cuvis_ai.unsupervised.sklearn_wrapped.GMM

class cuvis_ai.unsupervised.sklearn_wrapped.GMM(n_components=1, *, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)

Bases: Node, BaseUnsupervised, SklearnWrapped

__init__(n_components=1, *, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)

Methods

__init__([n_components, covariance_type, ...])
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)