# InhPMM

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Jump to navigationJump to searchby Ralf Eggeling, André Gohr, Pierre-Yves Bourguignon, Edgar Wingender, and Ivo Grosse

## Paper

The paper Inhomogeneous Parsimonious Markov Models has been published at ECMLPKDD 2013.

## Runnable JAR

The application learns an InhPMM (or InhVOMM) from a training data set, returns two representations of the model, and computes predictions on a test data set (if provided). The input files are expected to contain sequences over DNA alphabet of identical length as plain text. The sequence length in training and test files should be identical. Run by calling:

`java -jar InhPMM.jar modelClass order logKappa ess dataTrain dataTest`

The arguments have the following semantics:

name | comment | type |

modelClass | Determines the model class. Either parsimonious Markov model (type PMM) or variable order Markov model (type VOMM). | String |

order | The maximal depth of the (parsimonious) context trees. | Integer |

logKappa | The logarithm of the structure prior hyperparameter kappa. | Double |

ess | The equivalent sample size of the parameter prior. | Double (positive) |

dataTrain | Path to training data set. | String |

dataTest | Optional. Path to the test data set. If omitted, no predictive probabilities are computed. | String |

The application produces the following output:

- model.xml: Contains an xml-representation of the learned model for subsequent reloading the model into Jstacs.
- model.dot: Contains a graphViz-representation of the learned model. Convert to pdf by calling
`dot -Tpdf -o model.pdf model.dot`

(local graphViz installation required). - prediction.txt: Contains a list of log predictive probabilities of all sequences in the test data set given the learned model. Is only created if dataTest is set.

## Download

- Runnable Jar: Examplary command line application.
- Sources: Building requires Jstacs 2.1.