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All Algorithms implemented in Python
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* Added Pytests for Decission Tree Modified the mean_squared_error to be a static method Created the Test_Decision_Tree class Consists of two methods 1. helper_mean_squared_error_test: This method calculates the mean squared error manually without using numpy. Instead a for loop is used for the same. 2. test_one_mean_squared_error: This method considers a simple test case and compares the results by the helper function and the original mean_squared_error method of Decision_Tree class. This is done using asert keyword. Execution: PyTest installation pip3 install pytest OR pip install pytest Test function execution pytest decision_tree.py * Modified the pytests to be compatible with the doctest Added 2 doctest in the mean_squared_error method For its verification a static method helper_mean_squared_error(labels, prediction) is used It uses a for loop to calculate the error instead of the numpy inbuilt methods Execution ``` pytest .\decision_tree.py --doctest-modules ``` |
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| arithmetic_analysis | ||
| backtracking | ||
| blockchain | ||
| boolean_algebra | ||
| ciphers | ||
| compression | ||
| conversions | ||
| data_structures | ||
| digital_image_processing | ||
| divide_and_conquer | ||
| dynamic_programming | ||
| file_transfer | ||
| graphs | ||
| hashes | ||
| linear_algebra | ||
| machine_learning | ||
| maths | ||
| matrix | ||
| networking_flow | ||
| neural_network | ||
| other | ||
| project_euler | ||
| scripts | ||
| searches | ||
| sorts | ||
| strings | ||
| traversals | ||
| .gitignore | ||
| .travis.yml | ||
| CONTRIBUTING.md | ||
| DIRECTORY.md | ||
| LICENSE.md | ||
| README.md | ||
| requirements.txt | ||
The Algorithms - Python
All algorithms implemented in Python (for education)
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