flowsom.models.SOMEstimator

class flowsom.models.SOMEstimator(xdim=10, ydim=10, rlen=10, mst=1, alpha=(0.05, 0.01), init=False, initf=None, map=True, codes=None, importance=None, seed=None)

Estimate a Self-Organizing Map (SOM) clustering model.

Methods table

fit(X[, y])	Perform SOM clustering.
fit_predict(X[, y])	Fit the model and predict labels.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X[, y])	Predict labels using the model.
set_params(**params)	Set the parameters of this estimator.

Methods

SOMEstimator.fit(X, y=None)

Perform SOM clustering.

Parameters:

• inp (np.array) – An array of the columns to use for clustering

• xdim (int) – x dimension of SOM

• ydim (int) – y dimension of SOM

• rlen (int) – Number of times to loop over the training data for each MST

• importance – Array with numeric values. Parameters will be scaled

according to importance :type importance: np.array

SOMEstimator.fit_predict(X, y=None)

Fit the model and predict labels.

SOMEstimator.get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

-routing (MetadataRequest) A MetadataRequest encapsulating routing information.

SOMEstimator.get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

-params (dict) Parameter names mapped to their values.

SOMEstimator.predict(X, y=None)

Predict labels using the model.

SOMEstimator.set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self : estimator instance Estimator instance.