chen0040/js-graph-algorithms chen0040/js-graph-algorithms

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  • Language
    JavaScript
  • License
    MIT License
  • Created over 7 years ago
  • Updated almost 2 years ago
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chen0040/js-graph-algorithms
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Repository Details

Package provides javascript implementation of algorithms for graph processing

js-graph-algorithms

Package provides javascript implementation of algorithms for graph processing

Build Status Coverage Status

Features

  • Depth First Search (Link: HTML DEMO)
  • Breadth First Search
  • Connected Components for undirected graph (Link: HTML DEMO)
  • Topoloical Sort (Link: HTML DEMO)
  • Strongly Connected Components for directed graph (Link: HTML DEMO)
  • Minimum Spanning Tree for weighted graph (Kruskal, Prim Lazy, Prim Eager) (Link: HTML DEMO)
  • Shortest Paths (Dijkstra, Bellman-Ford, Topological Sort on DAG) (Link: HTML DEMO)
  • MaxFlow-MinCut (Ford-Fulkerson) (Link: HTML DEMO)

Install

npm install js-graph-algorithms

Usage

Create an undirected unweighted graph

The sample code below shows how to create a undirected and unweighted graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');

var g = new jsgraphs.Graph(6); // 6 is the number vertices in the graph
g.addEdge(0, 5); // add undirected edge connecting vertex 0 to vertex 5
g.addEdge(2, 4);
g.addEdge(2, 3);
g.addEdge(1, 2);
g.addEdge(0, 1);
g.addEdge(3, 4);
g.addEdge(3, 5);
g.addEdge(0, 2);

g.node(2).label = 'Hello'; // assigned 'Hello' as label for node 2
g.edge(0, 2).label = 'World'; // edge between 0 and 2

console.log(g.V); // display 6, which is the number of vertices in g
console.log(g.adj(0)); // display [5, 1, 2], which is the adjacent list to vertex 0

Create directed unweighted graph

The sample code below shows how to create a direted and unweighted graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');

var g = new jsgraphs.DiGraph(13); // 13 is the number vertices in the graph
g.addEdge(4,  2); // add directed edge from 4 to 2
g.addEdge(2,  3);
g.addEdge(3,  2);
g.addEdge(6,  0);
g.addEdge(0,  1);
g.addEdge(2,  0);
g.addEdge(11, 12);
g.addEdge(12,  9);
g.addEdge(9, 10);
g.addEdge(9, 11);
g.addEdge(7,  9);
g.addEdge(10, 12);
g.addEdge(11,  4);
g.addEdge(4,  3);
g.addEdge(3,  5);
g.addEdge(6,  8);
g.addEdge(8,  6);
g.addEdge(5,  4);
g.addEdge(0,  5);
g.addEdge(6,  4);
g.addEdge(6,  9);
g.addEdge(7,  6);

g.node(2).label = 'Hello'; // assign 'Hello' as label for node 2
g.edge(0, 5).label = 'World'; // edge from 0 to 5

console.log(g.V); // display 13, which is the number of vertices in g
console.log(g.adj(0)); // display the adjacency list which are vertices directed from vertex 0

Create undirected weighted graph

The sample code below shows show to create undirected weighted graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedGraph(8); // 8 is the number vertices in the graph
g.addEdge(new jsgraphs.Edge(0, 7, 0.16));
g.addEdge(new jsgraphs.Edge(2, 3, 0.17));
g.addEdge(new jsgraphs.Edge(1, 7, 0.19));
g.addEdge(new jsgraphs.Edge(0, 2, 0.26));
g.addEdge(new jsgraphs.Edge(5, 7, 0.28));
g.addEdge(new jsgraphs.Edge(1, 3, 0.29));
g.addEdge(new jsgraphs.Edge(1, 5, 0.32));
g.addEdge(new jsgraphs.Edge(2, 7, 0.34));
g.addEdge(new jsgraphs.Edge(4, 5, 0.35));
g.addEdge(new jsgraphs.Edge(1, 2, 0.36));
g.addEdge(new jsgraphs.Edge(4, 7, 0.37));
g.addEdge(new jsgraphs.Edge(0, 4, 0.38));
g.addEdge(new jsgraphs.Edge(6, 2, 0.4));
g.addEdge(new jsgraphs.Edge(3, 6, 0.52));
g.addEdge(new jsgraphs.Edge(6, 0, 0.58));
g.addEdge(new jsgraphs.Edge(6, 4, 0.93));

g.node(2).label = 'Hello'; // assign 'Hello' as label for node 2
g.edge(4, 5).label = 'World'; // edge between node 4 and 5

console.log(g.V); // display 13, which is the number of vertices in g
console.log(g.adj(0)); // display the adjacency list which are undirected edges connected to vertex 0

