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* Update gaussian_naive_bayes.py
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* initial commit
* first implementation of hard margin
* remove debugging print
* many commits squashed because pre-commit was buggy
* more kernels and improved kernel management
* remove unnecessary code + fix names + formatting + doctests
* rename to fit initial naming
* better naming and documentation
* better naming and documentation
* MAINT: Used f-string method
Updated the code with f-string methods wherever required for a better and cleaner understanding of the code.
* Updated files with f-string method
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* Add initial support for moving tests to GitHub
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* Fix GitHub action file for build to trigger on PR
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* add forecasting code
* add statsmodel
* sort import
* sort import fix
* fixing black
* sort requirement
* optimize code
* try with limited data
* sort again
* sort fix
* sort fix
* delete warning and black
* add code for forecasting
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* add more hints to describe
* add doctest
* finding whitespace
* fixing doctest
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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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* Fix errors
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* Added Standardization and Normalization algorithms with built-in stats
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* Added Lstm example for stock predection
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* Added Random Forest Regressor
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* camel case to snake case
* spelling fix
* review
* python --> Python # it is a brand name, not a snake
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* spaces in int list
* "!= None" to "is not None"
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* various spelling corrections in documentation & several variables naming conventions fix
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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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Simplify __sudoku.is_completed()__ using Python builtin function [__all()__](https://docs.python.org/3/library/functions.html#all).
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* e.args[0] = "msg"
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* converting generator object to a list object
* Refactor: converting generator object to a list object
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* Adding new file to the machine_learning directory
* 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
* changing code style in some lines
* Fixing y_generator function
* Refactoring 'predict_y_values' function by using list comprehensions
* Changing code style in import statements
* Refactoring CLI code block
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* No lines longer than 88 characters
* Remove code with side effects from main
When running tests withy pytest, some modules execute code in main scope
and open plot or browser windows.
Moves such code under `if __name__ == "__main__"`.
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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
```
* Added dbscan in two formats. A jupyter notebook file for the
storytelling and a .py file for people that just want to look at the
code. The code in both is essentially the same. With a few things
different in the .py file for plotting the clusters.
* fixed LGTM problems
* Some requested changes implemented.
Still need to do docstring
* implememted all changes as requested
* svm.py
for issue #840
I would like to add the Support Vector Machine algorithm implemented in Python 3.6.7
Requirements:
- sklearn
* update svm.py
* update svm.py
* Update and renamed to sorted_vector_machines.py
* Updated sorted_vector_machines.py
* Travis CI: Add pytest --doctest-modules neural_network
Fixes#987
```
neural_network/perceptron.py:123: in <module>
sample.insert(i, float(input('value: ')))
../lib/python3.7/site-packages/_pytest/capture.py:693: in read
raise IOError("reading from stdin while output is captured")
E OSError: reading from stdin while output is captured
-------------------------------------------------------------------------------- Captured stdout --------------------------------------------------------------------------------
('\nEpoch:\n', 399)
------------------------
value:
```
* Adding fix from #1056 -- thanks @QuantumNovice
* if __name__ == '__main__':
* pytest --ignore=virtualenv # do not test our dependencies
