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‎C++/Algorithms/SearchingAlgorithms/binarySearch.cpp

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// Binary search algorithm
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// This algorithm finds the index of an element in a sorted array
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// Time complexity : O(log n)
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// Space complexity :
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// 1. Recursive approach : O(log n)
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// 2. Iterative approach : O(1)
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#include <iostream>
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using namespace std;
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// Recursive approach
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int binarySearchRecursive(int a[], int l, int r, int t) {
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if(l <= r) {
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// middle element
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int m = l + (r - l) / 2;
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if(a[m] == t) {
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return m;
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}
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else if(a[m] > t) {
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return binarySearchRecursive(a, l, m - 1, t);
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}
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else {
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return binarySearchRecursive(a, m + 1, r, t);
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}
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}
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// If the element is not found return -1
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return -1;
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}
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// Iterative approach
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int binarySearchIterative(int a[], int l, int r, int t) {
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while(l <= r) {
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int m = l + (r - l) / 2;
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if(a[m] == t) {
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return m;
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}
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else if(a[m] > t) {
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r = m - 1;
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}
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else {
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l = m + 1;
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}
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}
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return -1;
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}
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int main() {
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int n = 10;
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int a[n] = {23, 37, 45, 67, 76, 78, 90, 93, 99, 100};
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int t = 37;
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cout<<"By recursive approach the index of element "<<t<<" is : ";
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cout<<binarySearchRecursive(a, 0, n - 1, t)<<endl;
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cout<<"By iterative approach the index of element "<<t<<" is : ";
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cout<<binarySearchIterative(a, 0, n - 1, t)<<endl;
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return 0;
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}

‎C++/Algorithms/Sieve Algorithms/Sieve_of_Eratosthenes.cpp

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/*
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Sieve of Eratosthenes:
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Given a number n, print all prime numbers less than n
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*/
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#include <iostream>
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using namespace std;
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void sieve_of_eratosthenes(int n)
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{
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/*
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This will print all
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prime number less than n
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*/
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// Initially all are prime
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bool *prime = new bool[n];
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for (int i = 0; i < n; i++)
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{
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prime[i] = true;
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}
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// Now we start marking multiples of every number non-prime
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for (int i = 2; i * i <= n; i++)
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{
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// We start from first multiple and go till n
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for (int j = 2 * i; j < n; j += i)
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{
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prime[j] = false;
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}
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}
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for (int i = 2; i < n; i++)
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{
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if (prime[i])
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{
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cout << i << " ";
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}
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}
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}
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int main()
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{
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sieve_of_eratosthenes(50);
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}

‎CONTRIBUTING.md

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# Contributing to Transcriptase
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# Contributing to Algorithms and Data Structures
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We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:
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- Proposing new features
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- Becoming a maintainer
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## We Develop with Github
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We use github to host code, to track issues and feature requests, as well as accept pull requests.
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## Steps to contribute
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## We Use [Github Flow](https://guides.github.com/introduction/flow/index.html), So All Code Changes Happen Through Pull Requests
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* Comment on the issue you want to work on. Make sure it's not assigned to someone else.
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Pull requests are the best way to propose changes to the codebase (we use [Github Flow](https://guides.github.com/introduction/flow/index.html)). We actively welcome your pull requests:
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* If you think an algorithm is missing, create an issue.
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1. Fork the repo and create your branch from `master`.
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2. If you've added code that should be tested, add tests.
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3. If you've changed APIs, update the documentation.
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4. Ensure the test suite passes.
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5. Make sure your code lints.
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6. Issue that pull request!
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### Making a PR
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## Any contributions you make will be under the MIT Software License
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> - Make sure you have been assigned the issue to which you are making a PR.
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> - If you make PR before being assigned, It will be labeled `invalid` and closed without merging.
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In short, when you submit code changes, your submissions are understood to be under the same [MIT License](http://choosealicense.com/licenses/mit/) that covers the project. Feel free to contact the maintainers if that's a concern.
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* Fork the repo and clone it on your machine.
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* Add a upstream link to main branch in your cloned repo
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```
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git remote add upstream https://github.com/Py-Contributors/AlgorithmsAndDataStructure.git
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```
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* Keep your cloned repo upto date by pulling from upstream (this will also avoid any merge conflicts while committing new changes)
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```
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git pull upstream master
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```
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* Create your feature branch
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```
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git checkout -b <feature-name>
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```
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* Commit all the changes
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```
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git commit -am "Meaningful commit message"
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```
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* Push the changes for review
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```
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git push origin <branch-name>
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```
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* Create a PR from our repo on Github.
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## Report bugs using Github's [issues](https://github.com/briandk/transcriptase-atom/issues)
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### Additional Notes
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We use GitHub issues to track public bugs. Report a bug by [opening a new issue](/ALGORITHM.md); it's that easy!
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* Code should be properly commented to ensure it's readability.
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* If you've added code that should be tested, add tests as comments.
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* In python use docstrings to provide tests.
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* Make sure your code properly formatted.
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* Issue that pull request!
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## Write bug reports with detail, background, and sample code
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[This is an example](http://stackoverflow.com/q/12488905/180626) of a bug report I wrote, and I think it's not a bad model. Here's [another example from Craig Hockenberry](http://www.openradar.me/11905408), an app developer whom I greatly respect.
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## Issue suggestions/Bug reporting
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**Great Bug Reports** tend to have:
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When you are creating an issue, make sure it's not already present. Furthermore, provide a proper description of the changes. If you are suggesting any code improvements, provide through details about the improvements.
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- A quick summary and/or background
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- Steps to reproduce
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**Great Issue suggestions** tend to have:
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- A quick summary of the changes.
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- In case of any bug provide steps to reproduce
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- Be specific!
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- Give sample code if you can. [My stackoverflow question](http://stackoverflow.com/q/12488905/180626) includes sample code that *anyone* with a base R setup can run to reproduce what I was seeing
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- What you expected would happen
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- What actually happens
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- Notes (possibly including why you think this might be happening, or stuff you tried that didn't work)
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- Give sample code if you can.
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- What you expected would happen
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- What actually happens
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- Notes (possibly including why you think this might be happening, or stuff you tried that didn't work)
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People *love* thorough bug reports. I'm not even kidding.
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## License
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By contributing, you agree that your contributions will be licensed under its MIT License.
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By contributing, you agree that your contributions will be licensed under its [MIT License](http://choosealicense.com/licenses/mit/).
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## References
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‎Python/Algorithms/README.md

