Merge branch 'master' of ssh://git.dcc.fc.up.pt/yap-6.3 into HEAD
This commit is contained in:
Vítor Santos Costa
commit
11282b44d7
@ -147,7 +147,8 @@ CPLINT_DOCS=\
|
||||
CPLINT_LEARNING_PROGRAMS=\
|
||||
$(CPLINT_SRCDIR)/em \
|
||||
$(CPLINT_SRCDIR)/rib \
|
||||
$(CPLINT_SRCDIR)/slipcase
|
||||
$(CPLINT_SRCDIR)/slipcase \
|
||||
$(CPLINT_SRCDIR)/slipcover
|
||||
|
||||
|
||||
all: $(SOBJS)
|
||||
@ -181,6 +182,7 @@ install: all
|
||||
cp $(CPLINT_SRCDIR)/em/*.pl $(DESTDIR)$(SHAREDIR)/cplint/
|
||||
cp $(CPLINT_SRCDIR)/rib/*.pl $(DESTDIR)$(SHAREDIR)/cplint/
|
||||
cp $(CPLINT_SRCDIR)/slipcase/*.pl $(DESTDIR)$(SHAREDIR)/cplint/
|
||||
cp $(CPLINT_SRCDIR)/slipcover/*.pl $(DESTDIR)$(SHAREDIR)/cplint/
|
||||
$(INSTALL_PROGRAM) slipcase/bddem.so $(DESTDIR)$(YAPLIBDIR)
|
||||
|
||||
installcheck:
|
||||
|
||||
@ -1,3 +1,22 @@
|
||||
@article{DBLP:journals/ai/Cohen95,
|
||||
author = {William W. Cohen},
|
||||
title = {Pac-Learning Non-Recursive Prolog Clauses},
|
||||
journal = {Artif. Intell.},
|
||||
volume = {79},
|
||||
number = {1},
|
||||
year = {1995},
|
||||
pages = {1-38},
|
||||
ee = {http://dx.doi.org/10.1016/0004-3702(94)00034-4},
|
||||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||||
}
|
||||
@article{BelRig13-TPLP-IJ,
|
||||
author = {Elena Bellodi and Fabrizio Riguzzi},
|
||||
title = {Structure Learning of Probabilistic Logic Programs by Searching the Clause Space},
|
||||
journal = {Theory and Practice of Logic Programming},
|
||||
publisher = {Cambridge University Press},
|
||||
copyright = {Cambridge University Press},
|
||||
year = {2013}
|
||||
}
|
||||
@article{BelRig11-IDA,
|
||||
author = {Elena Bellodi and Fabrizio Riguzzi},
|
||||
title = { Expectation {Maximization} over Binary Decision Diagrams for Probabilistic Logic Programs},
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
<meta name="originator" content="TeX4ht (http://www.cse.ohio-state.edu/~gurari/TeX4ht/)">
|
||||
<!-- html -->
|
||||
<meta name="src" content="manual.tex">
|
||||
<meta name="date" content="2013-07-16 15:51:00">
|
||||
<meta name="date" content="2013-09-04 21:32:00">
|
||||
<link rel="stylesheet" type="text/css" href="manual.css">
|
||||
</head><body
|
||||
>
|
||||
@ -21,18 +21,18 @@ class="cmr-12">Fabrizio Riguzzi</span>
|
||||
<br /><span
|
||||
class="cmr-12">fabrizio.riguzzi@unife.it</span></div><br />
|
||||
<div class="date" ><span
|
||||
class="cmr-12">July 16, 2013</span></div>
|
||||
class="cmr-12">September 4, 2013</span></div>
|
||||
</div>
|
||||
<h3 class="sectionHead"><span class="titlemark">1 </span> <a
|
||||
id="x1-10001"></a>Introduction</h3>
|
||||
<!--l. 31--><p class="noindent" ><span
|
||||
class="cmtt-10">cplint </span>is a suite of programs for reasoning with ICL <span class="cite">[<a
|
||||
href="#XDBLP:journals/ai/Poole97">13</a>]</span>, LPADs <span class="cite">[<a
|
||||
href="#XVenVer03-TR">22</a>, <a
|
||||
href="#XVenVer04-ICLP04-IC">23</a>]</span> and
|
||||
href="#XDBLP:journals/ai/Poole97">15</a>]</span>, LPADs <span class="cite">[<a
|
||||
href="#XVenVer03-TR">24</a>, <a
|
||||
href="#XVenVer04-ICLP04-IC">25</a>]</span> and
|
||||
CP-logic programs <span class="cite">[<a
|
||||
href="#XVenDenBru-JELIA06">20</a>, <a
|
||||
href="#XDBLP:journals/tplp/VennekensDB09">21</a>]</span>. It contains programs both for inference and
|
||||
href="#XVenDenBru-JELIA06">22</a>, <a
|
||||
href="#XDBLP:journals/tplp/VennekensDB09">23</a>]</span>. It contains programs both for inference and
|
||||
learning.
|
||||
<!--l. 33--><p class="noindent" >
|
||||
<h3 class="sectionHead"><span class="titlemark">2 </span> <a
|
||||
@ -121,7 +121,7 @@ h1:1 :- b1,...,bm,\+ c1,...,\+ cl.
|
||||
</div>
|
||||
<!--l. 83--><p class="nopar" >
|
||||
<!--l. 85--><p class="indent" > The coin example of <span class="cite">[<a
|
||||
href="#XVenVer04-ICLP04-IC">23</a>]</span> is represented as (see file <span
|
||||
href="#XVenVer04-ICLP04-IC">25</a>]</span> is represented as (see file <span
|
||||
class="cmtt-10">coin.cpl</span>)
|
||||
|
||||
<div class="verbatim" id="verbatim-7">
|
||||
@ -169,13 +169,13 @@ class="cmtt-10">cplint </span>contains various modules for answering queries.
|
||||
<ul class="itemize1">
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">lpadsld.pl</span>: uses the top-down procedure described in in <span class="cite">[<a
|
||||
href="#XRig-AIIA07-IC">14</a>]</span> and <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">15</a>]</span>. It
|
||||
href="#XRig-AIIA07-IC">16</a>]</span> and <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">17</a>]</span>. It
|
||||
is based on SLDNF resolution and is an adaptation of the interpreter for
|
||||
ProbLog <span class="cite">[<a
|
||||
href="#XDBLP:conf/ijcai/RaedtKT07">9</a>]</span>.
|
||||
href="#XDBLP:conf/ijcai/RaedtKT07">11</a>]</span>.
|
||||
<!--l. 130--><p class="noindent" >It was proved correct <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">15</a>]</span> with respect to the semantics of LPADs for
|
||||
href="#XRig-RCRA07-IC">17</a>]</span> with respect to the semantics of LPADs for
|
||||
range restricted acyclic programs <span class="cite">[<a
|
||||
href="#XDBLP:journals/ngc/AptB91">1</a>]</span> without function symbols.
|
||||
<!--l. 132--><p class="noindent" >It is also able to deal with extensions of LPADs and CP-logic: the clause
|
||||
@ -188,8 +188,8 @@ class="cmtt-10">setof </span>or <span
|
||||
class="cmtt-10">bagof </span>operator.
|
||||
These extended features have been introduced in order to represent
|
||||
CLP(BN) <span class="cite">[<a
|
||||
href="#XSanPagQaz03-UAI-IC">19</a>]</span> programs and PRM models <span class="cite">[<a
|
||||
href="#XGetoor+al:JMLR02">12</a>]</span>: <span
|
||||
href="#XSanPagQaz03-UAI-IC">21</a>]</span> programs and PRM models <span class="cite">[<a
|
||||
href="#XGetoor+al:JMLR02">14</a>]</span>: <span
|
||||
class="cmtt-10">setof </span>and <span
|
||||
class="cmtt-10">bagof </span>allow to
|
||||
express dependency of an attribute from an aggregate function of another
|
||||
@ -199,11 +199,11 @@ class="cmtt-10">bagof </span>allow to
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">picl.pl</span>: performs inference on ICL programs <span class="cite">[<a
|
||||
href="#XRig09-LJIGPL-IJ">16</a>]</span>
|
||||
href="#XRig09-LJIGPL-IJ">18</a>]</span>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">lpad.pl</span>: uses a top-down procedure based on SLG resolution <span class="cite">[<a
|
||||
href="#XDBLP:journals/jacm/ChenW96">8</a>]</span>. As a
|
||||
href="#XDBLP:journals/jacm/ChenW96">9</a>]</span>. As a
|
||||
consequence, it works for any sound LPADs, i.e., any LPAD such that
|
||||
each of its instances has a two valued well founded model.
|
||||
</li>
|
||||
@ -217,28 +217,28 @@ class="cmtt-10">cpl.pl</span>: uses a top-down procedure based on SLG resolution
|
||||
<ul class="itemize2">
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">deepit.pl </span>performs iterative deepening <span class="cite">[<a
|
||||
href="#XBraRig10-ILP10-IC">7</a>]</span>
|
||||
href="#XBraRig10-ILP10-IC">8</a>]</span>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">deepdyn.pl </span>performs dynamic iterative deepening <span class="cite">[<a
|
||||
href="#XBraRig10-ILP10-IC">7</a>]</span>
|
||||
href="#XBraRig10-ILP10-IC">8</a>]</span>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">bestk.pl </span>performs k-Best <span class="cite">[<a
|
||||
href="#XBraRig10-ILP10-IC">7</a>]</span>
|
||||
href="#XBraRig10-ILP10-IC">8</a>]</span>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">bestfirst.pl </span>performs best first <span class="cite">[<a
|
||||
href="#XBraRig10-ILP10-IC">7</a>]</span>
|
||||
href="#XBraRig10-ILP10-IC">8</a>]</span>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">montecarlo.pl </span>performs Monte Carlo <span class="cite">[<a
|
||||
href="#XBraRig10-ILP10-IC">7</a>]</span>
|
||||
href="#XBraRig10-ILP10-IC">8</a>]</span>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">mcintyre.pl</span>: implements the algorithm MCINTYRE (Monte Carlo
|
||||
INference wiTh Yap REcord) <span class="cite">[<a
|
||||
href="#XRig11-CILC11-NC">17</a>]</span></li></ul>
|
||||
href="#XRig11-CILC11-NC">19</a>]</span></li></ul>
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">approx/exact.pl </span>as <span
|
||||
@ -276,7 +276,7 @@ class="cmmi-10">P</span>, i.e.,
|
||||
sufficient causation, independent causation, no deus ex machina events
|
||||
and temporal precedence. It uses the definition of the semantics given in
|
||||
<span class="cite">[<a
|
||||
href="#XDBLP:journals/tplp/VennekensDB09">21</a>]</span>.</li></ul>
|
||||
href="#XDBLP:journals/tplp/VennekensDB09">23</a>]</span>.</li></ul>
|
||||
<!--l. 159--><p class="noindent" >
|
||||
<h4 class="subsectionHead"><span class="titlemark">4.1 </span> <a
|
||||
id="x1-50004.1"></a>Commands</h4>
|
||||
@ -827,55 +827,55 @@ class="cmtt-10">examples</span>:
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">alarm.cpl</span>: representation of the Bayesian network in Figure 2 of
|
||||
<span class="cite">[<a
|
||||
href="#XVenVer04-ICLP04-IC">23</a>]</span>.
|
||||
href="#XVenVer04-ICLP04-IC">25</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">coin.cpl</span>: coin example from <span class="cite">[<a
|
||||
href="#XVenVer04-ICLP04-IC">23</a>]</span>.
|
||||
href="#XVenVer04-ICLP04-IC">25</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">coin2.cpl</span>: coin example with two coins.
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">dice.cpl</span>: dice example from <span class="cite">[<a
|
||||
href="#XVenVer04-ICLP04-IC">23</a>]</span>.
|
||||
href="#XVenVer04-ICLP04-IC">25</a>]</span>.
|
||||
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">twosideddice.cpl,</span><span
|
||||
class="cmtt-10"> threesideddice.cpl</span></span></span> game with idealized dice
|
||||
with two or three sides. Used in the experiments in <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">15</a>]</span>.
|
||||
href="#XRig-RCRA07-IC">17</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">ex.cpl</span>: first example in <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">15</a>]</span>.
|
||||
href="#XRig-RCRA07-IC">17</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">exapprox.cpl</span>: example showing the problems of approximate
|
||||
inference (see <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">15</a>]</span>).
|
||||
href="#XRig-RCRA07-IC">17</a>]</span>).
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">exrange.cpl</span>: example showing the problems with non range
|
||||
restricted programs (see <span class="cite">[<a
|
||||
href="#XRig-RCRA07-IC">15</a>]</span>).
|
||||
href="#XRig-RCRA07-IC">17</a>]</span>).
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">female.cpl</span>: example showing the dependence of probabilities in the
|
||||
head from variables in the body (from <span class="cite">[<a
|
||||
href="#XVenVer04-ICLP04-IC">23</a>]</span>).
|
||||
href="#XVenVer04-ICLP04-IC">25</a>]</span>).
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">mendel.cpl, mendels.cpl</span>: programs describing the Mendelian
|
||||
rules of inheritance, taken from <span class="cite">[<a
|
||||
href="#XBlo04-ILP04WIP-IC">6</a>]</span>.
|
||||
href="#XBlo04-ILP04WIP-IC">7</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">paper_ref.cpl,</span><span
|
||||
class="cmtt-10"> paper_ref_simple.cpl</span></span></span>: paper citations examples,
|
||||
showing reference uncertainty, inspired by <span class="cite">[<a
|
||||
href="#XGetoor+al:JMLR02">12</a>]</span>.
|
||||
href="#XGetoor+al:JMLR02">14</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">paper_ref_not.cpl</span></span></span>: paper citations example showing that negation
|
||||
@ -895,13 +895,13 @@ class="cmtt-10">school.cpl</span>.
