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1081 lines
26 KiB
1081 lines
26 KiB
//==============================================================================
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//
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// Copyright (c) 2002-
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// Authors:
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// * Dave Parker <david.parker@comlab.ox.ac.uk> (University of Oxford)
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// * Christian von Essen <christian.vonessen@imag.fr> (Verimag, Grenoble)
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//
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//------------------------------------------------------------------------------
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//
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// This file is part of PRISM.
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//
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// PRISM is free software; you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation; either version 2 of the License, or
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// (at your option) any later version.
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//
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// PRISM is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with PRISM; if not, write to the Free Software Foundation,
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// Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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//
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//==============================================================================
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package explicit;
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import java.io.BufferedReader;
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import java.io.File;
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import java.io.FileReader;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.BitSet;
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import java.util.HashSet;
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import java.util.Iterator;
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import java.util.List;
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import java.util.Map;
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import java.util.Map.Entry;
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import java.util.TreeMap;
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import parser.State;
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import prism.PrismException;
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import prism.PrismUtils;
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import explicit.rewards.MDPRewards;
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/**
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* Sparse matrix (non-mutable) explicit-state representation of an MDP.
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* This is much faster to access than e.g. MDPSimple and should also be more compact.
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* The catch is that you have to create the model all in one go and then can't modify it.
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*/
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public class MDPSparse extends MDPExplicit
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{
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// Sparse matrix storing transition function (Steps)
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/** Probabilities for each transition (array of size numTransitions) */
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protected double nonZeros[];
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/** Column (destination) indices for each transition (array of size numTransitions) */
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protected int cols[];
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/** Indices into nonZeros/cols giving the start of the transitions for each choice (distribution);
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* array is of size numDistrs+1 and last entry is always equal to numTransitions */
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protected int choiceStarts[];
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/** Indices into choiceStarts giving the start of the choices for each state;
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* array is of size numStates+1 and last entry is always equal to numDistrs */
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protected int rowStarts[];
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// Action labels
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/** Array of action labels for choices;
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* if null, there are no actions; otherwise, is an array of size numDistrs */
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protected Object actions[];
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// Other statistics
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protected int numDistrs;
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protected int numTransitions;
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protected int maxNumDistrs;
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// Constructors
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/**
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* Copy constructor (from MDPSimple).
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*/
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public MDPSparse(MDPSimple mdp)
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{
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this(mdp, false);
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}
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/**
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* Copy constructor (from MDPSimple). Optionally, transitions within choices
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* are sorted (by ascending order of column index).
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* @param mdp The MDP to copy
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* @param sort Whether or not to sort column indices
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*/
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public MDPSparse(MDPSimple mdp, boolean sort)
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{
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int i, j, k, n;
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TreeMap<Integer, Double> sorted = null;
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initialise(mdp.getNumStates());
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copyFrom(mdp);
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// Copy stats
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numDistrs = mdp.getNumChoices();
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numTransitions = mdp.getNumTransitions();
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maxNumDistrs = mdp.getMaxNumChoices();
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// Copy transition function
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if (sort) {
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sorted = new TreeMap<Integer, Double>();
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}
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nonZeros = new double[numTransitions];
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cols = new int[numTransitions];
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choiceStarts = new int[numDistrs + 1];
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rowStarts = new int[numStates + 1];
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actions = mdp.actions == null ? null : new Object[numDistrs];
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j = k = 0;
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for (i = 0; i < numStates; i++) {
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rowStarts[i] = j;
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if (mdp.actions != null) {
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n = mdp.getNumChoices(i);
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for (int l = 0; l < n; l++) {
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actions[j + l] = mdp.getAction(i, l);
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}
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}
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for (Distribution distr : mdp.trans.get(i)) {
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choiceStarts[j] = k;
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for (Map.Entry<Integer, Double> e : distr) {
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if (sort) {
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sorted.put(e.getKey(), e.getValue());
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} else {
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cols[k] = (Integer) e.getKey();
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nonZeros[k] = (Double) e.getValue();
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k++;
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}
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}
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if (sort) {
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for (Map.Entry<Integer, Double> e : sorted.entrySet()) {
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cols[k] = (Integer) e.getKey();
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nonZeros[k] = (Double) e.getValue();
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k++;
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}
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sorted.clear();
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}
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j++;
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}
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}
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choiceStarts[numDistrs] = numTransitions;
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rowStarts[numStates] = numDistrs;
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}
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/**
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* Copy constructor (from MDPSimple). Optionally, transitions within choices
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* are sorted (by ascending order of column index). Also, optionally, a state
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* index permutation can be provided, i.e. old state index i becomes index permut[i].