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# [AlgorithmsImplementation](/ALGORITHM.md)
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Data Structure and Algorithm Implementation in Python.
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- [AlgorithmsImplementation](#algorithmsimplementation)
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- [BackTrackingAlgorithms](#backtrackingalgorithms)
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- [DeepLearningAlgorithms](#deeplearningalgorithms)
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- [DivideAndConquer](#divideandconquer)
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- [DynamicPrograming](#dynamicprograming)
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- [GreedyAlgorithms](#greedyalgorithms)
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- [MachineLearningAlgorithms](#machinelearningalgorithms)
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- [PathFindingAlgorithms](#pathfindingalgorithms)
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- [RecursionAlgorithms](#recursionalgorithms)
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- [SearchingAlgorithms](#searchingalgorithms)
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- [SortingAlgorithms](#sortingalgorithms)
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## BackTrackingAlgorithms
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## DeepLearningAlgorithms
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## DivideAndConquer
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## DynamicPrograming
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## GreedyAlgorithms
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## MachineLearningAlgorithms
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## PathFindingAlgorithms
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## RecursionAlgorithms
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## SearchingAlgorithms
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## SortingAlgorithms
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# Algorithm Implementation in Python
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List of Algorithms in Python contained in this repository
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- [Backtracking Algorithms](#backtrackingalgorithms)
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- [Deep Learning Algorithms](#deeplearningalgorithms)
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- [Divide And Conquer](#divideandconquer)
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- [Dynamic Programing](#dynamicprograming)
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- [Greedy Algorithms](#greedyalgorithms)
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- [Machine Learning Algorithms](#machinelearningalgorithms)
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- [Path Finding Algorithms](#pathfindingalgorithms)
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- [Recursion Algorithms](#recursionalgorithms)
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- [Searching Algorithms](#searchingalgorithms)
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- [Sorting Algorithms](#sortingalgorithms)
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### Backtracking Algorithms:
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Backtracking is a technique for solving problems recursively by trying to build a solution incrementally, one piece at a time, removing those solutions that fail to satisfy the constraints of the problem at any point of time.
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Example of implementation- Soduku solving.
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### Deep Learning Algorithms:
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Deep learning is part of machine learning, whose methods are based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
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Examples include- CNN, RNN, LSTM, GAN, RBM, etc.
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### Divide And Conquer:
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Divide and Conquer is an algorithm design paradigm based on multi-branched recursion. A divide-and-conquer algorithm works by recursively breaking down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly.
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Example of implementation- Quick Sort, Merge Sort.
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### Dynamic Programing:
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Dynamic Programming is primarily an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for same inputs, we can optimize it using Dynamic Programming. The idea is to store the results of subproblems, so that we do not have to re-compute them when needed later.
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Examples of implementation- Knapsack, Longest Common Subsequence.
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### Greedy Algorithms:
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A greedy algorithm is a simple, intuitive algorithm that is used in optimization problems. The algorithm makes the optimal choice at each step as it attempts to find the overall optimal way to solve the entire problem.
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Examples of implementation- Kruskal's algorithm, Prim's algorithm.
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### Machine Learning Algorithms:
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A machine learning algorithm is a method that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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Examples include- Linear Regression, Logistic Regression, Naïve Bayes, KNN, etc
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### Path Finding Algorithms:
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Pathfinding or pathing is the plotting, by a computer application, of the shortest route between two points. It is a more practical variant on solving mazes.
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Example of implemntation- A* search, Dijkstra's algorithm.
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### Recursion Algorithms:
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Recursion is a method of solving a problem where the solution depends on solutions to smaller instances of the same problem. Such problems can generally be solved by iteration, but this needs to identify and index the smaller instances at programming time.
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Examples of implementation- Tower of Hanoi, Tree traversals, DFS.
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### Searching Algorithms:
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The searching algorithms are used to search or find one or more than one element from a dataset. These type of algorithms are used to find elements from a specific data structures, which maybe sequential or not.
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Examples of implementation- Binary Search, Linear Search, Fibonacci Search.
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### Sorting Algorithms:
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A Sorting algorithm is an algorithm that puts elements of a list in a certain order. The most frequently used orders are numerical order and lexicographical order. Efficient sorting is important for optimizing the efficiency of other algorithms that require input data to be in sorted lists.
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Examples of implementation- Quick Sort, Merge Sort.

