Class definition for tsclustFamily
Formal S4 class with a family of functions used in tsclust().
The custom implementations also handle parallelization.
Since the distance function makes use of proxy, it also supports any extra proxy::dist()
parameters in ....
The prototype includes the cluster function for partitional methods, as well as a pass-through
preproc function. The initializer expects a control from tsclust-controls. See more below.
distThe function to calculate the distance matrices.
allcentThe function to calculate centroids on each iteration.
clusterThe function used to assign a series to a cluster.
preprocThe function used to preprocess the data (relevant for stats::predict()).
The family's dist() function works like proxy::dist() but supports parallelization and
optimized symmetric calculations. If you like, you can use the function more or less directly,
but provide a control argument when creating the family (see examples). However, bear in mind
the following considerations.
The second argument is called centroids (inconsistent with proxy::dist()).
If control$distmat is not NULL, the function will try to subset it.
If control$symmetric is TRUE, centroids is NULL, and there is no argument
pairwise that is TRUE, only half the distance matrix will be computed.
If the distance was registered in proxy::pr_DB with loop = TRUE, the bigmemory
package is available, and more than one parallel worker is detected, the computation will
be in parallel (using multi-processing with foreach::foreach()), otherwise it will be
sequential with proxy::dist().
The function always returns a crossdist matrix.
Note that all distances implemented as part of dtwclust have custom proxy loops that use multi-threading independently of foreach, so see their respective documentation to see what optimizations apply to each one.
For distances not included in dtwclust, the symmetric, parallel case mentioned above
makes chunks for parallel workers, but they are not perfectly balanced, so some workers might
finish before the others. You must load or attach the bigmemory package for this to
take place; see base::library() or base::loadNamespace().
The default partitional allcent() function is a closure with the implementations of the
included centroids. The ones for DBA(), shape_extraction() and sdtw_cent() can use
multi-process parallelization with foreach::foreach(). Its formal arguments are described in
the Centroid Calculation section from tsclust().
This class is meant to group together the relevant functions, but they are not linked with
each other automatically. In other words, neither dist nor allcent apply preproc. They
essentially don't know of each other's existence.
## Not run:
data(uciCT)
# See "GAK" documentation
fam <- new("tsclustFamily", dist = "gak")
# This is done with symmetric optimizations, regardless of control$symmetric
crossdist <- fam@dist(CharTraj, window.size = 18L)
# This is done without symmetric optimizations, regardless of control$symmetric
crossdist <- fam@dist(CharTraj, CharTraj, window.size = 18L)
# For non-dtwclust distances, symmetric optimizations only apply
# with an appropriate control AND a single data argument:
fam <- new("tsclustFamily", dist = "dtw",
control = partitional_control(symmetric = TRUE))
fam@dist(CharTraj[1L:5L])
# If you want the fuzzy family, use fuzzy = TRUE
ffam <- new("tsclustFamily", control = fuzzy_control(), fuzzy = TRUE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.