[922] | 1 | package de.ugoe.cs.autoquest.usageprofiles;
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[518] | 2 |
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| 3 | import java.util.ArrayList;
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| 4 | import java.util.Collection;
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| 5 | import java.util.LinkedList;
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| 6 | import java.util.List;
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| 7 | import java.util.Random;
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[639] | 8 | import java.util.logging.Level;
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[518] | 9 |
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[922] | 10 | import de.ugoe.cs.autoquest.eventcore.Event;
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[518] | 11 | import de.ugoe.cs.util.StringTools;
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| 12 | import de.ugoe.cs.util.console.Console;
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| 13 | import edu.uci.ics.jung.graph.Graph;
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| 14 | import edu.uci.ics.jung.graph.SparseMultigraph;
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| 15 | import edu.uci.ics.jung.graph.util.EdgeType;
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| 16 |
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| 17 | import Jama.Matrix;
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| 18 |
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| 19 | /**
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| 20 | * <p>
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[559] | 21 | * Implements first-order Markov models. The implementation is based on {@link HighOrderMarkovModel}
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| 22 | * and restricts the Markov order to 1. In comparison to {@link HighOrderMarkovModel}, more
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| 23 | * calculations are possible with first-order models, e.g., the calculation of the entropy (
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[518] | 24 | * {@link #calcEntropy()}).
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| 25 | * </p>
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| 26 | *
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| 27 | * @author Steffen Herbold
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| 28 | * @version 1.0
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| 29 | */
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[559] | 30 | public class FirstOrderMarkovModel extends HighOrderMarkovModel implements IDotCompatible {
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[518] | 31 |
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[559] | 32 | /**
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| 33 | * <p>
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| 34 | * Id for object serialization.
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| 35 | * </p>
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| 36 | */
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| 37 | private static final long serialVersionUID = 1L;
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[518] | 38 |
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[559] | 39 | /**
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| 40 | * <p>
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| 41 | * Maximum number of iterations when calculating the stationary distribution as the limit of
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| 42 | * multiplying the transmission matrix with itself.
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| 43 | * </p>
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| 44 | */
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| 45 | final static int MAX_STATDIST_ITERATIONS = 1000;
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[518] | 46 |
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[559] | 47 | /**
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| 48 | * <p>
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| 49 | * Constructor. Creates a new FirstOrderMarkovModel.
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| 50 | * </p>
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| 51 | *
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| 52 | * @param r
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| 53 | * random number generator used by probabilistic methods of the class
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| 54 | */
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| 55 | public FirstOrderMarkovModel(Random r) {
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| 56 | super(1, r);
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| 57 | }
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[518] | 58 |
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[559] | 59 | /**
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| 60 | * <p>
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| 61 | * Generates the transmission matrix of the Markov model.
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| 62 | * </p>
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| 63 | *
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| 64 | * @return transmission matrix
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| 65 | */
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| 66 | private Matrix getTransmissionMatrix() {
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| 67 | List<Event> knownSymbols = new ArrayList<Event>(trie.getKnownSymbols());
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| 68 | int numStates = knownSymbols.size();
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| 69 | Matrix transmissionMatrix = new Matrix(numStates, numStates);
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[518] | 70 |
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[559] | 71 | for (int i = 0; i < numStates; i++) {
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| 72 | Event currentSymbol = knownSymbols.get(i);
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| 73 | List<Event> context = new ArrayList<Event>();
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| 74 | context.add(currentSymbol);
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| 75 | for (int j = 0; j < numStates; j++) {
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| 76 | Event follower = knownSymbols.get(j);
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| 77 | double prob = getProbability(context, follower);
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| 78 | transmissionMatrix.set(i, j, prob);
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| 79 | }
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| 80 | }
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| 81 | return transmissionMatrix;
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| 82 | }
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[518] | 83 |
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[559] | 84 | /**
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| 85 | * <p>
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| 86 | * Calculates the entropy of the model. To make it possible that the model is stationary, a
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| 87 | * transition from {@link Event#ENDEVENT} to {@link Event#STARTEVENT} is added.
