RandomRBFGeneratorDrift

class capymoa.stream.generator.RandomRBFGeneratorDrift

Bases: MOAStream

Generates Random RBF concepts functions.

__init__(

model_random_seed: int = 1,

instance_random_seed: int = 1,

number_of_classes: int = 2,

number_of_attributes: int = 10,

number_of_centroids: int = 50,

number_of_drifting_centroids: int = 2,

magnitude_of_change: float = 0.0,

)

Construct a RBF Generator Classification/Clustering datastream generator.

Parameters:

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

• number_of_classes – The number of classes of the generated instances, defaults to 2

• number_of_attributes – The number of attributes of the generated instances, defaults to 10

• number_of_drifting_centroids – The number of drifting attributes, defaults to 2

• magnitude_of_change – Magnitude of change in the generated instances, defaults to 0.0

• noise_percentage – Percentage of noise to add to the data, defaults to 10

• sigma_percentage – Percentage of sigma to add to the data, defaults to 10

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.