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133. Vector multiplication
easy
Generalsenior
Vector multiplication is a core linear algebra operation used in ML for computing projections and model scores. In this task, you’ll implement the dot product of two vectors.
The dot product is defined as:
Requirements
Implement the function
python
Rules:
- Compute and return a single float value equal to the dot product of
xandy. - Do not use prebuilt dot-product helpers (e.g.,
numpy.dot,@,numpy.matmul). - You may use NumPy for basic elementwise multiplication (
*) and summation (sum). - Aim for clean, vectorized code.
Example
python
Output:
python
Input Signature
| Argument | Type |
|---|---|
| x | np.ndarray |
| y | np.ndarray |
Output Signature
| Return Name | Type |
|---|---|
| value | float |
Constraints
No numpy.dot/@/matmul allowed
Return a single float result
Keep solution inside dot_product()
Hint 1
This can be done with elementwise multiplication x * y followed by np.sum(...).
Hint 2
Ensure you return a native python float, e.g. float(np.sum(...)).
Roles
ML Engineer
AI Engineer
Data Scientist
Quantitative Analyst
Companies
GeneralLevels
senior
entry
Tags
dot-product
loops
linear-algebra
time-complexity
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Python EditorLn 1, Col 1