That completely slipped my head, I was troubleshooting something and failed to remember to update that! Thanks for telling me!

Thanks for the note, I completely forgot to add a test for calculate_loss.

For @test.it("Test Neural Network Initialization"):
These checks ensure that your network's weights are of the correct shape as per the architecture defined in the initialization (SimpleNeuralNetwork(3, 4, 2)).

And lastly @test.it("Test Forward Pass"):
This test ensures that when your neural network receives an input, it processes the input correctly through its layers and produces an output with the expected dimensions.

I thought future people attempting this would know what a forward pass was since it should be neural network fundamentals... but i'll be happy to update the description and clarify! Thanks a lot for your take on this, i'll update the description as soon as I implement the calculate loss test case. :)

Error margin is set up 0.55 and 5, but assert_approx_equals accepts maximum of relative/absolute error, which means the fixed tests accept +-0.55, and the random tests will accept everything.

It also means no training is neccessary to pass the kata: see this

    @test.it("Test Neural Network Initialization")
    def _():
        nn = SimpleNeuralNetwork(3, 4, 2)
        test.assert_equals(len(nn.hidden_weights), 3)
        test.assert_equals(len(nn.hidden_weights[0]), 4)
        test.assert_equals(len(nn.output_weights), 4)
        test.assert_equals(len(nn.output_weights[0]), 2)

    @test.it("Test Forward Pass")
    def _():
        nn = SimpleNeuralNetwork(3, 4, 2)
        inputs = np.random.rand(1, 3)
        output = nn.forward_pass(inputs)
        test.assert_equals(output.shape, (1, 2))

These tests are testing the specific implementation details that was not mentioned in the description at all.

Meanwhile, calculate_loss is not tested at all. So it's very unclear what is actually meant to be tested.

Lots of test cases added and small prompt change, hopefully should put people through hell :)

I see, i'll have that fixed soon!

No random tests. Also percentage is always 100 in the current tests, so the grade_percentage method is not properly tested.

alright thanks, i'll do that once i get home on my pc.

you can unpublish the kata, then it is private

sorry I just made this for the first years at my trade school, do you have any idea how I could private these?

Trivial kata, a simple class inheritance with some properties and string formatting has been done before.

Trivial kata, finding an item in a list (in a list) has been done before

In addition, the unit of noise level should be specified: the standard unit for noise level is decibel, which is a logarithmic scale.

obstacles in random tests are in a completely different format: in sample test it is

[
    { x1: 1, y1: 1, x2: 3, y2: 3, reduction: 20 }
]

But in random tests it is something like

[
  {
    obstacles: [
      { x1: 1, y1: 3, x2: 6, y2: 5, reduction: 40 },
      { x1: 2, y1: 2, x2: 6, y2: 9, reduction: 32 }
    ],
    source: { x: 1, y: 1 },
    reports: [
      { x: 0, y: 0, noiseLevel: 0 },
      { x: 1, y: 0, noiseLevel: 0 },
      { x: 2, y: 0, noiseLevel: 0 },
      { x: 3, y: 0, noiseLevel: 0 },
      ...
    ]
  },
  ...
]