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">student.cpl</span></span></span>: student example from Figure 1.3 of <span class="cite">[<a
|
||||
href="#XGetFri01-BC">11</a>]</span>.
|
||||
href="#XGetFri01-BC">13</a>]</span>.
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10">win.cpl, light.cpl, trigger.cpl, throws.cpl, hiv.cpl,</span><br
|
||||
class="newline" /> <span
|
||||
class="cmtt-10">invalid.cpl</span>: programs taken from <span class="cite">[<a
|
||||
href="#XDBLP:journals/tplp/VennekensDB09">21</a>]</span>. <span
|
||||
href="#XDBLP:journals/tplp/VennekensDB09">23</a>]</span>. <span
|
||||
class="cmtt-10">invalid.cpl </span>is an example
|
||||
of a program that is invalid but sound.</li></ul>
|
||||
<!--l. 432--><p class="noindent" >The files <span
|
||||
@ -922,11 +922,11 @@ class="cmtt-10">cplint </span>contains the following learning algorithms:
|
||||
class="cmtt-10">cplint </span>EM): an implementation of EM for learning parameters
|
||||
that is based on <span
|
||||
class="cmtt-10">lpadsld.pl </span><span class="cite">[<a
|
||||
href="#XRigDiM11-ML-IJ">18</a>]</span>
|
||||
href="#XRigDiM11-ML-IJ">20</a>]</span>
|
||||
</li>
|
||||
<li class="itemize">RIB (Relational Information Bottleneck): an algorithm for learning
|
||||
parameters based on the Information Bottleneck <span class="cite">[<a
|
||||
href="#XRigDiM11-ML-IJ">18</a>]</span>
|
||||
href="#XRigDiM11-ML-IJ">20</a>]</span>
|
||||
</li>
|
||||
<li class="itemize">EMBLEM (EM over Bdds for probabilistic Logic programs Efficient
|
||||
Mining): an implementation of EM for learning parameters that computes
|
||||
@ -935,14 +935,20 @@ href="#XBelRig11-IDA">5</a>, <a
|
||||
href="#XBelRig11-CILC11-NC">2</a>, <a
|
||||
href="#XBelRig11-TR">3</a>]</span>
|
||||
</li>
|
||||
<li class="itemize">SLIPCASE (Structure LearnIng of ProbabilistiC logic progrAmS with Em
|
||||
over bdds): an algorithm for learning the structure of program that is
|
||||
based on EMBLEM <span class="cite">[<a
|
||||
href="#XBelRig11-ILP11-IC">4</a>]</span></li></ul>
|
||||
<!--l. 445--><p class="noindent" >
|
||||
<li class="itemize">SLIPCASE (Structure LearnIng of ProbabilistiC logic progrAmS with
|
||||
Em over bdds): an algorithm for learning the structure of programs by
|
||||
searching directly the theory space <span class="cite">[<a
|
||||
href="#XBelRig11-ILP11-IC">4</a>]</span>
|
||||
</li>
|
||||
<li class="itemize">SLIPCOVER (Structure LearnIng of Probabilistic logic programs by
|
||||
searChing OVER the clause space): an algorithm for learning the structure
|
||||
of programs by searching the clause space and the theory space separatery
|
||||
<span class="cite">[<a
|
||||
href="#XBelRig13-TPLP-IJ">6</a>]</span></li></ul>
|
||||
<!--l. 446--><p class="noindent" >
|
||||
<h4 class="subsectionHead"><span class="titlemark">5.1 </span> <a
|
||||
id="x1-110005.1"></a>Input</h4>
|
||||
<!--l. 446--><p class="noindent" >To execute the learning algorithms, prepare four files in the same folder:
|
||||
<!--l. 447--><p class="noindent" >To execute the learning algorithms, prepare four files in the same folder:
|
||||
<ul class="itemize1">
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10"><stem>.kb</span>: contains the example interpretations
|
||||
@ -956,11 +962,13 @@ class="cmtt-10"><stem>.l</span>: contains language bias informatio
|
||||
</li>
|
||||
<li class="itemize"><span
|
||||
class="cmtt-10"><stem>.cpl</span>: contains the LPAD for you which you want to learn the
|
||||
parameters or the initial LPAD for SLIPCASE</li></ul>
|
||||
<!--l. 453--><p class="noindent" >where <span
|
||||
parameters or the initial LPAD for SLIPCASE. For SLIPCOVER, this file
|
||||
should be absent</li></ul>
|
||||
|
||||
<!--l. 454--><p class="noindent" >where <span
|
||||
class="cmtt-10"><stem> </span>is your dataset name. Examples of these files can be found in the dataset
|
||||
pages.
|
||||
<!--l. 455--><p class="indent" > In <span
|
||||
<!--l. 456--><p class="indent" > In <span
|
||||
class="cmtt-10"><stem>.kb </span>the example interpretations have to be given as a list of Prolog
|
||||
facts initiated by <span
|
||||
class="cmtt-10">begin(model(<name>)). </span>and terminated by <span
|
||||
@ -980,27 +988,27 @@ begin(model(b1)).
|
||||
 <br />actor(2).
|
||||
 <br />end(model(b1)).
|
||||
</div>
|
||||
<!--l. 469--><p class="nopar" > The interpretations may contain a fact of the form
|
||||
<!--l. 470--><p class="nopar" > The interpretations may contain a fact of the form
|
||||
|
||||
<div class="verbatim" id="verbatim-37">
|
||||
prob(0.3).
|
||||
</div>
|
||||
<!--l. 473--><p class="nopar" > assigning a probability (0.3 in this case) to the interpretations. If this is omitted, the
|
||||
<!--l. 474--><p class="nopar" > assigning a probability (0.3 in this case) to the interpretations. If this is omitted, the
|
||||
probability of each interpretation is considered equal to 1<span
|
||||
class="cmmi-10">∕n </span>where <span
|
||||
class="cmmi-10">n </span>is the total
|
||||
number of interpretations. <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">prob/1</span></span></span> can be used to set different multiplicity for the
|
||||
different interpretations.
|
||||
<!--l. 476--><p class="indent" > In order for RIB to work, the input interpretations must share the Herbrand
|
||||
<!--l. 477--><p class="indent" > In order for RIB to work, the input interpretations must share the Herbrand
|
||||
universe. If this is not the case, you have to translate the interpretations in this was,
|
||||
see for example the <span
|
||||
class="cmtt-10">sp1 </span>files in RIB’s folder, that are the results of the conversion of
|
||||
the first fold of the IMDB dataset.
|
||||
<!--l. 478--><p class="indent" > <span
|
||||
<!--l. 479--><p class="indent" > <span
|
||||
class="cmtt-10"><stem>.bg </span>can contain Prolog clauses that can be used to derive additional
|
||||
conclusions from the atoms in the interpretations.
|
||||
<!--l. 481--><p class="indent" > <span
|
||||
<!--l. 482--><p class="indent" > <span
|
||||
class="cmtt-10"><stem>.l </span>contains the declarations of the input and output predicates, of the
|
||||
unseen predicates and the commands for setting the algorithms’ parameters. Output
|
||||
predicates are declared as
|
||||
@ -1008,72 +1016,92 @@ predicates are declared as
|
||||
<div class="verbatim" id="verbatim-38">
|
||||
output(<predicate>/<arity>).
|
||||
</div>
|
||||
<!--l. 485--><p class="nopar" > and define the predicates whose atoms in the input interpretations are used as the
|
||||
<!--l. 486--><p class="nopar" > and define the predicates whose atoms in the input interpretations are used as the
|
||||
goals for the prediction of which you want to optimize the parameters. Derivations
|
||||
for these goals are built by the systems.
|
||||
<!--l. 488--><p class="indent" > Input predicates are those for the predictions of which you do not want to
|
||||
<!--l. 489--><p class="indent" > Input predicates are those for the predictions of which you do not want to
|
||||
optimize the parameters. You can declare closed world input predicates
|
||||
with
|
||||
|
||||
<div class="verbatim" id="verbatim-39">
|
||||
input_cw(<predicate>/<arity>).
|
||||
</div>
|
||||
<!--l. 491--><p class="nopar" > For these predicates, the only true atoms are those in the interpretations, the
|
||||
<!--l. 492--><p class="nopar" > For these predicates, the only true atoms are those in the interpretations, the
|
||||
clauses in the input program are not used to derive atoms not present in the
|
||||
interpretations.
|
||||
<!--l. 494--><p class="indent" > Open world input predicates are declared with
|
||||
<!--l. 495--><p class="indent" > Open world input predicates are declared with
|
||||
|
||||
<div class="verbatim" id="verbatim-40">
|
||||
input(<predicate>/<arity>).
|
||||
</div>
|
||||
<!--l. 497--><p class="nopar" > In this case, if a subgoal for such a predicate is encountered when deriving the atoms
|
||||
<!--l. 498--><p class="nopar" > In this case, if a subgoal for such a predicate is encountered when deriving the atoms
|
||||
for the output predicates, both the facts in the interpretations and the clauses of the
|
||||
input program are used.
|
||||
<!--l. 501--><p class="indent" > For RIB, if there are unseen predicates, i.e., predicates that are present in the
|
||||
<!--l. 502--><p class="indent" > For RIB, if there are unseen predicates, i.e., predicates that are present in the
|
||||
input program but not in the interpretations, you have to declare them
|
||||
with
|
||||
|
||||
<div class="verbatim" id="verbatim-41">
|
||||
unseen(<predicate>/<arity>).
|
||||
</div>
|
||||
<!--l. 504--><p class="nopar" >
|
||||
<!--l. 506--><p class="indent" > For SLIPCASE, you have to specify the language bias by means of mode
|
||||
declarations in the style of <a
|
||||
<!--l. 505--><p class="nopar" >
|
||||
<!--l. 507--><p class="indent" > For SLIPCASE and SLIPCOVER, you have to specify the language bias by
|
||||
means of mode declarations in the style of <a
|
||||
href="http://www.doc.ic.ac.uk/~shm/progol.html" > Progol </a>.
|
||||
|
||||
<div class="verbatim" id="verbatim-42">
|
||||
modeh(<recall>,<predicate>(<arg1>,...).
|
||||
</div>
|
||||
<!--l. 510--><p class="nopar" > specifies the atoms that can appear in the head of clauses, while
|
||||
<!--l. 511--><p class="nopar" > specifies the atoms that can appear in the head of clauses, while
|
||||
|
||||
<div class="verbatim" id="verbatim-43">
|
||||
modeb(<recall>,<predicate>(<arg1>,...).
|
||||
</div>
|
||||
<!--l. 514--><p class="nopar" > specifies the atoms that can appear in the body of clauses. <span
|
||||
<!--l. 515--><p class="nopar" > specifies the atoms that can appear in the body of clauses. <span
|
||||
class="cmtt-10"><recall> </span>can be an
|
||||
integer or <span
|
||||
class="cmtt-10">* </span>(currently unused).
|
||||
<!--l. 518--><p class="indent" > The arguments are of the form
|
||||
<!--l. 519--><p class="indent" > The arguments are of the form
|
||||
|
||||
<div class="verbatim" id="verbatim-44">
|
||||
+<type>
|
||||
</div>
|
||||
<!--l. 521--><p class="nopar" > for specifying an input variable of type <span
|
||||
<!--l. 522--><p class="nopar" > for specifying an input variable of type <span
|
||||
class="cmtt-10"><type></span>, or
|
||||
|
||||
<div class="verbatim" id="verbatim-45">
|
||||
-<type>
|
||||
</div>
|
||||
<!--l. 525--><p class="nopar" > for specifying an output variable of type <span
|
||||
<!--l. 526--><p class="nopar" > for specifying an output variable of type <span
|
||||
class="cmtt-10"><type></span>. or
|
||||
|
||||
<div class="verbatim" id="verbatim-46">
|
||||
<constant>
|
||||
</div>
|
||||
<!--l. 529--><p class="nopar" > for specifying a constant.
|
||||
<!--l. 532--><p class="indent" > An example of language bias for the UWCSE domain is
|
||||
<!--l. 530--><p class="nopar" > for specifying a constant.
|
||||
<!--l. 533--><p class="indent" > SLIPCOVER also allows the arguments
|
||||
|
||||
<div class="verbatim" id="verbatim-47">
|
||||
#<type>
|
||||
</div>
|
||||
<!--l. 536--><p class="nopar" > for specifying an argument which should be replaced by a constant of type <span
|
||||
class="cmtt-10"><type> </span>in
|
||||
the bottom clause but should not be used for replacing input variables of the
|
||||
following literals or
|
||||
|
||||
<div class="verbatim" id="verbatim-48">
|
||||
-#<type>
|
||||
</div>
|
||||
<!--l. 540--><p class="nopar" > for specifying an argument which should be replaced by a constant of type <span
|
||||
class="cmtt-10"><type> </span>in
|
||||
the bottom clause and that should be used for replacing input variables of
|
||||
the following literals. <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">#</span></span></span> and <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">-#</span></span></span> differ only in the creation of the bottom
|
||||
clause.
|
||||
<!--l. 543--><p class="indent" > An example of language bias for the UWCSE domain is
|
||||
|
||||
<div class="verbatim" id="verbatim-49">
|
||||
output(advisedby/2).
|
||||
 <br />
|
||||
 <br />input(student/1).
|
||||
@ -1090,18 +1118,57 @@ output(advisedby/2).
|
||||
 <br />modeb(*,samecourse(-course, +course)).
|
||||
 <br />....
|
||||
</div>
|
||||
<!--l. 549--><p class="nopar" >
|
||||
<!--l. 551--><p class="noindent" >
|
||||
<!--l. 560--><p class="nopar" > SLIPCOVER also requires facts for the <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">determination/2</span></span></span> predicate that indicate
|
||||
which predicates can appear in the body of clauses. For example
|
||||
|
||||
<div class="verbatim" id="verbatim-50">
|
||||
determination(professor/1,student/1).
|
||||
 <br />determination(student/1,hasposition/2).
