mGPLVM

Getting Started

  • Install

Examples

  • (Bayesian) GPFA
  • Supervised learning and decoding with manifold GPLVMs
  • Applying mGPLVM to synthetic neural data generated from circular latents
  • Fitting mGPLVM to data from the fly central complex

API

  • mgplvm package
mGPLVM
  • »
  • Overview: module code

All modules for which code is available

  • mgplvm.base
  • mgplvm.crossval.construct_model
  • mgplvm.crossval.crossval
  • mgplvm.crossval.crossval_bgpfa
  • mgplvm.crossval.train_model
  • mgplvm.dists
  • mgplvm.fast_utils.linear_cg
  • mgplvm.fast_utils.toeplitz
  • mgplvm.inducing_variables
  • mgplvm.kernels.kernel
  • mgplvm.kernels.linear
  • mgplvm.kernels.stationary
  • mgplvm.likelihoods
  • mgplvm.lpriors.common
  • mgplvm.lpriors.euclidean
  • mgplvm.lpriors.torus
  • mgplvm.manifolds.base
  • mgplvm.manifolds.euclid
  • mgplvm.manifolds.quaternion
  • mgplvm.manifolds.s3
  • mgplvm.manifolds.so3
  • mgplvm.manifolds.torus
  • mgplvm.models.bfa
  • mgplvm.models.gp_base
  • mgplvm.models.gplvm
  • mgplvm.models.lgplvm
  • mgplvm.models.svgp
  • mgplvm.models.svgplvm
  • mgplvm.optimisers.data
  • mgplvm.optimisers.stopping_criterions
  • mgplvm.optimisers.svgp
  • mgplvm.rdist.GP_circ
  • mgplvm.rdist.GP_diag
  • mgplvm.rdist.GPbase
  • mgplvm.rdist.common
  • mgplvm.rdist.relie
  • mgplvm.syndata.gen_data
  • mgplvm.utils

© Copyright 2021, Ta-Chu Kao and Kris Jensen.

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