+continue from, and will restart using it if found.  To force a new
+search instead of restarting an old one, give the parameter
+\fBforce_new_search_p\fP the value of true, or use the default.  If
+there is an existing search (<...>.results[-bin] & <...>.search), the
+user will be asked to confirm continuation since continuation will
+discard the existing search.
+.PP
+If a previous search is continued, the message "RESTARTING SEARCH" will
+be given instead of the usual "BEGINNING SEARCH".  It is generally
+better to continue a previous search than to start a new one, unless
+you are trying a significantly different search method, in which case
+statistics from the previous search may mislead the current one.
+.SS "STATUS REPORTS"
+A running commentary on the search will be printed to the screen and
+to the log file (unless \fBlog_file_p\fP is false).  Note that the
+".log" file will contain a listing of all default search params
+values, and the values of all params that are overridden.
+.PP
+After each try a very short report (only a few characters long) is
+given.  After each new best classification, a longer report is given,
+but no more often than \fBmin_report_period\fP (default is 30
+seconds).
+.SS "SEARCH VARIATIONS"
+AUTOCLASS -SEARCH by default uses a certain standard search method or
+"try function" (\fBtry_fn_type\fP = "converge_search_3").  Two others
+are also available: "converge_search_4" and "converge").  They are
+provided in case your problem is one that may happen to benefit from
+them.  In general the default method will result in finding better
+classifications at the expense of a longer search time.  The default
+was chosen so as to be robust, giving even performance across many
+problems.  The alternatives to the default may do better on some
+problems, but may do substantially worse on others.
+.PP
+"converge_search_3" uses an absolute stopping criterion
+(\fBrel_delta_range\fP, default value of 0.0025) which tests the variation
+of each class of the delta of the log approximate-marginal-likelihood
+of the class statistics with-respect-to the class hypothesis
+(class->log_a_w_s_h_j) divided by the class weight (class->w_j)
+between successive convergence cycles.  Increasing this value loosens
+the convergence and reduces the number of cycles.  Decreasing this
+value tightens the convergence and increases the number of
+cycles. \fBn_average\fP (default value of 3) specifies how many successive
+cycles must meet the stopping criterion before the trial terminates.
+.PP
+"converge_search_4" uses an absolute stopping criterion
+(\fBcs4_delta_range\fP, default value of 0.0025) which tests the
+variation of each class of the slope for each class of log
+approximate-marginal-likelihood of the class statistics
+with-respect-to the class hypothesis (class->log_a_w_s_h_j) divided by
+the class weight (class->w_j) over \fBsigma_beta_n_values\fP (default value
+6) convergence cycles.  Increasing the value of \fBcs4_delta_range\fP
+loosens the convergence and reduces the number of cycles.  Decreasing
+this value tightens the convergence and increases the number of
+cycles.  Computationally, this try function is more expensive than
+"converge_search_3", but may prove useful if the computational "noise"
+is significant compared to the variations in the computed values.  Key
+calculations are done in double precision floating point, and for the
+largest data base we have tested so far ( 5,420 cases of 93
+attributes), computational noise has not been a problem, although the
+value of \fBmax_cycles\fP needed to be increased to 400.
+.PP
+"converge" uses one of two absolute stopping criterion which test the
+variation of the classification (clsf) log_marginal (clsf->log_a_x_h)
+delta between successive convergence cycles.  The largest of
+\fBhalt_range\fP (default value 0.5) and \fBhalt_factor\fP *
+\fBcurrent_clsf_log_marginal\fP) is used (default value of
+\fBhalt_factor\fP is 0.0001).  Increasing these values loosens the
+convergence and reduces the number of cycles.  Decreasing these values
+tightens the convergence and increases the number of cycles.
+\fBn_average\fP (default value of 3) specifies how many cycles must
+meet the stopping criteria before the trial terminates.  This is a
+very approximate stopping criterion, but will give you some feel for
+the kind of classifications to expect.  It would be useful for
+"exploratory" searches of a data base.
+.PP
+The purpose of \fBreconverge_type\fP = "chkpt" is to complete an
+interrupted classification by continuing from its last checkpoint.
+The purpose of \fBreconverge_type\fP = "results" is to attempt further
+refinement of the best completed classification using a different
+value of \fBtry_fn_type\fP ("converge_search_3", "converge_search_4",
+"converge").  If \fBmax_n_tries\fP is greater than 1, then in each case,
+after the reconvergence has completed, \fBAutoClass\fP will perform
+further search trials based on the parameter values in the
+<...>.s-params file.
+.PP
+With the use of \fBreconverge_type\fP ( default value ""), you may
+apply more than one try function to a classification.  Say you
+generate several exploratory trials using \fBtry_fn_type\fP =
+"converge", and quit the search saving .search and .results[-bin]
+files.  Then you can begin another search with \fBtry_fn_type\fP =
+"converge_search_3", \fBreconverge_type\fP = "results", and
+\fBmax_n_tries\fP = 1.  This will result in the further convergence of
+the best classification generated with \fBtry_fn_type\fP = "converge",
+with \fBtry_fn_type\fP = "converge_search_3".  When \fBAutoClass\fP
+completes this search try, you will have an additional refined
+classification.
+.PP
+A good way to verify that any of the alternate \fBtry_fun_type\fP are
+generating a well converged classification is to run \fBAutoClass\fP
+in prediction mode on the same data used for generating the
+classification.  Then generate and compare the corresponding case or
+class cross reference files for the original classification and the
+prediction.  Small differences between these files are to be expected,
+while large differences indicate incomplete convergence.  Differences
+between such file pairs should, on average and modulo class deletions,
+decrease monotonically with further convergence.
+.PP
+The standard way to create a random classification to begin a try is
+with the default value of "random" for \fBstart_fn_type\fP.  At this
+point there are no alternatives.  Specifying "block" for
+\fBstart_fn_type\fP produces repeatable non-random searches.  That is
+how the <..>.s-params files in the autoclass-c/data/.. sub-directories
+are specified.  This is how development testing is done.
+.PP
+\fBmax_cycles\fP controls the maximum number of convergence cycles that will
+be performed in any one trial by the convergence functions.  Its default
+value is 200.  The screen output shows a period (".") for each cycle
+completed. If your search trials run for 200 cycles, then either your
+data base is very complex (increase the value), or the \fBtry_fn_type\fP
+is not adequate for situation (try another of the available ones, and
+use \fBconverge_print_p\fP to get more information on what is going on).