|
||||
</div>
|
||||
<!--l. 566--><p class="nopar" > state that <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">student/1</span></span></span> can appear in the body of clauses for <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">professor/1</span></span></span> and that
|
||||
<span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">hasposition/2</span></span></span> can appear in the body of clauses for <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">student/1</span></span></span>.
|
||||
<!--l. 570--><p class="indent" > SLIPCOVER also allows mode declarations of the form
|
||||
|
||||
<div class="verbatim" id="verbatim-51">
|
||||
modeh(<r>,[<s1>,...,<sn>],[<a1>,...,<an>],[<P1/Ar1>,...,<Pk/Ark>]).
|
||||
</div>
|
||||
<!--l. 573--><p class="nopar" > These mode declarations are used to generate clauses with more than two head
|
||||
atoms. In them, <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10"><s1>,...,<sn></span></span></span> are schemas, <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10"><a1>,...,<an></span></span></span> are atoms such that
|
||||
<span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10"><ai></span></span></span> is obtained from <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10"><si></span></span></span> by replacing placemarkers with variables, <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10"><Pi/Ari></span></span></span> are
|
||||
the predicates admitted in the body. <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10"><a1>,...,<an></span></span></span> are used to indicate which
|
||||
variables should be shared by the atoms in the head. An example of such a mode
|
||||
declaration is
|
||||
|
||||
<div class="verbatim" id="verbatim-52">
|
||||
modeh(*,
|
||||
 <br />  [advisedby(+person,+person),tempadvisedby(+person,+person)],
|
||||
 <br />  [advisedby(A,B),tempadvisedby(A,B)],
|
||||
 <br />  [professor/1,student/1,hasposition/2,inphase/2,
|
||||
 <br />  publication/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2]).
|
||||
</div>
|
||||
<!--l. 583--><p class="nopar" >
|
||||
<!--l. 587--><p class="noindent" >
|
||||
<h4 class="subsectionHead"><span class="titlemark">5.2 </span> <a
|
||||
id="x1-120005.2"></a>Parameters</h4>
|
||||
<!--l. 552--><p class="noindent" >In order to set the algorithms’ parameters, you have to insert in <span
|
||||
<!--l. 588--><p class="noindent" >In order to set the algorithms’ parameters, you have to insert in <span
|
||||
class="cmtt-10"><stem>.l </span>commands
|
||||
of the form
|
||||
|
||||
<div class="verbatim" id="verbatim-48">
|
||||
<div class="verbatim" id="verbatim-53">
|
||||
:- set(<parameter>,<value>).
|
||||
</div>
|
||||
<!--l. 555--><p class="nopar" > The available parameters are:
|
||||
<!--l. 591--><p class="nopar" > The available parameters are:
|
||||
<ul class="itemize1">
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">depth</span></span></span> (values: integer or <span class="obeylines-h"><span class="verb"><span
|
||||
@ -1114,10 +1181,10 @@ class="cmtt-10">true</span></span></span>
|
||||
class="cmtt-10">single_var</span></span></span> (values: <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">{true,false}</span></span></span>, default value: <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">false</span></span></span>, valid for CEM,
|
||||
EMBLEM and SLIPCASE): if set to <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">true</span></span></span>, there is a random variable for
|
||||
each clauses, instead of a separate random variable for each grounding of
|
||||
a clause
|
||||
EMBLEM, SLIPCASE and SLIPCOVER): if set to <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">true</span></span></span>, there is a
|
||||
random variable for each clauses, instead of a separate random variable
|
||||
for each grounding of a clause
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">sample_size</span></span></span> (values: integer, default value: 1000): total number of
|
||||
@ -1128,37 +1195,38 @@ class="cmtt-10">prob(P).</span></span></span>
|
||||
class="cmtt-10">sample_size*P</span></span></span> examples
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">epsilon_em</span></span></span> (values: real, default value: 0.1, valid for CEM, EMBLEM
|
||||
and SLIPCASE): if the difference in the log likelihood in two successive
|
||||
EM iteration is smaller than <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">epsilon_em</span></span></span> (values: real, default value: 0.1, valid for CEM, EMBLEM,
|
||||
SLIPCASE and SLIPCOVER): if the difference in the log likelihood in
|
||||
two successive EM iteration is smaller than <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">epsilon_em</span></span></span>, then EM stops
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">epsilon_em_fraction</span></span></span> (values: real, default value: 0.01, valid for CEM,
|
||||
EMBLEM and SLIPCASE): if the difference in the log likelihood in two
|
||||
successive EM iteration is smaller than <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">epsilon_em_fraction</span></span></span>*(-current
|
||||
log likelihood), then EM stops
|
||||
class="cmtt-10">epsilon_em_fraction</span></span></span> (values: real, default value: 0.01, valid for
|
||||
CEM, EMBLEM, SLIPCASE and SLIPCOVER): if the difference in
|
||||
the log likelihood in two successive EM iteration is smaller than
|
||||
<span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">epsilon_em_fraction</span></span></span>*(-current log likelihood), then EM stops
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">iter</span></span></span> (values: integer, defualt value: 1, valid for EMBLEM and
|
||||
SLIPCASE): maximum number of iteration of EM parameter learning. If
|
||||
set to -1, no maximum number of iterations is imposed
|
||||
class="cmtt-10">iter</span></span></span> (values: integer, defualt value: 1, valid for EMBLEM, SLIPCASE and
|
||||
SLIPCOVER): maximum number of iteration of EM parameter learning.
|
||||
If set to -1, no maximum number of iterations is imposed
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">iterREF</span></span></span> (values: integer, defualt value: 1, valid for SLIPCASE): maximum
|
||||
number of iteration of EM parameter learning for refinements. If set to -1,
|
||||
no maximum number of iterations is imposed.
|
||||
class="cmtt-10">iterREF</span></span></span> (values: integer, defualt value: 1, valid for SLIPCASE and
|
||||
SLIPCOVER): maximum number of iteration of EM parameter learning
|
||||
for refinements. If set to -1, no maximum number of iterations is imposed.
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">random_restarts_number</span></span></span> (values: integer, default value: 1, valid for
|
||||
CEM, EMBLEM and SLIPCASE): number of random restarts of EM
|
||||
learning
|
||||
CEM, EMBLEM, SLIPCASE and SLIPCOVER): number of random
|
||||
restarts of EM learning
|
||||
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">random_restarts_REFnumber</span></span></span> (values: integer, default value: 1, valid for
|
||||
SLIPCASE): number of random restarts of EM learning for refinements
|
||||
SLIPCASE and SLIPCOVER): number of random restarts of EM learning
|
||||
for refinements
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">setrand</span></span></span> (values: rand(integer,integer,integer)): seed for the random
|
||||
@ -1185,7 +1253,7 @@ class="cmtt-10">logsize_fraction</span></span></span> times its maximum
|
||||
class="cmsy-10">|</span><span
|
||||
class="cmmi-10">CH,T</span><span
|
||||
class="cmsy-10">|</span>, see <span class="cite">[<a
|
||||
href="#XDBLP:journals/jmlr/ElidanF05">10</a>]</span>)
|
||||
href="#XDBLP:journals/jmlr/ElidanF05">12</a>]</span>)
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">delta</span></span></span> (values: negative integer, default value -10, valid for RIB): value
|
||||
@ -1195,7 +1263,7 @@ class="cmtt-10">delta</span></span></span> (values: negative integer, default va
|
||||
class="cmtt-10">epsilon_fraction</span></span></span> (values: integer, default value 100, valid for RIB):
|
||||
in the computation of the step, the value of <span
|
||||
class="cmmi-10">ϵ </span>of <span class="cite">[<a
|
||||
href="#XDBLP:journals/jmlr/ElidanF05">10</a>]</span> is obtained as
|
||||
href="#XDBLP:journals/jmlr/ElidanF05">12</a>]</span> is obtained as
|
||||
log <span
|
||||
class="cmsy-10">|</span><span
|
||||
class="cmmi-10">CH,T</span><span
|
||||
@ -1211,7 +1279,7 @@ class="cmtt-10">max_rules</span></span></span> (values: integer, default valu
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">logzero</span></span></span> (values: negative real, default value log(0<span
|
||||
class="cmmi-10">.</span>000001), valid for
|
||||
SLIPCASE): value assigned to log 0
|
||||
SLIPCASE and SLIPCOVER): value assigned to log 0
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">examples</span></span></span> (values: <span class="obeylines-h"><span class="verb"><span
|
||||
@ -1233,94 +1301,130 @@ class="cmtt-10">group</span></span></span> (values: integer, default value: 1, v
|
||||
target atoms in the groups that are used to build BDDs
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">nax_iter</span></span></span> (values: integer, default value: 10, valid for SLIPCASE): number
|
||||
of interations of beam search
|
||||
class="cmtt-10">nax_iter</span></span></span> (values: integer, default value: 10, valid for SLIPCASE and
|
||||
SLIPCOVER): number of interations of beam search
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">max_var</span></span></span> (values: integer, default value: 1, valid for SLIPCASE): maximum
|
||||
number of distinct variables in a clause
|
||||
class="cmtt-10">max_var</span></span></span> (values: integer, default value: 1, valid for SLIPCASE and
|
||||
SLIPCOVER): maximum number of distinct variables in a clause
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">verbosity</span></span></span> (values: integer in [1,3], default value: 1): level of verbosity of
|
||||
the algorithms
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">beamsize</span></span></span> (values: integer, default value: 20, valid for SLIPCASE): size of
|
||||
the beam in SLIPCASE
|
||||
</li></ul>
|
||||
<!--l. 590--><p class="noindent" >
|
||||
class="cmtt-10">beamsize</span></span></span> (values: integer, default value: 20, valid for SLIPCASE and
|
||||
SLIPCOVER): size of the beam
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">megaex_bottom</span></span></span> (values: integer, default value: 1, valid for SLIPCOVER):
|
||||
number of mega-examples on which to build the bottom clauses
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">initial_clauses_per_megaex</span></span></span> (values: integer, default value: 1, valid for
|
||||
SLIPCOVER): number of bottom clauses to build for each mega-example
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">d</span></span></span> (values: integer, default value: 10000, valid for SLIPCOVER): number
|
||||
of saturation steps when building the bottom clause
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">max_iter_structure</span></span></span> (values: integer, default value: 1, valid for
|
||||
SLIPCOVER): maximum number of theory search iterations
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">background_clauses</span></span></span> (values: integer, default value: 50, valid for
|
||||
SLIPCOVER): maximum numbers of background clauses
|
||||
</li>
|
||||
<li class="itemize"><span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">maxdepth_var</span></span></span> (values: integer, default value: 2, valid for SLIPCOVER):
|
||||
maximum depth of variables in clauses (as defined in <span class="cite">[<a
|
||||
href="#XDBLP:journals/ai/Cohen95">10</a>]</span>).</li></ul>
|
||||
<!--l. 633--><p class="noindent" >
|
||||
<h4 class="subsectionHead"><span class="titlemark">5.3 </span> <a
|
||||
id="x1-130005.3"></a>Commands</h4>
|
||||
<!--l. 591--><p class="noindent" >To execute CEM, load <span
|
||||
<!--l. 634--><p class="noindent" >To execute CEM, load <span
|
||||
class="cmtt-10">em.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-49">
|
||||
<div class="verbatim" id="verbatim-54">
|
||||
?:- use_module(library(’cplint/em’)).
|
||||
</div>
|
||||
<!--l. 594--><p class="nopar" > and call:
|
||||
|
||||
<div class="verbatim" id="verbatim-50">
|
||||
?:- em(stem).
|
||||
</div>
|
||||
<!--l. 598--><p class="nopar" > To execute RIB, load <span
|
||||
class="cmtt-10">rib.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-51">
|
||||
?:- use_module(library(’cplint/rib’)).
|
||||
</div>
|
||||
<!--l. 602--><p class="nopar" > and call:
|
||||
|
||||
<div class="verbatim" id="verbatim-52">
|
||||
?:- ib_par(stem).
|
||||
</div>
|
||||
<!--l. 606--><p class="nopar" > To execute EMBLEM, load <span
|
||||
class="cmtt-10">slipcase.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-53">
|
||||
?:- use_module(library(’cplint/slipcase’)).
|
||||
</div>
|
||||
<!--l. 610--><p class="nopar" > and call
|
||||
|
||||
<div class="verbatim" id="verbatim-54">
|
||||
?:- em(stem).
|
||||
</div>
|
||||
<!--l. 614--><p class="nopar" > To execute SLIPCASE, load <span
|
||||
class="cmtt-10">slipcase.pl </span>with
|
||||
<!--l. 637--><p class="nopar" > and call:
|
||||
|
||||
<div class="verbatim" id="verbatim-55">
|
||||
?:- use_module(library(’cplint/slipcase’)).
|
||||
?:- em(stem).
|
||||
</div>
|
||||
<!--l. 618--><p class="nopar" > and call
|
||||
<!--l. 641--><p class="nopar" > To execute RIB, load <span
|
||||
class="cmtt-10">rib.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-56">
|
||||
?:- use_module(library(’cplint/rib’)).
|
||||
</div>
|
||||
<!--l. 645--><p class="nopar" > and call:
|
||||
|
||||
<div class="verbatim" id="verbatim-57">
|
||||
?:- ib_par(stem).
|
||||
</div>
|
||||
<!--l. 649--><p class="nopar" > To execute EMBLEM, load <span
|
||||
class="cmtt-10">slipcase.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-58">
|
||||
?:- use_module(library(’cplint/slipcase’)).
|
||||
</div>
|
||||
<!--l. 653--><p class="nopar" > and call
|
||||
|
||||
<div class="verbatim" id="verbatim-59">
|
||||
?:- em(stem).
|
||||
</div>
|
||||
<!--l. 657--><p class="nopar" > To execute SLIPCASE, load <span
|
||||
class="cmtt-10">slipcase.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-60">
|
||||
?:- use_module(library(’cplint/slipcase’)).