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| 88 | * </p>
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| 89 | *
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| 90 | * @return entropy of the model or NaN if it could not be calculated
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| 91 | */
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| 92 | public double calcEntropy() {
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| 93 | Matrix transmissionMatrix = getTransmissionMatrix();
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| 94 | List<Event> knownSymbols = new ArrayList<Event>(trie.getKnownSymbols());
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| 95 | int numStates = knownSymbols.size();
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[518] | 96 |
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[559] | 97 | List<Integer> startIndexList = new LinkedList<Integer>();
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| 98 | List<Integer> endIndexList = new LinkedList<Integer>();
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| 99 | for (int i = 0; i < knownSymbols.size(); i++) {
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| 100 | String id = knownSymbols.get(i).getId();
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| 101 | if (id.equals(Event.STARTEVENT.getId()) ||
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| 102 | id.contains(Event.STARTEVENT.getId() + "-=-"))
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| 103 | {
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| 104 | startIndexList.add(i);
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| 105 | }
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| 106 | if (id.equals(Event.ENDEVENT.getId()) || id.contains("-=-" + Event.ENDEVENT.getId())) {
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| 107 | endIndexList.add(i);
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| 108 | }
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| 109 | }
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[518] | 110 |
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[559] | 111 | if (startIndexList.isEmpty()) {
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| 112 | Console
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| 113 | .printerrln("Error calculating entropy. Initial state of markov chain not found.");
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| 114 | return Double.NaN;
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| 115 | }
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| 116 | if (endIndexList.isEmpty()) {
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| 117 | Console.printerrln("Error calculating entropy. End state of markov chain not found.");
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| 118 | return Double.NaN;
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| 119 | }
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| 120 | for (Integer i : endIndexList) {
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| 121 | for (Integer j : startIndexList) {
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| 122 | transmissionMatrix.set(i, j, 1);
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| 123 | }
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| 124 | }
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[518] | 125 |
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[559] | 126 | // Calculate stationary distribution by raising the power of the
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| 127 | // transmission matrix.
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| 128 | // The rank of the matrix should fall to 1 and each two should be the
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| 129 | // vector of the stationory distribution.
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| 130 | int iter = 0;
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| 131 | int rank = transmissionMatrix.rank();
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| 132 | Matrix stationaryMatrix = (Matrix) transmissionMatrix.clone();
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| 133 | while (iter < MAX_STATDIST_ITERATIONS && rank > 1) {
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| 134 | stationaryMatrix = stationaryMatrix.times(stationaryMatrix);
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| 135 | rank = stationaryMatrix.rank();
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| 136 | iter++;
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| 137 | }
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[518] | 138 |
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[559] | 139 | if (rank != 1) {
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[639] | 140 | Console.traceln(Level.FINE, "rank: " + rank);
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[559] | 141 | Console.printerrln("Unable to calculate stationary distribution.");
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| 142 | return Double.NaN;
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| 143 | }
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[518] | 144 |
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[559] | 145 | double entropy = 0.0;
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| 146 | for (int i = 0; i < numStates; i++) {
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| 147 | for (int j = 0; j < numStates; j++) {
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| 148 | if (transmissionMatrix.get(i, j) != 0 && transmissionMatrix.get(i, j) != 1) {
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| 149 | double tmp = stationaryMatrix.get(0, i);
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| 150 | tmp *= transmissionMatrix.get(i, j);
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| 151 | tmp *= Math.log(transmissionMatrix.get(i, j)) / Math.log(2);
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| 152 | entropy -= tmp;
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| 153 | }
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| 154 | }
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| 155 | }
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| 156 | return entropy;
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| 157 | }
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[518] | 158 |
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[559] | 159 | /**
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| 160 | * <p>
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| 161 | * The dot represenation of {@link FirstOrderMarkovModel}s is its graph representation with the
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| 162 | * states as nodes and directed edges weighted with transition probabilities.
