Clustering and Prediction using Multi-Task Gaussian Processes with Common Mean

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Documentation for package ‘MagmaClustR’ version 1.0.0

• DESCRIPTION file.

Help Pages

chol_inv_jitter	Inverse a matrix using an adaptive jitter term
data_allocate_cluster	Allocate training data into the most probable cluster
dmnorm	Compute the Multivariate Gaussian likelihood
draw	Draw a number
elbo_clust_multi_GP	Evidence Lower Bound for a mixture of GPs
elbo_clust_multi_GP_common_hp_i	Penalised elbo for multiple individual GPs with common HPs
elbo_GP_mod_common_hp_k	Penalised elbo for multiple mean GPs with common HPs
elbo_monitoring_VEM	Evidence Lower Bound maximised in MagmaClust
e_step	E-Step of the EM algorithm
gr_clust_multi_GP	Gradient of the elbo for a mixture of GPs
gr_clust_multi_GP_common_hp_i	Gradient of the penalised elbo for multiple individual GPs with common HPs
gr_GP	Gradient of the logLikelihood of a Gaussian Process
gr_GP_mod	Gradient of the modified logLikelihood for GPs in Magma
gr_GP_mod_common_hp	Gradient of the modified logLikelihood with common HPs for GPs in Magma
gr_GP_mod_common_hp_k	Gradient of the penalised elbo for multiple mean GPs with common HPs
gr_sum_logL_GP_clust	Gradient of the mixture of Gaussian likelihoods
hp	Generate random hyper-parameters
hyperposterior	Compute the hyper-posterior distribution in Magma
hyperposterior_clust	Compute the hyper-posterior distribution for each cluster in MagmaClust
ini_kmeans	Run a k-means algoithm to initialise clusters' allocation
ini_mixture	Mixture initialisation with kmeans
kern_to_cov	Create covariance matrix from a kernel
kern_to_inv	Create inverse of a covariance matrix from a kernel
lin_kernel	Linear Kernel
list_kern_to_cov	Compute a covariance matrix for multiple individuals
list_kern_to_inv	Compute an inverse covariance matrix for multiple individuals
logL_GP	Log-Likelihood function of a Gaussian Process
logL_GP_mod	Modified log-Likelihood function for GPs
logL_GP_mod_common_hp	Modified log-Likelihood function with common HPs for GPs
logL_monitoring	Log-Likelihood for monitoring the EM algorithm in Magma
MagmaClustR	MagmaClustR : Clustering and Prediction using Multi-Task Gaussian Processes
m_step	M-Step of the EM algorithm
perio_kernel	Periodic Kernel
plot_db	Plot smoothed curves of raw data
plot_gif	Create a GIF of Magma or GP predictions
plot_gp	Plot Magma or GP predictions
plot_magmaclust	Plot MagmaClust predictions
pred_gif	Magma prediction for ploting GIFs
pred_gp	Gaussian Process prediction
pred_magma	Magma prediction
pred_magmaclust	MagmaClust prediction
proba_max_cluster	Indicates the most probable cluster
rq_kernel	Rational Quadratic Kernel
sample_gp	Display Realisation From Posterior GP
select_nb_cluster	Select the optimal number of clusters
se_kernel	Squared Exponential Kernel
simu_db	Simulate a dataset tailored for MagmaClustR
simu_indiv_se	Simulate a batch a data
sum_logL_GP_clust	Compute a mixture of Gaussian log-likelihoods
train_gp	Learning hyper-parameters of a Gaussian Process
train_gp_clust	Prediction in MagmaClust: learning new HPs and mixture probabilities
train_magma	Training Magma with an EM algorithm
train_magmaclust	Training MagmaClust with a Variational EM algorithm
update_mixture	Update the mixture probabilities for each individual and each cluster
ve_step	E-Step of the VEM algorithm
vm_step	V-Step of the VEM algorithm