[922] | 1 | package de.ugoe.cs.autoquest.usageprofiles;
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[518] | 2 |
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| 3 | import java.util.Collection;
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| 4 | import java.util.LinkedList;
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| 5 | import java.util.List;
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| 6 | import java.util.Random;
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| 7 |
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[922] | 8 | import de.ugoe.cs.autoquest.eventcore.Event;
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[518] | 9 |
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| 10 | /**
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| 11 | * <p>
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[559] | 12 | * Implements Prediction by Partial Match (PPM) based on the following formula (LaTeX-style
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| 13 | * notation):<br>
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[518] | 14 | * P_{PPM}(X_n|X_{n-1},...,X_{n-k}) = \sum_{i=k}^min escape^{k-i} P_{MM^i}(X_n
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| 15 | * |X_{n-1},...,X_{n-i})(1-escape)+escape^(k-min)P(X_n|X_{n-i},... ,X_{n-min})<br>
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| 16 | * P_{MM^i} denotes the probability in an i-th order Markov model.
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| 17 | * </p>
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| 18 | *
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| 19 | * @author Steffen Herbold
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| 20 | *
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| 21 | */
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| 22 | public class PredictionByPartialMatch extends TrieBasedModel {
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| 23 |
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[559] | 24 | /**
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| 25 | * <p>
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| 26 | * Id for object serialization.
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| 27 | * </p>
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| 28 | */
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| 29 | private static final long serialVersionUID = 1L;
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[518] | 30 |
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[559] | 31 | /**
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| 32 | * <p>
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| 33 | * Minimum order of the Markov model.
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| 34 | * </p>
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| 35 | */
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| 36 | protected int minOrder;
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[518] | 37 |
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[559] | 38 | /**
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| 39 | * <p>
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| 40 | * Probability to use a lower-order Markov model
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| 41 | * </p>
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| 42 | */
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| 43 | protected double probEscape;
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[518] | 44 |
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[559] | 45 | /**
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| 46 | * <p>
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| 47 | * Constructor. Creates a new PredictionByPartialMatch model with a given Markov order and a
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| 48 | * default escape probability of 0.1.
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| 49 | * </p>
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| 50 | *
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| 51 | * @param markovOrder
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| 52 | * Markov order of the model
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| 53 | * @param r
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| 54 | * random number generator used by probabilistic methods of the class
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| 55 | */
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| 56 | public PredictionByPartialMatch(int markovOrder, Random r) {
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| 57 | this(markovOrder, r, 0.1);
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| 58 | }
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[518] | 59 |
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[559] | 60 | /**
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| 61 | * <p>
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| 62 | * Creates a new PredictionByPartialMatch model with a given Markov order and escape
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| 63 | * probability.
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| 64 | * </p>
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| 65 | *
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| 66 | * @param markovOrder
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| 67 | * Markov order of the model
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| 68 | * @param r
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| 69 | * random number generator used by probabilistic methods of the class
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| 70 | * @param probEscape
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| 71 | * escape probability used by the model
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| 72 | */
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| 73 | public PredictionByPartialMatch(int markovOrder, Random r, double probEscape) {
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| 74 | this(markovOrder, 0, r, probEscape);
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| 75 | }
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[518] | 76 |
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[559] | 77 | /**
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| 78 | * <p>
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| 79 | * Creates a new PredictionByPartialMatch model with a given Markov order and escape
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| 80 | * probability.
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| 81 | * </p>
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| 82 | *
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| 83 | * @param markovOrder
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| 84 | * Markov order of the model
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| 85 | * @param minOrder
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| 86 | * minimum order of the model; if this order is reached, there is no escape
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| 87 | * @param r
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| 88 | * random number generator used by probabilistic methods of the class
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| 89 | * @param probEscape
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| 90 | * escape probability used by the model
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[766] | 91 | * @throws IllegalArgumentException
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[559] | 92 | * thrown if minOrder is less than 0 or greater than markovOrder or probEscape is
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| 93 | * not in the interval (0,1)
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| 94 | */
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| 95 | public PredictionByPartialMatch(int markovOrder, int minOrder, Random r, double probEscape) {
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| 96 | super(markovOrder, r);
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| 97 | if (minOrder < 0) {
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[766] | 98 | throw new IllegalArgumentException("minOrder must be greather than or equal to 0");
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[559] | 99 | }
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| 100 | if (minOrder > markovOrder) {
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[766] | 101 | throw new IllegalArgumentException(
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[559] | 102 | "minOrder must be less than or equal to markovOrder");
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| 103 | }
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| 104 | if (probEscape <= 0.0 || probEscape >= 1.0) {
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[766] | 105 | throw new IllegalArgumentException("probEscape must be in the interval (0,1)");
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[559] | 106 | }
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| 107 | this.probEscape = probEscape;
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| 108 | this.minOrder = minOrder;
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| 109 | }
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[518] | 110 |
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[559] | 111 | /**
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| 112 | * <p>
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| 113 | * Sets the escape probability of the model.