|
||||
</div>
|
||||
<!--l. 661--><p class="nopar" > and call
|
||||
|
||||
<div class="verbatim" id="verbatim-61">
|
||||
?:- sl(stem).
|
||||
</div>
|
||||
<!--l. 622--><p class="nopar" >
|
||||
<!--l. 665--><p class="nopar" > To execute SLIPCOVER, load <span
|
||||
class="cmtt-10">slipcover.pl </span>with
|
||||
|
||||
<div class="verbatim" id="verbatim-62">
|
||||
?:- use_module(library(’cplint/slipcover’)).
|
||||
</div>
|
||||
<!--l. 669--><p class="nopar" > and call
|
||||
|
||||
<div class="verbatim" id="verbatim-63">
|
||||
?:- sl(stem).
|
||||
</div>
|
||||
<!--l. 673--><p class="nopar" >
|
||||
<h4 class="subsectionHead"><span class="titlemark">5.4 </span> <a
|
||||
id="x1-140005.4"></a>Learning Examples</h4>
|
||||
<!--l. 624--><p class="noindent" >The subfolders <span class="obeylines-h"><span class="verb"><span
|
||||
<!--l. 675--><p class="noindent" >The subfolders <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">em</span></span></span>, <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">rib</span></span></span> and <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">slipcase</span></span></span> of the <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">packages/cplint</span></span></span> folder in Yap
|
||||
git distribution contain examples of input and output files for the learning
|
||||
class="cmtt-10">rib</span></span></span>, <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">slipcase</span></span></span> and <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">slipcover</span></span></span> of the <span class="obeylines-h"><span class="verb"><span
|
||||
class="cmtt-10">packages/cplint</span></span></span> folder in
|
||||
Yap git distribution contain examples of input and output files for the learning
|
||||
algorithms.
|
||||
<!--l. 627--><p class="noindent" >
|
||||
<!--l. 678--><p class="noindent" >
|
||||
<h3 class="sectionHead"><span class="titlemark">6 </span> <a
|
||||
id="x1-150006"></a>License</h3>
|
||||
<!--l. 632--><p class="noindent" ><span
|
||||
<!--l. 683--><p class="noindent" ><span
|
||||
class="cmtt-10">cplint</span>, as Yap, follows the Artistic License 2.0 that you can find in Yap CVS root
|
||||
dir. The copyright is by Fabrizio Riguzzi.
|
||||
<!--l. 635--><p class="indent" > The modules in the approx subdirectory use SimplecuddLPADs, a modification of
|
||||
<!--l. 686--><p class="indent" > The modules in the approx subdirectory use SimplecuddLPADs, a modification of
|
||||
the <a
|
||||
href="http://dtai.cs.kuleuven.be/problog/download.html" > Simplecudd </a> library whose copyright is by Katholieke Universiteit Leuven and
|
||||
that follows the Artistic License 2.0.
|
||||
<!--l. 638--><p class="indent" > Some modules use the library <a
|
||||
<!--l. 689--><p class="indent" > Some modules use the library <a
|
||||
href="http://vlsi.colorado.edu/~fabio/" > CUDD </a> for manipulating BDDs that is included in
|
||||
glu. For the use of CUDD, the following license must be accepted:
|
||||
<!--l. 643--><p class="indent" > Copyright (c) 1995-2004, Regents of the University of Colorado
|
||||
<!--l. 645--><p class="indent" > All rights reserved.
|
||||
<!--l. 647--><p class="indent" > Redistribution and use in source and binary forms, with or without modification,
|
||||
<!--l. 694--><p class="indent" > Copyright (c) 1995-2004, Regents of the University of Colorado
|
||||
<!--l. 696--><p class="indent" > All rights reserved.
|
||||
<!--l. 698--><p class="indent" > Redistribution and use in source and binary forms, with or without modification,
|
||||
are permitted provided that the following conditions are met:
|
||||
<ul class="itemize1">
|
||||
<li class="itemize">Redistributions of source code must retain the above copyright notice, this
|
||||
@ -1334,7 +1438,7 @@ are permitted provided that the following conditions are met:
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.</li></ul>
|
||||
|
||||
<!--l. 664--><p class="noindent" >THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS <br
|
||||
<!--l. 715--><p class="noindent" >THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS <br
|
||||
class="newline" />AND CONTRIBUTORS ”AS IS” AND ANY EXPRESS OR IMPLIED
|
||||
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
||||
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
@ -1348,7 +1452,7 @@ class="newline" />AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
||||
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
|
||||
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
<!--l. 678--><p class="indent" > <span
|
||||
<!--l. 729--><p class="indent" > <span
|
||||
class="cmtt-10">lpad.pl</span>, <span
|
||||
class="cmtt-10">semlpad.pl </span>and <span
|
||||
class="cmtt-10">cpl.pl </span>are based on the SLG system by Weidong
|
||||
@ -1402,6 +1506,14 @@ class="cmti-10">Intel. Data Anal.</span>,
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[6]<span class="bibsp">   </span></span><a
|
||||
id="XBelRig13-TPLP-IJ"></a>Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic
|
||||
logic programs by searching the clause space. <span
|
||||
class="cmti-10">Theory and Practice of Logic</span>
|
||||
<span
|
||||
class="cmti-10">Programming</span>, 2013.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[7]<span class="bibsp">   </span></span><a
|
||||
id="XBlo04-ILP04WIP-IC"></a>H. Blockeel. Probabilistic logical models for mendel’s experiments: An
|
||||
exercise. In <span
|
||||
class="cmti-10">Inductive Logic Programming (ILP 2004), Work in Progress</span>
|
||||
@ -1409,7 +1521,7 @@ class="cmti-10">Inductive Logic Programming (ILP 2004), Work in Progress</span>
|
||||
class="cmti-10">Track</span>, 2004.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[7]<span class="bibsp">   </span></span><a
|
||||
[8]<span class="bibsp">   </span></span><a
|
||||
id="XBraRig10-ILP10-IC"></a>Stefano Bragaglia and Fabrizio Riguzzi. Approximate inference for logic
|
||||
programs with annotated disjunctions. In Paolo Frasconi and Francesca
|
||||
Lisi, editors, <span
|
||||
@ -1420,36 +1532,43 @@ class="cmti-10">ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers</spa
|
||||
class="cmti-10">LNCS</span>, pages 30–37. Springer, 2011.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[8]<span class="bibsp">   </span></span><a
|
||||
[9]<span class="bibsp">   </span></span><a
|
||||
id="XDBLP:journals/jacm/ChenW96"></a>Weidong Chen and David Scott Warren. Tabled evaluation with
|
||||
delaying for general logic programs. <span
|
||||
class="cmti-10">Journal of the ACM</span>, 43(1):20–74, 1996.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[9]<span class="bibsp">   </span></span><a
|
||||
[10]<span class="bibsp">   </span></span><a
|
||||
id="XDBLP:journals/ai/Cohen95"></a>William W. Cohen. Pac-learning non-recursive prolog clauses. <span
|
||||
class="cmti-10">Artif.</span>
|
||||
<span
|
||||
class="cmti-10">Intell.</span>, 79(1):1–38, 1995.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[11]<span class="bibsp">   </span></span><a
|
||||
id="XDBLP:conf/ijcai/RaedtKT07"></a>L. De Raedt, A. Kimmig, and H. Toivonen. ProbLog: A probabilistic
|
||||
Prolog and its application in link discovery. In <span
|
||||
class="cmti-10">International Joint</span>
|
||||
<span
|
||||
class="cmti-10">Conference on Artificial Intelligence</span>, pages 2462–2467, 2007.
|
||||
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[10]<span class="bibsp">   </span></span><a
|
||||
[12]<span class="bibsp">   </span></span><a
|
||||
id="XDBLP:journals/jmlr/ElidanF05"></a>G. Elidan and N. Friedman. Learning hidden variable networks: The
|
||||
information bottleneck approach. <span
|
||||
class="cmti-10">Journal of Machine Learning Research</span>,
|
||||
6:81–127, 2005.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[11]<span class="bibsp">   </span></span><a
|
||||
[13]<span class="bibsp">   </span></span><a
|
||||
id="XGetFri01-BC"></a>L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning
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||||
probabilistic relational models. In Saso Dzeroski and Nada Lavrac, editors,
|
||||
<span
|
||||
class="cmti-10">Relational Data Mining</span>. Springer-Verlag, Berlin, 2001.
|
||||
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[12]<span class="bibsp">   </span></span><a
|
||||
[14]<span class="bibsp">   </span></span><a
|
||||
id="XGetoor+al:JMLR02"></a>L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning
|
||||
probabilistic models of relational structure. <span
|
||||
class="cmti-10">Journal of Machine Learning</span>
|
||||
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|
||||
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||||
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|
||||
<p class="bibitem" ><span class="biblabel">
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||||
[13]<span class="bibsp">   </span></span><a
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||||
[15]<span class="bibsp">   </span></span><a
|
||||
id="XDBLP:journals/ai/Poole97"></a>David Poole. The independent choice logic for modelling multiple agents
|
||||
under uncertainty. <span
|
||||
class="cmti-10">Artificial Intelligence</span>, 94(1-2):7–56, 1997.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
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||||
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||||
[16]<span class="bibsp">   </span></span><a
|
||||
id="XRig-AIIA07-IC"></a>Fabrizio Riguzzi. A top down interpreter for LPAD and CP-logic. In
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||||
<span
|
||||
class="cmti-10">Congress of the Italian Association for Artificial Intelligence</span>, volume 4733
|
||||
@ -1471,7 +1590,7 @@ class="cmti-10">Congress of the Italian Association for Artificial Intelligence<
|
||||
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|
||||
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|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[15]<span class="bibsp">   </span></span><a
|
||||
[17]<span class="bibsp">   </span></span><a
|
||||
id="XRig-RCRA07-IC"></a>Fabrizio Riguzzi. A top down interpreter for LPAD and CP-logic.
|
||||
In <span
|
||||
class="cmti-10">Proceedings of the 14th RCRA workshop Experimental Evaluation of</span>
|
||||
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||||
class="cmti-10">Algorithms for Solving Problems with Combinatorial Explosion</span>, 2007.
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||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
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||||
[16]<span class="bibsp">   </span></span><a
|
||||
[18]<span class="bibsp">   </span></span><a
|
||||
id="XRig09-LJIGPL-IJ"></a>Fabrizio Riguzzi. Extended semantics and inference for the Independent
|
||||
Choice Logic. <span
|
||||
class="cmti-10">Logic Journal of the IGPL</span>, 17(6):589–629, 2009.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[17]<span class="bibsp">   </span></span><a
|
||||
[19]<span class="bibsp">   </span></span><a
|
||||
id="XRig11-CILC11-NC"></a>Fabrizio Riguzzi. MCINTYRE: A Monte Carlo algorithm for
|
||||
probabilistic logic programming. In <span
|
||||
class="cmti-10">Proceedings of the 26th Italian</span>
|
||||
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|
||||
class="cmti-10">Conference on Computational Logic (CILC2011), Pescara, Italy, 31</span>
|
||||
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|
||||
class="cmti-10">August-2 September, 2011</span>, 2011.
|
||||
|
||||
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|
||||
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|
||||
[18]<span class="bibsp">   </span></span><a
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||||
[20]<span class="bibsp">   </span></span><a
|
||||
id="XRigDiM11-ML-IJ"></a>Fabrizio Riguzzi and Nicola Di Mauro. Applying the information
|
||||
bottleneck to statistical relational learning. <span
|
||||
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|
||||
appear.
|
||||
</p>
|
||||
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|
||||
[19]<span class="bibsp">   </span></span><a
|
||||
[21]<span class="bibsp">   </span></span><a
|
||||
id="XSanPagQaz03-UAI-IC"></a>V. Santos Costa, D. Page, M. Qazi, and J. Cussens. CLP(<span
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||||
class="cmsy-10"><img
|
||||
src="cmsy10-42.png" alt="B" class="10x-x-42" /><img
|
||||
@ -1511,10 +1631,9 @@ src="cmsy10-4e.png" alt="N" class="10x-x-4e" /></span>):
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||||
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|
||||
<span
|
||||
class="cmti-10">in Artificial Intelligence</span>. Morgan Kaufmann, 2003.
|
||||
|
||||
</p>
|
||||
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|
||||
[20]<span class="bibsp">   </span></span><a
|
||||
[22]<span class="bibsp">   </span></span><a
|
||||
id="XVenDenBru-JELIA06"></a>J. Vennekens, M. Denecker, and M. Bruynooghe. Representing causal
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||||
information about a probabilistic process. In <span
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||||
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||||
@ -1523,19 +1642,19 @@ class="cmti-10">European Conference on Logics in Artificial Intelligence</span>,
|
||||
September 2006.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[21]<span class="bibsp">   </span></span><a
|
||||
[23]<span class="bibsp">   </span></span><a
|
||||
id="XDBLP:journals/tplp/VennekensDB09"></a>J. Vennekens, Marc Denecker, and Maurice Bruynooghe. CP-logic:
|
||||
A language of causal probabilistic events and its relation to logic
|
||||
programming. <span
|
||||
class="cmti-10">Theory Pract. Log. Program.</span>, 9(3):245–308, 2009.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[22]<span class="bibsp">   </span></span><a
|
||||
[24]<span class="bibsp">   </span></span><a
|
||||
id="XVenVer03-TR"></a>J. Vennekens and S. Verbaeten. Logic programs with annotated
|
||||
disjunctions. Technical Report CW386, K. U. Leuven, 2003.
|
||||
</p>
|
||||
<p class="bibitem" ><span class="biblabel">
|
||||
[23]<span class="bibsp">   </span></span><a
|
||||
[25]<span class="bibsp">   </span></span><a
|
||||
id="XVenVer04-ICLP04-IC"></a>J. Vennekens, S. Verbaeten, and M. Bruynooghe. Logic programs
|
||||
with annotated disjunctions. In <span
|
||||
class="cmti-10">International Conference on Logic</span>
|
||||
|
||||
Binary file not shown.
