| | 49 | * {{{deleteObject <objectname>}}} |
| | 50 | - Deletes an object from the storage. |
| | 51 | - Examples: |
| | 52 | - {{{deleteObject markovModel}}} |
| | 53 | - {{{deleteObject sequences}}} |
| | 54 | * {{{generateFixedLengthSequences <modelname> <sequencesName> <minlenght> <maxlength> {<all>} {<numSequences>}}}} |
| | 55 | - Generates sequences of a predefined length. This command first generates all possible sequences of the defined lengths. In case {{{<all>}}} is {{{true}}}, all generated sequences are then stored. In case {{{<all>}}} is false {{{<numSequences>}}} sequences will be randomly drawn from all the sequences, based on their probability according to {{{<modelname>}}}. The default value for {{{<all>}}} is true. |
| | 56 | - Examples: |
| | 57 | - {{{generateFixedLengthSequences markovModel generatedSequences 3 5}}} |
| | 58 | - {{{generateFixedLengthSequences markovModel generatedSequences 3 5 true}}} |
| | 59 | - {{{generateFixedLengthSequences markovModel generatedSequences 3 5 false 1000}}} |
| | 60 | * {{{generateRandomReplay <modelname> <filename> {<numSessions>}}}} |
| | 61 | - Generates a randomly generated replay from a previously learned usage model. The default number of sessions generate is 1. With this command, is it not possible to configure the length of the generated sequences. |
| | 62 | - Examples: |
| | 63 | - {{{generateRandomReplay markovModel replay.xml}}} |
| | 64 | - {{{generateRandomReplay markovModel replay.xml 5}}} |
| | 65 | * {{{generateRandomSequenecs <modelName> <sequencesName> <numSessions> <minLength> <maxLength> {<maxIter>}}}} |
| | 66 | - Generates sequences of a predefined length. This command randomly generates sequences from the usage profile. In case the length of a generated session has the desired length, it is added to the set of sequences. The maximum number of randomly generated sequences is defined by {{{<maxIter>}}}, which is by default {{{<numSessions>}}}*10. |
| | 67 | * {{{generateReplayfile <filename> <sequences>}}} |
| | 68 | - Generates a replay file from a set of sequences. |
| | 69 | - Examples: |
| | 70 | - {{{generateReplayfile d:/data/replay.xml sequences}}} |
| | 71 | * {{{listStates <modelName> {<sort>}}}} |
| | 72 | - Lists all states of a stochastic process. If {{{<sort>}}} is true, the states will be sorted alphabetically. |
| | 73 | - Examples: |
| | 74 | - {{{listStates markovModel}}} |
| | 75 | - {{{listStates markovModel true}}} |
| | 76 | * {{{load <filename>}}} |
| | 77 | - Loads a data container from a file. All currently stored data is discarded. |
| | 78 | - Examples: |
| | 79 | - {{{load d:/data.dat}}} |
| | 80 | * {{{loadObject <filename> <objectName>}}} |
| | 81 | - Loads an object from a file to the storage. |
| | 82 | - Examples: |
| | 83 | - {{{loadObject d:/markovModel.obj markovModel}}} |
| | 84 | * {{{modelSize <modelname>}}} |
| | 85 | - Prints information about the size of a usage profile. |
| | 86 | - Examples: |
| | 87 | - {{{modelSize markovModel>}}} |
| | 88 | * {{{printDot <modelname>}}} |
| | 89 | - Prints the [http://en.wikipedia.org/wiki/DOT_language Dot] graph representation of a model to the console. |
| | 90 | - Examples: |
| | 91 | - {{{printDot markovModel}}} |
| | 92 | * {{{printTrieDot <modelname>}}} |
| | 93 | - Prints the [http://en.wikipedia.org/wiki/DOT_language Dot] graph representation of a trie used by a model to the console. |
| | 94 | - Examples: |
| | 95 | - {{{printTrieDot markovModel}}} |
| | 96 | - {{{printTrieDot predictionByPartialMatch}}} |
| | 97 | * {{{save <filename>}}} |
| | 98 | - Saves all currently stored data to a file. |
| | 99 | - Examples: |
| | 100 | - {{{save d:/data.dat}}} |
| | 101 | * {{{saveObject <filename> <objectname>}}} |
| | 102 | - Saves a single object from the storage to a file. |
| | 103 | - Examples: |
| | 104 | - {{{saveObject d:/markovModel.obj markovModel}}} |
| | 105 | * {{{sequenceStatistics <sequencesName>}}} |
| | 106 | - Prints statistics about a collection of sequences. |
| | 107 | - Examples: |
| | 108 | - {{{sequenceStatistics sequences}}} |
| | 109 | * {{{showMarkovModel <modelname> {<showNodeNames>}}}} |
| | 110 | - Opens a window that displays a first-order Markov model as a directed graph. Per default, the node names are not shown, as the graph gets very ugly and has overlapping nodes if they are shown. |
| | 111 | - Examples: |
| | 112 | - {{{showMarkovModel markovModel}}} |
| | 113 | - {{{showMarkovModel markovModel true}}} |
| | 114 | * {{{showSequences <sequencesName>}}} |