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* Note: a states list, if present, will not be permuted and should be set
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* separately afterwards if required.
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* @param mdp The MDP to copy
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* @param sort Whether or not to sort column indices
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* @param permut State space permutation
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*/
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public MDPSparse(MDPSimple mdp, boolean sort, int permut[])
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{
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int i, j, k, n;
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TreeMap<Integer, Double> sorted = null;
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int permutInv[];
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initialise(mdp.getNumStates());
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copyFrom(mdp, permut);
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// Copy stats
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numDistrs = mdp.getNumChoices();
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numTransitions = mdp.getNumTransitions();
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maxNumDistrs = mdp.getMaxNumChoices();
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// Compute the inverse of the permutation
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permutInv = new int[numStates];
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for (i = 0; i < numStates; i++) {
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permutInv[permut[i]] = i;
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}
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// Copy transition function
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if (sort) {
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sorted = new TreeMap<Integer, Double>();
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}
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nonZeros = new double[numTransitions];
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cols = new int[numTransitions];
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choiceStarts = new int[numDistrs + 1];
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rowStarts = new int[numStates + 1];
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actions = mdp.actions == null ? null : new Object[numDistrs];
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j = k = 0;
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for (i = 0; i < numStates; i++) {
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rowStarts[i] = j;
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if (mdp.actions != null) {
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n = mdp.getNumChoices(permutInv[i]);
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for (int l = 0; l < n; l++) {
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actions[j + l] = mdp.getAction(permutInv[i], l);
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}
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}
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for (Distribution distr : mdp.trans.get(permutInv[i])) {
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choiceStarts[j] = k;
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for (Map.Entry<Integer, Double> e : distr) {
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if (sort) {
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sorted.put(permut[e.getKey()], e.getValue());
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} else {
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cols[k] = (Integer) permut[e.getKey()];
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nonZeros[k] = (Double) e.getValue();
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k++;
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}
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}
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if (sort) {
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for (Map.Entry<Integer, Double> e : sorted.entrySet()) {
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cols[k] = (Integer) e.getKey();
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nonZeros[k] = (Double) e.getValue();
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k++;
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}
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sorted.clear();
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}
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j++;
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}
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}
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choiceStarts[numDistrs] = numTransitions;
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rowStarts[numStates] = numDistrs;
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}
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/**
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* Copy constructor for a (sub-)MDP from a given MDP.
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* The states and actions will be indexed as given by the order
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* of the lists {@code states} and {@code actions}.