+.PP
+Specifying \fBconverge_print_p\fP to be true will generate a brief
+print-out for each cycle which will provide information so that you
+can modify the default values of \fBrel_delta_range\fP &
+\fBn_average\fP for "converge_search_3"; \fBcs4_delta_range\fP &
+\fBsigma_beta_n_values\fP for "converge_search_4"; and
+\fBhalt_range\fP, \fBhalt_factor\fP, and \fBn_average\fP for
+"converge".  Their default values are given in the <..>.s-params files
+in the autoclass-c/data/..  sub-directories.
+.SS "HOW MANY CLASSES?"
+Each new try begins with a certain number of classes and may end up
+with a smaller number, as some classes may drop out of the convergence.
+In general, you want to begin the try with some number of classes that
+previous tries have indicated look promising, and you want to be sure 
+you are fishing around elsewhere in case you missed something before.
+.PP
+\fBn_classes_fn_type\fP = "random_ln_normal" is the default way to make this
+choice.  It fits a log normal to the number of classes (usually called "j"
+for short) of the 10 best classifications found so far, and randomly
+selects from that.  There is currently no alternative.
+.PP
+To start the game off, the default is to go down \fBstart_j_list\fP
+for the first few tries, and then switch to \fBn_classes_fn_type\fP.
+If you believe that the probable number of classes in your data base
+is say 75, then instead of using the default value of \fBstart_j_list\fP (2,
+3, 5, 7, 10, 15, 25), specify something like 50, 60, 70, 80, 90, 100.
+.PP
+If one wants to always look for, say, three classes, one can use
+\fBfixed_j\fP and override the above.  Search status reports will describe 
+what the current method for choosing j is.
+.SS "DO I HAVE ENOUGH MEMORY AND DISK SPACE?"
+Internally, the storage requirements in the current system are of
+order n_classes_per_clsf * (n_data + n_stored_clsfs * n_attributes *
+n_attribute_values).  This depends on the number of cases, the number
+of attributes, the values per attribute (use 2 if a real value), and
+the number of classifications stored away for comparison to see if
+others are duplicates -- controlled by \fBmax_n_store\fP (default
+value = 10).  The search process does not itself consume significant
+memory, but storage of the results may do so.
+.PP
+\fBAutoClass C\fP is configured to handle a maximum of 999 attributes.
+If you attempt to run with more than that you will get array bound
+violations.  In that case, change these configuration parameters in
+prog/autoclass.h and recompile \fBAutoClass C\fP:
+.nf
+
+#define ALL_ATTRIBUTES                  999   
+#define VERY_LONG_STRING_LENGTH         20000 
+#define VERY_LONG_TOKEN_LENGTH          500 
+
+.fi
+For example, these values will handle several thousand attributes:
+.nf
+
+#define ALL_ATTRIBUTES                  9999
+#define VERY_LONG_STRING_LENGTH         50000
+#define VERY_LONG_TOKEN_LENGTH          50000
+
+.fi
+Disk space taken up by the "log" file will of course depend on the
+duration of the search.  \fBn_save\fP (default value = 2) determines how
+many best classifications are saved into the ".results[-bin]" file.
+\fBsave_compact_p\fP controls whether the "results" and "checkpoint" files
+are saved as binary.  Binary files are faster and more compact, but
+are not portable.  The default value of \fBsave_compact_p\fP is true, which
+causes binary files to be written.
+.PP
+If the time taken to save the "results" files is a problem, consider
+increasing \fBmin_save_period\fP (default value = 1800 seconds or 30
+minutes).  Files are saved to disk this often if there is anything
+different to report.
+.SS "JUST HOW SLOW IS IT?"
+Compute time is of order n_data * n_attributes * n_classes * n_tries
+* converge_cycles_per_try. The major uncertainties in this are the
+number of basic back and forth cycles till convergence in each try, and of
+course the number of tries.  The number of cycles per trial is typically 
+10-100 for \fBtry_fn_type\fP "converge", and 10-200+ for "converge_search_3" 
+and "converge_search-4".  The maximum number is specified by \fBmax_n_tries\fP
+(default value = 200).  The number of trials is up to you and your
+available computing resources.
+.PP
+The running time of very large data sets will be quite uncertain.  We
+advise that a few small scale test runs be made on your system to
+determine a baseline.  Specify \fBn_data\fP to limit how many data vectors
+are read.  Given a very large quantity of data, \fBAutoClass\fP may
+find its most probable classifications at upwards of a hundred
+classes, and this will require that \fBstart_j_list\fP be specified
+appropriately (See above section \fBHOW MANY CLASSES?\fP).  If you are
+quite certain that you only want a few classes, you can force
+\fBAutoClass\fP to search with a fixed number of classes specified by
+\fBfixed_j\fP.  You will then need to run separate searches with each
+different fixed number of classes.
+.SS "CHANGING FILENAMES IN A SAVED CLASSIFICATION FILE"
+\fBAutoClass\fP caches the data, header, and model file pathnames in
+the saved classification structure of the binary (".results-bin") or
+ASCII (".results") "results" files.  If the "results" and "search"
+files are moved to a different directory location, the search cannot
+be successfully restarted if you have used absolute pathnames.  Thus
+it is advantageous to run invoke \fBAutoClass\fP in a parent directory
+of the data, header, and model files, so that relative pathnames can
+be used.  Since the pathnames cached will then be relative, the files
+can be moved to a different host or file system and restarted --
+providing the same relative pathname hierarchy exists.
+.PP
+However, since the ".results" file is ASCII text, those pathnames
+could be changed with a text editor (\fBsave_compact_p\fP must be
+specified as false).
+.SS "SEARCH PARAMETERS"
+The search is controlled by the ".s-params" file.  In this file, an
+empty line or a line starting with one of these characters is treated
+as a comment: "#", "!", or ";".  The parameter name and its value can
+be separated by an equal sign, a space, or a tab:
+.sp
+.nf
+	n_clsfs 1
+	n_clsfs = 1
+	n_clsfs<tab>1
+.fi
+.sp
+Spaces are ignored if "=" or "<tab>" are used as separators.  Note
+there are no trailing semicolons.
+.PP
+The search parameters, with their default values, are as follows:
+.IP "\fBrel_error\fP = 0.01"
+Specifies the relative difference measure used by clsf-DS-%=, when 
+deciding if a new clsf is a duplicate of an old one.  
