# `TinyBlobs`

### *class* capymoa.anomaly.datasets.TinyBlobs[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/datasets.py#L9)

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

A tiny stream for running unit tests for anomaly detection.

```python
import matplotlib.pyplot as plt
from capymoa.anomaly.datasets import TinyBlobs

stream = TinyBlobs()
x, y = stream._x_data, stream._y_data
plt.scatter(x[y == 0, 0], x[y == 0, 1], marker=".")
plt.scatter(x[y == 1, 0], x[y == 1, 1], marker="x")
```

![image](/plot_directive/api/modules/capymoa-anomaly-datasets-TinyBlobs-1.png)

#### \_\_init_\_(in_samples: [int](https://docs.python.org/3/builtins/functions.html#int) = 1000, out_samples: [int](https://docs.python.org/3/builtins/functions.html#int) = 100, features: [int](https://docs.python.org/3/builtins/functions.html#int) = 4, clusters: [int](https://docs.python.org/3/builtins/functions.html#int) = 3, seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 0, center_box: [tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[float](https://docs.python.org/3/builtins/functions.html#float), [float](https://docs.python.org/3/builtins/functions.html#float)] = (-10.0, 10.0), cluster_std: [float](https://docs.python.org/3/builtins/functions.html#float) | [list](https://docs.python.org/3/builtins/stdtypes.html#list)[[float](https://docs.python.org/3/builtins/functions.html#float)] = 1.0)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/datasets.py#L24)

Construct TinyBlobs.

* **Parameters:**
  * **in_samples** – In distribution samples.
  * **out_samples** – Out of distribution samples.
  * **features** – Number of features.
  * **clusters** – Number of clusters.
  * **seed** – Random seed for generating data.
  * **center_box** – Range features may take.
  * **cluster_std** – Variance of each blob center.

#### \_\_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.anomaly.datasets.TinyBlobs.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#L379)

Return a help message

#### 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#L402)

Get the MOA stream object if it exists.

#### get_schema()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L625)

Return the schema of the stream.

#### has_more_instances()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L602)

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#L605)

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#L628)

Restart the stream to read instances from the beginning.