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* @param mdp MDP to copy from
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* @param states States to copy
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* @param actions Actions to copy
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*/
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public MDPSparse(MDP mdp, List<Integer> states, List<List<Integer>> actions)
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{
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initialise(states.size());
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for (int in : mdp.getInitialStates()) {
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addInitialState(in);
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}
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for (int dl : mdp.getDeadlockStates()) {
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addDeadlockState(dl);
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}
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statesList = new ArrayList<State>();
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for (int s : states) {
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statesList.add(mdp.getStatesList().get(s));
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}
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numDistrs = 0;
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numTransitions = 0;
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maxNumDistrs = 0;
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for (int i = 0; i < states.size(); i++) {
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int s = states.get(i);
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final int numChoices = actions.get(s).size();
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numDistrs += numChoices;
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if (numChoices > maxNumDistrs) {
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maxNumDistrs = numChoices;
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}
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for (int a : actions.get(s)) {
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numTransitions += mdp.getNumTransitions(s, a);
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}
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}
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nonZeros = new double[numTransitions];
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cols = new int[numTransitions];
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choiceStarts = new int[numDistrs + 1];
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rowStarts = new int[numStates + 1];
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this.actions = new Object[numDistrs];
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int choiceIndex = 0;
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int colIndex = 0;
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int[] reverseStates = new int[mdp.getNumStates()];
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for (int i = 0; i < states.size(); i++) {
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reverseStates[states.get(i)] = i;
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}
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for (int i = 0; i < states.size(); i++) {
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int s = states.get(i);
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rowStarts[i] = choiceIndex;
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for (int a : actions.get(s)) {
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choiceStarts[choiceIndex] = colIndex;
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this.actions[choiceIndex] = mdp.getAction(s, a);
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choiceIndex++;
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Iterator<Entry<Integer, Double>> it = mdp.getTransitionsIterator(s, a);
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while (it.hasNext()) {
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Entry<Integer, Double> next = it.next();
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cols[colIndex] = reverseStates[next.getKey()];
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nonZeros[colIndex] = next.getValue();
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colIndex++;
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}
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}
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}
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choiceStarts[numDistrs] = numTransitions;
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rowStarts[numStates] = numDistrs;
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}
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// Mutators (other)
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@Override
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public void initialise(int numStates)
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{
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super.initialise(numStates);
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numDistrs = numTransitions = maxNumDistrs = 0;
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actions = null;
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}
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@Override
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public void buildFromPrismExplicit(String filename) throws PrismException
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{