+.IP "\fBstart_j_list\fP = 2, 3, 5, 7, 10, 15, 25"
+Initially try these numbers of classes, so as not to narrow the
+search too quickly.  The state of this list is saved in the <..>.search 
+file and used on restarts, unless an override
+specification of \fBstart_j_list\fP is made in the .s-params file for the
+restart run.  This list should bracket your expected number of
+classes, and by a wide margin!
+"start_j_list = -999" specifies an empty list (allowed only on restarts)
+.IP "\fBn_classes_fn_type\fP = ""random_ln_normal"""
+Once \fBstart_j_list\fP is exhausted, \fBAutoClass\fP will call this
+function to decide how many classes to start with on the next try,
+based on the 10 best classifications found so far.  Currently only
+"random_ln_normal" is available.
+.IP "\fBfixed_j\fP = 0"
+When \fBfixed_j\fP > 0, overrides \fBstart_j_list\fP and
+\fBn_classes_fn_type,\fP and \fBAutoClass\fP will always use this value for
+the initial number of classes.
+.IP "\fBmin_report_period\fP = 30"
+Wait at least this time (in seconds) since last report until reporting
+verbosely again.  Should be set longer than the expected run time when
+checking for repeatability of results.  For repeatable results, also
+see \fBforce_new_search_p,\fP \fBstart_fn_type\fP and
+\fBrandomize_random_p\fP. \fINOTE\fP: At least one of "interactive_p",
+"max_duration", and "max_n_tries" must be active.  Otherwise
+\fBAutoClass\fP will run indefinitely.  See below.
+.IP "\fBinteractive_p\fP = true"
+When false, allows run to continue until otherwise halted.
+When true, standard input is queried on each cycle for the quit
+character "q", which, when detected, triggers an immediate halt. 
+.IP "\fBmax_duration\fP = 0"
+When = 0, allows run to continue until otherwise halted.
+When > 0, specifies the maximum number of seconds to run.  
+.IP "\fBmax_n_tries\fP = 0"
+When = 0, allows run to continue until otherwise halted.
+When > 0, specifies the maximum number of tries to make.
+.IP "\fBn_save\fP = 2"
+Save this many clsfs to disk in the .results[-bin] and .search files.
+if 0, don't save anything (no .search & .results[-bin] files). 
+.IP "\fBlog_file_p\fP = true"
+If false, do not write a log file.
+.IP "\fBsearch_file_p\fP = true"
+If false, do not write a search file. 
+.IP "\fBresults_file_p\fP = true"
+If false, do not write a results file.
+.IP "\fBmin_save_period\fP = 1800"
+CPU crash protection.  This specifies the maximum time, in seconds,
+that \fBAutoClass\fP will run before it saves the current results to
+disk.  The default time is 30 minutes.
+.IP "\fBmax_n_store\fP = 10"
+Specifies the maximum number of classifications stored internally.
+.IP "\fBn_final_summary\fP = 10"
+Specifies the number of trials to be printed out after search ends.
+.IP "\fBstart_fn_type\fP = ""random"""
+One of {"random", "block"}.  This specifies the type of class
+initialization.  For normal search, use "random", which randomly
+selects instances to be initial class means, and adds appropriate
+variances. For testing with repeatable search, use "block", which
+partitions the database into successive blocks of near equal size.
+For repeatable results, also see \fBforce_new_search_p\fP,
+\fBmin_report_period\fP, and \fBrandomize_random_p\fP.
+.IP "\fBtry_fn_type\fP = ""converge_search_3"""
+One of {"converge_search_3", "converge_search_4", "converge"}. 
+These specify alternate search stopping criteria.  
+"converge" merely tests the rate of change of the log_marginal
+classification probability (clsf->log_a_x_h), without checking
+rate of change of individual classes(see \fBhalt_range\fP and
+\fBhalt_factor\fP).  
+"converge_search_3" and "converge_search_4" each monitor the ratio
+class->log_a_w_s_h_j/class->w_j for all classes, and continue
+convergence until all pass the quiescence criteria for \fBn_average\fP
+cycles.  "converge_search_3" tests differences between successive
+convergence cycles (see \fBrel_delta_range\fP).  This provides a
+reasonable, general purpose stopping criteria.
+"converge_search_4" averages the ratio over "sigma_beta_n_values"
+cycles (see \fBcs4_delta_range\fP).  This is preferred when
+converge_search_3 produces many similar classes.
+.IP "\fBinitial_cycles_p\fP = true"
+If true, perform base_cycle in initialize_parameters.
+false is used only for testing.
+.IP "\fBsave_compact_p\fP = true"
+true saves classifications as machine dependent binary
+(.results-bin & .chkpt-bin).   
+false saves as ascii text (.results & .chkpt)
+.IP "\fBread_compact_p\fP = true"
+true reads classifications as machine dependent binary 
+(.results-bin & .chkpt-bin).
+false reads as ascii text (.results & .chkpt).
+.IP "\fBrandomize_random_p\fP = true"
+false seeds lrand48, the pseudo-random number function with 1 
+to give repeatable test cases.  true uses universal time clock 
+as the seed, giving semi-random searches.
+For repeatable results, also see \fBforce_new_search_p\fP, 
+\fBmin_report_period\fP and \fBstart_fn_type\fP.
+.IP "\fBn_data\fP = 0"
+With n_data = 0, the entire database is read from .db2.  
+With n_data > 0, only this number of data are read. 
+.IP "\fBhalt_range\fP = 0.5"
+Passed to try_fn_type "converge".  With the "converge"
+try_fn_type, convergence is halted when the larger of halt_range
+and (halt_factor * current_log_marginal) exceeds the difference
+between successive cycle values of the classification log_marginal
+(clsf->log_a_x_h).  Decreasing this value may tighten the
+convergence and increase the number of cycles.
+.IP "\fBhalt_factor\fP = 0.0001"
+Passed to try_fn_type "converge".  With the "converge"
+try_fn_type, convergence is halted when the larger of halt_range
+and (halt_factor * current_log_marginal) exceeds the difference
+between successive cycle values of the classification log_marginal
+(clsf->log_a_x_h).  Decreasing this value may tighten the
+convergence and increase the number of cycles.
+.IP "\fBrel_delta_range\fP = 0.0025"
+Passed to try function "converge_search_3", which monitors the
+ratio of log approx-marginal-likelihood of class statistics
+with-respect-to the class hypothesis (class->log_a_w_s_h_j)
+divided by the class weight (class->w_j), for each class.
