LEDGenerator#

class capymoa.stream.generator.LEDGenerator[source]#

Bases: Stream

An LED Generator

>>> from capymoa.stream.generator import LEDGenerator
...
>>> stream = LEDGenerator()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.LEDGenerator ),
    x=ndarray(..., 24),
    y_index=5,
    y_label='5'
)
>>> stream.next_instance().x
array([1., 1., 1., 0., 1., 1., 0., 0., 0., 1., 0., 1., 0., 1., 1., 0., 1.,
       0., 0., 1., 1., 0., 1., 1.])
__init__(
instance_random_seed: int = 1,
noise_percentage: int = 10,
reduce_data: bool = False,
)[source]#

Construct an LED Generator

Parameters:

  • instance_random_seed – Seed for random generation of instances.

  • noise_percentage – Percentage of noise to add to the data

  • reduce_data – Reduce the data to only contain 7 relevant binary attributes

CLI_help() → str[source]#

Return cli help string for the stream.

get_moa_stream() → InstanceStream | None[source]#

Get the MOA stream object if it exists.

get_schema() → Schema[source]#

Return the schema of the stream.

has_more_instances() → bool[source]#

Return True if the stream have more instances to read.

next_instance() → LabeledInstance | RegressionInstance[source]#

Return the next instance in the stream.

Raises:

ValueError – If the machine learning task is neither a regression nor a classification task.

Returns:

A labeled instances or a regression depending on the schema.

restart()[source]#

Restart the stream to read instances from the beginning.