vastorbit.machine_learning.memmodel.cluster.KPrototypes.transform

KPrototypes.transform(X: Annotated[list | ndarray, 'Array Like Structure']) → ndarray

Transforms and returns the distance to each cluster.

Parameters:

X (ArrayLike) – The data on which to make the transformation.

Returns:

Transformed values.

Return type:

numpy.array

Examples

Import the required module.

from vastorbit.machine_learning.memmodel.cluster import KPrototypes

We will use the following attributes:

clusters = [
    [0.5, 'high'],
    [1, 'low'],
    [100, 'high'],
]
p = 2
gamma = 1.0
is_categorical = [0, 1]

Let’s create a model.

model_kp = KPrototypes(clusters, p, gamma, is_categorical)

Create a dataset.

data = [[2, 'low']]

Transform the data.

model_kp.transform(data)

Note

Refer to KPrototypes for more information about the different methods and usages.