+"converge_search_3" halts convergence when the difference between
+cycles, of this ratio, for every class, has been exceeded by
+"rel_delta_range" for "n_average" cycles.  Decreasing
+"rel_delta_range" tightens the convergence and increases the
+number of cycles.
+.IP "\fBcs4_delta_range\fP = 0.0025"
+Passed to try function "converge_search_4", which monitors the
+ratio of (class->log_a_w_s_h_j)/(class->w_j), for each class,
+averaged over "sigma_beta_n_values" convergence cycles.
+"converge_search_4" halts convergence when the maximum difference
+in average values of this ratio falls below "cs4_delta_range".
+Decreasing "cs4_delta_range" tightens the convergence and
+increases the number of cycles.
+.IP "\fBn_average\fP = 3"
+Passed to try functions "converge_search_3" and "converge".
+The number of cycles for which the convergence criterion
+must be satisfied for the trial to terminate.
+.IP "\fBsigma_beta_n_values\fP = 6"
+Passed to try_fn_type "converge_search_4".  The number of past 
+values to use in computing sigma^2 (noise) and beta^2 (signal).
+.IP "\fBmax_cycles\fP = 200"
+This is the maximum number of cycles permitted for any one convergence 
+of a classification, regardless of any other stopping criteria.  This
+is very dependent upon your database and choice of model and
+convergence parameters, but should be about twice the average number
+of cycles reported in the screen dump and .log file 
+.IP "\fBconverge_print_p\fP = false"
+If true, the selected try function will print to the screen values
+useful in specifying non-default values for \fBhalt_range\fP,
+\fBhalt_factor\fP, \fBrel_delta_range\fP, \fBn_average\fP,
+\fBsigma_beta_n_values\fP, and \fBrange_factor\fP.
+.IP "\fBforce_new_search_p\fP = true"
+If true, will ignore any previous search results, discarding the 
+existing .search and .results[-bin] files after confirmation by the 
+user; if false, will continue the search using the 
+existing .search and .results[-bin] files. 
+For repeatable results, also see \fBmin_report_period\fP,
+\fBstart_fn_type\fP and \fBrandomize_random_p\fP.
+.IP "\fBcheckpoint_p\fP = false"
+If true, checkpoints of the current classification will be written
+every "min_checkpoint_period" seconds, with file extension
+\&.chkpt[-bin]. This is only useful for very large classifications
+.IP "\fBmin_checkpoint_period\fP = 10800"
+If checkpoint_p = true, the checkpointed classification will be 
+written this often - in seconds (default = 3 hours)
+.IP "\fBreconverge_type\fP = """
+Can be either "chkpt" or "results".  If "checkpoint_p" = true and
+"reconverge_type" = "chkpt", then continue convergence of the
+classification contained in <...>.chkpt[-bin].  If "checkpoint_p "
+= false and "reconverge_type" = "results", continue convergence of
+the best classification contained in <...>.results[-bin].  
+.IP "\fBscreen_output_p\fP = true"
+If false, no output is directed to the screen.  Assuming 
+log_file_p = true, output will be directed to the log file only.
+.IP "\fBbreak_on_warnings_p\fP = true"
+The default value asks the user whether or not to continue, when data
+definition warnings are found.  If specified as false, then
+\fBAutoClass\fP will continue, despite warnings -- the warning will
+continue to be output to the terminal and the log file.
+.IP "\fBfree_storage_p\fP = true"
+The default value tells \fBAutoClass\fP to free the majority of its
+allocated storage.  This is not required, and in the case of the DEC
+Alpha causes core dump [is this still true?].  If specified as false,
+\fBAutoClass\fP will not attempt to free storage.
+.SS "HOW TO GET AUTOCLASS C TO PRODUCE REPEATABLE RESULTS"
+In some situations, repeatable classifications are required: comparing
+basic \fBAutoClass C\fP integrity on different platforms, porting
+\fBAutoClass C\fP to a new platform, etc.  In order to accomplish this
+two things are necessary: 1) the same random number generator must be
+used, and 2) the search parameters must be specified properly.
+.PP
+Random Number Generator. This implementation of \fBAutoClass C\fP uses the
+Unix srand48/lrand48 random number generator which generates
+pseudo-random numbers using the well-known linear congruential
+algorithm and 48-bit integer arithmetic.  lrand48() returns non-
+negative long integers uniformly distributed over the interval [0,
+2**31].
+.PP
+Search Parameters.
+The following .s-params file parameters should be specified:
+.nf
+
+force_new_search_p = true
+start_fn_type   "block"
+randomize_random_p = false
+;; specify the number of trials you wish to run
+max_n_tries = 50
+;; specify a time greater than duration of run
+min_report_period = 30000
+
+.fi
+Note that no current best classification reports will be produced.
+Only a final classification summary will be output.
+.SH CHECKPOINTING
+With very large databases there is a significant probability of a
+system crash during any one classification try.  Under such
+circumstances it is advisable to take the time to checkpoint the
+calculations for possible restart.
+.PP
+Checkpointing is initiated by specifying "\fBcheckpoint_p\fP = true"
+in the ".s-params" file.  This causes the inner convergence step, to
+save a copy of the classification onto the checkpoint file each time
+the classification is updated, providing a certain period of time has
+elapsed.  The file extension is ".chkpt[-bin]".
+.PP
+Each time a AutoClass completes a cycle, a "." is output to the screen
+to provide you with information to be used in setting the
+\fBmin_checkpoint_period\fP value (default 10800 seconds or 3 hours).
+There is obviously a trade-off between frequency of checkpointing and
+the probability that your machine may crash, since the repetitive
+writing of the checkpoint file will slow the search process.
+.PP
+Restarting AutoClass Search:
+.PP
+To recover the classification and continue the search after rebooting
+and reloading AutoClass, specify \fBreconverge_type\fP = "chkpt" in
+the ".s-params" file (specify \fBforce_new_search_p\fP as false).
+.PP
+AutoClass will reload the appropriate database and models, provided
+there has been no change in their filenames since the time they were
+loaded for the checkpointed classification run.  The ".s-params" file
+contains any non-default arguments that were provided to the original
+call.
+.PP
+In the beginning of a search, before \fBstart_j_list\fP has been
+emptied, it will be necessary to trim the original list to what would
+have remained in the crashed search.  This can be determined by
+looking at the ".log" file to determine what values were already used.
