Java tutorial
/* LICENSE Copyright (c) 2013-2016, Jesse Hostetler (jessehostetler@gmail.com) All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS 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 PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED 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. */ /** * */ package edu.oregonstate.eecs.mcplan.search.pats; import java.util.ArrayList; import java.util.LinkedHashMap; import com.google.common.collect.Iterables; import com.google.common.collect.Sets; import edu.oregonstate.eecs.mcplan.Representation; import edu.oregonstate.eecs.mcplan.State; import edu.oregonstate.eecs.mcplan.util.MeanVarianceAccumulator; /** * @author jhostetler * */ public class PatsStateNode<S extends State, A> implements AbstractionGraph.Listener<S, A> { private final PatsActionNode<S, A> parent; public final Representation<S> x; private final BoundedValueModel<S, A> model; private final LinkedHashMap<A, PatsActionNode<S, A>> successors = new LinkedHashMap<>(); // FIXME: elements needs to either be a Multiset, or we need to count // multiplicity separately. /*package*/ final ArrayList<BoundedStateNode<S, A>> elements = new ArrayList<>(); private final ActionSet<A> abstraction = null; private final boolean terminal = false; private final boolean backed_up = false; private final boolean pure = true; private final boolean freed = false; public final int depth; private final MeanVarianceAccumulator R = new MeanVarianceAccumulator(); private double U = Double.NaN; private double L = Double.NaN; public PatsStateNode(final Representation<S> x, final PatsActionNode<S, A> parent, final BoundedValueModel<S, A> model) { this.parent = parent; this.x = x; this.model = model; this.depth = parent.depth() - 1; } public int n() { return elements.size(); } public PatsActionNode<S, A> successor(final A a) { return successors.get(a); } public Iterable<PatsActionNode<S, A>> successors() { return Iterables.unmodifiableIterable(successors.values()); } public int Nsuccessors() { return successors.size(); } /*package*/ void addElement(final BoundedStateNode<S, A> ss) { elements.add(ss); } public boolean isTerminal() { return depth == 0 || terminal; } private boolean isExpanded() { return Nsuccessors() > 0; } public boolean isPure() { return pure; } // public boolean isReadyToClose() // { // return isExpanded() && (isTerminal() || isPure()); // } // public boolean isActive() // { // return isExpanded() && !isTerminal() && !isPure(); // } public boolean isClosed() { return freed; } public double R() { return R.mean(); } public double U() { return U; } public double L() { return L; } public void backup() { assert (Nsuccessors() > 0); double max_u = -Double.MAX_VALUE; double max_l = -Double.MAX_VALUE; for (final PatsActionNode<S, A> an : successors()) { final double u = an.U(); if (u > max_u) { max_u = u; } final double l = an.L(); if (l > max_l) { max_l = l; } } U = R.mean() + max_u; L = R.mean() + max_l; // FIXME: Where whould we do this? for (final BoundedStateNode<S, A> gsn : elements) { gsn.backup(); } backed_up = true; } /** * Returns all actions that achieve the maximum value of U(s, a). * @return */ public ArrayList<PatsActionNode<S, A>> greatestUpperBound() { final ArrayList<PatsActionNode<S, A>> best = new ArrayList<>(); double Ustar = -Double.MAX_VALUE; for (final PatsActionNode<S, A> an : successors()) { final double U = an.U(); if (U > Ustar) { Ustar = U; best.clear(); best.add(an); } else if (U >= Ustar) { best.add(an); } } return best; } /** * Returns all actions that achieve the maximum value of L(s, a). * @return */ public ArrayList<PatsActionNode<S, A>> greatestLowerBound() { final ArrayList<PatsActionNode<S, A>> best = new ArrayList<>(); double Lstar = -Double.MAX_VALUE; for (final PatsActionNode<S, A> an : successors()) { final double L = an.L(); if (L > Lstar) { Lstar = L; best.clear(); best.add(an); } else if (L >= Lstar) { best.add(an); } } return best; } private PatsActionNode<S, A> createSuccessor(final A a) { for (final PatsActionNode<S, A> bn : successors()) { if (bn.a().equals(a)) { throw new IllegalStateException("Successor already exists for '" + a + "'"); } } final PatsActionNode<S, A> an = new PatsActionNode<S, A>(a, this, model, abstraction); } @Override public void updateAbstraction(final ActionSet<A> an) { if (isExpanded()) { for (final A a : Sets.difference(an, abstraction)) { } } } @Override public void updateAbstraction(final StateAbstraction<S> X, final ArrayList<Representation<S>> changed) { } private void transferSubtreeStructureTo(final PatsStateNode<S, A> subtree) { // for (final BoundedStateNode<S, A> ss : subtree.elements) { for (final BoundedActionNode<S, A> sa : ss.successors()) { final PatsActionNode<S, A> tsucc = this.successor(sa.a()); PatsActionNode<S, A> a = subtree.successor(tsucc.a()); if (a == null) { a = tsucc.makeEmptyCopyBelow(subtree); } a.addElement(sa); } } for (final PatsActionNode<S, A> a : subtree.successors()) { this.successor(a.a()).transferSubtreeStructureTo(a); } // FIXME: Need to de-allocate old nodes someplace } }