1 | package de.ugoe.cs.eventbench.models;
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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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8 | import de.ugoe.cs.eventbench.data.Event;
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9 |
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10 | /**
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11 | * <p>
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12 | * Implements Prediction by Partial Match (PPM) based on the following formula
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13 | * (LaTeX-style notation):<br>
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14 | * P_{PPM}(X_n|X_{n-1},...,X_{n-k}) = \sum_{i=k}^1 escape^{i-1}
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15 | * P_{MM^i}(X_n|X_{n-1},...,X_{n-i})(1-escape)<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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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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30 |
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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 | private int minOrder;
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37 |
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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 | double probEscape;
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44 |
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45 | /**
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46 | * <p>
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47 | * Constructor. Creates a new PredictionByPartialMatch model with a given
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48 | * Markov order and a 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
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55 | * class
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56 | */
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57 | public PredictionByPartialMatch(int markovOrder, Random r) {
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58 | this(markovOrder, r, 0.1);
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59 | }
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60 |
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61 | /**
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62 | * <p>
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63 | * Creates a new PredictionByPartialMatch model with a given Markov order
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64 | * and escape probability.
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65 | * </p>
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66 | *
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67 | * @param markovOrder
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68 | * Markov order of the model
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69 | * @param r
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70 | * random number generator used by probabilistic methods of the
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71 | * class
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72 | * @param probEscape
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73 | * escape probability used by the model
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74 | */
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75 | public PredictionByPartialMatch(int markovOrder, Random r, double probEscape) {
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76 | this(markovOrder, 0, r, probEscape);
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77 | }
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78 |
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79 | /**
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80 | * <p>
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81 | * Creates a new PredictionByPartialMatch model with a given Markov order
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82 | * and escape probability.
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83 | * </p>
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84 | *
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85 | * @param markovOrder
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86 | * Markov order of the model
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87 | * @param minOrder
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88 | * minimum order of the model; if this order is reached, there is
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89 | * no escape
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90 | * @param r
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91 | * random number generator used by probabilistic methods of the
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92 | * class
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93 | * @param probEscape
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94 | * escape probability used by the model
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95 | */
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96 | public PredictionByPartialMatch(int markovOrder, int minOrder, Random r,
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97 | double probEscape) {
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98 | super(markovOrder, r);
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99 | this.probEscape = probEscape;
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100 | this.minOrder = minOrder;
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101 | }
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102 |
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103 | /**
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104 | * <p>
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105 | * Sets the escape probability of the model.
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106 | * </p>
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107 | *
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108 | * @param probEscape
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109 | * new escape probability
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110 | */
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111 | public void setProbEscape(double probEscape) {
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112 | this.probEscape = probEscape;
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113 | }
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114 |
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115 | /**
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116 | * <p>
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117 | * Returns the escape probability of the model.
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118 | * </p>
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119 | *
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120 | * @return escape probability of the model
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121 | */
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122 | public double getProbEscape() {
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123 | return probEscape;
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124 | }
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125 |
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126 | /**
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127 | * <p>
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128 | * Calculates the probability of the next event based on the formula:<br>
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129 | * P_{PPM}(X_n|X_{n-1},...,X_{n-k}) = \sum_{i=k}^min escape^{k-i}
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130 | * P_{MM^i}(X_n
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131 | * |X_{n-1},...,X_{n-i})(1-escape)+escape^(k-min)P(X_n|X_{n-i},...
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132 | * ,X_{n-min})<br>
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133 | * P_{MM^i} denotes the probability in an i-th order Markov model.
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134 | * </p>
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135 | */
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136 | @Override
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137 | public double getProbability(List<? extends Event<?>> context,
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138 | Event<?> symbol) {
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139 | double result = 0.0d;
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140 | double resultCurrentContex = 0.0d;
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141 | double resultShorterContex = 0.0d;
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142 |
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143 | List<Event<?>> contextCopy;
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144 | if (context.size() >= trieOrder) {
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145 | contextCopy = new LinkedList<Event<?>>(context.subList(
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146 | context.size() - trieOrder + 1, context.size()));
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147 | } else {
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148 | contextCopy = new LinkedList<Event<?>>(context);
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149 | }
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150 |
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151 | Collection<Event<?>> followers = trie.getFollowingSymbols(contextCopy); // \Sigma'
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152 | int sumCountFollowers = 0; // N(s\sigma')
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153 | for (Event<?> follower : followers) {
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154 | sumCountFollowers += trie.getCount(contextCopy, follower);
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155 | }
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156 |
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157 | int countSymbol = trie.getCount(contextCopy, symbol); // N(s\sigma)
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158 | if (sumCountFollowers == 0) {
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159 | resultCurrentContex = 0.0;
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160 | } else {
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161 | resultCurrentContex = (double) countSymbol / sumCountFollowers;
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162 | }
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163 | if (contextCopy.size() != minOrder) {
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164 | resultCurrentContex *= (1 - probEscape);
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165 | contextCopy.remove(0);
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166 | if (contextCopy.size() >= minOrder) {
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167 | double probSuffix = getProbability(contextCopy, symbol);
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168 |
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169 | if (followers.size() == 0) {
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170 | resultShorterContex = probSuffix;
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171 | } else {
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172 | resultShorterContex = probEscape * probSuffix;
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173 | }
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174 | }
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175 | }
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176 | result = resultCurrentContex + resultShorterContex;
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177 |
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178 | return result;
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179 | }
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180 | }
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