* 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
* Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory
* Removed .vs/ folder per #893
* Rename matrix_multiplication_addition.py to matrix_operation.py
* Fixing lgtm issue in basic_graphs per ##1024
* Fixed lgtm issue per @cclauss recommendation in #1024
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* some pep8 cleanup too
* ENH: refactored longest common subsequence, also fixed a bug with the sequence returned
* renamed function
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* function for the knapsack problem which returns one of the optimal subsets
* some pep8 cleanup too
* divide and conquer and brute force algorithms for array-inversions counting
* divide and conquer and brute force algorithms for array-inversions counting
* divide and conquer and brute force algorithms for array-inversions counting
* a naive and divide-and-conquer algorithms for the convex-hull problem
* two convex-hull algorithms, a divide-and-conquer and a naive algorithm
* two convex-hull algorithms, a divide-and-conquer and a naive algorithm
* two convex-hull algorithms, a divide-and-conquer and a naive algorithm
* divide and conquer and brute force algorithms for array-inversions counting
* divide and conquer and brute force algorithms for array-inversions counting
* divide and conquer and brute force algorithms for array-inversions counting
* Added new algorithm which takes points as an input and outputs a polynom connecting them
* Rename Python-Polynom-for-points.py to python-polynom-for-points.py
* Update python-polynom-for-points.py
* Update python-polynom-for-points.py
* Update python-polynom-for-points.py
* Update python-polynom-for-points.py
* Update python-polynom-for-points.py
* Update python-polynom-for-points.py
* Update python-polynom-for-points.py
* Add doctests and run thru psf/black
* 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
* added sample file to transfer
* split client and server into separate files
* client and server now work in python2
* server works on python3
* client works on python3
* allow configurable ONE_CONNECTION_ONLY for testing server
* allow testing of ftp server + client
* use f-strings
* removed single letter vars
* fixed bad quote marks
* clearer file handler names
* 'with open() as' syntax
* unicode and emojis in the test data
* s -> sock
* consistent comment spacing
* remove closing formalities
* swap in and out_file
* f-string
* if __name__ == '__main__':
* Create Quadratic Equations(Complexes Numbers)
Created function that solves quadratic equations treating the cases with complexes numbers. Giving an answer with the imaginary unit "i".
* Update Quadratic Equations(Complexes Numbers)
Since there was no response from the owner of this PR, I made this little change which I hope will solve the issue!
* Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory
* Removed .vs/ folder per #893
* Rename matrix_multiplication_addition.py to matrix_operation.py
* Unicode test on strings/rabin_karp.py per #1067
* bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function.
* bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function.
* Added doctests. bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function.
* Added doctests. bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function.
* Added doctests. bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function.
* doctests still failing.
* Doctests added.
* Update tim_sort.py
Update tim_sort.py
The previous algorithm was skipping numbers, according to issue #959, and my own tests.
The version I am applying uses a while loop, which works correctly and is easier to compute, as there is no break statement.
* Update tim_sort.py
* Add Lucas_Lehmer_primality_test
* Add explanation for Lucas_Lehmer_primality_test
* Update and rename Lucas_Lehmer_primality_test.py to lucas_lehmer_primality_test.py
* LargestOfVeryLargeNumbers
Finds the largest among two very large numbers of the form x^y. Numbers like 512^513 etc
* Rename LargestOfVeryLargeNumbers to LargestOfVeryLargeNumbers.py
* Input() statements have been indented.
input() statements are indented under if __name__ == "__main__":
* largest_of_very_large_numbers.py