@ -439,7 +439,8 @@ The files \texttt{*.uni} that are present for some of the examples are used by
|
||||
\item CEM (\texttt{cplint} EM): an implementation of EM for learning parameters that is based on \texttt{lpadsld.pl} \cite{RigDiM11-ML-IJ}
|
||||
\item RIB (Relational Information Bottleneck): an algorithm for learning parameters based on the Information Bottleneck \cite{RigDiM11-ML-IJ}
|
||||
\item EMBLEM (EM over Bdds for probabilistic Logic programs Efficient Mining): an implementation of EM for learning parameters that computes expectations directly on BDDs \cite{BelRig11-IDA,BelRig11-CILC11-NC,BelRig11-TR}
|
||||
\item SLIPCASE (Structure LearnIng of ProbabilistiC logic progrAmS with Em over bdds): an algorithm for learning the structure of program that is based on EMBLEM \cite{BelRig11-ILP11-IC}
|
||||
\item SLIPCASE (Structure LearnIng of ProbabilistiC logic progrAmS with Em over bdds): an algorithm for learning the structure of programs by searching directly the theory space \cite{BelRig11-ILP11-IC}
|
||||
\item SLIPCOVER (Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space): an algorithm for learning the structure of programs by searching the clause space and the theory space separatery \cite{BelRig13-TPLP-IJ}
|
||||
\end{itemize}
|
||||
|
||||
\subsection{Input}
|
||||
@ -448,7 +449,7 @@ To execute the learning algorithms, prepare four files in the same folder:
|
||||
\item \texttt{<stem>.kb}: contains the example interpretations
|
||||
\item \texttt{<stem>.bg}: contains the background knowledge, i.e., knowledge valid for all interpretations
|
||||
\item \texttt{<stem>.l}: contains language bias information
|
||||
\item \texttt{<stem>.cpl}: contains the LPAD for you which you want to learn the parameters or the initial LPAD for SLIPCASE
|
||||
\item \texttt{<stem>.cpl}: contains the LPAD for you which you want to learn the parameters or the initial LPAD for SLIPCASE. For SLIPCOVER, this file should be absent
|
||||
\end{itemize}
|
||||
where \texttt{<stem>} is your dataset name. Examples of these files can be found in the dataset pages.
|
||||
|
||||
@ -503,7 +504,7 @@ For RIB, if there are unseen predicates, i.e., predicates that are present in th
|
||||
unseen(<predicate>/<arity>).
|
||||
\end{verbatim}
|
||||
|
||||
For SLIPCASE, you have to specify the language bias by means of mode declarations in the style of
|
||||
For SLIPCASE and SLIPCOVER, you have to specify the language bias by means of mode declarations in the style of
|
||||
\href{http://www.doc.ic.ac.uk/\string ~shm/progol.html}{Progol}.
|
||||
\begin{verbatim}
|
||||
modeh(<recall>,<predicate>(<arg1>,...).
|
||||
@ -529,6 +530,16 @@ for specifying an output variable of type \texttt{<type>}. or
|
||||
\end{verbatim}
|
||||
for specifying a constant.
|
||||
|
||||
SLIPCOVER also allows the arguments
|
||||
\begin{verbatim}
|
||||
#<type>
|
||||
\end{verbatim}
|
||||
for specifying an argument which should be replaced by a constant of type \texttt{<type>} in the bottom clause but should not be used for replacing input variables of the following literals or
|
||||
\begin{verbatim}
|
||||
-#<type>
|
||||
\end{verbatim}
|
||||
for specifying an argument which should be replaced by a constant of type \texttt{<type>} in the bottom clause and that should be used for replacing input variables of the following literals. \verb|#| and \verb|-#| differ only in the creation of the bottom clause.
|
||||
|
||||
An example of language bias for the UWCSE domain is
|
||||
\begin{verbatim}
|
||||
output(advisedby/2).
|
||||
@ -547,6 +558,31 @@ modeb(*,samecourse(+course, -course)).
|
||||
modeb(*,samecourse(-course, +course)).
|
||||
....
|
||||
\end{verbatim}
|
||||
SLIPCOVER also requires facts for the \verb|determination/2| predicate that indicate which predicates can appear in the body of clauses.
|
||||
For example
|
||||
\begin{verbatim}
|
||||
determination(professor/1,student/1).
|
||||
determination(student/1,hasposition/2).
|
||||
\end{verbatim}
|
||||
state that \verb|student/1| can appear in the body of clauses for \verb|professor/1| and that \verb|hasposition/2| can appear in
|
||||
the body of clauses for \verb|student/1|.
|
||||
|
||||
SLIPCOVER also allows mode declarations of the form
|
||||
\begin{verbatim}
|
||||
modeh(<r>,[<s1>,...,<sn>],[<a1>,...,<an>],[<P1/Ar1>,...,<Pk/Ark>]).
|
||||
\end{verbatim}
|
||||
These mode declarations are used to generate clauses with more than two head atoms. In them, \verb|<s1>,...,<sn>| are schemas, \verb|<a1>,...,<an>| are atoms such that \verb|<ai>| is obtained from $\verb|<si>|$ by replacing placemarkers with variables,
|
||||
\verb|<Pi/Ari>| are the predicates admitted in the body. \verb|<a1>,...,<an>| are used to indicate which variables should be shared by the atoms in the head.
|
||||
An example of such a mode declaration is
|
||||
\begin{verbatim}
|
||||
modeh(*,
|
||||
[advisedby(+person,+person),tempadvisedby(+person,+person)],
|
||||
[advisedby(A,B),tempadvisedby(A,B)],
|
||||
[professor/1,student/1,hasposition/2,inphase/2,
|
||||
publication/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2]).
|
||||
\end{verbatim}
|
||||
|
||||
|
||||
|
||||
\subsection{Parameters}
|
||||
In order to set the algorithms' parameters, you have to insert in \texttt{<stem>.l} commands of the form
|
||||
@ -556,20 +592,17 @@ In order to set the algorithms' parameters, you have to insert in \texttt{<stem>
|
||||
The available parameters are:
|
||||
\begin{itemize}
|
||||
\item \verb|depth| (values: integer or \verb|inf|, default value: 3): depth of derivations if \verb|depth_bound| is set to \verb|true|
|
||||
\item \verb|single_var| (values: \verb|{true,false}|, default value: \verb|false|, valid for CEM, EMBLEM and SLIPCASE): if set to \verb|true|, there is a random variable for each clauses, instead of a separate random variable for each grounding of a clause
|
||||
\item \verb|single_var| (values: \verb|{true,false}|, default value: \verb|false|, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): if set to \verb|true|, there is a random variable for each clauses, instead of a separate random variable for each grounding of a clause
|
||||
\item \verb|sample_size| (values: integer, default value: 1000): total number of examples in case in which the models in the \verb|.kb| file contain a \verb|prob(P).| fact. In that case, one model corresponds to \verb|sample_size*P| examples
|
||||
\item \verb|epsilon_em| (values: real, default value: 0.1, valid for CEM, EMBLEM and SLIPCASE): if the difference in the log likelihood in two successive EM iteration is smaller
|
||||
\item \verb|epsilon_em| (values: real, default value: 0.1, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): if the difference in the log likelihood in two successive EM iteration is smaller
|
||||
than \verb|epsilon_em|, then EM stops
|
||||
\item \verb|epsilon_em_fraction| (values: real, default value: 0.01, valid for CEM, EMBLEM and SLIPCASE): if the difference in the log likelihood in two successive EM iteration is smaller
|
||||
\item \verb|epsilon_em_fraction| (values: real, default value: 0.01, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): if the difference in the log likelihood in two successive EM iteration is smaller
|
||||
than \verb|epsilon_em_fraction|*(-current log likelihood), then EM stops
|
||||
\item \verb|iter| (values: integer, defualt value: 1, valid for EMBLEM and SLIPCASE): maximum number of iteration of EM parameter learning. If set to -1, no maximum number of iterations is imposed
|
||||
\item \verb|iterREF| (values: integer, defualt value: 1, valid for SLIPCASE):
|
||||
\item \verb|iter| (values: integer, defualt value: 1, valid for EMBLEM, SLIPCASE and SLIPCOVER): maximum number of iteration of EM parameter learning. If set to -1, no maximum number of iterations is imposed
|
||||
\item \verb|iterREF| (values: integer, defualt value: 1, valid for SLIPCASE and SLIPCOVER):
|
||||
maximum number of iteration of EM parameter learning for refinements. If set to -1, no maximum number of iterations is imposed.
|
||||
|
||||
\item \verb|random_restarts_number| (values: integer, default value: 1, valid for CEM, EMBLEM and SLIPCASE): number of random restarts of EM learning
|
||||
\item \verb|random_restarts_REFnumber| (values: integer, default value: 1, valid for SLIPCASE): number of random restarts of EM learning for refinements
|
||||
|
||||
|
||||
\item \verb|random_restarts_number| (values: integer, default value: 1, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): number of random restarts of EM learning
|
||||
\item \verb|random_restarts_REFnumber| (values: integer, default value: 1, valid for SLIPCASE and SLIPCOVER): number of random restarts of EM learning for refinements
|
||||
\item \verb|setrand| (values: rand(integer,integer,integer)): seed for the random functions, see Yap manual for allowed values
|
||||
\item \verb|minimal_step| (values: [0,1], default value: 0.005, valid for RIB): minimal increment of $\gamma$
|
||||
\item \verb|maximal_step| (values: [0,1], default value: 0.1, valid for RIB): maximal increment of $\gamma$
|
||||
@ -577,15 +610,25 @@ than \verb|epsilon_em_fraction|*(-current log likelihood), then EM stops
|
||||
\item \verb|delta| (values: negative integer, default value -10, valid for RIB): value assigned to $\log 0$
|
||||
\item \verb|epsilon_fraction| (values: integer, default value 100, valid for RIB): in the computation of the step, the value of $\epsilon$ of \cite{DBLP:journals/jmlr/ElidanF05} is obtained as $\log |CH,T|\times$\verb|epsilon_fraction|
|
||||
\item \verb|max_rules| (values: integer, default value: 6000, valid for RIB and SLIPCASE): maximum number of ground rules. Used to set the size of arrays for storing internal statistics. Can be increased as much as memory allows.
|
||||
\item \verb|logzero| (values: negative real, default value $\log(0.000001)$, valid for SLIPCASE): value assigned to $\log 0$
|
||||
\item \verb|logzero| (values: negative real, default value $\log(0.000001)$, valid for SLIPCASE and SLIPCOVER): value assigned to $\log 0$
|
||||
\item \verb|examples| (values: \verb|atoms|,\verb|interpretations|, default value \verb|atoms|, valid for SLIPCASE): determines how BDDs are built: if set to \verb|interpretations|, a BDD for the conjunction of all the atoms for the target predicates in each interpretations is built.
|
||||
If set to \verb|atoms|, a BDD is built for the conjunction of a group of atoms for the target predicates in each interpretations. The number of atoms in each group is determined by the parameter \verb|group|
|
||||
\item \verb|group| (values: integer, default value: 1, valid for SLIPCASE): number of target atoms in the groups that are used to build BDDs
|
||||
\item \verb|nax_iter| (values: integer, default value: 10, valid for SLIPCASE): number of interations of beam search
|
||||
\item \verb|max_var| (values: integer, default value: 1, valid for SLIPCASE): maximum number of distinct variables in a clause
|
||||
\item \verb|nax_iter| (values: integer, default value: 10, valid for SLIPCASE and SLIPCOVER): number of interations of beam search
|
||||
\item \verb|max_var| (values: integer, default value: 1, valid for SLIPCASE and SLIPCOVER): maximum number of distinct variables in a clause
|
||||
\item \verb|verbosity| (values: integer in [1,3], default value: 1): level of verbosity of the algorithms
|
||||
\item \verb|beamsize| (values: integer, default value: 20, valid for SLIPCASE): size of the beam in SLIPCASE
|
||||
|
||||
\item \verb|beamsize| (values: integer, default value: 20, valid for SLIPCASE and SLIPCOVER): size of the beam
|
||||
\item \verb|megaex_bottom| (values: integer, default value: 1, valid for SLIPCOVER): number of mega-examples on which to build the bottom clauses
|
||||
\item \verb|initial_clauses_per_megaex| (values: integer, default value: 1, valid for SLIPCOVER):
|
||||
number of bottom clauses to build for each mega-example
|
||||
\item \verb|d| (values: integer, default value: 10000, valid for SLIPCOVER):
|
||||
number of saturation steps when building the bottom clause
|
||||
\item \verb|max_iter_structure| (values: integer, default value: 1, valid for SLIPCOVER):
|
||||
maximum number of theory search iterations
|
||||
\item \verb|background_clauses| (values: integer, default value: 50, valid for SLIPCOVER):
|
||||
maximum numbers of background clauses
|
||||
\item \verb|maxdepth_var| (values: integer, default value: 2, valid for SLIPCOVER): maximum depth of
|
||||
variables in clauses (as defined in \cite{DBLP:journals/ai/Cohen95}).
|
||||
\end{itemize}
|
||||
\subsection{Commands}
|
||||
To execute CEM, load \texttt{em.pl} with
|
||||
@ -620,8 +663,16 @@ and call
|
||||
\begin{verbatim}
|
||||
?:- sl(stem).
|
||||
\end{verbatim}
|
||||
To execute SLIPCOVER, load \texttt{slipcover.pl} with
|
||||
\begin{verbatim}
|
||||
?:- use_module(library('cplint/slipcover')).
|
||||
\end{verbatim}
|
||||
and call
|
||||
\begin{verbatim}
|
||||
?:- sl(stem).
|
||||
\end{verbatim}
|
||||
\subsection{Learning Examples}
|
||||
The subfolders \verb|em|, \verb|rib| and \verb|slipcase| of the \verb|packages/cplint| folder in Yap git distribution
|
||||
The subfolders \verb|em|, \verb|rib|, \verb|slipcase| and \verb|slipcover| of the \verb|packages/cplint| folder in Yap git distribution
|
||||
contain examples of input and output files for the learning algorithms.
