001package org.cpsolver.ifs.example.csp; 002 003import java.util.Iterator; 004import java.util.Random; 005 006import org.cpsolver.ifs.model.Constraint; 007import org.cpsolver.ifs.model.Model; 008 009 010/** 011 * Random Binary CSP with uniform distribution. <br> 012 * <br> 013 * A random CSP is defined by a four-tuple (n, d, p1, p2), where n denotes the 014 * number of variables and d denotes the domain size of each variable, p1 and p2 015 * are two probabilities. They are used to generate randomly the binary 016 * constraints among the variables. p1 represents the probability that a 017 * constraint exists between two different variables and p2 represents the 018 * probability that a pair of values in the domains of two variables connected 019 * by a constraint are incompatible. <br> 020 * <br> 021 * We use a so called model B of Random CSP (n, d, n1, n2) where n1 = 022 * p1*n*(n-1)/2 pairs of variables are randomly and uniformly selected and 023 * binary constraints are posted between them. For each constraint, n2 = p1*d^2 024 * randomly and uniformly selected pairs of values are picked as incompatible. 025 * 026 * @version IFS 1.3 (Iterative Forward Search)<br> 027 * Copyright (C) 2006 - 2014 Tomáš Müller<br> 028 * <a href="mailto:muller@unitime.org">muller@unitime.org</a><br> 029 * <a href="http://muller.unitime.org">http://muller.unitime.org</a><br> 030 * <br> 031 * This library is free software; you can redistribute it and/or modify 032 * it under the terms of the GNU Lesser General Public License as 033 * published by the Free Software Foundation; either version 3 of the 034 * License, or (at your option) any later version. <br> 035 * <br> 036 * This library is distributed in the hope that it will be useful, but 037 * WITHOUT ANY WARRANTY; without even the implied warranty of 038 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 039 * Lesser General Public License for more details. <br> 040 * <br> 041 * You should have received a copy of the GNU Lesser General Public 042 * License along with this library; if not see 043 * <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>. 044 */ 045public class CSPModel extends Model<CSPVariable, CSPValue> { 046 047 /** 048 * Constructor 049 * 050 * @param nrVariables 051 * number of variables in the problem 052 * @param nrValues 053 * number of values of each variable 054 * @param nrConstraints 055 * number of constraints in the problem 056 * @param nrCompatiblePairs 057 * number of compatible pairs of values for every constraint 058 * @param seed 059 * seed for random number generator (use 060 * {@link System#currentTimeMillis} if not bother) 061 */ 062 public CSPModel(int nrVariables, int nrValues, int nrConstraints, int nrCompatiblePairs, long seed) { 063 generate(nrVariables, nrValues, nrConstraints, nrCompatiblePairs, seed); 064 } 065 066 public CSPModel() { 067 } 068 069 private void swap(CSPVariable[][] allPairs, int first, int second) { 070 CSPVariable[] a = allPairs[first]; 071 allPairs[first] = allPairs[second]; 072 allPairs[second] = a; 073 } 074 075 private void buildBinaryConstraintGraph(Random rnd) { 076 int numberOfAllPairs = variables().size() * (variables().size() - 1) / 2; 077 CSPVariable[][] allPairs = new CSPVariable[numberOfAllPairs][]; 078 int idx = 0; 079 for (CSPVariable v1 : variables()) { 080 for (CSPVariable v2 : variables()) { 081 if (v1.getId() >= v2.getId()) 082 continue; 083 allPairs[idx++] = new CSPVariable[] { v1, v2 }; 084 } 085 } 086 idx = 0; 087 for (Iterator<Constraint<CSPVariable, CSPValue>> i = constraints().iterator(); i.hasNext();) { 088 CSPBinaryConstraint c = (CSPBinaryConstraint) i.next(); 089 swap(allPairs, idx, idx + (int) (rnd.nextDouble() * (numberOfAllPairs - idx))); 090 c.addVariable(allPairs[idx][0]); 091 c.addVariable(allPairs[idx][1]); 092 c.init(rnd); 093 idx++; 094 } 095 } 096 097 private void generate(int nrVariables, int nrValues, int nrConstraints, int nrCompatiblePairs, long seed) { 098 Random rnd = new Random(seed); 099 100 for (int i = 0; i < nrVariables; i++) { 101 CSPVariable var = new CSPVariable(i + 1, nrValues); 102 addVariable(var); 103 } 104 105 for (int i = 0; i < nrConstraints; i++) { 106 CSPBinaryConstraint c = new CSPBinaryConstraint(i + 1, nrCompatiblePairs); 107 addConstraint(c); 108 } 109 110 buildBinaryConstraintGraph(rnd); 111 } 112}