SineGenerator

class capymoa.stream.generator.SineGenerator

Bases: MOAStream

An SineGenerator

>>> from capymoa.stream.generator import SineGenerator
...
>>> stream = SineGenerator()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.SineGenerator ),
    x=[0.731 0.41  0.208 0.333],
    y_index=0,
    y_label='positive'
)
>>> stream.next_instance().x
array([0.96775591, 0.00611718, 0.9637048 , 0.93986539])

__init__(

instance_random_seed: int = 1,

classification_function: int = 1,

suppress_irrelevant_attributes: bool = False,

balance_classes: bool = False,

)

Construct a SineGenerator .

Parameters:

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

• classification_function – Classification function used, as defined in the original paper.

• suppress_irrelevant_attributes – Reduce the data to only contain 2 relevant numeric attributes

• balance – Balance the number of instances of each class.

CLI_help() → str

Return cli help string for the stream.

__iter__() → Iterator[_AnyInstance]

Get an iterator over the stream.

This will NOT restart the stream if it has already been iterated over. Please use the restart() method to restart the stream.

Yield:

An iterator over the stream.

__next__() → _AnyInstance

Get the next instance in the stream.

Returns:

The next instance in 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() → _AnyInstance

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.