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| 163 | * </p>
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| 164 | *
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[922] | 165 | * @see de.ugoe.cs.autoquest.usageprofiles.IDotCompatible#getDotRepresentation()
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[559] | 166 | */
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| 167 | @Override
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| 168 | public String getDotRepresentation() {
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| 169 | StringBuilder stringBuilder = new StringBuilder();
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| 170 | stringBuilder.append("digraph model {" + StringTools.ENDLINE);
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[518] | 171 |
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[559] | 172 | List<Event> knownSymbols = new ArrayList<Event>(trie.getKnownSymbols());
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| 173 | for (Event symbol : knownSymbols) {
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| 174 | final String thisSaneId = symbol.getId().replace("\"", "\\\"").replaceAll("[\r\n]", "");
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| 175 | stringBuilder.append(" " + knownSymbols.indexOf(symbol) + " [label=\"" + thisSaneId +
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| 176 | "\"];" + StringTools.ENDLINE);
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| 177 | List<Event> context = new ArrayList<Event>();
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| 178 | context.add(symbol);
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| 179 | Collection<Event> followers = trie.getFollowingSymbols(context);
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| 180 | for (Event follower : followers) {
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| 181 | stringBuilder.append(" " + knownSymbols.indexOf(symbol) + " -> " +
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| 182 | knownSymbols.indexOf(follower) + " ");
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| 183 | stringBuilder.append("[label=\"" + getProbability(context, follower) + "\"];" +
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| 184 | StringTools.ENDLINE);
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| 185 | }
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| 186 | }
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| 187 | stringBuilder.append('}' + StringTools.ENDLINE);
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| 188 | return stringBuilder.toString();
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| 189 | }
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[518] | 190 |
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[559] | 191 | /**
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| 192 | * <p>
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| 193 | * Returns a {@link Graph} representation of the model with the states as nodes and directed
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| 194 | * edges weighted with transition probabilities.
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| 195 | * </p>
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| 196 | *
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| 197 | * @return {@link Graph} of the model
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| 198 | */
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| 199 | public Graph<String, MarkovEdge> getGraph() {
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| 200 | Graph<String, MarkovEdge> graph = new SparseMultigraph<String, MarkovEdge>();
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[518] | 201 |
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[559] | 202 | List<Event> knownSymbols = new ArrayList<Event>(trie.getKnownSymbols());
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[518] | 203 |
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[559] | 204 | for (Event symbol : knownSymbols) {
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| 205 | String from = symbol.getId();
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| 206 | List<Event> context = new ArrayList<Event>();
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| 207 | context.add(symbol);
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[518] | 208 |
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[559] | 209 | Collection<Event> followers = trie.getFollowingSymbols(context);
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[518] | 210 |
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[559] | 211 | for (Event follower : followers) {
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| 212 | String to = follower.getId();
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| 213 | MarkovEdge prob = new MarkovEdge(getProbability(context, follower));
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| 214 | graph.addEdge(prob, from, to, EdgeType.DIRECTED);
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| 215 | }
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| 216 | }
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| 217 | return graph;
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| 218 | }
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[518] | 219 |
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[559] | 220 | /**
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| 221 | * Inner class used for the {@link Graph} representation of the model.
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| 222 | *
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| 223 | * @author Steffen Herbold
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| 224 | * @version 1.0
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| 225 | */
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| 226 | static public class MarkovEdge {
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| 227 | /**
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| 228 | * <p>
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| 229 | * Weight of the edge, i.e., its transition probability.
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| 230 | * </p>
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| 231 | */
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| 232 | double weight;
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[518] | 233 |
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[559] | 234 | /**
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| 235 | * <p>
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| 236 | * Constructor. Creates a new MarkovEdge.
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| 237 | * </p>
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| 238 | *
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| 239 | * @param weight
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| 240 | * weight of the edge, i.e., its transition probability
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| 241 | */
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| 242 | MarkovEdge(double weight) {
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| 243 | this.weight = weight;
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| 244 | }
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[518] | 245 |
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[559] | 246 | /**
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| 247 | * <p>
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| 248 | * The weight of the edge as {@link String}.
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| 249 | * </p>
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| 250 | */
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| 251 | public String toString() {
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| 252 | return "" + weight;
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| 253 | }
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| 254 | }
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| 255 |
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[518] | 256 | }
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