+If the \fBstart_j_list\fP has been emptied, then an empty
+\fBstart_j_list\fP should be specified in the ".s-params" file.  This
+is done either by
+.sp
+        \fBstart_j_list\fP =
+.sp
+or
+.sp
+        \fBstart_j_list\fP = -9999 
+.sp
+Here is an a set of scripts to demonstrate check-pointing:
+.nf
+
+autoclass -search data/glass/glassc.db2 data/glass/glass-3c.hd2 \\
+     data/glass/glass-mnc.model data/glass/glassc-chkpt.s-params
+
+Run 1)
+  ## glassc-chkpt.s-params
+  max_n_tries = 2
+  force_new_search_p = true
+  ## --------------------
+  ;; run to completion
+
+Run 2)
+  ## glassc-chkpt.s-params
+  force_new_search_p = false
+  max_n_tries = 10
+  checkpoint_p = true
+  min_checkpoint_period = 2
+  ## --------------------
+  ;; after 1 checkpoint, ctrl-C to simulate cpu crash
+
+Run 3)
+  ## glassc-chkpt.s-params
+  force_new_search_p = false
+  max_n_tries = 1
+  checkpoint_p = true
+  min_checkpoint_period = 1
+  reconverge_type = "chkpt"
+  ## --------------------
+  ;; checkpointed trial should finish 
+
+.fi
+.SH "OUTPUT FILES"
+The standard reports are 
+.IP 1) 
+Attribute influence values: presents the relative influence or 
+significance of the data's attributes both globally (averaged over
+all classes), and locally (specifically for each class). A
+heuristic
+for relative class strength is also listed;
+.IP 2) 
+Cross-reference by case (datum) number: lists the primary class 
+probability for each datum, ordered by case number.  When
+report_mode = "data", additional lesser class probabilities 
+(greater than or equal to 0.001) are listed for each datum;
+.IP 3) 
+Cross-reference by class number: for each class the primary class
+probability and any lesser class probabilities (greater than or
+equal to 0.001) are listed for each datum in the class, ordered by 
+case number. It is also possible to list, for each datum, the values
+of attributes, which you select.
+.PP
+The attribute influence values report attempts to provide relative
+measures of the "influence" of the data attributes on the classes
+found by the classification.  The normalized class strengths, the
+normalized attribute influence values summed over all classes, and the
+individual influence values (I[jkl]) are all only relative measures
+and should be interpreted with more meaning than rank ordering, but
+not like anything approaching absolute values.
+.PP
+The reports are output to files whose names and pathnames are taken
+from the ".r-params" file pathname.  The report file types (extensions)
+are:
+.IP "\fBinfluence values report\fP"
+"influ-o-text-\fIn\fP" or "influ-no-text-\fIn\fP"
+.IP "\fBcross-reference by case\fP"
+"case-text-\fIn\fP"
+.IP "\fBcross-reference by class\fP"
+"class-text-\fIn\fP" 
+.PP
+or, if report_mode is overridden to "data":
+.IP "\fBinfluence values report\fP"
+"influ-o-data-\fIn\fP" or "influ-no-data-\fIn\fP"
+.IP "\fBcross-reference by case\fP"
+"case-data-\fIn\fP"
+.IP "\fBcross-reference by class\fP"
+"class-data-\fIn\fP" 
+.PP
+where \fIn\fP is the classification number from the "results" file.
+The first or best classification is numbered 1, the next best 2, etc.
+The default is to generate reports only for the best classification in
+the "results" file.  You can produce reports for other saved
+classifications by using report params keywords \fBn_clsfs\fP and
+\fBclsf_n_list\fP.  The "influ-o-text-\fIn\fP" file type is the
+default (\fBorder_attributes_by_influence_p\fP = true), and lists each
+class's attributes in descending order of attribute influence value.
+If the value of \fBorder_attributes_by_influence_p\fP is overridden to
+be false in the <...>.r-params file, then each class's attributes will
+be listed in ascending order by attribute number.  The extension of
+the file generated will be "influ-no-text-\fIn\fP".  This method of
+listing facilitates the visual comparison of attribute values between
+classes.
+.PP
+For example, this command:
+.sp
+.nf
+	autoclass -reports sample/imports-85c.results-bin 
+		sample/imports-85c.search sample/imports-85c.r-params
+.fi
+.sp
+with this line in the ".r-params" file:
+.sp
+	xref_class_report_att_list = 2, 5, 6
+.sp
+will generate these output files:
+.sp
+.nf
+	imports-85.influ-o-text-1
+	imports-85.case-text-1
+	imports-85.class-text-1
+.fi
+.PP
+The \fBAutoClass C\fP reports provide the capability to compute sigma
+class contour values for specified pairs of real valued attributes,
+when generating the influence values report with the data option
+(report_mode = "data").  Note that sigma class contours are not
+generated from discrete type attributes.
+.PP
+The sigma contours are the two dimensional equivalent of n-sigma error
+bars in one dimension.  Specifically, for two independent attributes
+the n-sigma contour is defined as the ellipse where
+.PP
+((x - xMean) / xSigma)^2 + ((y - yMean) / ySigma)^2 == n 
+.PP
+With covariant attributes, the n-sigma contours are defined
+identically, in the rotated coordinate system of the distribution's
+principle axes.  Thus independent attributes give ellipses oriented
+parallel with the attribute axes, while the axes of sigma contours of
+covariant attributes are rotated about the center determined by the
+means.  In either case the sigma contour represents a line where the
+class probability is constant, irrespective of any other class
+probabilities.
+.PP
+With three or more attributes the n-sigma contours become
+k-dimensional ellipsoidal surfaces.  This code takes advantage of the
+fact that the parallel projection of an n-dimensional ellipsoid, onto
+any 2-dim plane, is bounded by an ellipse.  In this simplified case of
+projecting the single sigma ellipsoid onto the coordinate planes, it
+is also true that the 2-dim covariances of this ellipse are equal to
+the corresponding elements of the n-dim ellipsoid's covariances.  The
+Eigen-system of the 2-dim covariance then gives the variances
+w.r.t. the principal components of the eclipse, and the rotation that
+aligns it with the data.  This represents the best way to display a
+distribution in the marginal plane.
