f88b272f79
* chore: migrate instructions to learn * chore: apply sem's review suggestions Co-authored-by: Sem Bauke <46919888+Sembauke@users.noreply.github.com> * chore: apply tom's review suggestion Co-authored-by: Tom <20648924+moT01@users.noreply.github.com> Co-authored-by: Sem Bauke <46919888+Sembauke@users.noreply.github.com> Co-authored-by: Tom <20648924+moT01@users.noreply.github.com>
78 lines
2.6 KiB
Markdown
78 lines
2.6 KiB
Markdown
---
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id: 5e46f8d6ac417301a38fb92d
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title: Rock Paper Scissors
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challengeType: 10
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forumTopicId: 462376
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dashedName: rock-paper-scissors
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---
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# --description--
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You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-rock-paper-scissors).
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We are still developing the interactive instructional part of the machine learning curriculum. For now, you will have to use other resources to learn how to pass this challenge.
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# --instructions--
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Create a function named `calculate()` in `mean_var_std.py` that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix.
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The input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix.
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The returned dictionary should follow this format:
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```py
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{
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'mean': [axis1, axis2, flattened],
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'variance': [axis1, axis2, flattened],
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'standard deviation': [axis1, axis2, flattened],
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'max': [axis1, axis2, flattened],
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'min': [axis1, axis2, flattened],
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'sum': [axis1, axis2, flattened]
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}
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```
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If a list containing less than 9 elements is passed into the function, it should raise a `ValueError` exception with the message: "List must contain nine numbers." The values in the returned dictionary should be lists and not Numpy arrays.
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For example, `calculate([0,1,2,3,4,5,6,7,8])` should return:
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```py
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{
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'mean': [[3.0, 4.0, 5.0], [1.0, 4.0, 7.0], 4.0],
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'variance': [[6.0, 6.0, 6.0], [0.6666666666666666, 0.6666666666666666, 0.6666666666666666], 6.666666666666667],
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'standard deviation': [[2.449489742783178, 2.449489742783178, 2.449489742783178], [0.816496580927726, 0.816496580927726, 0.816496580927726], 2.581988897471611],
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'max': [[6, 7, 8], [2, 5, 8], 8],
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'min': [[0, 1, 2], [0, 3, 6], 0],
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'sum': [[9, 12, 15], [3, 12, 21], 36]
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}
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```
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The unit tests for this project are in `test_module.py`.
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## Development
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For development, you can use `main.py` to test your `calculate()` function. Click the "run" button and `main.py` will run.
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## Testing
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We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
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## Submitting
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Copy your project's URL and submit it below.
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# --hints--
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It should pass all Python tests.
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```js
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```
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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