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- Add type hints and improve code structure - Make the code cleaner and readable
- Add tests for objective functions (Ackley, Rastrigin, Rosenbrock, Griewank, Michalewicz, Schwefel) - Implement tests for DeepActiveLearning class - Add tests for TreeExploration and OptTask classes - Include tests for utility functions and Tracker class - Add tests for various SurrogateModel implementations This commit significantly improves test coverage for the optimization library, ensuring robustness and reliability of key components.
- Add detailed installation steps including pip and local installation - Include a sample code snippet demonstrating basic DANTE usage - Provide instructions on how to integrate custom functions with DANTE - Add information about running tests - Update the flowchart image to visualise DANTE's process
test: fix `number_count` to `num_inital_sample`
doc: print additional information in the sample code to better understand the learning progress
Update README.md
* Update obj_functions.py to display the real-time best results
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