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.LinkedList;
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18 | import java.util.List;
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19 | import java.util.Random;
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20 |
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21 | import de.ugoe.cs.autoquest.eventcore.Event;
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22 | import de.ugoe.cs.autoquest.eventcore.StringEventType;
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23 |
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24 | /**
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25 | * <p>
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26 | * This class provides functions to create flattened first-order Markov models from
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27 | * {@link HighOrderMarkovModel}s and {@link PredictionByPartialMatch} models through state
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28 | * splitting.
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29 | * </p>
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30 | * <p>
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31 | * If possible, the normal high-order markov model should be used, as the Events may be broken by
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32 | * the flattener, as, e.g., the information {@link ReplayableEvent}s contain is not preserved.
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33 | * </p>
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34 | *
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35 | * @author Steffen Herbold
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36 | * @version 1.0
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37 | */
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38 | public class ModelFlattener {
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39 |
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40 | private static final String EVENT_SEPARATOR = "-=-";
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41 |
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42 | Trie<Event> firstOrderTrie;
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43 |
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44 | /**
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45 | * <p>
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46 | * Takes a {@link HighOrderMarkovModel} and returns a {@link FirstOrderMarkovModel} that
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47 | * conserves the high-order memory through state splitting.
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48 | * </p>
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49 | *
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50 | * @param model
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51 | * model that is flattened
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52 | * @return flattened first-order Markov model
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53 | */
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54 | public FirstOrderMarkovModel flattenHighOrderMarkovModel(HighOrderMarkovModel model) {
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55 | int markovOrder = model.trieOrder - 1;
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56 | FirstOrderMarkovModel firstOrderModel = new FirstOrderMarkovModel(new Random());
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57 | firstOrderModel.trieOrder = 2;
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58 | if (markovOrder == 1) {
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59 | firstOrderModel.trie = new Trie<Event>(model.trie);
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60 | firstOrderModel.trieOrder = 2;
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61 | }
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62 | else {
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63 | firstOrderTrie = new Trie<Event>();
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64 | TrieNode<Event> rootNode = model.trie.find(null);
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65 | generateFirstOrderTrie(rootNode, new LinkedList<String>(), markovOrder);
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66 | firstOrderTrie.updateKnownSymbols();
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67 | firstOrderModel.trie = firstOrderTrie;
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68 | }
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69 |
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70 | return firstOrderModel;
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71 | }
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72 |
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73 | /**
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74 | * <p>
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75 | * <b>This method is not available yet and always return null!</b>
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76 | * </p>
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77 | * <p>
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78 | * Takes a {@link PredictionByPartialMatch} model and returns a {@link FirstOrderMarkovModel}
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79 | * that conserves the high-order memory through state splitting.
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80 | * </p>
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81 | *
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82 | * @param model
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83 | * model that is flattened
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84 | * @return flattened first-order Markov model
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85 | */
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86 | public FirstOrderMarkovModel flattenPredictionByPartialMatch(PredictionByPartialMatch model) {
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87 | // TODO implement method
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88 | return null;
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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 | * Converts all nodes of the given depth into first-order node through state-splitting. For each
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94 | * node at the given depth a new node is created and appropriate transitions will be added.
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95 | * </p>
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96 | * <p>
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97 | * This method traverses through the tree recursively. If the recursion reaches the desired
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98 | * depth in the tree, node are added.
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99 | * </p>
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100 | *
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101 | * @param currentNode
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102 | * node whose sub-trie is currently traversed
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103 | * @param parentIDs
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104 | * ID strings of the ancestors of the currentNode
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105 | * @param depth
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106 | * depth to go - NOT the current depth.
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107 | */
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108 | private void generateFirstOrderTrie(TrieNode<Event> currentNode,
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109 | List<String> parentIDs,
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110 | int depth)
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111 | {
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112 | for (TrieNode<Event> child : currentNode.getChildren()) {
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113 | String currentId = child.getSymbol().getId();
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114 | if (depth > 1) {
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115 | List<String> childParentIDs = new LinkedList<String>(parentIDs);
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116 | childParentIDs.add(currentId);
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117 | generateFirstOrderTrie(child, childParentIDs, depth - 1);
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118 |
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119 | }
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120 | else {
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121 | StringBuilder firstOrderID = new StringBuilder();
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122 | for (String parentID : parentIDs) {
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123 | firstOrderID.append(parentID + EVENT_SEPARATOR);
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124 | }
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125 | firstOrderID.append(currentId);
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126 | TrieNode<Event> firstOrderNode =
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127 | firstOrderTrie.getChildCreate(new Event(new StringEventType(firstOrderID
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128 | .toString())));
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129 | firstOrderNode.setCount(child.getCount());
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130 | for (TrieNode<Event> transitionChild : child.getChildren()) {
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131 | StringBuilder transitionID = new StringBuilder();
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132 | for (String parentID : parentIDs.subList(1, parentIDs.size())) {
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133 | transitionID.append(parentID + EVENT_SEPARATOR);
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134 | }
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135 | transitionID.append(currentId + EVENT_SEPARATOR);
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136 | transitionID.append(transitionChild.getSymbol().getId());
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137 | TrieNode<Event> firstOrderTransitionChild =
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138 | firstOrderNode.getChildCreate(new Event(new StringEventType(transitionID
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139 | .toString())));
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140 | firstOrderTransitionChild.setCount(transitionChild.getCount());
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141 | }
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142 | }
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143 | }
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144 | }
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145 | }
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