Identity and diagonal matrices

6. Identity and diagonal matrices

easy

General

senior

Identity and diagonal matrices show up everywhere in ML/AI pipelines, from feature scaling to simple linear layers. In this task, you’ll apply a diagonal linear transform to a vector using efficient NumPy operations.

Requirements

Implement the function

python

Rules:

Use the definition $y_i = d_i \cdot x_i$ where $D = \mathrm{diag}(d)$ .
Do not construct the full $n \times n$ diagonal matrix (keep it efficient).
Return the result as a NumPy array.
Use only NumPy.
Keep the function self-contained (single function).

Example

python

Output:

python

Input Signature

Argument	Type
x	np.ndarray
diag	np.ndarray

Output Signature

Return Name	Type
value	np.ndarray

Constraints

Do not build an n×n diagonal matrix.
Use only NumPy.
Return a NumPy array.

Hint 1

A diagonal matrix multiply $y = D x$ is equivalent to element-wise multiplication of the diagonal component vector and the input vector.

Hint 2

Use the * operator or np.multiply() in NumPy to perform element-wise multiplication.

Hint 3

Avoid constructing the full $n \times n$ diagonal matrix to save memory and computation time.

Roles

ML Engineer

AI Engineer

Companies

General

Levels

senior

entry

Input Arguments

Edit values below to test with custom inputs

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