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verticapy.machine_learning.memmodel.decomposition.SVD.transform#

SVD.transform(X: list | ndarray) → ndarray#

Transforms and applies the SVD model to the input matrix.

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.decomposition import SVD

We will use the following attributes:

vectors = [
    [0.4, 0.5],
    [0.3, 0.2],
]


values = [0.1, 0.3]

Let’s create a model.

model_svd = SVD(vectors, values)

Create a dataset.

data = [[0.3, 0.5]]

Transform the data.

model_svd.transform(data)
Out[6]: array([[2.7       , 0.83333333]])

Note

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