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All Algorithms implemented in Python
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* Trying to time every solution * Proposal 2 for timing PE solutions: - Use pytest fixture along with --capture=no flag to print out the top DURATIONS slowest solution at the end of the test sessions. - Remove the print part and try ... except ... block from the test function. * Proposal 3 for timing PE solutions: Completely changed the way I was performing the tests. Instead of parametrizing the problem numbers and expected output, I will parametrize the solution file path. Steps: - Collect all the solution file paths - Convert the paths into a Python module - Call solution on the module - Assert the answer with the expected results For assertion, it was needed to convert the JSON list object to Python dictionary object which required changing the JSON file itself. * Add type hints for variables * Fix whitespace in single_qubit_measure |
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|---|---|---|
| .github | ||
| arithmetic_analysis | ||
| backtracking | ||
| bit_manipulation | ||
| blockchain | ||
| boolean_algebra | ||
| cellular_automata | ||
| ciphers | ||
| compression | ||
| computer_vision | ||
| conversions | ||
| data_structures | ||
| digital_image_processing | ||
| divide_and_conquer | ||
| dynamic_programming | ||
| file_transfer | ||
| fuzzy_logic | ||
| genetic_algorithm | ||
| geodesy | ||
| graphics | ||
| graphs | ||
| greedy_method | ||
| hashes | ||
| images | ||
| linear_algebra | ||
| machine_learning | ||
| maths | ||
| matrix | ||
| networking_flow | ||
| neural_network | ||
| other | ||
| project_euler | ||
| quantum | ||
| scheduling | ||
| scripts | ||
| searches | ||
| sorts | ||
| strings | ||
| traversals | ||
| web_programming | ||
| .coveragerc | ||
| .gitignore | ||
| .gitpod.yml | ||
| .pre-commit-config.yaml | ||
| .travis.yml | ||
| CONTRIBUTING.md | ||
| DIRECTORY.md | ||
| LICENSE.md | ||
| pytest.ini | ||
| README.md | ||
| requirements.txt | ||
| Travis_CI_tests_are_failing.md | ||
The Algorithms - Python
All algorithms implemented in Python (for education)
These implementations are for learning purposes only. Therefore they may be less efficient than the implementations in the Python standard library.
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List of Algorithms
See our directory.