Python and NumPy
Inspect every error before summarising the report
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
Subtract the target from each reading and square each difference, without averaging yet.
Botie wants one average squared deviation from a supplied target. Opposite temperature deviations must not cancel.
Inspect the component calculation
Square(
Value or list: Subtract(
Starting value: Greenhouse temperatures
Value to remove: Reference temperature
)
)Examples and results
- Greenhouse temperatures
- 018
- 122
- 226
- Reference temperature
20
- Required result
- 04
- 14
- 236
- Greenhouse temperatures
- 020
- 120
- Reference temperature
20
- Required result
- 00
- 10
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 target is supplied. This example evaluates a fixed reference rather than learning a prediction rule. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
Your last tested pieces can start the full construction. An existing draft there keeps its own pieces.
Equal departures on opposite sides
This check uses the pieces from “Average deviations after measuring their sizes”.
BOTIE
- Greenhouse temperatures
- 017
- 123
- Reference temperature
20
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.
How would averaging before squaring change what this number measures?
The order of an elementwise operation and a reduction can change the statistical question.
The target is supplied. This example evaluates a fixed reference rather than learning a prediction rule.
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