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BufferedReader in;
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String s, ss[];
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int i, j, k, iLast, kLast, jCount, kCount, n, lineNum = 0;
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double prob;
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try {
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// Open file
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in = new BufferedReader(new FileReader(new File(filename)));
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// Parse first line to get num states
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s = in.readLine();
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lineNum = 1;
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if (s == null) {
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in.close();
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throw new PrismException("Missing first line of .tra file");
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}
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ss = s.split(" ");
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n = Integer.parseInt(ss[0]);
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// Initialise
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initialise(n);
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// Set initial state (assume 0)
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initialStates.add(0);
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// Store stats
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numDistrs = Integer.parseInt(ss[1]);
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numTransitions = Integer.parseInt(ss[2]);
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// Go though list of transitions in file
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iLast = -1;
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kLast = -1;
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jCount = kCount = 0;
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s = in.readLine();
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lineNum++;
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while (s != null) {
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s = s.trim();
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if (s.length() > 0) {
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ss = s.split(" ");
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i = Integer.parseInt(ss[0]);
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k = Integer.parseInt(ss[1]);
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j = Integer.parseInt(ss[2]);
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prob = Double.parseDouble(ss[3]);
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// For a new state
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if (i != iLast) {
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rowStarts[i] = kCount;
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}
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// For a new state or distribution
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if (i != iLast || k != kLast) {
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choiceStarts[kCount] = jCount;
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kCount++;
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}
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// Store transition
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cols[jCount] = j;
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nonZeros[jCount] = prob;
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// Prepare for next iter
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iLast = i;
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kLast = k;
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jCount++;
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}
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s = in.readLine();
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lineNum++;
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}
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choiceStarts[numDistrs] = numTransitions;
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rowStarts[numStates] = numDistrs;
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// Compute maxNumDistrs
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maxNumDistrs = 0;
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for (i = 0; i < numStates; i++) {
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maxNumDistrs = Math.max(maxNumDistrs, getNumChoices(i));
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}
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// Close file
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in.close();
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// Sanity checks
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if (kCount != numDistrs) {
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throw new PrismException("Choice count is wrong in tra file (" + kCount + "!=" + numTransitions + ")");
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}
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if (jCount != numTransitions) {
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throw new PrismException("Transition count is wrong in tra file (" + kCount + "!=" + numTransitions + ")");
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}
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} catch (IOException e) {
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System.exit(1);
