1 | // Copyright 2012 Georg-August-Universität Göttingen, Germany
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2 | //
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3 | // Licensed under the Apache License, Version 2.0 (the "License");
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4 | // you may not use this file except in compliance with the License.
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5 | // You may obtain a copy of the License at
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6 | //
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7 | // http://www.apache.org/licenses/LICENSE-2.0
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8 | //
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9 | // Unless required by applicable law or agreed to in writing, software
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10 | // distributed under the License is distributed on an "AS IS" BASIS,
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11 | // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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12 | // See the License for the specific language governing permissions and
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13 | // limitations under the License.
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14 |
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15 | package de.ugoe.cs.autoquest.usageprofiles;
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16 |
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17 | import java.util.Collection;
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18 | import java.util.LinkedList;
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19 | import java.util.List;
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20 | import java.util.Random;
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21 |
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22 | import de.ugoe.cs.autoquest.eventcore.Event;
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23 |
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24 | /**
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25 | * <p>
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26 | * Implements Prediction by Partial Match (PPM) based on the following formula (LaTeX-style
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27 | * notation):<br>
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28 | * 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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29 | * |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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30 | * P_{MM^i} denotes the probability in an i-th order Markov model.
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31 | * </p>
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32 | *
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33 | * @author Steffen Herbold
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34 | *
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35 | */
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36 | public class PredictionByPartialMatch extends TrieBasedModel {
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37 |
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38 | /**
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39 | * <p>
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40 | * Id for object serialization.
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41 | * </p>
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42 | */
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43 | private static final long serialVersionUID = 1L;
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44 |
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45 | /**
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46 | * <p>
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47 | * Minimum order of the Markov model.
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48 | * </p>
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49 | */
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50 | protected int minOrder;
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51 |
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52 | /**
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53 | * <p>
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54 | * Probability to use a lower-order Markov model
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55 | * </p>
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56 | */
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57 | protected double probEscape;
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58 |
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59 | /**
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60 | * <p>
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61 | * Constructor. Creates a new PredictionByPartialMatch model with a given Markov order and a
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62 | * default escape probability of 0.1.
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63 | * </p>
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64 | *
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65 | * @param markovOrder
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66 | * Markov order of the model
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67 | * @param r
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68 | * random number generator used by probabilistic methods of the class
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69 | */
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70 | public PredictionByPartialMatch(int markovOrder, Random r) {
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71 | this(markovOrder, r, 0.1);
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72 | }
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73 |
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74 | /**
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75 | * <p>
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76 | * Creates a new PredictionByPartialMatch model with a given Markov order and escape
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77 | * probability.
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78 | * </p>
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79 | *
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80 | * @param markovOrder
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81 | * Markov order of the model
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82 | * @param r
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83 | * random number generator used by probabilistic methods of the class
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84 | * @param probEscape
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85 | * escape probability used by the model
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86 | */
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87 | public PredictionByPartialMatch(int markovOrder, Random r, double probEscape) {
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88 | this(markovOrder, 0, r, probEscape);
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89 | }
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90 |
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91 | /**
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92 | * <p>
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93 | * Creates a new PredictionByPartialMatch model with a given Markov order and escape
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94 | * probability.
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95 | * </p>
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96 | *
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97 | * @param markovOrder
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98 | * Markov order of the model
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99 | * @param minOrder
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100 | * minimum order of the model; if this order is reached, there is no escape
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101 | * @param r
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102 | * random number generator used by probabilistic methods of the class
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103 | * @param probEscape
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104 | * escape probability used by the model
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105 | * @throws IllegalArgumentException
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106 | * thrown if minOrder is less than 0 or greater than markovOrder or probEscape is
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107 | * not in the interval (0,1)
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108 | */
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109 | public PredictionByPartialMatch(int markovOrder, int minOrder, Random r, double probEscape) {
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110 | super(markovOrder, r);
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111 | if (minOrder < 0) {
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112 | throw new IllegalArgumentException("minOrder must be greather than or equal to 0");
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113 | }
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114 | if (minOrder > markovOrder) {
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115 | throw new IllegalArgumentException(
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116 | "minOrder must be less than or equal to markovOrder");
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117 | }
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118 | if (probEscape <= 0.0 || probEscape >= 1.0) {
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119 | throw new IllegalArgumentException("probEscape must be in the interval (0,1)");
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120 | }
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121 | this.probEscape = probEscape;
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122 | this.minOrder = minOrder;
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123 | }
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124 |
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125 | /**
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126 | * <p>
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127 | * Sets the escape probability of the model.
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128 | * </p>
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129 | *
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130 | * @param probEscape
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131 | * new escape probability
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132 | */
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133 | public void setProbEscape(double probEscape) {
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134 | this.probEscape = probEscape;
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135 | }
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136 |
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137 | /**
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138 | * <p>
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139 | * Returns the escape probability of the model.
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140 | * </p>
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141 | *
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142 | * @return escape probability of the model
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143 | */
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144 | public double getProbEscape() {
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145 | return probEscape;
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146 | }
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147 |
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148 | /**
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149 | * <p>
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150 | * Calculates the probability of the next event based on the formula:<br>
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151 | * 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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152 | * |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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153 | * P_{MM^i} denotes the probability in an i-th order Markov model.
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154 | * </p>
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155 | *
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156 | * @see de.ugoe.cs.autoquest.usageprofiles.IStochasticProcess#getProbability(java.util.List,
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157 | * de.ugoe.cs.autoquest.eventcore.Event)
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158 | */
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159 | @Override
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160 | public double getProbability(List<Event> context, Event symbol) {
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161 | if (context == null) {
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162 | throw new IllegalArgumentException("context must not be null");
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163 | }
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164 | if (symbol == null) {
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165 | throw new IllegalArgumentException("symbol must not be null");
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166 | }
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167 | double result = 0.0d;
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168 | double resultCurrentContex = 0.0d;
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169 | double resultShorterContex = 0.0d;
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170 |
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171 | List<Event> contextCopy;
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172 | if (context.size() >= trieOrder) {
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173 | contextCopy =
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174 | new LinkedList<Event>(context.subList(context.size() - trieOrder + 1,
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175 | context.size()));
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176 | }
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177 | else {
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178 | contextCopy = new LinkedList<Event>(context);
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179 | }
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180 |
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181 | Collection<Event> followers = trie.getFollowingSymbols(contextCopy); // \Sigma'
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182 | int sumCountFollowers = 0; // N(s\sigma')
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183 | for (Event follower : followers) {
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184 | sumCountFollowers += trie.getCount(contextCopy, follower);
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185 | }
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186 |
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187 | int countSymbol = trie.getCount(contextCopy, symbol); // N(s\sigma)
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188 | if (sumCountFollowers == 0) {
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189 | resultCurrentContex = 0.0;
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190 | }
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191 | else {
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192 | resultCurrentContex = (double) countSymbol / sumCountFollowers;
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193 | }
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194 | if (contextCopy.size() > minOrder) {
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195 | resultCurrentContex *= (1 - probEscape);
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196 | contextCopy.remove(0);
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197 | if (contextCopy.size() >= minOrder) {
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198 | double probSuffix = getProbability(contextCopy, symbol);
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199 |
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200 | if (followers.size() == 0) {
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201 | resultShorterContex = probSuffix;
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202 | }
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203 | else {
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204 | resultShorterContex = probEscape * probSuffix;
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205 | }
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206 | }
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207 | }
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208 | result = resultCurrentContex + resultShorterContex;
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209 |
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210 | return result;
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211 | }
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212 | }
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