extractFDADTWKernel

(<code>character(1)</code>)
How should the reference curves be obtained?
Method <code>random</code> draws <code>n.refs</code> random reference curves, while <code>all</code> uses all curves as references.
In order to use user-provided reference curves, this parameter is set to <code>fixed</code>.

ref.method

(<code>numeric(1)</code>)
Number of reference curves to be drawn (as a fraction of the number of observations in the training data).

n.refs

(<code>matrix</code>|<code>integer(n)</code>)
Integer vector of training set row indices or a matrix of reference curves with the same length as
the functionals in the training data. Overwrites <code>ref.method</code> and <code>n.refs</code>.

refs

(<code>numeric(1)</code>)
Size of the warping window size (as a proportion of query length).

dtwwindow

The function extracts features from functional data based on the DTW distance with a reference dataframe.

Interface to a large number of classification and
regression techniques, including machine-readable parameter
descriptions. There is also an experimental extension for survival
analysis, clustering and general, example-specific cost-sensitive
learning. Generic resampling, including cross-validation,
bootstrapping and subsampling.  Hyperparameter tuning with modern
optimization techniques, for single- and multi-objective problems.
Filter and wrapper methods for feature selection. Extension of basic
learners with additional operations common in machine learning, also
allowing for easy nested resampling.  Most operations can be
parallelized.

Patrick Schratz

Machine Learning in R

Bernd Bischl

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

Annette Spooner

extractFDADTWKernel function

<dl><dt>ref.method</dt>
<dd>(<code>character(1)</code>) 
How should the reference curves be obtained?
Method <code>random</code> draws <code>n.refs</code> random reference curves, while <code>all</code> uses all curves as references.
In order to use user-provided reference curves, this parameter is set to <code>fixed</code>.</dd>
<dt>n.refs</dt>
<dd>(<code>numeric(1)</code>) 
Number of reference curves to be drawn (as a fraction of the number of observations in the training data).</dd>
<dt>refs</dt>
<dd>(<code>matrix</code>|<code>integer(n)</code>) 
Integer vector of training set row indices or a matrix of reference curves with the same length as
the functionals in the training data. Overwrites <code>ref.method</code> and <code>n.refs</code>.</dd>
<dt>dtwwindow</dt>
<dd>(<code>numeric(1)</code>) 
Size of the warping window size (as a proportion of query length).</dd></dl>

Arguments

DTW kernel features — extractFDADTWKernel

<dl>

<dt>ref.method</dt>
<dd>(<code>character(1)</code>) 
How should the reference curves be obtained?
Method <code>random</code> draws <code>n.refs</code> random reference curves, while <code>all</code> uses all curves as references.
In order to use user-provided reference curves, this parameter is set to <code>fixed</code>.</dd>


<dt>n.refs</dt>
<dd>(<code>numeric(1)</code>) 
Number of reference curves to be drawn (as a fraction of the number of observations in the training data).</dd>


<dt>refs</dt>
<dd>(<code>matrix</code>|<code>integer(n)</code>) 
Integer vector of training set row indices or a matrix of reference curves with the same length as
the functionals in the training data. Overwrites <code>ref.method</code> and <code>n.refs</code>.</dd>


<dt>dtwwindow</dt>
<dd>(<code>numeric(1)</code>) 
Size of the warping window size (as a proportion of query length).</dd>

</dl>

DTW kernel features

extractFDADTWKernel: DTW kernel features

Description

Usage

Value

Arguments

See Also