‎Python/DataStructure/Addition Without Operator/Addition.py

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# Title : Python3 Program to add two numbers without using arithmetic operator
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def Add(a, b):
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# Iterate till there is no carry
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while (b!= 0):
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# carry now contains common set bits of x and y
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carry = a & b
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# Sum of bits of x and y where at least one of the bits is not set
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a = a ^ b
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# Carry is shifted by one so that adding it to x gives the required sum
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b = carry << 1
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return a
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# Declear Print statement and Pass Parameter on them i.e Print(Add(20,10))
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print(Add(10,20))

‎README.md

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# [AlgorithmsAndDataStructure](/Algorithms/README.md)
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![header](https://capsule-render.vercel.app/api?type=rect&color=666666&height=100&section=header&text=Algorithms%20And%20Data%20Structures&fontSize=55%&fontColor=ffffff)
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![python](https://img.shields.io/badge/language-Python-blue?style=for-the-badge)
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![C++](https://img.shields.io/badge/language-C++-blue?style=for-the-badge)
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![Discord](https://img.shields.io/discord/758030555005714512?style=for-the-badge)
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![last commit](https://img.shields.io/github/last-commit/py-contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![contributors](https://img.shields.io/github/contributors/py-contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![hacktober-issue](https://img.shields.io/github/hacktoberfest/2020/py-contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![forks](https://img.shields.io/github/forks/Py-Contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![stars](https://img.shields.io/github/stars/Py-Contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![license](https://img.shields.io/github/license/Py-Contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![open issues](https://img.shields.io/github/issues-raw/Py-Contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![close issue](https://img.shields.io/github/issues-closed-raw/py-contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![open_pr](https://img.shields.io/github/issues-pr-raw/Py-contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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![open_pr](https://img.shields.io/github/issues-pr-closed-raw/Py-contributors/AlgorithmsAndDataStructure?style=for-the-badge)
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- [AlgorithmsAndDataStructure](#algorithmsanddatastructure)
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- [Introduction](#introduction)
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## Project Progress
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### Data Structures
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<summary><h3>Data Structures</h3></summary>
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<details>
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| Data Structure | C++ | Python | Status/Remarks |
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|---------------- |---------------- |----------------- |-------------------- |
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| Linked List | Yes | Yes | Being improved #23 |
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| Sets | Yes | Yes | Implemented |
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| Stack | Yes | In progress #13 | |
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| Queue | In progress #7 | In progress #12 | |
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</details>
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<summary><h3>Algorithms</h3></summary>
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<details>
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### Algorithms
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| Algorithm | C++ | Python | Remarks |
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|-------------------------------- |----------------- |----------------- |-----------------|
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| **Searching** | | | |
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| Decision Tree | No | In progress #37 | |
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| K-Nearest Neighbours | No | In progress #38 | |
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</details>
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## Contributing
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Before submitting a bug, please do the following:
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Check [Contribution](/CONTRIBUTING.md) Guide Before Contribution.
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Perform basic troubleshooting steps:
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- Make sure you are on the latest version. If you are not on the most recent version, your problem may have been solved already! Upgrading is always the best first step.
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- Try older versions. If you are already on the latest release, try rolling back a few minor versions (e.g. if on 1.7, try 1.5 or 1.6) and see if the problem goes away. This will help the devs narrow down when the problem first arose in the commit log.
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- Try switching up dependency versions. If the software in question has dependencies (other libraries, etc) try upgrading/downgrading those as well.
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- Create separate issues for Python and C++.
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- You can only work on issues that you have been assigned to.
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- Use Flake8 locally for linting Python Code. `pip install flake8`.
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(We have linting checks so if your code fails it we will not merge the PR.)
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## Authors and acknowledgment
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131153
- [ExpressHermes](https://github.com/ExpressHermes)
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- [Ayush Modi](https://github.com/hot9cups)
133155
- [rex_divakar](https://github.com/rexdivakar)
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- [Shubham Pawar](https://github.com/shubham5351)

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