|
||||
|
||||
\section{License}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
13960
packages/cplint/slipcover/ai_train.kb
Normal file
13960
packages/cplint/slipcover/ai_train.kb
Normal file
File diff suppressed because it is too large
Load Diff
205
packages/cplint/slipcover/ai_train.l
Normal file
205
packages/cplint/slipcover/ai_train.l
Normal file
@ -0,0 +1,205 @@
|
||||
output(advisedby/2).
|
||||
|
||||
|
||||
input(student/1).
|
||||
|
||||
input(professor/1).
|
||||
|
||||
input(inphase/2).
|
||||
|
||||
input(hasposition/2).
|
||||
|
||||
input(publication/2).
|
||||
|
||||
input(yearsinprogram/2).
|
||||
|
||||
input(taughtby/3).
|
||||
|
||||
input(ta/3).
|
||||
|
||||
input(courselevel/2).
|
||||
|
||||
input(tempadvisedby/2).
|
||||
|
||||
|
||||
determination(professor/1,student/1).
|
||||
determination(professor/1,hasposition/2).
|
||||
determination(professor/1,publication/2).
|
||||
determination(professor/1,courselevel/2).
|
||||
determination(professor/1,inphase/2).
|
||||
determination(professor/1,advisedby/2).
|
||||
determination(professor/1,tempadvisedby/2).
|
||||
determination(professor/1,yearsinprogram/2).
|
||||
determination(professor/1,taughtby/3).
|
||||
determination(professor/1,ta/3).
|
||||
|
||||
determination(student/1,professor/1).
|
||||
determination(student/1,hasposition/2).
|
||||
determination(student/1,publication/2).
|
||||
determination(student/1,courselevel/2).
|
||||
determination(student/1,inphase/2).
|
||||
determination(student/1,advisedby/2).
|
||||
determination(student/1,tempadvisedby/2).
|
||||
determination(student/1,yearsinprogram/2).
|
||||
determination(student/1,taughtby/3).
|
||||
determination(student/1,ta/3).
|
||||
|
||||
determination(hasposition/2,professor/1).
|
||||
determination(hasposition/2,student/1).
|
||||
determination(hasposition/2,publication/2).
|
||||
determination(hasposition/2,courselevel/2).
|
||||
determination(hasposition/2,inphase/2).
|
||||
determination(hasposition/2,advisedby/2).
|
||||
determination(hasposition/2,tempadvisedby/2).
|
||||
determination(hasposition/2,yearsinprogram/2).
|
||||
determination(hasposition/2,taughtby/3).
|
||||
determination(hasposition/2,ta/3).
|
||||
|
||||
determination(inphase/2,professor/1).
|
||||
determination(inphase/2,student/1).
|
||||
determination(inphase/2,hasposition/2).
|
||||
determination(inphase/2,publication/2).
|
||||
determination(inphase/2,courselevel/2).
|
||||
determination(inphase/2,advisedby/2).
|
||||
determination(inphase/2,tempadvisedby/2).
|
||||
determination(inphase/2,yearsinprogram/2).
|
||||
determination(inphase/2,taughtby/3).
|
||||
determination(inphase/2,ta/3).
|
||||
|
||||
determination(taughtby/3,professor/1).
|
||||
determination(taughtby/3,student/1).
|
||||
determination(taughtby/3,hasposition/2).
|
||||
determination(taughtby/3,publication/2).
|
||||
determination(taughtby/3,courselevel/2).
|
||||
determination(taughtby/3,inphase/2).
|
||||
determination(taughtby/3,advisedby/2).
|
||||
determination(taughtby/3,tempadvisedby/2).
|
||||
determination(taughtby/3,yearsinprogram/2).
|
||||
determination(taughtby/3,ta/3).
|
||||
|
||||
determination(advisedby/2,professor/1).
|
||||
determination(advisedby/2,student/1).
|
||||
determination(advisedby/2,hasposition/2).
|
||||
determination(advisedby/2,publication/2).
|
||||
determination(advisedby/2,courselevel/2).
|
||||
determination(advisedby/2,inphase/2).
|
||||
determination(advisedby/2,yearsinprogram/2).
|
||||
determination(advisedby/2,taughtby/3).
|
||||
determination(advisedby/2,ta/3).
|
||||
|
||||
determination(tempadvisedby/2,professor/1).
|
||||
determination(tempadvisedby/2,student/1).
|
||||
determination(tempadvisedby/2,hasposition/2).
|
||||
determination(tempadvisedby/2,publication/2).
|
||||
determination(tempadvisedby/2,courselevel/2).
|
||||
determination(tempadvisedby/2,inphase/2).
|
||||
determination(tempadvisedby/2,yearsinprogram/2).
|
||||
determination(tempadvisedby/2,taughtby/3).
|
||||
determination(tempadvisedby/2,ta/3).
|
||||
|
||||
determination(ta/3,professor/1).
|
||||
determination(ta/3,student/1).
|
||||
determination(ta/3,hasposition/2).
|
||||
determination(ta/3,publication/2).
|
||||
determination(ta/3,courselevel/2).
|
||||
determination(ta/3,inphase/2).
|
||||
determination(ta/3,advisedby/2).
|
||||
determination(ta/3,tempadvisedby/2).
|
||||
determination(ta/3,yearsinprogram/2).
|
||||
determination(ta/3,taughtby/3).
|
||||
|
||||
determination(yearsinprogram/2,professor/1).
|
||||
determination(yearsinprogram/2,student/1).
|
||||
determination(yearsinprogram/2,hasposition/2).
|
||||
determination(yearsinprogram/2,publication/2).
|
||||
determination(yearsinprogram/2,courselevel/2).
|
||||
determination(yearsinprogram/2,inphase/2).
|
||||
determination(yearsinprogram/2,advisedby/2).
|
||||
determination(yearsinprogram/2,tempadvisedby/2).
|
||||
determination(yearsinprogram/2,taughtby/3).
|
||||
determination(yearsinprogram/2,ta/3).
|
||||
|
||||
determination(courselevel/2,professor/1).
|
||||
determination(courselevel/2,student/1).
|
||||
determination(courselevel/2,hasposition/2).
|
||||
determination(courselevel/2,publication/2).
|
||||
determination(courselevel/2,inphase/2).
|
||||
determination(courselevel/2,advisedby/2).
|
||||
determination(courselevel/2,tempadvisedby/2).
|
||||
determination(courselevel/2,yearsinprogram/2).
|
||||
determination(courselevel/2,taughtby/3).
|
||||
determination(courselevel/2,ta/3).
|
||||
|
||||
determination(publication/2,professor/1).
|
||||
determination(publication/2,student/1).
|
||||
determination(publication/2,hasposition/2).
|
||||
determination(publication/2,courselevel/2).
|
||||
determination(publication/2,inphase/2).
|
||||
determination(publication/2,advisedby/2).
|
||||
determination(publication/2,tempadvisedby/2).
|
||||
determination(publication/2,yearsinprogram/2).
|
||||
determination(publication/2,taughtby/3).
|
||||
determination(publication/2,ta/3).
|
||||
|
||||
|
||||
modeh(*,professor(+person)).
|
||||
modeh(*,student(+person)).
|
||||
modeh(*,hasposition(+person, faculty)).
|
||||
modeh(*,inphase(+person, pre_quals)).
|
||||
modeh(*,taughtby(+course, +person, +quarter)).
|
||||
modeh(*,advisedby(+person,+person)).
|
||||
modeh(*,tempadvisedby(+person,+person)).
|
||||
modeh(*,ta(+course,+person,+quarter)).
|
||||
modeh(*,courselevel(+course,#level)).
|
||||
modeh(*,yearsinprogram(+person,#year)).
|
||||
|
||||
|
||||
modeh(*,[advisedby(+person,+person),tempadvisedby(+person,+person)],[advisedby(A,B),tempadvisedby(A,B)],[professor/1,student/1,hasposition/2,inphase/2,publication/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2]).
|
||||
|
||||
modeh(*,[student(+person),professor(+person)],[student(P),professor(P)],[hasposition/2,inphase/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2,advisedby/2,tempadvisedby/2,publication/2]).
|
||||
|
||||
modeh(*,
|
||||
[hasposition(+person, faculty),hasposition(+person, faculty_affiliate),hasposition(+person, faculty_adjunct),hasposition(+person, faculty_emeritus),hasposition(+person, faculty_visiting)],
|
||||
[hasposition(P, faculty),hasposition(P, faculty_affiliate),hasposition(P, faculty_adjunct),hasposition(P, faculty_emeritus),hasposition(P, faculty_visiting)],
|
||||
[professor/1,student/1,inphase/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2,advisedby/2,tempadvisedby/2]).
|
||||
|
||||
modeh(*,
|
||||
[yearsinprogram(+person, year_1) , ta(+course, +person, +quarter)],
|
||||
[yearsinprogram(P, year_1) , ta(_C, P, _Q)],
|
||||
[professor/1,student/1,inphase/2,taughtby/3,courselevel/2,advisedby/2,tempadvisedby/2,hasposition/2]).
|
||||
|
||||
modeh(*,[yearsinprogram(+person, year_1) ,yearsinprogram(+person, year_2)],[yearsinprogram(P, year_1) ,yearsinprogram(P, year_2)],
|
||||
[professor/1,student/1,inphase/2,taughtby/3,ta/3,courselevel/2,advisedby/2,tempadvisedby/2,hasposition/2]).
|
||||
|
||||
modeh(*,[ inphase(+person,pre_quals) , inphase(+person,post_quals) , inphase(+person,post_generals)],[ inphase(P,pre_quals) , inphase(P,post_quals) , inphase(P,post_generals)],
|
||||
[professor/1,student/1,taughtby/3,ta/3,courselevel/2,yearsinprogram/2,advisedby/2,tempadvisedby/2,hasposition/2]).
|
||||
|
||||
modeh(*,[courselevel(+course,level_100) , advisedby(+person,+person)],[courselevel(_C,level_100) , advisedby(_S,_P)],
|
||||
[professor/1,student/1,inphase/2,taughtby/3,ta/3,yearsinprogram/2,tempadvisedby/2,hasposition/2,publication/2]).
|
||||
|
||||
modeh(*,[ta(+course,+person,+quarter) , courselevel(+course,level_100)],[ta(C,_P,_Q) , courselevel(C,level_100)],
|
||||
[professor/1,student/1,inphase/2,taughtby/3,yearsinprogram/2,advisedby/2,tempadvisedby/2,hasposition/2,publication/2]).
|
||||
|
||||
modeh(*,[taughtby(+course,+person,+quarter) , courselevel(+course,level_100)],[taughtby(C,_P,_Q) , courselevel(C,level_100)],
|
||||
[professor/1,student/1,inphase/2,ta/3,yearsinprogram/2,advisedby/2,tempadvisedby/2,hasposition/2,publication/2]).
|
||||
|
||||
|
||||
modeb(*,publication(-title, +person)).
|
||||
modeb(*,publication(+title, -person)).
|
||||
modeb(*,professor(+person)).
|
||||
modeb(*,student(+person)).
|
||||
modeb(*,taughtby(-course, +person, -quarter)).
|
||||
modeb(*,taughtby(+course, -person, -quarter)).
|
||||
modeb(*,ta(+course, -person, +quarter)).
|
||||
modeb(*,ta(-course, +person, -quarter)).
|
||||
modeb(*,hasposition(+person, -position)).
|
||||
modeb(*,hasposition(+person, #position)).
|
||||
modeb(*,inphase(+person, -phase)).
|
||||
modeb(*,inphase(+person, #phase)).
|
||||
modeb(*,tempadvisedby(+person, -person)).
|
||||
modeb(*,tempadvisedby(-person, +person)).
|
||||
modeb(*,yearsinprogram(+person, -year)).
|
||||
modeb(*,courselevel(+course, #level)).
|
||||
|
||||
lookahead(ta(_A,_B,_C),[taughtby(_A,_D,_C)]).
|
||||
lookahead(publication(_A,_B),[publication(_A,_C), professor(_B),student(_C)]).
|
||||
669
packages/cplint/slipcover/revise_sl.pl
Normal file
669
packages/cplint/slipcover/revise_sl.pl
Normal file
@ -0,0 +1,669 @@
|
||||
/*
|
||||
|
||||
EMBLEM and SLIPCASE
|
||||
|
||||
Copyright (c) 2011, Fabrizio Riguzzi, Nicola di Mauro and Elena Bellodi
|
||||
|
||||
*/
|
||||
:- use_module(library(terms)).
|
||||
:- use_module(library(lists)).
|
||||
|
||||
:- set_prolog_flag(discontiguous_warnings,on).
|
||||
:- set_prolog_flag(single_var_warnings,on).
|
||||
|
||||
|
||||
theory_revisions_op(Theory,TheoryRevs):-
|
||||
setof(RevOp, Theory^revise_theory(Theory,RevOp), TheoryRevs),!.
|
||||
|
||||
theory_revisions_op(_Theory,[]).
|
||||
|
||||
|
||||
theory_revisions(Theory,TheoryRevs):-
|
||||
theory_revisions_op(Theory,TheoryRevs1),
|
||||
apply_operators(TheoryRevs1,Theory,TheoryRevs).
|
||||
|
||||
|
||||
apply_operators([],_Theory,[]).
|
||||
|
||||
apply_operators([add(Rule)|RestOps],Theory,[NewTheory|RestTheory]) :-
|
||||
append(Theory, [Rule], NewTheory),
|
||||
% nl,write(NewTheory),
|
||||
apply_operators(RestOps,Theory,RestTheory).
