RandomRBFGenerator#

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

Bases: Stream

An Random RBF Generator

>>> from capymoa.stream.generator import RandomRBFGenerator
...
>>> stream = RandomRBFGenerator()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.RandomRBFGenerator ),
    x=ndarray(..., 10),
    y_index=1,
    y_label='class2'
)
>>> stream.next_instance().x
array([0.68807095, 0.62508298, 0.36161375, 0.29484898, 0.46067958,
       0.83491016, 0.69794979, 0.75702471, 0.79436834, 0.7605141 ])
__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,
)[source]#

Construct a Random RBF 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

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.