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} catch (NumberFormatException e) {
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throw new PrismException("Problem in .tra file (line " + lineNum + ") for " + getModelType());
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}
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}
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// Accessors (for Model)
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@Override
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public int getNumTransitions()
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{
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return numTransitions;
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}
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@Override
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public Iterator<Integer> getSuccessorsIterator(final int s)
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{
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// Need to build set to avoid duplicates
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// So not necessarily the fastest method to access successors
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int start = choiceStarts[rowStarts[s]];
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int end = choiceStarts[rowStarts[s + 1]];
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HashSet<Integer> succs = new HashSet<Integer>();
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for (int i = start; i < end; i++) {
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succs.add(cols[i]);
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}
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return succs.iterator();
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}
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@Override
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public boolean isSuccessor(int s1, int s2)
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{
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int j, k, l1, h1, l2, h2;
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l1 = rowStarts[s1];
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h1 = rowStarts[s1 + 1];
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for (j = l1; j < h1; j++) {
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l2 = choiceStarts[j];
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h2 = choiceStarts[j + 1];
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for (k = l2; k < h2; k++) {
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// Assume that only non-zero entries are stored
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if (cols[k] == s2) {
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return true;
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}
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}
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}
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return false;
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}
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@Override
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public boolean allSuccessorsInSet(int s, BitSet set)
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{
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int j, k, l1, h1, l2, h2;
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l1 = rowStarts[s];
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h1 = rowStarts[s + 1];
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for (j = l1; j < h1; j++) {
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l2 = choiceStarts[j];
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h2 = choiceStarts[j + 1];
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for (k = l2; k < h2; k++) {
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// Assume that only non-zero entries are stored
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if (!set.get(cols[k])) {
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return false;
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}
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}
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}
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return true;
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}
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@Override
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public boolean someSuccessorsInSet(int s, BitSet set)
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{
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int j, k, l1, h1, l2, h2;
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l1 = rowStarts[s];
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h1 = rowStarts[s + 1];
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for (j = l1; j < h1; j++) {
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l2 = choiceStarts[j];
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h2 = choiceStarts[j + 1];
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for (k = l2; k < h2; k++) {
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// Assume that only non-zero entries are stored
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if (set.get(cols[k])) {
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return true;
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}
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}
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}
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return false;
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}
