CODEWITHSUNDEEP
pythonscikit-learnlinear-algebra
Incognito
Incognito

Reputation: 351

Normalize function in Sklearn requires 2D array

In linear algebra, vectors are normalized when they are divided by their norm, that is, the squared sum of their components.

Yet, sklearn.preprocessing.normalize method does not accept vectors, only matrices of at least two columns:

"ValueError: Expected 2D array, got 1D array instead"

Why?

Upvotes: 1

Views: 529

Answers (2)

Samuel
Samuel

Reputation: 3053

According to the documentation for sklearn.preprocessing.normalize, the parameter x is the data to normalize, element by element, and has the shape [n_samples, n_features]. The function normalize perform this operation on a single array-like dataset, either using the L1 or L2 norms.

Upvotes: 0

Prune
Prune

Reputation: 77827

normalize works on a data set, not a vector. You have the wrong definition of "normalize" for this function. It works on individual vectors. If you give it a 2D array of a single column (shape of [N, 1]), you can get your vector normalized in the "normal" fashion.

Upvotes: 1

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