+.PP
+To get contour values, set the keyword \fBsigma_contours_att_list\fP
+to a list of real valued attribute indices (from .hd2 file), and
+request an influence values report with the data option.  For example,
+.sp
+.nf
+	report_mode = "data"
+	sigma_contours_att_list = 3, 4, 5, 8, 15
+.fi
+.SS "OUTPUT REPORT PARAMETERS"
+The contents of the output report are controlled by the ".r-params"
+file.  In this file, an empty line or a line starting with one of
+these characters is treated as a comment: "#", "!", or ";".  The
+parameter name and its value can be separated by an equal sign, a
+space, or a tab:
+.sp
+.nf
+	n_clsfs 1
+	n_clsfs = 1
+	n_clsfs<tab>1
+.fi
+.sp
+Spaces are ignored if "=" or "<tab>" are used as separators.  Note
+there are no trailing semicolons.
+.PP
+The following are the allowed parameters and their default values:
+.IP "\fBn_clsfs\fP = 1"
+number of clsfs in the .results file for which to generate reports,
+starting with the first or "best".
+.IP "\fBclsf_n_list\fP = "
+if specified, this is a one-based index list of clsfs in the clsf
+sequence read from the .results file.  It overrides "n_clsfs".
+For example: 
+.sp
+	clsf_n_list = 1, 2 
+.sp
+will produce the same output as
+.sp
+	n_clsfs = 2
+.sp
+but
+.sp
+	clsf_n_list = 2
+.sp
+will only output the "second best" classification report.
+.IP "\fBreport_type\fP = \"all\""
+type of reports to generate: "all", "influence_values", "xref_case", or
+"xref_class".
+.IP "\fBreport_mode\fP = \"text\""
+mode of reports to generate. "text" is formatted text layout.  "data"
+is numerical -- suitable for further processing.
+.IP "\fBcomment_data_headers_p\fP = false"
+the default value does not insert # in column 1 of most 
+report_mode = "data" header lines.  If specified as true, the comment 
+character will be inserted in most header lines.
+.IP "\fBnum_atts_to_list\fP = "
+if specified, the number of attributes to list in influence values report.
+if not specified, \fIall\fP attributes will be listed. 
+(e.g. "num_atts_to_list = 5")
+.IP "\fBxref_class_report_att_list\fP = "
+if specified, a list of attribute numbers (zero-based), whose values will 
+be output in the "xref_class" report along with the case probabilities.  
+if not specified, no attributes values will be output. 
+(e.g. "xref_class_report_att_list = 1, 2, 3")
+.IP "\fBorder_attributes_by_influence_p\fP = true"
+The default value lists each class's attributes in descending order of
+attribute influence value, and uses ".influ-o-text-n" as the
+influence values report file type.  If specified as false, then each 
+class's attributes will be listed in ascending order by attribute number.  
+The extension of the file generated will be "influ-no-text-n".
+.IP "\fBbreak_on_warnings_p\fP = true"
+The default value asks the user whether to continue or not when data
+definition warnings are found.  If specified as false, then \fBAutoClass\fP
+will continue, despite warnings -- the warning will continue to be
+output to the terminal.
+.IP "\fBfree_storage_p\fP = true"
+The default value tells \fBAutoClass\fP to free the majority of its
+allocated storage.  This is not required, and in the case of the DEC
+Alpha causes a core dump [is this still true?].  If specified as
+false, \fBAutoClass\fP will not attempt to free storage.
+.IP "\fBmax_num_xref_class_probs\fP = 5"
+Determines how many lessor class probabilities will be printed for the 
+case and class cross-reference reports.  The default is to print the
+most probable class probability value and up to 4 lessor class prob-
+ibilities.  Note this is true for both the "text" and "data" class
+cross-reference reports, but only true for the "data" case cross-
+reference report.  The "text" case cross-reference report only has the
+most probable class probability.
+.IP "\fBsigma_contours_att_list\fP = "
+If specified, a list of real valued attribute indices (from .hd2 file) 
+will be to compute sigma class contour values, when generating 
+influence values report with the data option (report_mode = "data"). 
+If not specified, there will be no sigma class contour output.
+(e.g. "sigma_contours_att_list = 3, 4, 5, 8, 15")
+.SH "INTERPRETATION OF AUTOCLASS RESULTS"
+.br
+.sp
+.SS "WHAT HAVE YOU GOT?"
+Now you have run \fBAutoClass\fP on your data set -- what have you got?
+Typically, the \fBAutoClass\fP search procedure finds many classifications,
+but only saves the few best.  These are now available for inspection
+and interpretation.  The most important indicator of the relative
+merits of these alternative classifications is Log total posterior
+probability value.  Note that since the probability lies between 1 and
+0, the corresponding Log probability is negative and ranges from 0 to
+negative infinity. The difference between these Log probability values
+raised to the power e gives the relative probability of the
+alternatives classifications.  So a difference of, say 100, implies
+one classification is e^100 ~= 10^43 more likely than the other.
+However, these numbers can be very misleading, since they give the
+relative probability of alternative classifications under the
+\fBAutoClass\fP \fIassumptions\fP.
+.SS "ASSUMPTIONS"
+Specifically, the most important \fBAutoClass\fP assumptions are the use of normal
+models for real variables, and the assumption of independence of attributes
+within a class.  Since these assumptions are often violated in practice, the
+difference in posterior probability of alternative classifications can be
+partly due to one classification being closer to satisfying the assumptions
+than another, rather than to a real difference in classification quality.
+Another source of uncertainty about the utility of Log probability values is 
+that they do not take into account any specific prior knowledge the user may 
+have about the domain.  This means that it is often worth looking at 
+alternative classifications to see if you can interpret them, but it is worth 
+starting from the most probable first.  Note that if the Log probability value
+is much greater than that for the one class case, it is saying that there is 
+overwhelming evidence for \fIsome\fP structure in the data, and part of this 
+structure has been captured by the \fBAutoClass\fP classification.
+.SS "INFLUENCE REPORT "
+So you have now picked a classification you want to examine, based on
+its Log probability value; how do you examine it?  The first thing to
+do is to generate an "influence" report on the classification using
+the report generation facilities documented in
+\fI/usr/share/doc/autoclass/reports-c.text\fP.  An influence report is
+designed to summarize the important information buried in the
+\fBAutoClass\fP data structures.
+.PP
+The first part of this report gives the heuristic class "strengths".
+Class "strength" is here defined as the geometric mean probability that
+any instance "belonging to" class, would have been generated from the
+class probability model.  It thus provides a heuristic measure of how
+strongly each class predicts "its" instances.
