# `NCM`

### *class* capymoa.ocl.strategy.NCM[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ncm.py#L26)

Bases: [`BatchClassifier`](capymoa.base.BatchClassifier.md#capymoa.base.BatchClassifier)

Nearest Class Mean.

Nearest Class Mean (NCM) <sup>[1](#f0)</sup> is a prototype based classifier that uses the mean
of each class as a prototype. It calculates the distance from each input to
the class means and assigns class with the closest mean as the predicted
class.

```pycon
>>> from capymoa.ocl.strategy import NCM
>>> from capymoa.ocl.datasets import TinySplitMNIST
>>> from capymoa.ocl.evaluation import ocl_train_eval_loop
>>> scenario = TinySplitMNIST()
>>> learner = NCM(scenario.schema)
>>> results = ocl_train_eval_loop(
...     learner,
...     scenario.train_loaders(32),
...     scenario.test_loaders(32),
... )
>>> print(f"{results.accuracy_final*100:.1f}%")
71.5%
```

* <a id='f0'>**[1]**</a> [Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017, July). iCaRL: Incremental Classifier and Representation Learning. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).](https://arxiv.org/abs/1611.07725)

#### \_\_init_\_(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), pre_processor: [Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module) | [None](https://docs.python.org/3/builtins/constants.html#None) = None, num_features: [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None) = None, device: [device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device) | [str](https://docs.python.org/3/builtins/stdtypes.html#str) | [None](https://docs.python.org/3/builtins/constants.html#None) = None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ncm.py#L55)

Initialize a NCM classifier head.

* **Parameters:**
  * **schema** – Describes the shape and type of the data.
  * **pre_processor** – A pre-processing module to apply to the input
    data, defaults to an identity module.
  * **num_features** – Number of features once pre-processed, defaults to
    the number of attributes in the schema.
  * **device** – Device to run the model on, defaults to CPU.

#### batch_predict(x: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) → [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_batch_classifier.py#L115)

Predict the labels for a batch of instances.

* **Parameters:**
  **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.NCM.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Predicted batch of [`y_dtype`](#capymoa.ocl.strategy.NCM.y_dtype) valued labels
  `(batch_size,)`.

#### batch_predict_proba(x: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) → [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ncm.py#L96)

Predict the probabilities of the classes for a batch of instances.

* **Parameters:**
  **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.NCM.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Batch of [`x_dtype`](#capymoa.ocl.strategy.NCM.x_dtype) valued predicted probabilities
  `(batch_size, num_classes)`.

#### batch_train(x: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor), y: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ncm.py#L83)

Train with a batch of instances.

* **Parameters:**
  * **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.NCM.x_dtype) valued feature vectors
    `(batch_size, num_features)`
  * **y** – Batch of [`y_dtype`](#capymoa.ocl.strategy.NCM.y_dtype) valued labels `(batch_size,)`.

#### *classmethod* from_params(schema: [Any](https://docs.python.org/3/library/typing.html#typing.Any) = None, params: [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)] | [None](https://docs.python.org/3/builtins/constants.html#None) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1) → [Any](https://docs.python.org/3/library/typing.html#typing.Any)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L170)

Construct an instance from parameters produced by `get_params`.

#### get_params() → [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L163)

Return the hyper-parameters captured from the constructor.

#### predict(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L56)

Predict the label of an instance.

The base implementation calls [`predict_proba()`](#capymoa.ocl.strategy.NCM.predict_proba) and returns the
label with the highest probability.

* **Parameters:**
  **instance** – The instance to predict the label for.
* **Returns:**
  The predicted label or `None` if the classifier is unable
  to make a prediction.

#### predict_proba(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]] | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_batch_classifier.py#L134)

Calls [`batch_predict_proba()`](#capymoa.ocl.strategy.NCM.batch_predict_proba) with a batch of size 1.

#### train(instance: [LabeledInstance](capymoa.core.LabeledInstance.md#capymoa.core.LabeledInstance)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_batch_classifier.py#L125)

Calls [`batch_train()`](#capymoa.ocl.strategy.NCM.batch_train) with a batch of size 1.

#### device *: [torch.device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device)* *= device(type='cpu')*

Device on which the batch will be processed.

#### random_seed *: [int](https://docs.python.org/3/builtins/functions.html#int)*

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

#### schema *: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)*

The schema representing the instances.

#### x_dtype *: [torch.dtype](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)* *= torch.float32*

Data type for the input features.

#### y_dtype *: [torch.dtype](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)* *= torch.int64*

Data type for the target value/labels.