| | 115 | - Opens a dialog that display a list of all sequences that are part of the current sequence set, including their sizes. The sequences can be edited using this dialog. |
| | 116 | * {{{showTimer <timerName>}}} |
| | 117 | - Prints how many milliseconds elapsed since the start of the time. |
| | 118 | - Examples: |
| | 119 | - {{{showTimer timer}}} |
| | 120 | * {{{showTrie <modelname>}}} |
| | 121 | - Opens a window that displays a trie underlying a stochastic process. |
| | 122 | - Examples: |
| | 123 | - {{{showTrie markovModel}}} |
| | 124 | - {{{showTrie predictionByPartialMatch}}} |
| | 125 | * {{{startTimer <timerName>}}} |
| | 126 | - Starts a timer. |
| | 127 | - Examples: |
| | 128 | - {{{startTimer timer}}} |
| | 129 | * {{{trainDFA <modelname> <sequencesName>}}} |
| | 130 | - Trains a Deterministic Finite Automaton (DFA) from a collection of sequences. |
| | 131 | - Examples: |
| | 132 | - {{{trainDFA dfa trainingSequences}}} |
| | 133 | * {{{trainMarkovModel <modelName> <sequencesName> {<order>}}}} |
| | 134 | - Trains a Markov model based from a collection of seqeuences. The default {{{<order>}}} of the model is 1. |
| | 135 | - Examples: |
| | 136 | - {{{trainMarkovModel markovModel trainingSequences}}} |
| | 137 | - {{{trainMarkovModel markovModel trainingSequences 3}}} |
| | 138 | * {{{trainPPM <modelName> <sequencesName> <probEscape> <maxOrder> {<minOrder>}}}} |
| | 139 | - Trains a Prediction by Partial Match (PPM) model based from a collection of sequences. The default {{{<minOrder>}}} is 0, i.e., random selection. |
| | 140 | - Examples: |
| | 141 | - {{{trainPPM PPMModel trainingSequences 0.01 3}}} |
| | 142 | - {{{trainPPM PPMModel trainingSequences 0.01 3 1}}} |
| | 143 | * {{{updateModel <modelname> <sequencesName>}}} |
| | 144 | - Updates a usage profile with a collection of sequences. This reinforces the model, i.e., it is not completly retrained, but the probabilities are merely updated using the new information. |
| | 145 | - Examples: |
| | 146 | - {{{updateModel markovModel newTrainingSequences}}} |
| | 147 | |
| | 148 | === Commands for handling data generated by [wiki:Software/userlog MFCUsageMonitor] === |
| 68 | | * {{{printDot <modelname>}}} |
| 69 | | - Prints the [http://en.wikipedia.org/wiki/DOT_language Dot] graph representation of a model to the console. |
| 70 | | - Examples: |
| 71 | | - {{{printDot markovModel}}} |
| 72 | | * {{{showMarkovModel <modelname> {<showNodeNames>}}}} |
| 73 | | - Opens a window that display a first-order Markov model as a directed graph. Per default, the node names are not shown, as the graph gets very ugly and has overlapping nodes if they are shown. |
| 74 | | - Examples: |
| 75 | | - {{{showMarkovModel markovModel}}} |
| 76 | | - {{{showMarkovModel markovModel true}}} |
| 77 | | * {{{trainMarkovModel <modelname> {<order>}}}} |
| 78 | | - Trains a Markov model based on the currently loaded sessions. The default order of the model is 1, and may be changed using the second parameter. |
| 79 | | - Examples: |
| 80 | | - {{{trainMarkovModel markovModel}}} |
| 81 | | - {{{trainMarkovModel markovModel 3}}} |
| 82 | | * {{{trainPPM <modelname> <order>}}} |
| 83 | | - Trains a Prediction by Partial Match (PPM) model based on the currently loaded sessions with the specified order. |
| 84 | | - Examples: |
| 85 | | - {{{trainPPM PPMModel 3}}} |
| | 164 | |
| | 165 | === Commands for handling data generated by [wiki:Software/PHPMonitor PHPMonitor] === |
| | 166 | * {{{loadWebSequences <filename> <sequencesName> {<serverUrl>} {<timeout> <minSessionLength> <maxSessionLength>} {<frequentUserThreshold>}}}} |
| | 167 | - Parses a logfile generated by [wiki:Software/PHPMonitor PHPMonitor] and creates a collection of sequences from it. The default {{{<timeout>}}} is 3600000 milliseconds, the default {{{<minSessionLength>}}} is 2, the default {{{<maxSessionLength>}}} is 100. In case a {{{<frequenceUserThreshold>}}} is defined, additional collection of sequences will be defined for all users that have more sessions than this threshold, that fulfill the session length requirements. |
| | 168 | - Examples: |
| | 169 | - {{{loadWebSequences d:/websessions.log web-sequences}}} |
| | 170 | - {{{loadWebSequences d:/websessions.log web-seqeunces http://www.swe.informatik.uni-goettingen.de}}} |
| | 171 | - {{{loadWebSequences d:/websessions.log web-seqeunces http://www.swe.informatik.uni-goettingen.de 7200000 4 50}}} |
| | 172 | - {{{loadWebSequences d:/websessions.log web-seqeunces http://www.swe.informatik.uni-goettingen.de 7200000 4 50 20}}} |