+.PP
+The second part is a listing of the overall "influence" of each of the
+attributes used in the classification.  These give a rough heuristic
+measure of the relative importance of each attribute in the
+classification.  Attribute "influence values" are a class probability
+weighted average of the "influence" of each attribute in the classes, as
+described below.
+.PP
+The next part of the report is a summary description of each of the
+classes.  The classes are arbitrarily numbered from 0 up to n, in order
+of descending class weight.  A class weight of say 34.1 means that the
+weighted sum of membership probabilities for class is 34.1.  Note that
+a class weight of 34 does not necessarily mean that 34 cases belong to
+that class, since many cases may have only partial membership in that
+class.  Within each class, attributes or attribute sets are ordered by
+the "influence" of their model term.
+.SS "CROSS ENTROPY "
+A commonly used measure of the divergence between two probability
+distributions is the cross entropy: the sum over all possible values x,
+of P(x|c...)*log[P(x|c...)/P(x|g...)], where c... and g... define the
+distributions.  It ranges from zero, for identical distributions, to
+infinite for distributions placing probability 1 on differing values of
+an attribute.  With conditionally independent terms in the probability
+distributions, the cross entropy can be factored to a sum over these
+terms.  These factors provide a measure of the corresponding modeled
+attribute's influence in differentiating the two distributions.
+.PP
+We define the modeled term's "influence" on a class to be the cross
+entropy term for the class distribution w.r.t. the global class
+distribution of the single class classification.  "Influence" is thus a
+measure of how strongly the model term helps differentiate the class
+from the whole data set.  With independently modeled attributes, the
+influence can legitimately be ascribed to the attribute itself.  With
+correlated or covariant attributes sets, the cross entropy factor is a
+function of the entire set, and we distribute the influence value
+equally over the modeled attributes.
+.SS "ATTRIBUTE INFLUENCE VALUES"
+In the "influence" report on each class, the attribute parameters for
+that class are given in order of highest influence value for the model
+term attribute sets.  Only the first few attribute sets usually have
+significant influence values.  If an influence value drops below about
+20% of the highest value, then it is probably not significant, but all
+attribute sets are listed for completeness.  In addition to the
+influence value for each attribute set, the values of the attribute
+set parameters in that class are given along with the corresponding
+"global" values.  The global values are computed directly from the
+data independent of the classification.  For example, if the class
+mean of attribute "temperature" is 90 with standard deviation of 2.5,
+but the global mean is 68 with a standard deviation of 16.3, then this
+class has selected out cases with much higher than average
+temperature, and a rather small spread in this high range.  Similarly,
+for discrete attribute sets, the probability of each outcome in that
+class is given, along with the corresponding global probability --
+ordered by its significance: the absolute value of (log
+{<local-probability> / <global-probability>}).  The sign of the
+significance value shows the direction of change from the global
+class.  This information gives an overview of how each class differs
+from the average for all the data, in order of the most significant
+differences.
+.SS "CLASS AND CASE REPORTS"
+Having gained a description of the classes from the "influence"
+report, you may want to follow-up to see which classes your favorite
+cases ended up in.  Conversely, you may want to see which cases belong
+to a particular class.  For this kind of cross-reference information
+two complementary reports can be generated.  These are more fully
+documented in \fI/usr/share/doc/autoclass/reports-c.text\fP. The
+"class" report, lists all the cases which have significant membership
+in each class and the degree to which each such case belongs to that
+class.  Cases whose class membership is less than 90% in the current
+class have their other class membership listed as well.  The cases
+within a class are ordered in increasing case number.  The alternative
+"cases" report states which class (or classes) a case belongs to, and
+the membership probability in the most probable class.  These two
+reports allow you to find which cases belong to which classes or the
+other way around.  If nearly every case has close to 99% membership in
+a single class, then it means that the classes are well separated,
+while a high degree of cross-membership indicates that the classes are
+heavily overlapped.  Highly overlapped classes are an indication that
+the idea of classification is breaking down and that groups of
+mutually highly overlapped classes, a kind of meta class, is probably
+a better way of understanding the data.
+.SS "COMPARING CLASS WEIGHTS AND CLASS/CASE REPORT ASSIGNMENTS"
+The class weight given as the class probability parameter, is
+essentially the sum over all data instances, of the normalized
+probability that the instance is a member of the class.  It is
+probably an error on our part that we format this number as an integer
+in the report, rather than emphasizing its real nature.  You will find
+the actual real value recorded as the w_j parameter in the class_DS
+structures on any .results[-bin] file.
+.PP
+The .case and .class reports give probabilities that cases are members
+of classes.  Any assignment of cases to classes requires some decision
+rule.  The maximum probability assignment rule is often implicitly
+assumed, but it cannot be expected that the resulting partition sizes
+will equal the class weights unless nearly all class membership
+probabilities are effectively one or zero.  With non-1/0 membership
+probabilities, matching the class weights requires summing the
+probabilities.
+.PP
+In addition, there is the question of completeness of the EM
+(expectation maximization) convergence.  EM alternates between
+estimating class parameters and estimating class membership
+probabilities.  These estimates converge on each other, but never
+actually meet.  \fBAutoClass\fP implements several convergence algorithms
+with alternate stopping criteria using appropriate parameters in 
+the .s-params file.  Proper setting of these parameters, to get reasonably
+complete and efficient convergence may require experimentation.
+.SS "ALTERNATIVE CLASSIFICATIONS "
+In summary, the various reports that can be generated give you a way
+of viewing the current classification.  It is usually a good idea to
+look at alternative classifications even though they do not have the
+minimum Log probability values.  These other classifications usually
+have classes that correspond closely to strong classes in other
+classifications, but can differ in the weak classes.  The "strength"
+of a class within a classification can usually be judged by how
+dramatically the highest influence value attributes in the class
+differ from the corresponding global attributes.  If none of the
+classifications seem quite satisfactory, it is always possible to run
+\fBAutoClass\fP again to generate new classifications.
+.SS "WHAT NEXT?"
+Finally, the question of what to do after you have found an
+insightful classification arises.  Usually, classification is a
+preliminary data analysis step for examining a set of cases (things,
+examples, etc.) to see if they can be grouped so that members of the
+group are "similar" to each other.  \fBAutoClass\fP gives such a grouping
+without the user having to define a similarity measure.  The built-in
+"similarity" measure is the mutual predictiveness of the cases.  The
+next step is to try to "explain" why some objects are more like others
+than those in a different group.  Usually, domain knowledge suggests
+an answer.  For example, a classification of people based on income,
+buying habits, location, age, etc., may reveal particular social
+classes that were not obvious before the classification analysis.  To
+obtain further information about such classes, further information,
+such as number of cars, what TV shows are watched, etc., would reveal
+even more information.  Longitudinal studies would give information
+about how social classes arise and what influences their attitudes --
+all of which is going way beyond the initial classification.
