mlr_task_generators_spirals {mlr3}

Spiral Classification Task Generator

Description

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)

Super class

mlr3::TaskGenerator -> TaskGeneratorSpirals

Methods

Public methods

• TaskGeneratorSpirals$new()

• TaskGeneratorSpirals$plot()

• TaskGeneratorSpirals$clone()

Inherited methods

• mlr3::TaskGenerator$format()
• mlr3::TaskGenerator$generate()
• mlr3::TaskGenerator$print()

Method new()

Creates a new instance of this R6 class.

Usage

TaskGeneratorSpirals$new()

Method 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().

Method clone()

The objects of this class are cloneable with this method.

Usage

TaskGeneratorSpirals$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_simplex, mlr_task_generators_smiley, mlr_task_generators_xor

Examples

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

task = generator$generate(200)
str(task$data())