Python and NumPy
Apply a rule to the greenhouse feature table
BOTIE
Examples, experiments and workspace controls
Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.
Try your own inputs
Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.
Context for this challenge
Apply the coefficient vector to each row, adding its weighted contributions, then add the supplied offset.
Botie has feature rows and a shared coefficient vector. A row's contributions must be combined before the common offset is added.
Apply the supplied weights to each feature row, then add the shared offset to the resulting score vector.
Examples and results
- Observation rows
10 40 20 60 - Feature coefficients
- 02
- 10.1
- Shared offset
3
- Required result
- 027
- 149
Try a fresh construction
This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.
Stuck? Inspect a worked construction
The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.
Assumptions and test precision
The coefficients are supplied. Python separately introduces exact solving and least-squares fitting. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
A third feature and a negative coefficient
This check uses the pieces from “Produce one weighted score per observation”.
BOTIE
- Observation rows
2 5 1 0 3 4 - Feature coefficients
- 0-1
- 12
- 20.5
- Shared offset
-2
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
Where would an offset be counted too many times if it were added to individual feature contributions first?
Separate a row's weighted sum from its shared bias. Supplied coefficients calculate scores but do not train a model.
The coefficients are supplied. Python separately introduces exact solving and least-squares fitting.
Work through the Python challenges →