# `AgrawalGenerator`

### *class* capymoa.stream.generator.AgrawalGenerator[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/generator.py#L579)

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

An Agrawal Generator

```pycon
>>> from capymoa.stream.generator import AgrawalGenerator
...
>>> stream = AgrawalGenerator()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.AgrawalGenerator ),
    x=[1.105e+05 0.000e+00 5.400e+01 3.000e+00 1.400e+01 4.000e+00 1.350e+05
        3.000e+01 3.547e+05],
    y_index=1,
    y_label='groupB'
)
>>> stream.next_instance().x
array([1.40893779e+05, 0.00000000e+00, 4.40000000e+01, 4.00000000e+00,
       1.90000000e+01, 7.00000000e+00, 1.35000000e+05, 2.00000000e+00,
       3.95015339e+05])
```

#### \_\_init_\_(instance_random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1, classification_function: [int](https://docs.python.org/3/builtins/functions.html#int) = 1, peturbation: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.05, balance_classes: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/generator.py#L600)

Construct an Agrawal Generator

* **Parameters:**
  * **instance_random_seed** – Seed for random generation of instances.
  * **classification_function** – Classification function used, as defined in the original paper.
  * **peturbation** – The amount of peturbation (noise) introduced to numeric values
  * **balance** – Balance the number of instances of each class.

#### \_\_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.AgrawalGenerator.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.