Algoplus{BETA Version}

AlgoPlus is a C++ library that includes ready-to-use complex data structures and algorithms.

See the full documentation here

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Example:

#include <machine_learning/clustering/kmeans/kmeans.h>

// AlgoPlus now has Machine Learning classes!
int main(){
    std::vector<std::vector<double> > data;
    int CLUSTERS;
    kmeans a(data, CLUSTERS);
    ...
    // returns the cluster centers and assignments of the kmeans clustering
    std::pair<std::vector<std::vector<double> >, std::map<std::vector<double>, int64_t> > ans = a.fit();
}

#include <machine_learning/image/edge_detection/sobel_operator.h>

// As well as image processing algorithms!
int main(){
  ...
  std::vector<std::vector<int32_t> > img(data);

  // Performs edge detection on image img
  std::vector<std::vector<int32_t> > resulted = Sobel(img);
  ...
}


#include <graph.h>

// And of course, every data structure that you need!
int main(){
  weighted_graph<int> g("undirected");
  g.add_edge(1, 4, 2);
  g.add_edge(4, 5, 6);
  g.add_edge(5, 2, 9);
  g.add_edge(2, 8, 10);

  // returns the shortest path from 1 to 2.
  std::cout << g.shortest_path(1, 2) << '\n';
  g.visualize() // You can visualize almost any of our implemented data structures!
}

You can see more examples or follow the Tutorials.

Classes

Graphs

• Di-Graph
• Graph

Trees

• AVL Tree
• Binary Search Tree
• Splay Tree
• Trie
• Interval Tree
• 234 Tree
• Min/Max Heap

Lists

• Single Linked List
• Doubly Linked List
• Circular Linked List
• Skip List

Other

• Disjoint set
• Stack
• Queue
• Hash Table

Machine Learning(NEW!)

• Clustering Algorithms
• Encoders
• Regression Algorithms
• Shortest Path Algorithms
• Image Processing Algorithms

How to run test cases

We have unit tests for every function of our implemented algorithms & data structures.It is very important to make sure that your code works before making any pull requests!

Linux/MacOS

mkdir build && cd build
cmake ..
make
cd tests
./runUnitTests

Windows

mkdir build && cd build
cmake -G "MinGW Makefiles" ..
make
cd tests
./runUnitTests

Our contributors

How to contribute

1. Povide new implementations on our already implemented data structures & algorithms.

2. Implement new classes / algorithms.

3. Contribute to unit testing by writting unit tests for our classes and algorithms.

4. Create and contribute to APIs for other languages.

5. Promote the repository on your local workshop/seminar and get a shout-out.

See more here.

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