Spiral Classification Task Generator

A TaskGenerator for the spirals task in mlbench::mlbench.spirals().

Dictionary

This TaskGenerator can be instantiated via the dictionary mlr_task_generators or with the associated sugar function tgen():

mlr_task_generators$get("spirals")
tgen("spirals")

Parameters

Id	Type	Default	Range
cycles	integer	1	\([1, \infty)\)
sd	numeric	0	\([0, \infty)\)

See also

• Dictionary of TaskGenerators: mlr_task_generators

• as.data.table(mlr_task_generators) for a table of available TaskGenerators in the running session (depending on the loaded packages).

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

Other TaskGenerator: TaskGenerator, mlr_task_generators, mlr_task_generators_2dnormals, mlr_task_generators_cassini, mlr_task_generators_circle, mlr_task_generators_friedman1, mlr_task_generators_moons, mlr_task_generators_peak, mlr_task_generators_simplex, mlr_task_generators_smiley, mlr_task_generators_xor

Super class

TaskGenerator -> TaskGeneratorSpirals

Methods

Public methods

• TaskGeneratorSpirals$new()

• TaskGeneratorSpirals$plot()

• TaskGeneratorSpirals$clone()

Inherited methods

• TaskGenerator$format()
• TaskGenerator$generate()
• TaskGenerator$print()

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TaskGeneratorSpirals$new()

Creates a new instance of this R6 class.

Usage

TaskGeneratorSpirals$new()

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TaskGeneratorSpirals$plot()

Creates a simple plot of generated data.

Usage

TaskGeneratorSpirals$plot(n = 200L, pch = 19L, ...)

Arguments

n

(integer(1))
Number of samples to draw for the plot. Default is 200.

pch

(integer(1))
Point char. Passed to plot().

...

(any)
Additional arguments passed to plot().

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TaskGeneratorSpirals$clone()

The objects of this class are cloneable with this method.

Usage

TaskGeneratorSpirals$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

generator = tgen("spirals")
plot(generator, n = 200)


task = generator$generate(200)
str(task$data())
#> Classes ‘data.table’ and 'data.frame':	200 obs. of  3 variables:
#>  $ y : Factor w/ 2 levels "A","B": 2 2 1 1 1 2 2 1 2 2 ...
#>  $ x1: num  -0.255 0.292 0.333 0.339 0.344 ...
#>  $ x2: num  -0.3506 0.7376 0 0.0213 0.0434 ...
#>  - attr(*, ".internal.selfref")=<pointer: 0x562039fedea0>