This PR introduces a suite of unit tests for arithmetic_operations.py located within the within the mathematical functions directory. The primary functions in this file are based around multiplication and division operations.
Multiplication and Division Operation Tests
General Functionality: Tests confirm that the library accurately handles multiplication and division across various array shapes and dimensions, ensuring these operations perform correctly from scalars up to four-dimensional arrays. This encompasses verifying the maintenance of result dimensions in accordance with input shapes.
Data Type and Edge Case Handling: The library's robustness is tested against a comprehensive set of data types including integers, floating-points, and complex numbers (where applicable), affirming operation compatibility and precision. Additionally, scenarios involving shape mismatches, negative dimensions, and zero-sized arrays are explored, expecting runtime errors to prevent undefined behaviors.
Operational Integrity with Diverse Inputs: Special attention is given to handling arrays of incompatible shapes and different dimensionalities, testing the library’s ability to apply broadcasting rules correctly. For division operations, a small constant is added to denominators to avoid division by zero, focusing tests on the correctness of operations and corresponding sizes rather than mathematical anomalies.
This PR introduces a suite of unit tests for arithmetic_operations.py located within the within the mathematical functions directory. The primary functions in this file are based around multiplication and division operations.
Multiplication and Division Operation Tests General Functionality: Tests confirm that the library accurately handles multiplication and division across various array shapes and dimensions, ensuring these operations perform correctly from scalars up to four-dimensional arrays. This encompasses verifying the maintenance of result dimensions in accordance with input shapes.
Data Type and Edge Case Handling: The library's robustness is tested against a comprehensive set of data types including integers, floating-points, and complex numbers (where applicable), affirming operation compatibility and precision. Additionally, scenarios involving shape mismatches, negative dimensions, and zero-sized arrays are explored, expecting runtime errors to prevent undefined behaviors.
Operational Integrity with Diverse Inputs: Special attention is given to handling arrays of incompatible shapes and different dimensionalities, testing the library’s ability to apply broadcasting rules correctly. For division operations, a small constant is added to denominators to avoid division by zero, focusing tests on the correctness of operations and corresponding sizes rather than mathematical anomalies.