|
||||
|
||||
apply_operators([add_body(Rule1,Rule2,_A)|RestOps],Theory,[NewTheory|RestTheory]) :-
|
||||
delete_matching(Theory,Rule1,Theory1),
|
||||
append(Theory1, [Rule2], NewTheory),
|
||||
% nl,write(NewTheory),
|
||||
apply_operators(RestOps,Theory,RestTheory).
|
||||
|
||||
apply_operators([remove_body(Rule1,Rule2,_A)|RestOps],Theory,[NewTheory|RestTheory]) :-
|
||||
delete_matching(Theory,Rule1,Theory1),
|
||||
append(Theory1, [Rule2], NewTheory),
|
||||
% nl,write(NewTheory),
|
||||
apply_operators(RestOps,Theory,RestTheory).
|
||||
|
||||
apply_operators([add_head(Rule1,Rule2,_A)|RestOps],Theory,[NewTheory|RestTheory]) :-
|
||||
delete_matching(Theory,Rule1,Theory1),
|
||||
append(Theory1, [Rule2], NewTheory),
|
||||
% nl,write(NewTheory),
|
||||
apply_operators(RestOps,Theory,RestTheory).
|
||||
|
||||
apply_operators([remove_head(Rule1,Rule2,_A)|RestOps],Theory,[NewTheory|RestTheory]) :-
|
||||
delete_matching(Theory,Rule1,Theory1),
|
||||
append(Theory1, [Rule2], NewTheory),
|
||||
% nl,write(NewTheory),
|
||||
apply_operators(RestOps,Theory,RestTheory).
|
||||
|
||||
apply_operators([remove(Rule)|RestOps],Theory,[NewTheory|RestTheory]) :-
|
||||
delete_matching(Theory,Rule,NewTheory),
|
||||
% nl,write(NewTheory),
|
||||
apply_operators(RestOps,Theory,RestTheory).
|
||||
|
||||
|
||||
revise_theory(Theory,Ref):-
|
||||
specialize_theory(Theory,Ref).
|
||||
|
||||
revise_theory(Theory,Ref):-
|
||||
generalize_theory(Theory,Ref).
|
||||
|
||||
|
||||
generalize_theory(Theory,Ref):-
|
||||
Theory \== [],
|
||||
choose_rule(Theory,Rule),
|
||||
generalize_rule(Rule,Ref).
|
||||
|
||||
generalize_theory(Theory,Ref):-
|
||||
length(Theory,LT),
|
||||
setting(max_rules,MR),
|
||||
LT<MR,
|
||||
add_rule(Ref).
|
||||
|
||||
|
||||
generalize_rule(Rule,Ref):-
|
||||
generalize_head(Rule,Ref).
|
||||
|
||||
generalize_rule(Rule,Ref):-
|
||||
generalize_body(Rule,Ref).
|
||||
|
||||
|
||||
add_rule(add(rule(ID,Head,[],Lits))):-
|
||||
setting(specialization,bottom),!,
|
||||
database(DB),
|
||||
sample(1,DB,[M]),
|
||||
get_head_atoms(O),
|
||||
member(A,O),
|
||||
functor(A,F,N),
|
||||
functor(F1,F,N),
|
||||
F1=..[F|Arg],
|
||||
Pred1=..[F,M|Arg],
|
||||
A=..[F|ArgM],
|
||||
keep_const(ArgM,Arg),
|
||||
findall((A,Pred1),call(Pred1),L),
|
||||
sample(1,L,LH),
|
||||
generate_body(LH,[rule(ID,Head,[],Lits)]).
|
||||
|
||||
add_rule(add(rule(ID,Head,[],true))):-
|
||||
findall(HL , modeh(_,HL), HLS),
|
||||
length(HLS,L),
|
||||
L1 is L+1,
|
||||
P is 1/L1,
|
||||
generate_head(HLS,P,Head),
|
||||
get_next_rule_number(ID).
|
||||
|
||||
|
||||
generate_head([H|_T],_P,[H1:0.5,'':0.5]):-
|
||||
H=..[Pred|Args],
|
||||
length(Args,LA),
|
||||
length(Args1,LA),
|
||||
H1=..[Pred|Args1].
|
||||
|
||||
generate_head([_H|T],P,Head):-
|
||||
generate_head(T,P,Head).
|
||||
|
||||
|
||||
generalize_head(Rule,Ref):-
|
||||
Rule = rule(ID,LH,BL),
|
||||
generalize_head1(LH,LH1,NewAt),
|
||||
Ref = add_head(Rule,rule(ID,LH1,BL),NewAt).
|
||||
|
||||
|
||||
generalize_head1(LH,LH1,NH):-
|
||||
findall(HL , modeh(_,HL), HLS),
|
||||
generalize_head2(HLS,LH,LH1,NH).
|
||||
|
||||
|
||||
generalize_head2([X|_R],LH,LH1,PH) :-
|
||||
X =.. [P|A],
|
||||
length(A,LA),
|
||||
length(A1,LA),
|
||||
PH =.. [P|A1],
|
||||
\+ member(PH:_, LH),
|
||||
(setting(new_head_atoms_zero_prob,true)->
|
||||
delete_matching(LH,'':PNull,LH0),
|
||||
append(LH0,[PH:0.0,'':PNull],LH1)
|
||||
;
|
||||
length(LH,NH),
|
||||
add_to_head(LH,NH,PH,LH1)
|
||||
).
|
||||
|
||||
generalize_head2([_X|R],LH,LH1) :-
|
||||
generalize_head2(R,LH,LH1).
|
||||
|
||||
|
||||
add_to_head(['':PN],NH,At,[At:PA,'':PN1]):-!,
|
||||
PN1 is PN*NH/(NH+1),
|
||||
PA is 1/(NH+1).
|
||||
|
||||
add_to_head([H:PH|T],NH,At,[H:PH1|T1]):-
|
||||
PH1 is PH*NH/(NH+1),
|
||||
add_to_head(T,NH,At,T1).
|
||||
|
||||
|
||||
get_module_var(LH,Module):-
|
||||
member(H:_,LH),!,
|
||||
H=..[_F,Module|_].
|
||||
|
||||
|
||||
generalize_body(Rule,Ref):-
|
||||
Rule = rule(ID,LH,BL),
|
||||
delete_one(BL,BL1,A),
|
||||
remove_prob(LH,LH1),
|
||||
delete(LH1,'',LH2),
|
||||
linked_clause(BL1,LH2),
|
||||
Ref = remove_body(Rule,rule(ID,LH,BL1),A).
|
||||
|
||||
|
||||
specialize_theory(Theory,Ref):-
|
||||
Theory \== [],
|
||||
choose_rule(Theory,Rule),
|
||||
specialize_rule(Rule,SpecRule,Lit),
|
||||
Ref = add_body(Rule,SpecRule,Lit).
|
||||
|
||||
%used by cycle_clauses in slipcover.pl
|
||||
specialize_rule(Rule,SpecRule,Lit):-
|
||||
setting(specialization,bottom),
|
||||
Rule = rule(ID,LH,BL,Lits),
|
||||
delete_one(Lits,RLits,Lit),
|
||||
\+ lookahead_cons(Lit,_),
|
||||
\+ lookahead_cons_var(Lit,_),
|
||||
\+ member_eq(Lit,BL),
|
||||
append(BL,[Lit],BL1),
|
||||
remove_prob(LH,LH1),
|
||||
delete(LH1,'',LH2),
|
||||
append(LH2,BL1,ALL2),
|
||||
dv(LH2,BL1,DList), %-DList: list of couples (variable,depth)
|
||||
extract_fancy_vars(ALL2,Vars1),
|
||||
length(Vars1,NV),
|
||||
setting(max_var,MV),
|
||||
NV=<MV,
|
||||
linked_clause(BL1,LH2),
|
||||
setting(maxdepth_var,MD),
|
||||
exceed_depth(DList,MD),
|
||||
\+ banned_clause(LH2,BL1),
|
||||
SpecRule=rule(ID,LH,BL1,RLits).
|
||||
|
||||
specialize_rule(Rule,SpecRule,Lit):-
|
||||
setting(specialization,bottom),
|
||||
Rule = rule(ID,LH,BL,Lits),
|
||||
delete_one(Lits,RLits,Lit),
|
||||
\+ member_eq(Lit,BL),
|
||||
append(BL,[Lit],BL0),
|
||||
\+lookahead_cons_var(Lit,_),
|
||||
(lookahead(Lit,LLit1);lookahead_cons(Lit,LLit1)),
|
||||
copy_term(LLit1,LLit2),
|
||||
specialize_rule_la_bot(LLit2,RLits,RLits1,BL0,BL1),
|
||||
remove_prob(LH,LH1),
|
||||
delete(LH1,'',LH2),
|
||||
append(LH2,BL1,ALL2),
|
||||
dv(LH2,BL1,DList),
|
||||
extract_fancy_vars(ALL2,Vars1),
|
||||
length(Vars1,NV),
|
||||
setting(max_var,MV),
|
||||
NV=<MV,
|
||||
linked_clause(BL1,LH2),
|
||||
setting(maxdepth_var,MD),
|
||||
exceed_depth(DList,MD),
|
||||
\+ banned_clause(LH2,BL1),
|
||||
SpecRule=rule(ID,LH,BL1,RLits1).
|
||||
|
||||
specialize_rule(Rule,SpecRule,Lit):-
|
||||
setting(specialization,bottom),
|
||||
Rule = rule(ID,LH,BL,Lits),
|
||||
delete_one(Lits,RLits,Lit),
|
||||
\+ member_eq(Lit,BL),
|
||||
append(BL,[Lit],BL0),
|
||||
lookahead_cons_var(Lit,LLit2),
|
||||
specialize_rule_la_bot(LLit2,RLits,_RLits1,BL0,BL1),
|
||||
remove_prob(LH,LH1),
|
||||
delete(LH1,'',LH2),
|
||||
append(LH2,BL1,ALL2),
|
||||
dv(LH2,BL1,DList),
|
||||
extract_fancy_vars(ALL2,Vars1),
|
||||
length(Vars1,NV),
|
||||
setting(max_var,MV),
|
||||
NV=<MV,
|
||||
linked_clause(BL1,LH2),
|
||||
setting(maxdepth_var,MD),
|
||||
exceed_depth(DList,MD),
|
||||
\+ banned_clause(LH2,BL1),
|
||||
SpecRule=rule(ID,LH,BL1,[]).
|
||||
|
||||
specialize_rule(Rule,SpecRule,Lit):-
|
||||
setting(specialization,mode),%!,
|
||||
findall(BL , modeb(_,BL), BLS),
|
||||
specialize_rule(BLS,Rule,SpecRule,Lit).
|
||||
|
||||
%specializes the clause's head
|
||||
specialize_rule(rule(ID,LH,BL,Lits),rule(ID,LH2,BL,Lits),Lit):-
|
||||
length(LH,L),
|
||||
L>2,
|
||||
delete_one(LH,LH1,Lit), %deletes Lit
|
||||
Lit\=' ',
|
||||
update_head1(LH1,L-1,LH2). %updates parameters
|
||||
|
||||
update_head1([],_N,[]):-!.
|
||||
|
||||
update_head1([H:_P|T],N,[H:P|T1]):-
|
||||
P is 1/N,
|
||||
update_head1(T,N,T1).
|
||||
|
||||
write_list([A]):-!,
|
||||
format("\t~p.~n~n",[A]).
|
||||
write_list([A|T]):-
|
||||
format("\t~p,",[A]),
|
||||
write_list(T).
|
||||
|
||||
|
||||
banned_clause(H,B):-
|
||||
numbervars((H,B),0,_N),
|
||||
banned(H2,B2),
|
||||
mysublist(H2,H),
|
||||
mysublist(B2,B).
|
||||
|
||||
|
||||
mysublist([],_).
|
||||
|
||||
mysublist([H|T],L):-
|
||||
member(H,L),
|
||||
mysublist(T,L).
|
||||
|
||||
|
||||
check_ref(H,B):-
|
||||
copy_term((H,B),(H1,B1)),
|
||||
numbervars((H1,B1),0,_N),
|
||||
(ref(H1,B1)->
|
||||
fail
|
||||
;
|
||||
assert(ref(H1,B1))
|
||||
).
|
||||
|
||||
specialize_rule([Lit|_RLit],Rule,SpecRul,SLit):-
|
||||
Rule = rule(ID,LH,BL,true),
|
||||
remove_prob(LH,LH1),
|
||||
append(LH1,BL,ALL),
|
||||
specialize_rule1(Lit,ALL,SLit),
|
||||
append(BL,[SLit],BL1),
|
||||
(lookahead(SLit,LLit1);lookahead_cons(SLit,LLit1)),
|
||||
specialize_rule_la(LLit1,LH1,BL1,BL2),
|
||||
append(LH1,BL2,ALL2),
|
||||
extract_fancy_vars(ALL2,Vars1),
|
||||
length(Vars1,NV),
|
||||
setting(max_var,MV),
|
||||
NV=<MV,
|
||||
SpecRul = rule(ID,LH,BL2,true).
|
||||
|
||||
specialize_rule([Lit|_RLit],Rule,SpecRul,SLit):-
|
||||
Rule = rule(ID,LH,BL,true),
|
||||
remove_prob(LH,LH1),
|
||||
append(LH1,BL,ALL),
|
||||
specialize_rule1(Lit,ALL,SLit),
|
||||
\+ lookahead_cons(SLit,_),
|
||||
append(BL,[SLit],BL1),
|
||||
append(LH1,BL1,ALL1),
|
||||
extract_fancy_vars(ALL1,Vars1),
|
||||
length(Vars1,NV),
|
||||
setting(max_var,MV),
|
||||
NV=<MV,
|
||||
SpecRul = rule(ID,LH,BL1,true).
|
||||
|
||||
specialize_rule([_|RLit],Rule,SpecRul,Lit):-
|
||||
specialize_rule(RLit,Rule,SpecRul,Lit).
|
||||
|
||||
|
||||
specialize_rule_la([],_LH1,BL1,BL1).
