Structured PCA Model
The matrix of data
The number of samples
Simensionality of observations
Covariance function hyperparameters
covariance matrix generated from the covariance function and hyperparameters
Partial derivatived of K w.r.t each of the hyperparameters
Function giving partial derivatives of covariance function w.r.t hyperparameters
MAP estimate of mu
MAP estimate of W
MAP estimate of sigSq
The posterior mean of each latent variable
The posterior variance around each latent variable
log of the approximate evidence
sequence of posteriors obtained in fitting. Un-normalised if sparse
Partial derivatives of evidence w.r.t hyperparameters
The precision matrix for the gaussian approximation to the posterior around each w
the maximisation object returned in hyperparameter tuning
Whether theta has been optimised to convergence (if false, beta has probably converged, not theta)
betaHist history of betas in optimisation
History of logEvidence and logPosteror; used to assess convergence
whether this is a sparse stpca model
The sparsity hyperparameter in a sparse model.
crossvalidate(nFolds = 3, nThreads = 1)
Perform k-fold cross-validation (possibly multithreaded) to determine the held out log likelihood of examples. This could be used as an alternative to computing the approximate evidence in model selection.
Decode points in the latent space into observations
Encode a matrix of observations into latent distributions.
Setter for 'b'
Sets beta to a new value, recomputes K, KD and H.
Sets the locations, and recomputes K. Be careful with this method. Does not update theta
Setter for 'sparse'
Sets X, updates mu. Does not opdate anything else so it is recommended to call update() or something. Be careful with this method.
simulate(n = 1, Wknown = TRUE)
Simulates new synthetic data from a fitted model.
update(tune.maxit = 10, tune.tol = 1e-05, EM.maxit = 500, EM.bftol = 1e-05, ...)
The major method for performing inference. This iterates between updating theta (using update_theta) and updating beta (using update_beta).
Optimises the approximate evidence with respect to the hyperparameters.
update_theta(maxit = 500, bftol = 1e-05)
Finds the MAP theta using EM.
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