cuvis_ai.unsupervised.sklearn_wrapped.KMeans

class cuvis_ai.unsupervised.sklearn_wrapped.KMeans(n_clusters=8, *, init='k-means++', n_init='auto', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd')[source]

Bases: Node, BaseUnsupervised, SklearnWrapped

__init__(n_clusters=8, *, init='k-means++', n_init='auto', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd')[source]

Methods

`__init__`([n_clusters, init, n_init, ...])
`check_input_dim`(X)	Check that the parameters for the input data data match user expectations
`check_output_dim`(X)	Check that the parameters for the output data data match user expectations
`fit`(X[, warm_start])	_summary_
`forward`(X)	Transform the input data.
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`(params, data_dir)	Load from serialized format into an object
`serialize`(data_dir)	Convert the class into a serialized representation
`set_fit_meta_request`(**kwargs)
`set_forward_meta_request`(**kwargs)

Attributes

`input_dim`	Returns the needed shape for the input data.
`output_dim`	Returns the shape for the output data.

check_input_dim(X)

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

fit(X: ndarray, warm_start=False)

_summary_

Parameters:: X (Any) – Generic method to initialize a classifier with data.

forward(X: ndarray)

Transform the input data.

Parameters: X (array-like): Input data.

Returns: Transformed data.

get_fit_requested_meta()

get_forward_requested_meta()

property input_dim

Returns the needed shape for the input data. If a dimension is not important, it will return -1 in the specific position.

Returns: (tuple) needed shape for data

load(params: dict, data_dir: Path) → None: Load from serialized format into an object

property output_dim

Returns the shape for the output data. If a dimension is dependent on the input, it will return -1 in the specific position.

Returns: (tuple) expected output shape for data

serialize(data_dir: Path) → dict: Convert the class into a serialized representation

set_fit_meta_request(**kwargs)

set_forward_meta_request(**kwargs)