|
||||
|
||||
specialize_rule_la([Lit1|T],LH1,BL1,BL3):-
|
||||
copy_term(Lit1,Lit2),
|
||||
modeb(_,Lit2),
|
||||
append(LH1,BL1,ALL1),
|
||||
specialize_rule1(Lit2,ALL1,SLit1),
|
||||
append(BL1,[SLit1],BL2),
|
||||
specialize_rule_la(T,LH1,BL2,BL3).
|
||||
|
||||
|
||||
specialize_rule_la_bot([],Bot,Bot,BL,BL).
|
||||
|
||||
specialize_rule_la_bot([Lit|T],Bot0,Bot,BL1,BL3):-
|
||||
delete_one(Bot0,Bot1,Lit),
|
||||
\+ member_eq(Lit,BL1),
|
||||
append(BL1,[Lit],BL2),
|
||||
specialize_rule_la_bot(T,Bot1,Bot,BL2,BL3).
|
||||
|
||||
|
||||
remove_prob(['':_P],[]):-!.
|
||||
|
||||
remove_prob([X:_|R],[X|R1]):-
|
||||
remove_prob(R,R1).
|
||||
|
||||
|
||||
specialize_rule1(Lit,Lits,SpecLit):-
|
||||
Lit =.. [Pred|Args],
|
||||
exctract_type_vars(Lits,TypeVars0),
|
||||
remove_duplicates(TypeVars0,TypeVars),
|
||||
take_var_args(Args,TypeVars,Args1),
|
||||
SpecLit =.. [Pred|Args1],
|
||||
\+ member_eq(SpecLit,Lits).
|
||||
|
||||
|
||||
convert_to_input_vars([],[]):-!.
|
||||
|
||||
convert_to_input_vars([+T|RT],[+T|RT1]):-
|
||||
!,
|
||||
convert_to_input_vars(RT,RT1).
|
||||
|
||||
convert_to_input_vars([-T|RT],[+T|RT1]):-
|
||||
convert_to_input_vars(RT,RT1).
|
||||
|
||||
|
||||
member_eq(X,[Y|_List]) :-
|
||||
X == Y.
|
||||
|
||||
member_eq(X,[_|List]) :-
|
||||
member_eq(X,List).
|
||||
|
||||
|
||||
remove_eq(X,[Y|R],R):-
|
||||
X == Y,
|
||||
!.
|
||||
|
||||
remove_eq(X,[_|R],R1):-
|
||||
remove_eq(X,R,R1).
|
||||
|
||||
|
||||
linked_clause(X):-
|
||||
linked_clause(X,[]).
|
||||
|
||||
linked_clause([],_).
|
||||
|
||||
linked_clause([L|R],PrevLits):-
|
||||
term_variables(PrevLits,PrevVars),
|
||||
input_variables(L,InputVars),
|
||||
linked(InputVars,PrevVars),!,
|
||||
linked_clause(R,[L|PrevLits]).
|
||||
|
||||
|
||||
linked([],_).
|
||||
|
||||
linked([X|R],L) :-
|
||||
member_eq(X,L),
|
||||
!,
|
||||
linked(R,L).
|
||||
|
||||
|
||||
input_variables(\+ LitM,InputVars):-
|
||||
!,
|
||||
LitM=..[P|Args],
|
||||
length(Args,LA),
|
||||
length(Args1,LA),
|
||||
Lit1=..[P|Args1],
|
||||
copy_term(LitM,Lit0),
|
||||
modeb(_,Lit1),
|
||||
Lit1 =.. [P|Args1],
|
||||
convert_to_input_vars(Args1,Args2),
|
||||
Lit2 =.. [P|Args2],
|
||||
input_vars(Lit0,Lit2,InputVars).
|
||||
|
||||
input_variables(LitM,InputVars):-
|
||||
LitM=..[P|Args],
|
||||
length(Args,LA),
|
||||
length(Args1,LA),
|
||||
Lit1=..[P|Args1],
|
||||
modeb(_,Lit1),
|
||||
input_vars(LitM,Lit1,InputVars).
|
||||
|
||||
input_variables(LitM,InputVars):-
|
||||
LitM=..[P|Args],
|
||||
length(Args,LA),
|
||||
length(Args1,LA),
|
||||
Lit1=..[P|Args1],
|
||||
modeh(_,Lit1),
|
||||
input_vars(LitM,Lit1,InputVars).
|
||||
|
||||
input_vars(Lit,Lit1,InputVars):-
|
||||
Lit =.. [_|Vars],
|
||||
Lit1 =.. [_|Types],
|
||||
input_vars1(Vars,Types,InputVars).
|
||||
|
||||
|
||||
input_vars1([],_,[]).
|
||||
|
||||
input_vars1([V|RV],[+_T|RT],[V|RV1]):-
|
||||
!,
|
||||
input_vars1(RV,RT,RV1).
|
||||
|
||||
input_vars1([_V|RV],[_|RT],RV1):-
|
||||
input_vars1(RV,RT,RV1).
|
||||
|
||||
|
||||
exctract_type_vars([],[]).
|
||||
|
||||
exctract_type_vars([Lit|RestLit],TypeVars):-
|
||||
Lit =.. [Pred|Args],
|
||||
length(Args,L),
|
||||
length(Args1,L),
|
||||
Lit1 =.. [Pred|Args1],
|
||||
take_mode(Lit1),
|
||||
type_vars(Args,Args1,Types),
|
||||
exctract_type_vars(RestLit,TypeVars0),
|
||||
!,
|
||||
append(Types,TypeVars0,TypeVars).
|
||||
|
||||
|
||||
take_mode(Lit):-
|
||||
modeh(_,Lit),!.
|
||||
|
||||
take_mode(Lit):-
|
||||
modeb(_,Lit),!.
|
||||
|
||||
take_mode(Lit):-
|
||||
mode(_,Lit),!.
|
||||
|
||||
|
||||
type_vars([],[],[]).
|
||||
|
||||
type_vars([V|RV],[+T|RT],[V=T|RTV]):-
|
||||
!,
|
||||
type_vars(RV,RT,RTV).
|
||||
|
||||
type_vars([V|RV],[-T|RT],[V=T|RTV]):-atom(T),!,
|
||||
type_vars(RV,RT,RTV).
|
||||
|
||||
type_vars([_V|RV],[_T|RT],RTV):-
|
||||
type_vars(RV,RT,RTV).
|
||||
|
||||
|
||||
take_var_args([],_,[]).
|
||||
|
||||
take_var_args([+T|RT],TypeVars,[V|RV]):-
|
||||
!,
|
||||
member(V=T,TypeVars),
|
||||
take_var_args(RT,TypeVars,RV).
|
||||
|
||||
take_var_args([-T|RT],TypeVars,[_V|RV]):-
|
||||
atom(T),
|
||||
take_var_args(RT,TypeVars,RV).
|
||||
|
||||
take_var_args([-T|RT],TypeVars,[V|RV]):-
|
||||
member(V=T,TypeVars),
|
||||
take_var_args(RT,TypeVars,RV).
|
||||
|
||||
take_var_args([T|RT],TypeVars,[T|RV]):-
|
||||
T\= + _,(T\= - _; T= - A,number(A)),
|
||||
take_var_args(RT,TypeVars,RV).
|
||||
|
||||
|
||||
choose_rule(Theory,Rule):-
|
||||
member(Rule,Theory).
|
||||
|
||||
|
||||
add_rule(Theory,add(rule(ID,H,[],true))):-
|
||||
new_id(ID),
|
||||
findall(HL , modeh(_,HL), HLS),
|
||||
length(HLS,NH),
|
||||
P is 1/(NH+1),
|
||||
add_probs(HLS,H,P),
|
||||
\+ member(rule(_,H,[],true),Theory).
|
||||
|
||||
add_rule(Theory,TheoryGen):-
|
||||
findall(HL , modeh(_,HL), HLS),
|
||||
add_rule(HLS,Theory,TheoryGen).
|
||||
|
||||
add_rule([X|_R],Theory,TheoryGen) :-
|
||||
new_id(ID),
|
||||
X =.. [P|A],
|
||||
length(A,LA),
|
||||
length(A1,LA),
|
||||
PH =.. [P|A1],
|
||||
TheoryGen = add(rule(ID,[PH:0.5,'':0.5],[],true)),
|
||||
\+ member(rule(_,[PH:_,'':_],[],true),Theory).
|
||||
|
||||
add_rule([_X|R],Theory,TheoryGen) :-
|
||||
add_rule(R,Theory,TheoryGen).
|
||||
|
||||
|
||||
add_probs([],['':P],P):-!.
|
||||
|
||||
add_probs([H|T],[H:P|T1],P):-
|
||||
add_probs(T,T1,P).
|
||||
|
||||
|
||||
extract_fancy_vars(List,Vars):-
|
||||
term_variables(List,Vars0),
|
||||
fancy_vars(Vars0,1,Vars).
|
||||
|
||||
|
||||
fancy_vars([],_,[]).
|
||||
|
||||
fancy_vars([X|R],N,[NN2=X|R1]):-
|
||||
name(N,NN),
|
||||
append([86],NN,NN1),
|
||||
name(NN2,NN1),
|
||||
N1 is N + 1,
|
||||
fancy_vars(R,N1,R1).
|
||||
|
||||
|
||||
delete_one([X|R],R,X).
|
||||
|
||||
delete_one([X|R],[X|R1],D):-
|
||||
delete_one(R,R1,D).
|
||||
|
||||
|
||||
remove_last([_X],[]) :-
|
||||
!.
|
||||
|
||||
remove_last([X|R],[X|R1]):-
|
||||
remove_last(R,R1).
|
||||
|
||||
|
||||
delete_matching([],_El,[]).
|
||||
|
||||
delete_matching([El|T],El,T1):-!,
|
||||
delete_matching(T,El,T1).
|
||||
|
||||
delete_matching([H|T],El,[H|T1]):-
|
||||
delete_matching(T,El,T1).
|
||||
|
||||
|
||||
%Computation of the depth of the variables in the clause's head/body
|
||||
dv(H,B,DV1):- %DV1: returns a list of couples (Variable, Max depth)
|
||||
term_variables(H,V),
|
||||
head_depth(V,DV0),
|
||||
findall((MD-DV),var_depth(B,DV0,DV,0,MD),LDs),
|
||||
get_max(LDs,-1,-,DV1).
|
||||
|
||||
|
||||
input_variables_b(LitM,InputVars):-
|
||||
LitM=..[P|Args],
|
||||
length(Args,LA),
|
||||
length(Args1,LA),
|
||||
Lit1=..[P|Args1],
|
||||
modeb(_,Lit1),
|
||||
input_vars(LitM,Lit1,InputVars).
|
||||
|
||||
|
||||
|
||||
%associates depth 0 to each variable in the clause's head
|
||||
head_depth([],[]).
|
||||
head_depth([V|R],[[V,0]|R1]):-
|
||||
head_depth(R,R1).
|
||||
|
||||
%associates a depth to each variable in the clause's body
|
||||
var_depth([],PrevDs1,PrevDs1,MD,MD):-!.
|
||||
|
||||
var_depth([L|R],PrevDs,PrevDs1,_MD,MD):- %L = a body literal, MD = maximum depth set by the user
|
||||
input_variables_b(L,InputVars),
|
||||
term_variables(L, BodyAtomVars),
|
||||
output_vars(BodyAtomVars,InputVars,OutputVars),
|
||||
depth_InputVars(InputVars,PrevDs,0,MaxD), %MaxD: maximum depth of the input variables in the body literal
|
||||
D is MaxD+1,
|
||||
compute_depth(OutputVars,D,PrevDs,PrevDs0), %Computes the depth for the output variables in the body literal
|
||||
var_depth(R,PrevDs0,PrevDs1,D,MD).
|
||||
|
||||
get_max([],_,Ds,Ds).
|
||||
|
||||
get_max([(MD-DsH)|T],MD0,_Ds0,Ds):-
|
||||
MD>MD0,!,
|
||||
get_max(T,MD,DsH,Ds).
|
||||
|
||||
get_max([_H|T],MD,Ds0,Ds):-
|
||||
get_max(T,MD,Ds0,Ds).
|
||||
|
||||
|
||||
output_vars(OutVars,[],OutVars):-!.
|
||||
output_vars(BodyAtomVars,[I|InputVars],OutVars):-
|
||||
delete(BodyAtomVars, I, Residue),
|
||||
output_vars(Residue,InputVars, OutVars).
|
||||
|
||||
% returns D as the maximum depth of the variables in the list (first argument)
|
||||
depth_InputVars([],_,D,D).
|
||||
depth_InputVars([I|Input],PrevDs,D0,D):-
|
||||
member_l(PrevDs,I,MD),
|
||||
(MD>D0->
|
||||
D1=MD
|
||||
;
|
||||
D1=D0
|
||||
),
|
||||
depth_InputVars(Input,PrevDs,D1,D).
|
||||
|
||||
member_l([[L,D]|_P],I,D):-
|
||||
I==L,!.
|
||||
member_l([_|P],I,D):-
|
||||
member_l(P,I,D).
|
||||
|
||||
compute_depth([],_,PD,PD):-!.
|
||||
compute_depth([O|Output],D,PD,RestO):-
|
||||
member_l(PD,O,_),!,
|
||||
compute_depth(Output,D,PD,RestO).
|
||||
|
||||
compute_depth([O|Output],D,PD,[[O,D]|RestO]):-
|
||||
compute_depth(Output,D,PD,RestO).
|
||||
|
||||
|
||||
|
||||
%checks if a variable's depth exceeds the setting
|
||||
exceed_depth([],_):-!.
|
||||
exceed_depth([H|T],MD):-
|
||||
nth1(2,H,Dep),
|
||||
Dep<MD, %setting(maxdepth_var,MD),
|
||||
exceed_depth(T,MD).
|
||||
|
||||
1241
packages/cplint/slipcover/slipcover.pl
Normal file
1241
packages/cplint/slipcover/slipcover.pl
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user