Segway 2.0 Application Note Datasets

<p>Learned parameters and resulting segmentation&nbsp;corresponding to the analyses shown in the Segway 2.0 application note.</p> <p>Directory structure:</p> <p><strong>GMM</strong> (datasets corresponding to the mixture of Gaussians analysis)</p> <ul> <li>1-component <ul> <li>traindir/ <ul> <li>log/ (training log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> <li>identifydir/ <ul> <li>segway.bed.gz (segmentation)</li> </ul> </li> </ul> </li> <li>3-component <ul> <li>traindir/ <ul> <li>log/ (training&nbsp;log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> <li>identifydir/ <ul> <li>segway.bed.gz (segmentation)</li> </ul> </li> </ul> </li> </ul> <p><strong>minibatch-fixed</strong> (datasets corresponding to the minibatch learning analysis)</p> <ul> <li>fixed/ <ul> <li>traindir/ <ul> <li>log/ (training and validation log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> </ul> </li> <li>minibatch/ <ul> <li>traindir/ <ul> <li>log/ (training and validation log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> </ul> </li> </ul> <p><strong>TSS_prediction</strong> (datasets corresponding to the TSS prediction analysis) (where k=component number=1-5, n=random start number=1-10)</p> <ul> <li>outputs_[date]_k/ <ul> <li>traindir/ <ul> <li>log/ (training and validation log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> <li>identifydir_n/ <ul> <li>segway.bed.gz (segmentation)</li> </ul> </li> </ul> </li> </ul>