Write unit tests for symbolic_kernel functions

[blackboxprogramming]

Description

The symbolic_kernel.py module provides various functions for computing psychological metrics and other symbolic operations, but there are no accompanying unit tests to ensure their correctness. Adding a test suite will help detect regressions and clarify expected behaviour.

Proposed work

• Create a tests/ directory in the Remember repository with a test_symbolic_kernel.py file.
• For each public function in symbolic_kernel.py, write test cases that cover common inputs and edge cases. For instance, verify that valence() handles positive and negative numbers and that dominance() behaves consistently with zero or negative denominators.
• Use pytest and parametrized tests to reduce boilerplate where functions can be tested with multiple inputs and expected outputs.
• Update the README with instructions for running the test suite (pip install -r requirements.txt && pytest).

Acceptance criteria

• Executing pytest runs the new tests without failures.
• Each function in symbolic_kernel.py has at least one corresponding test case covering nominal and edge cases.
• Clear instructions for running the tests are provided in the project documentation.