rsatoolbox.rdm.transform module¶
transforms, which can be applied to RDMs
- rsatoolbox.rdm.transform.geodesic_transform(rdms: RDMs) RDMs[source]¶
applies a geodesic transform to the dissimilarities and returns a new RDMs object.
Reference: Lin, B., & Kriegeskorte, N. (2023). The Topology and Geometry of Neural Representations. arXiv preprint arXiv:2309.11028.
- rsatoolbox.rdm.transform.geotopological_transform(rdms: RDMs, low: float, up: float) RDMs[source]¶
applies a geo-topological transform to the dissimilarities and returns a new RDMs object.
Reference: Lin, B., & Kriegeskorte, N. (2023). The Topology and Geometry of Neural Representations. arXiv preprint arXiv:2309.11028.
- rsatoolbox.rdm.transform.minmax_transform(rdms: RDMs) RDMs[source]¶
applies a minmax transform to the dissimilarities and returns a new RDMs object.
- rsatoolbox.rdm.transform.positive_transform(rdms: RDMs) RDMs[source]¶
sets all negative entries in an RDM to zero and returns a new RDMs
- rsatoolbox.rdm.transform.rank_transform(rdms: RDMs, method: str = 'average') RDMs[source]¶
applies a rank_transform and generates a new RDMs object This assigns a rank to each dissimilarity estimate in the RDM, deals with rank ties and saves ranks as new dissimilarity estimates. As an effect, all non-diagonal entries of the RDM will range from 1 to (n_dim²-n_dim)/2, if the RDM has the dimensions n_dim x n_dim.