# `LEDGeneratorDrift`

### *class* capymoa.stream.generator.LEDGeneratorDrift[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/generator.py#L710)

Bases: [`MOAStream`](capymoa.stream.MOAStream.md#capymoa.stream.MOAStream)

An LED Generator Drift

```pycon
>>> from capymoa.stream.generator import LEDGeneratorDrift
...
>>> stream = LEDGeneratorDrift()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.LEDGeneratorDrift -d 7),
    x=[1. 1. 0. ... 0. 0. 0.],
    y_index=5,
    y_label='5'
)
>>> stream.next_instance().x
array([0., 0., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0., 1., 0., 1.,
       0., 0., 1., 1., 0., 1., 1.])
```

#### \_\_init_\_(instance_random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1, noise_percentage: [int](https://docs.python.org/3/builtins/functions.html#int) = 10, reduce_data: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False, number_of_attributes_with_drift: [int](https://docs.python.org/3/builtins/functions.html#int) = 7)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/generator.py#L729)

Construct an LED Generator Drift

* **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
  * **number_of_attributes_with_drift** – Number of attributes with drift

#### \_\_iter_\_() → [Self](https://docs.python.org/3/library/typing.html#typing.Self)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L356)

Get an iterator over the stream.

This will NOT restart the stream if it has already been iterated over.
Please use the [`restart()`](#capymoa.stream.generator.LEDGeneratorDrift.restart) method to restart the stream.

* **Yield:**
  An iterator over the stream.

#### \_\_next_\_() → \_AnyInstance[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L366)

Get the next instance in the stream.

* **Returns:**
  The next instance in the stream.

#### cli_help() → [str](https://docs.python.org/3/builtins/stdtypes.html#str)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L466)

Return cli help string for the stream.

#### get_moa_stream() → InstanceStream | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L502)

Get the MOA stream object if it exists.

#### get_schema() → [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L498)

Return the schema of the stream.

#### has_more_instances() → [bool](https://docs.python.org/3/builtins/functions.html#bool)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L474)

Return True if the stream have more instances to read.

#### next_instance() → \_AnyInstance[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L478)

Return the next instance in the stream.

* **Raises:**
  [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#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]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L506)

Restart the stream to read instances from the beginning.