ini_kmeans

Run a k-means algorithm to initialise clusters' allocation

internal

An implementation for the multi-task Gaussian processes with common
mean framework. Two main algorithms, called 'Magma' and 'MagmaClust',
are available to perform predictions for supervised learning problems, in
particular for time series or any functional/continuous data applications.
The corresponding articles has been respectively proposed by Arthur Leroy,
Pierre Latouche, Benjamin Guedj and Servane Gey (2022)
<doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche,
Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>.
Theses approaches leverage the learning of cluster-specific mean processes,
which are common across similar tasks, to provide enhanced prediction
performances (even far from data) at a linear computational cost (in
the number of tasks). 'MagmaClust' is a generalisation of 'Magma'
where the tasks are simultaneously clustered into groups, each being
associated to a specific mean process. User-oriented functions in the
package are decomposed into training, prediction and plotting
functions. Some basic features (classic kernels, training, prediction) of
standard Gaussian processes are also implemented.

Arthur Leroy

MagmaClustR

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

Pierre Latouche

Pierre Pathé

Alexia Grenouillat

Hugo Lelievre

ini_kmeans function

<dl><dt>data</dt>
<dd>A tibble containing common Input and associated Output values
to cluster.</dd>
<dt>k</dt>
<dd>A number of clusters assumed for running the kmeans algorithm.</dd>
<dt>nstart</dt>
<dd>A number, indicating how many re-starts of kmeans are set.</dd>
<dt>summary</dt>
<dd>A boolean, indicating whether we want an outcome summary</dd></dl>

Arguments

Run a k-means algorithm to initialise clusters' allocation — ini_kmeans

<dl>

<dt>data</dt>
<dd>A tibble containing common Input and associated Output values
to cluster.</dd>


<dt>k</dt>
<dd>A number of clusters assumed for running the kmeans algorithm.</dd>


<dt>nstart</dt>
<dd>A number, indicating how many re-starts of kmeans are set.</dd>


<dt>summary</dt>
<dd>A boolean, indicating whether we want an outcome summary</dd>

</dl>

ini_kmeans: Run a k-means algorithm to initialise clusters' allocation

Description

Usage

Value

Arguments

Examples