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verticapy.machine_learning.memmodel.cluster.KPrototypes.transform

KPrototypes.transform(X: list | ndarray) → ndarray

Transforms and returns the distance to each cluster.

Parameters

X: ArrayLike

The data on which to make the transformation.

Returns

numpy.array

Transformed values.

Examples

Import the required module.

from verticapy.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)
Out[8]: array([[3.250e+00, 1.000e+00, 9.605e+03]])

Note

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