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@Override
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public int getNumChoices(int s)
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{
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return rowStarts[s + 1] - rowStarts[s];
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}
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@Override
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public void findDeadlocks(boolean fix) throws PrismException
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{
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for (int i = 0; i < numStates; i++) {
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// Note that no distributions is a deadlock, not an empty distribution
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if (getNumChoices(i) == 0) {
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addDeadlockState(i);
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if (fix) {
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throw new PrismException("Can't fix deadlocks in an MDPSparse since it cannot be modified after construction");
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}
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}
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}
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}
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@Override
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public void checkForDeadlocks(BitSet except) throws PrismException
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{
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for (int i = 0; i < numStates; i++) {
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if (getNumChoices(i) == 0 && (except == null || !except.get(i)))
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throw new PrismException("MDP has a deadlock in state " + i);
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}
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}
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// Accessors (for MDP)
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@Override
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public int getNumChoices()
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{
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return numDistrs;
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}
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@Override
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public int getMaxNumChoices()
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{
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return maxNumDistrs;
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}
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@Override
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public Object getAction(int s, int i)
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{
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return actions == null ? null : actions[rowStarts[s] + i];
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}
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@Override
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public int getNumTransitions(int s, int i)
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{
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return choiceStarts[rowStarts[s] + i + 1] - choiceStarts[rowStarts[s] + i];
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}
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@Override
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public Iterator<Entry<Integer, Double>> getTransitionsIterator(final int s, final int i)
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{
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return new Iterator<Entry<Integer, Double>>()
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{
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final int start = choiceStarts[rowStarts[s] + i];
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int col = start;
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final int end = choiceStarts[rowStarts[s] + i + 1];
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@Override
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public boolean hasNext()
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{
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|
return col < end;
|
|
}
|
|
|
|
@Override
|
|
public Entry<Integer, Double> next()
|
|
{
|
|
assert (col < end);
|
|
final int i = col;
|
|
col++;
|
|
return new Entry<Integer, Double>()
|
|
{
|
|
int key = cols[i];
|
|
double value = nonZeros[i];
|
|
|
|
@Override
|
|
public Integer getKey()
|
|
{
|
|
return key;
|
|
}
|
|
|
|
@Override
|
|
public Double getValue()
|
|
{
|
|
return value;
|
|
}
|
|
|
|
@Override
|
|
public Double setValue(Double arg0)
|
|
{
|
|
throw new UnsupportedOperationException();
|
|
}
|
|
};
|
|
}
|
|
|
|
@Override
|
|
public void remove()
|
|
{
|
|
throw new UnsupportedOperationException();
|
|
}
|
|
};
|
|
}
|
|
|
|
@Override
|
|
public boolean allSuccessorsInSet(int s, int i, BitSet set)
|
|
{
|
|
int j, k, l2, h2;
|
|
j = rowStarts[s] + i;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
// Assume that only non-zero entries are stored
|
|
if (!set.get(cols[k])) {
|
|
return false;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
@Override
|
|
public void prob0step(BitSet subset, BitSet u, boolean forall, BitSet result)
|
|
{
|
|
int i, j, k, l1, h1, l2, h2;
|
|
boolean b1, some;
|
|
for (i = 0; i < numStates; i++) {
|
|
if (subset.get(i)) {
|
|
