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* initial commit
* first implementation of hard margin
* remove debugging print
* many commits squashed because pre-commit was buggy
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* remove unnecessary code + fix names + formatting + doctests
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* Updated files with f-string method
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* Add initial support for moving tests to GitHub
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* fixing black
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* optimize code
* try with limited data
* sort again
* sort fix
* sort fix
* delete warning and black
* add code for forecasting
* use black
* add more hints to describe
* add doctest
* finding whitespace
* fixing doctest
* delete
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* try trimming whitespace
* try adding doctypeand etc
* fixing reviews
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* add description for safety checker
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* Add GitHub action file for pre-commit
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* Fix all errors mentioned in pre-commit run:
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* NLP Word Frequency Algorithms
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* Fix line length for flake8
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* Added Lstm example for stock predection
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* Added Random Forest Regressor
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* python --> Python # it is a brand name, not a snake
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* "!= None" to "is not None"
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* Adding doctests into <gaussian_distribution> function
* Adding doctests into <y_generator> function
* Adding doctests into <calculate_mean> function
* Adding doctests into <calculate_probabilities> function
* Adding doctests into <calculate_variance> function
* Adding doctests into <predict_y_values> function
* Adding doctests into <accuracy> function
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* e.args[0] = "msg"
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* Refactor: converting generator object to a list object
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* Adding initial documentation
* importing modules
* Adding Normal_gen function
* Adding Y_gen function
* Adding mean_calc function
* Adding prob_calc function
* Adding var_calc function
* Adding predict function
* Adding accuracy function
* Adding main function
* Renaming LDA file
* Adding requested changes
* Renaming some of functions
* Refactoring str.format() statements to f-string
* Removing unnecessary list objects inside two functions
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* Fixing y_generator function
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and open plot or browser windows.
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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
```
