Segway 2.0 Application Note Datasets

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