b1 = forall; // there exists or for all
|
|
l1 = rowStarts[i];
|
|
h1 = rowStarts[i + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
some = false;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
// Assume that only non-zero entries are stored
|
|
if (u.get(cols[k])) {
|
|
some = true;
|
|
continue;
|
|
}
|
|
}
|
|
if (forall) {
|
|
if (!some) {
|
|
b1 = false;
|
|
continue;
|
|
}
|
|
} else {
|
|
if (some) {
|
|
b1 = true;
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
result.set(i, b1);
|
|
}
|
|
}
|
|
}
|
|
|
|
@Override
|
|
public void prob1Astep(BitSet subset, BitSet u, BitSet v, BitSet result)
|
|
{
|
|
int i, j, k, l1, h1, l2, h2;
|
|
boolean b1, some, all;
|
|
for (i = 0; i < numStates; i++) {
|
|
if (subset.get(i)) {
|
|
b1 = true;
|
|
l1 = rowStarts[i];
|
|
h1 = rowStarts[i + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
some = false;
|
|
all = true;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
// Assume that only non-zero entries are stored
|
|
if (!u.get(cols[k])) {
|
|
all = false;
|
|
continue; // Stop early (already know b1 will be set to false)
|
|
}
|
|
if (v.get(cols[k])) {
|
|
some = true;
|
|
}
|
|
}
|
|
if (!(some && all)) {
|
|
b1 = false;
|
|
continue;
|
|
}
|
|
}
|
|
result.set(i, b1);
|
|
}
|
|
}
|
|
}
|
|
|
|
@Override
|
|
public void prob1Estep(BitSet subset, BitSet u, BitSet v, BitSet result, int strat[])
|
|
{
|
|
int i, j, k, l1, h1, l2, h2, stratCh = -1;
|
|
boolean b1, some, all;
|
|
for (i = 0; i < numStates; i++) {
|
|
if (subset.get(i)) {
|
|
b1 = false;
|
|
l1 = rowStarts[i];
|
|
h1 = rowStarts[i + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
some = false;
|
|
all = true;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
// Assume that only non-zero entries are stored
|
|
if (!u.get(cols[k])) {
|
|
all = false;
|
|
continue; // Stop early (already know b1 will not be set to true)
|
|
}
|
|
if (v.get(cols[k])) {
|
|
some = true;
|
|
}
|
|
}
|
|
if (some && all) {
|
|
b1 = true;
|
|
// If strategy generation is enabled, remember optimal choice
|
|
if (strat != null)
|
|
stratCh = j - l1;
|
|
continue;
|
|
}
|
|
}
|
|
// If strategy generation is enabled, store optimal choice
|
|
// (only if this the first time we add the state to S^yes)
|
|
if (strat != null & b1 & !result.get(i)) {
|
|
strat[i] = stratCh;
|
|
}
|
|
// Store result
|
|
result.set(i, b1);
|
|
}
|
|
}
|
|
}
|
|
|
|
@Override
|
|
public void prob1step(BitSet subset, BitSet u, BitSet v, boolean forall, BitSet result)
|
|
{
|
|
int i, j, k, l1, h1, l2, h2;
|
|
boolean b1, some, all;
|
|
for (i = 0; i < numStates; i++) {
|
|
if (subset.get(i)) {
|
|
b1 = forall; // there exists or for all
|
|
l1 = rowStarts[i];
|
|
h1 = rowStarts[i + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
some = false;
|
|
all = true;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
// Assume that only non-zero entries are stored
|
|
if (v.get(cols[k])) {
|
|
some = true;
|
|
}
|
|
if (!u.get(cols[k])) {
|
|
all = false;
|
|
}
|
|
}
|
|
if (forall) {
|
|
if (!(some && all)) {
|
|
b1 = false;
|
|
continue;
|
|
}
|
|
} else {
|
|
if (some && all) {
|
|
b1 = true;
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
result.set(i, b1);
|
|
}
|
|
}
|
|
}
|
|
|
|
@Override
|
|
public double mvMultMinMaxSingle(int s, double vect[], boolean min, int strat[])
|
|
{
|
|
int j, k, l1, h1, l2, h2, stratCh = -1;
|
|
double d, minmax;
|
|
boolean first;
|
|
|
|
minmax = 0;
|
|
first = true;
|
|
l1 = rowStarts[s];
|
|
h1 = rowStarts[s + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
// Compute sum for this distribution
|
|
d = 0.0;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
}
|
|
// Check whether we have exceeded min/max so far
|
|
if (first || (min && d < minmax) || (!min && d > minmax)) {
|
|
minmax = d;
|
|
// If strategy generation is enabled, remember optimal choice
|
|
if (strat != null)
|
|
stratCh = j - l1;
|
|
}
|
|
first = false;
|
|
}
|
|
// If strategy generation is enabled, store optimal choice
|
|
if (strat != null & !first) {
|
|
// For max, only remember strictly better choices
|
|
if (min) {
|
|
strat[s] = stratCh;
|
|
} else if (strat[s] == -1 || minmax > vect[s]) {
|
|
strat[s] = stratCh;
|
|
}
|
|
}
|
|
|
|
return minmax;
|
|
}
|
|
|
|
@Override
|
|
public List<Integer> mvMultMinMaxSingleChoices(int s, double vect[], boolean min, double val)
|
|
{
|
|
int j, k, l1, h1, l2, h2;
|
|
double d;
|
|
List<Integer> res;
|
|
|
|
// Create data structures to store strategy
|
|
res = new ArrayList<Integer>();
|
|
// One row of matrix-vector operation
|
|
l1 = rowStarts[s];
|
|
h1 = rowStarts[s + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
// Compute sum for this distribution
|
|
d = 0.0;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
}
|
|
// Store strategy info if value matches
|
|
if (PrismUtils.doublesAreClose(val, d, 1e-12, false)) {
|
|
res.add(j - l1);
|
|
}
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
@Override
|
|
public double mvMultSingle(int s, int k, double vect[])
|
|
{
|
|
int j, l2, h2;
|
|
double d;
|
|
|
|
j = rowStarts[s] + k;
|
|
// Compute sum for this distribution
|
|
d = 0.0;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
}
|
|
|
|
return d;
|
|
}
|
|
|
|
@Override
|
|
public double mvMultJacMinMaxSingle(int s, double vect[], boolean min, int strat[])
|
|
{
|
|
int j, k, l1, h1, l2, h2, stratCh = -1;
|
|
double diag, d, minmax;
|
|
boolean first;
|
|
|
|
minmax = 0;
|
|
first = true;
|
|
l1 = rowStarts[s];
|
|
h1 = rowStarts[s + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
diag = 1.0;
|
|
// Compute sum for this distribution
|
|
d = 0.0;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
if (cols[k] != s) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
