import unittest
import torch
import importlib
import io
import sys


def _dynamic(test, cond, ok, fail):
    if cond:
        test._testMethodName = ok
        test.assertTrue(True)
    else:
        test._testMethodName = fail
        test.fail(fail)


class TestUserCode(unittest.TestCase):

    def setUp(self):
        # Reference tensor for comparison
        self.tensor = torch.tensor([[3, 7, 1],
                                    [2, 5, 9],
                                    [8, 6, 4]], dtype=torch.float32)

    # --- Variable existence ---
    def test_row_sum_exists(self):
        import user_code
        cond = hasattr(user_code, "row_sum")
        _dynamic(
            self,
            cond,
            "`row_sum` variable is defined.",
            "`row_sum` is missing. Compute it using `tensor.sum(dim=1, keepdim=True)`."
        )

    def test_column_mean_exists(self):
        import user_code
        cond = hasattr(user_code, "column_mean")
        _dynamic(
            self,
            cond,
            "`column_mean` variable is defined.",
            "`column_mean` is missing. Compute it using `tensor.mean(dim=0)`."
        )

    def test_row_max_exists(self):
        import user_code
        cond = hasattr(user_code, "row_max")
        _dynamic(
            self,
            cond,
            "`row_max` variable is defined.",
            "`row_max` is missing. Compute it using `tensor.max(dim=1)[0]`."
        )

    def test_normalized_exists(self):
        import user_code
        cond = hasattr(user_code, "normalized_tensor")
        _dynamic(
            self,
            cond,
            "`normalized_tensor` variable is defined.",
            "`normalized_tensor` is missing. Normalize using `tensor / tensor.sum()`."
        )

    # --- Correct calculations ---
    def test_row_sum_correct(self):
        import user_code
        expected = self.tensor.sum(dim=1, keepdim=True)
        cond = torch.allclose(user_code.row_sum, expected)
        _dynamic(
            self,
            cond,
            "`row_sum` is computed correctly.",
            "`row_sum` is incorrect. Use `tensor.sum(dim=1, keepdim=True)`."
        )

    def test_column_mean_correct(self):
        import user_code
        expected = self.tensor.mean(dim=0)
        cond = torch.allclose(user_code.column_mean, expected)
        _dynamic(
            self,
            cond,
            "`column_mean` is computed correctly.",
            "`column_mean` is incorrect. Use `tensor.mean(dim=0)`."
        )

    def test_row_max_correct(self):
        import user_code
        expected = self.tensor.max(dim=1)[0]
        cond = torch.allclose(user_code.row_max, expected)
        _dynamic(
            self,
            cond,
            "`row_max` is computed correctly.",
            "`row_max` is incorrect. Use `tensor.max(dim=1)[0]`."
        )

    def test_normalized_tensor_correct(self):
        import user_code
        expected = self.tensor / self.tensor.sum()
        cond = torch.allclose(user_code.normalized_tensor, expected)
        _dynamic(
            self,
            cond,
            "`normalized_tensor` is computed correctly.",
            "`normalized_tensor` is incorrect. Normalize using `tensor / tensor.sum()`."
        )

    # --- Ensure something was printed ---
    def test_print_output(self):
        import user_code

        buffer = io.StringIO()
        sys.stdout = buffer
        importlib.reload(user_code)
        sys.stdout = sys.__stdout__

        cond = len(buffer.getvalue().strip()) > 0

        _dynamic(
            self,
            cond,
            "Output is successfully printed.",
            "No output detected. Make sure to print all required values."
        )


if __name__ == "__main__":
    unittest.main()


test_code.py

Lær de grundlæggende og avancerede koncepter, der er nødvendige for at arbejde effektivt med PyTorch. Opnå en solid forståelse af tensorer, herunder oprettelse, operationer og omformning. Udforsk det essentielle inden for gradienter, backpropagation og lineær regression, før du går videre til håndtering af datasæt. Behersk de færdigheder, der er nødvendige for at opbygge, træne og evaluere neurale netværk.

Undersøg grundlæggende elementer i PyTorch med fokus på tensors—den centrale datastruktur til beregninger. Du vil lære om oprettelse af tensors, tilfældig initialisering, matematiske operationer og formmanipulation.

Udforsk centrale begreber for træning af modeller i PyTorch, herunder beregning af gradienter og udførelse af multi-trins backpropagation. Behersk lineær regression som en grundlæggende maskinlæringsmodel og introducer effektiv håndtering af datasæt.

Opdag, hvordan man opbygger, træner og evaluerer neurale netværk ved hjælp af PyTorch. Du vil lære at definere et simpelt feedforward neuralt netværk, optimere dets parametre gennem træning og vurdere dets ydeevne.

Udfordring: Udførelse af Matematiske Operationer

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