+.SH PREDICTIONS
+Classifications can be used to predict class membership for new
+cases.  So in addition to possibly giving you some insight into the
+structure behind your data, you can now use \fBAutoClass\fP directly
+to make predictions, and compare \fBAutoClass\fP to other learning
+systems.
+.PP
+This technique for predicting class probabilities is applicable to all
+attributes, regardless of data type/sub_type or likelihood model term type.
+.PP
+In the event that the class membership of a data case does not exceed
+0.0099999 for any of the "training" classes, the following message will appear
+in the screen output for each case:
+.sp
+        xref_get_data: case_num xxx => class 9999
+.sp
+Class 9999 members will appear in the "case" and "class" cross-reference 
+reports with a class membership of 1.0.
+.PP
+Cautionary Points:
+.PP
+The usual way of using \fBAutoClass\fP is to put all of your data in a
+data_file, describe that data with model and header files, and run
+"autoclass -search".  Now, instead of one data_file you will have two,
+a training_data_file and a test_data_file.
+.PP
+It is most important that both databases have the same \fBAutoClass\fP
+internal representation.  Should this not be true, \fBAutoClass\fP
+will exit, or possibly in in some situations, crash.  The prediction
+mode is designed to hopefully direct the user into conforming to this
+requirement.
+.PP
+Preparation:
+.PP
+Prediction requires having a training classification and a test
+database.  The training classification is generated by the running of
+"autoclass -search" on the training data_file
+("data/soybean/soyc.db2"), for example:
+.sp
+.nf
+    autoclass -search data/soybean/soyc.db2 data/soybean/soyc.hd2 
+        data/soybean/soyc.model data/soybean/soyc.s-params
+.fi
+.sp
+This will produce "soyc.results-bin" and "soyc.search".  Then create a
+"reports" parameter file, such as "soyc.r-params" (see
+\fI/usr/share/doc/autoclass/reports-c.text\fP), and run
+\fBAutoClass\fP in "reports" mode, such as:
+.sp
+.nf
+    autoclass -reports data/soybean/soyc.results-bin 
+        data/soybean/soyc.search data/soybean/soyc.r-params
+.fi
+.sp
+This will generate class and case cross-reference files, and an influence
+values file.  The file names are based on the ".r-params" file name:
+.sp
+.nf
+        data/soybean/soyc.class-text-1
+        data/soybean/soyc.case-text-1
+        data/soybean/soyc.influ-text-1
+.fi
+.sp
+These will describe the classes found in the training_data_file.
+Now this classification can be used to predict the probabilistic class
+membership of the test_data_file cases ("data/soybean/soyc-predict.db2")
+in the training_data_file classes.
+.sp
+.nf
+    autoclass -predict data/soybean/soyc-predict.db2
+        data/soybean/soyc.results-bin data/soybean/soyc.search
+        data/soybean/soyc.r-params
+.fi
+.sp
+This will generate class and case cross-reference files for the
+test_data_file cases predicting their probabilistic class memberships
+in the training_data_file classes.  The file names are based on the
+".db2" file name:
+.sp
+.nf
+        data/soybean/soyc-predict.class-text-1
+        data/soybean/soyc-predict.case-text-1
+.fi
+.sp
+.SH "SEE ALSO"
+\fBAutoClass\fP is documented fully here:
+.LP
+.I /usr/share/doc/autoclass/introduction-c.text
+Guide to the documentation 
+.LP
+.I /usr/share/doc/autoclass/preparation-c.text
+How to prepare data for use by AutoClass
+.LP
+.I /usr/share/doc/autoclass/search-c.text
+How to run AutoClass to find classifications.
+.LP
+.I /usr/share/doc/autoclass/reports-c.text
+How to examine the classification in various ways.
+.LP
+.I /usr/share/doc/autoclass/interpretation-c.text
+How to interpret AutoClass results.
+.LP
+.I /usr/share/doc/autoclass/checkpoint-c.text
+Protocols for running a checkpointed search.
+.LP
+.I /usr/share/doc/autoclass/prediction-c.text
+Use classifications to predict class membership for new cases.  
+.PP
+These provide supporting documentation:
+.LP
+.I /usr/share/doc/autoclass/classes-c.text
+What classification is all about, for beginners.
+.LP
+.I /usr/share/doc/autoclass/models-c.text
+Brief descriptions of the model term implementations.
+.PP
+The mathematical theory behind \fBAutoClass\fP is explained in these
+documents:
+.LP
+.I /usr/share/doc/autoclass/kdd-95.ps
+Postscript file containing:
+P. Cheeseman, J. Stutz, "Bayesian Classification (AutoClass):
+Theory and Results", in "Advances in Knowledge Discovery and
+Data Mining", Usama M. Fayyad, Gregory Piatetsky-Shapiro,
+Padhraic Smyth, & Ramasamy Uthurusamy, Eds. The AAAI Press, 
+Menlo Park, expected fall 1995.
+.LP
+.I /usr/share/doc/autoclass/tr-fia-90-12-7-01.ps
+Postscript file containing:
+R. Hanson, J. Stutz, P. Cheeseman, "Bayesian Classification
+Theory", Technical Report FIA-90-12-7-01, NASA Ames Research
+Center, Artificial Intelligence Branch, May 1991
+(The figures are not included, since they were inserted by
+"cut-and-paste" methods into the original "camera-ready"
+copy.)
+.SH AUTHORS
+.nf
+Dr. Peter Cheeseman
+Principal Investigator - NASA Ames, Computational Sciences Division
+cheesem@ptolemy.arc.nasa.gov
+
+John Stutz
+Research Programmer - NASA Ames, Computational Sciences Division
+stutz@ptolemy.arc.nasa.gov
+
+Will Taylor
+Support Programmer - NASA Ames, Computational Sciences Division
+taylor@ptolemy.arc.nasa.gov
+.fi
+.\" .PP
+.\" This manual page was written by James R. Van Zandt <jrv@debian.org>,
+.\" for the Debian GNU/Linux system (but may be used by others).
+.SH "SEE ALSO"
+.BR multimix (1).
