Merge branch 'master' of github.com:friguzzi/cplint

prolog/examples/bitcoin_attack.pl

@@ -1,3 +1,11 @@
+/*
+Computing the probability of success of an attack to the Bitcoin protocol
+From Damiano Azzolini, Fabrizio Riguzzi, Evelina Lamma, Elena Bellodi, 
+and Riccardo Zese.  
+Modeling bitcoin protocols with probabilistic logic programming. 
+PLP 2018 http://ceur-ws.org/Vol-2219/paper6.pdf
+ 
+*/
 :- use_module(library(mcintyre)).
 :- mc.
 :- begin_lpad.
@@ -43,4 +51,4 @@
 /** <examples>
 ?- mc_prob(success_poisson,Prob).
 ?- mc_prob(success_pascal,Prob).
-*/
+*/

prolog/examples/bitcoin_two_phase.pl

@@ -1,4 +1,11 @@
-% Empty with mcintyre loaded
+/*
+Computing the probability of mining the next block with a two-phase
+Proof-of-Work protocol in a Bitcoin network
+From Damiano Azzolini, Fabrizio Riguzzi, Evelina Lamma, Elena Bellodi,
+and Riccardo Zese.
+Modeling bitcoin protocols with probabilistic logic programming.
+PLP 2018 http://ceur-ws.org/Vol-2219/paper6.pdf
+*/
 :- use_module(library(mcintyre)).
 
 :- if(current_predicate(use_rendering/1)).
@@ -95,4 +102,4 @@
 ?- mc_sample(reach(a0,0,a2),100,PA).
 ?- mc_sample(reach(b0,0,b2),100,PB).
 ?- mc_prob(reach(a0,0,a2),PA).
-*/
+*/

prolog/examples/h_index.pl

@@ -0,0 +1,66 @@
+/* Computation of the h-index of an author given his number of papers
+assuming that the citations per paper follow a Poisson distribution with 
+given average.
+It shows that the expected h-index is directly proportional to the
+average number of citations (expected h-index=avg. n. cit. +5)
+*/
+:- use_module(library(mcintyre)).
+:- if(current_predicate(use_rendering/1)).
+:- use_rendering(c3).
+:- endif.
+:- mc.
+
+:- begin_lpad.
+
+citations(AverageCit,_,Cit):poisson(Cit,AverageCit).
+
+h_index(NumPapers,AverageCit,HIndex):-
+    numlist(1,NumPapers,Papers),
+    maplist(citations(AverageCit),Papers,Citations),
+    sort(0,  @>=, Citations,  Sorted),
+    compute_index(Sorted,1,HIndex).
+
+compute_index([0|_],_,0):-!.
+
+compute_index([],I0,I):-!,
+    I is I0-1.
+
+compute_index([I|_T],I,I):-!.
+
+compute_index([H|_T],I0,I):-
+    H<I0,!,
+    I is I0-1.
+
+compute_index([H|T],I0,I):-
+    H>I0,
+    I1 is I0+1,
+    compute_index(T,I1,I).
+
+
+
+:- end_lpad.
+
+h_vs_avg_cit(Papers,MaxAvg,Chart):-
+  findall(E,(
+    between(1,MaxAvg,N),mc_expectation(h_index(Papers,N,T),10,T,E)
+  ),V),
+  numlist(1,MaxAvg,X),
+  findall(N,(between(1,MaxAvg,N0),N is 5+N0),Dep),
+  Chart=c3{data:_{x:x, columns:[[x|X],['Expected h-index'|V],['Dependency'|Dep]]}}.
+
+/** <examples> Your example queries go here, e.g.
+
+?- mc_sample_arg_first(h_index(200,10,H),1000,H,HList),density(HList,Dens,[nbins(20)]).
+compute the distribution of the h_index given that the authors wrote
+200 papers and each paper receives on average 10 citations
+?- mc_expectation(h_index(200,10,H),1000,H,HExp).
+compute the expected value of the h_index given that the authors wrote
+200 papers and each paper receives on average 10 citations
+?- mc_sample_arg_first(citations(10,200,Cit),1000,Cit,CitList),density(CitList,Dens,[nbins(20)]).
+?- h_vs_avg_cit(100,40,Chart).
+Plot of the depedency of the expected h-index as a function of the average
+number of citations per article (max 40) given that there are 100 papers.
+The graphs shows that the expected h-index is directly proportional to the
+average number of citations (expected h-index=avg. n. cit. +5)
+*/
+