WaveformGeneratorDrift

class capymoa.stream.generator.WaveformGeneratorDrift

Bases: Stream

An Waveform Generator Drift

>>> from capymoa.stream.generator import WaveformGeneratorDrift
...
>>> stream = WaveformGeneratorDrift()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.WaveformGeneratorDrift -d 10),
    x=ndarray(..., 21),
    y_index=1,
    y_label='class2'
)
>>> stream.next_instance().x
array([ 0.54985074,  2.17089406,  0.6142235 ,  3.18809944, -1.81293483,
       -0.11717947, -1.77198821, -0.14927903, -0.49779111, -1.33272998,
       -0.38139892, -1.49682927,  1.49204371,  2.65344343,  4.25116434,
        3.39751393,  2.90259886,  4.21403878,  1.98411715,  3.33956917,
        4.08153654])

__init__(

instance_random_seed: int = 1,

noise: bool = False,

number_of_attributes_with_drift: int = 10,

)

Construct a WaveformGeneratorDrift Generator .

Parameters:

• instance_random_seed – Seed for random generation of instances, defaults to 1

• noise – Adds noise for a total of 40 attributes

• number_of_attributes_with_drift – Number of attributes with drift

CLI_help() → str

Return cli help string for the stream.

get_moa_stream() → InstanceStream | None

Get the MOA stream object if it exists.

get_schema() → Schema

Return the schema of the stream.

has_more_instances() → bool

Return True if the stream have more instances to read.

next_instance() → LabeledInstance | RegressionInstance

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()

Restart the stream to read instances from the beginning.