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| 114 | * </p>
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| 115 | *
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| 116 | * @param probEscape
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| 117 | * new escape probability
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| 118 | */
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| 119 | public void setProbEscape(double probEscape) {
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| 120 | this.probEscape = probEscape;
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| 121 | }
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[518] | 122 |
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[559] | 123 | /**
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| 124 | * <p>
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| 125 | * Returns the escape probability of the model.
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| 126 | * </p>
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| 127 | *
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| 128 | * @return escape probability of the model
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| 129 | */
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| 130 | public double getProbEscape() {
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| 131 | return probEscape;
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| 132 | }
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[518] | 133 |
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[559] | 134 | /**
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| 135 | * <p>
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| 136 | * Calculates the probability of the next event based on the formula:<br>
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| 137 | * P_{PPM}(X_n|X_{n-1},...,X_{n-k}) = \sum_{i=k}^min escape^{k-i} P_{MM^i}(X_n
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| 138 | * |X_{n-1},...,X_{n-i})(1-escape)+escape^(k-min)P(X_n|X_{n-i},... ,X_{n-min})<br>
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| 139 | * P_{MM^i} denotes the probability in an i-th order Markov model.
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| 140 | * </p>
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| 141 | *
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[922] | 142 | * @see de.ugoe.cs.autoquest.usageprofiles.IStochasticProcess#getProbability(java.util.List,
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| 143 | * de.ugoe.cs.autoquest.eventcore.Event)
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[559] | 144 | */
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| 145 | @Override
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| 146 | public double getProbability(List<Event> context, Event symbol) {
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| 147 | if (context == null) {
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[766] | 148 | throw new IllegalArgumentException("context must not be null");
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[559] | 149 | }
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| 150 | if (symbol == null) {
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[766] | 151 | throw new IllegalArgumentException("symbol must not be null");
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[559] | 152 | }
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| 153 | double result = 0.0d;
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| 154 | double resultCurrentContex = 0.0d;
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| 155 | double resultShorterContex = 0.0d;
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[518] | 156 |
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[559] | 157 | List<Event> contextCopy;
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| 158 | if (context.size() >= trieOrder) {
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| 159 | contextCopy =
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| 160 | new LinkedList<Event>(context.subList(context.size() - trieOrder + 1,
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| 161 | context.size()));
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| 162 | }
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| 163 | else {
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| 164 | contextCopy = new LinkedList<Event>(context);
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| 165 | }
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[518] | 166 |
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[559] | 167 | Collection<Event> followers = trie.getFollowingSymbols(contextCopy); // \Sigma'
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| 168 | int sumCountFollowers = 0; // N(s\sigma')
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| 169 | for (Event follower : followers) {
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| 170 | sumCountFollowers += trie.getCount(contextCopy, follower);
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| 171 | }
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[518] | 172 |
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[559] | 173 | int countSymbol = trie.getCount(contextCopy, symbol); // N(s\sigma)
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| 174 | if (sumCountFollowers == 0) {
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| 175 | resultCurrentContex = 0.0;
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| 176 | }
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| 177 | else {
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| 178 | resultCurrentContex = (double) countSymbol / sumCountFollowers;
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| 179 | }
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| 180 | if (contextCopy.size() > minOrder) {
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| 181 | resultCurrentContex *= (1 - probEscape);
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| 182 | contextCopy.remove(0);
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| 183 | if (contextCopy.size() >= minOrder) {
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| 184 | double probSuffix = getProbability(contextCopy, symbol);
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[518] | 185 |
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[559] | 186 | if (followers.size() == 0) {
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| 187 | resultShorterContex = probSuffix;
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| 188 | }
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| 189 | else {
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| 190 | resultShorterContex = probEscape * probSuffix;
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| 191 | }
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| 192 | }
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| 193 | }
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| 194 | result = resultCurrentContex + resultShorterContex;
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[518] | 195 |
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[559] | 196 | return result;
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| 197 | }
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[518] | 198 | }
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