} else {
|
|
diag -= nonZeros[k];
|
|
}
|
|
}
|
|
if (diag > 0)
|
|
d /= diag;
|
|
// Check whether we have exceeded min/max so far
|
|
if (first || (min && d < minmax) || (!min && d > minmax)) {
|
|
minmax = d;
|
|
// If strategy generation is enabled, remember optimal choice
|
|
if (strat != null)
|
|
stratCh = j - l1;
|
|
}
|
|
first = false;
|
|
}
|
|
// If strategy generation is enabled, store optimal choice
|
|
if (strat != null & !first) {
|
|
// For max, only remember strictly better choices
|
|
if (min) {
|
|
strat[s] = stratCh;
|
|
} else if (strat[s] == -1 || minmax > vect[s]) {
|
|
strat[s] = stratCh;
|
|
}
|
|
}
|
|
|
|
return minmax;
|
|
}
|
|
|
|
@Override
|
|
public double mvMultJacSingle(int s, int k, double vect[])
|
|
{
|
|
int j, l2, h2;
|
|
double diag, d;
|
|
|
|
j = rowStarts[s] + k;
|
|
diag = 1.0;
|
|
// Compute sum for this distribution
|
|
d = 0.0;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
if (cols[k] != s) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
} else {
|
|
diag -= nonZeros[k];
|
|
}
|
|
}
|
|
if (diag > 0)
|
|
d /= diag;
|
|
|
|
return d;
|
|
}
|
|
|
|
@Override
|
|
public double mvMultRewMinMaxSingle(int s, double vect[], MDPRewards mdpRewards, boolean min, int strat[])
|
|
{
|
|
int j, k, l1, h1, l2, h2, stratCh = -1;
|
|
double d, minmax;
|
|
boolean first;
|
|
|
|
minmax = 0;
|
|
first = true;
|
|
l1 = rowStarts[s];
|
|
h1 = rowStarts[s + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
// Compute sum for this distribution
|
|
d = mdpRewards.getTransitionReward(s, j - l1);
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
}
|
|
// Check whether we have exceeded min/max so far
|
|
if (first || (min && d < minmax) || (!min && d > minmax)) {
|
|
minmax = d;
|
|
// If strategy generation is enabled, remember optimal choice
|
|
if (strat != null)
|
|
stratCh = j - l1;
|
|
}
|
|
first = false;
|
|
}
|
|
// If strategy generation is enabled, store optimal choice
|
|
if (strat != null & !first) {
|
|
// Only remember strictly better choices (required for max)
|
|
if (strat[s] == -1 || (min && minmax < vect[s]) || (!min && minmax > vect[s])) {
|
|
strat[s] = stratCh;
|
|
}
|
|
}
|
|
// Add state reward (doesn't affect min/max)
|
|
minmax += mdpRewards.getStateReward(s);
|
|
|
|
return minmax;
|
|
}
|
|
|
|
@Override
|
|
public double mvMultRewJacMinMaxSingle(int s, double vect[], MDPRewards mdpRewards, boolean min)
|
|
{
|
|
int j, k, l1, h1, l2, h2;
|
|
double diag, d, minmax;
|
|
boolean first;
|
|
|
|
minmax = 0;
|
|
first = true;
|
|
l1 = rowStarts[s];
|
|
h1 = rowStarts[s + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
diag = 1.0;
|
|
// Compute sum for this distribution
|
|
d = mdpRewards.getTransitionReward(s, j - l1);
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
if (cols[k] != s) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
} else {
|
|
diag -= nonZeros[k];
|
|
}
|
|
}
|
|
if (diag > 0)
|
|
d /= diag;
|
|
// Check whether we have exceeded min/max so far
|
|
if (first || (min && d < minmax) || (!min && d > minmax))
|
|
minmax = d;
|
|
first = false;
|
|
}
|
|
// Add state reward (doesn't affect min/max)
|
|
minmax += mdpRewards.getStateReward(s);
|
|
|
|
return minmax;
|
|
}
|
|
|
|
@Override
|
|
public List<Integer> mvMultRewMinMaxSingleChoices(int s, double vect[], MDPRewards mdpRewards, boolean min, double val)
|
|
{
|
|
int j, k, l1, h1, l2, h2;
|
|
double d;
|
|
List<Integer> res;
|
|
|
|
// Create data structures to store strategy
|
|
res = new ArrayList<Integer>();
|
|
// One row of matrix-vector operation
|
|
l1 = rowStarts[s];
|
|
h1 = rowStarts[s + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
// Compute sum for this distribution
|
|
d = mdpRewards.getTransitionReward(s, j);
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
d += nonZeros[k] * vect[cols[k]];
|
|
}
|
|
// Store strategy info if value matches
|
|
if (PrismUtils.doublesAreClose(val, d, 1e-12, false)) {
|
|
res.add(j - l1);
|
|
}
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
@Override
|
|
public void mvMultRight(int[] states, int[] strat, double[] source, double[] dest)
|
|
{
|
|
for (int s : states) {
|
|
int j, l2, h2;
|
|
int k = strat[s];
|
|
j = rowStarts[s] + k;
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
dest[cols[k]] += nonZeros[k] * source[s];
|
|
}
|
|
}
|
|
}
|
|
|
|
// Standard methods
|
|
|
|
@Override
|
|
public String toString()
|
|
{
|
|
int i, j, k, l1, h1, l2, h2;
|
|
Object o;
|
|
String s = "";
|
|
s = "[ ";
|
|
for (i = 0; i < numStates; i++) {
|
|
if (i > 0)
|
|
s += ", ";
|
|
s += i + ": [";
|
|
l1 = rowStarts[i];
|
|
h1 = rowStarts[i + 1];
|
|
for (j = l1; j < h1; j++) {
|
|
if (j > l1)
|
|
s += ",";
|
|
o = getAction(i, j - l1);
|
|
if (o != null)
|
|
s += o + ":";
|
|
s += "{";
|
|
l2 = choiceStarts[j];
|
|
h2 = choiceStarts[j + 1];
|
|
for (k = l2; k < h2; k++) {
|
|
if (k > l2)
|
|
s += ", ";
|
|
s += cols[k] + ":" + nonZeros[k];
|
|
}
|
|
s += "}";
|
|
}
|
|
s += "]";
|
|
}
|
|
s += " ]";
|
|
|
|
return s;
|
|
}
|
|
|
|
@Override
|
|
public boolean equals(Object o)
|
|
{
|
|
if (o == null || !(o instanceof MDPSparse))
|
|
return false;
|
|
MDPSparse mdp = (MDPSparse) o;
|
|
if (numStates != mdp.numStates)
|
|
return false;
|
|
if (!initialStates.equals(mdp.initialStates))
|
|
return false;
|
|
if (!Utils.doubleArraysAreEqual(nonZeros, mdp.nonZeros))
|
|
return false;
|
|
if (!Utils.intArraysAreEqual(cols, mdp.cols))
|
|
return false;
|
|
if (!Utils.intArraysAreEqual(choiceStarts, mdp.choiceStarts))
|
|
return false;
|
|
if (!Utils.intArraysAreEqual(rowStarts, mdp.rowStarts))
|
|
return false;
|
|
// TODO: compare actions (complicated: null = null,null,null,...)
|
|
return true;
|
|
}
|
|
}
|