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All Algorithms implemented in Python
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| .vs | ||
| analysis/compression_analysis | ||
| arithmetic_analysis | ||
| binary_tree | ||
| boolean_algebra | ||
| ciphers | ||
| compression | ||
| data_structures | ||
| digital_image_processing | ||
| dynamic_programming | ||
| file_transfer_protocol | ||
| graphs | ||
| Graphs | ||
| hashes | ||
| linear_algebra_python | ||
| machine_learning | ||
| maths | ||
| matrix | ||
| networking_flow | ||
| neural_network | ||
| other | ||
| project_euler | ||
| searches | ||
| simple_client | ||
| sorts | ||
| strings | ||
| traversals | ||
| .gitignore | ||
| .lgtm.yml | ||
| .travis.yml | ||
| License | ||
| README.md | ||
The Algorithms - Python
All algorithms implemented in Python (for education)
These implementations are for learning purposes. They may be efficient than the implementations in the Python standard library.
Contribution Guidelines
- File name should be in camel case.
- Write proper documentation of the code.
- Avoid input methods as far as possible. Assign values to the variables statically. This will make the code easy to understand and algorithm can be traced easily.
- Add a corresponding explaination to Algorithms-Explanation (Optional but recommended).
- Avoid importing external libraries for basic algorithms.