Create directed weighted graph

The sample code below shows show to create directed weighted graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedDiGraph(8); // 8 is the number vertices in the graph
g.addEdge(new jsgraphs.Edge(0, 7, 0.16));
g.addEdge(new jsgraphs.Edge(2, 3, 0.17));
g.addEdge(new jsgraphs.Edge(1, 7, 0.19));
g.addEdge(new jsgraphs.Edge(0, 2, 0.26));
g.addEdge(new jsgraphs.Edge(5, 7, 0.28));
g.addEdge(new jsgraphs.Edge(1, 3, 0.29));
g.addEdge(new jsgraphs.Edge(1, 5, 0.32));
g.addEdge(new jsgraphs.Edge(2, 7, 0.34));
g.addEdge(new jsgraphs.Edge(4, 5, 0.35));
g.addEdge(new jsgraphs.Edge(1, 2, 0.36));
g.addEdge(new jsgraphs.Edge(4, 7, 0.37));
g.addEdge(new jsgraphs.Edge(0, 4, 0.38));
g.addEdge(new jsgraphs.Edge(6, 2, 0.4));
g.addEdge(new jsgraphs.Edge(3, 6, 0.52));
g.addEdge(new jsgraphs.Edge(6, 0, 0.58));
g.addEdge(new jsgraphs.Edge(6, 4, 0.93));

g.node(2).label = 'Hello';
g.edge(4, 5).label = 'World'; // edge from node 4 to node 5

console.log(g.V); // display 13, which is the number of vertices in g
console.log(g.adj(0)); // display the adjacency list which are directed edges from vertex 0

Depth First Search

The sample code below show how to perform depth first search of an undirected graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');

var g = new jsgraphs.Graph(6);
g.addEdge(0, 5);
g.addEdge(2, 4);
g.addEdge(2, 3);
g.addEdge(1, 2);
g.addEdge(0, 1);
g.addEdge(3, 4);
g.addEdge(3, 5);
g.addEdge(0, 2);
var s = 0;
var dfs = new jsgraphs.DepthFirstSearch(g, s);


for(var v=0; v < g.V; ++v) {
 if(dfs.hasPathTo(v)) {
    console.log(s + " is connected to " + v);
    console.log("path: " + dfs.pathTo(v));
 } else {
     console.log('No path from ' + s + ' to ' + v);
 }
}

Connected Components

The sample code below show how to obtain the number of connected components in an undirected graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');

var g = new jsgraphs.Graph(13);
g.addEdge(0, 5);
g.addEdge(4, 3);
g.addEdge(0, 1);
g.addEdge(9, 12);
g.addEdge(6, 4);
g.addEdge(5, 4);
g.addEdge(0, 2);
g.addEdge(11, 12);
g.addEdge(9,10);
g.addEdge(0, 6);
g.addEdge(7, 8);
g.addEdge(9, 11);
g.addEdge(5, 3); 

var cc = new jsgraphs.ConnectedComponents(g);
console.log(cc.componentCount()); // display 3
for (var v = 0; v < g.V; ++v) {
    console.log('id[' + v + ']: ' + cc.componentId(v));
}

Topological Sort

The sample code below show how to obtain the reverse post order of a topological sort in a directed acyclic graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');

var dag = new jsgraphs.DiGraph(7); // must be directed acyclic graph

dag.addEdge(0, 5);
dag.addEdge(0, 2);
dag.addEdge(0, 1);
dag.addEdge(3, 6);
dag.addEdge(3, 5);
dag.addEdge(3, 4);
dag.addEdge(5, 4);
dag.addEdge(6, 4);
dag.addEdge(6, 0);
dag.addEdge(3, 2);
dag.addEdge(1, 4);

var ts = new jsgraphs.TopologicalSort(dag);

var order = ts.order();
console.log(order); // display array which is the topological sort order

Strongly Connected Components for Directed Graph

The sample code below show how to obtain the strongly connected components from a directed graph (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');

var graph = new jsgraphs.DiGraph(13);
graph.addEdge(4, 2);
graph.addEdge(2, 3);
graph.addEdge(3, 2);
graph.addEdge(6, 0);
graph.addEdge(0, 1);
graph.addEdge(2, 0);
graph.addEdge(11, 12);
graph.addEdge(12, 9);
graph.addEdge(9, 10);
graph.addEdge(9, 11);
graph.addEdge(8, 9);
graph.addEdge(10, 12);
graph.addEdge(11, 4);
graph.addEdge(4, 3);
graph.addEdge(3, 5);
graph.addEdge(7, 8);
graph.addEdge(8, 7);
graph.addEdge(5, 4);
graph.addEdge(0, 5);
graph.addEdge(6, 4);
graph.addEdge(6, 9);
graph.addEdge(7, 6);
var scc = new jsgraphs.StronglyConnectedComponents(graph);
console.log(scc.componentCount()); // display 5
for (var v = 0; v < graph.V; ++v) {
    console.log('id[' + v + ']: ' + scc.componentId(v));
}

Use Kruskal algorithm to find the minimum spanning tree of a weighted graph

The sample code below show how to obtain the minimum spanning tree from a weighted graph using Kruskal algorithm (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedGraph(8);

g.addEdge(new jsgraphs.Edge(0, 7, 0.16));
g.addEdge(new jsgraphs.Edge(2, 3, 0.17));
g.addEdge(new jsgraphs.Edge(1, 7, 0.19));
g.addEdge(new jsgraphs.Edge(0, 2, 0.26));
g.addEdge(new jsgraphs.Edge(5, 7, 0.28));
g.addEdge(new jsgraphs.Edge(1, 3, 0.29));
g.addEdge(new jsgraphs.Edge(1, 5, 0.32));
g.addEdge(new jsgraphs.Edge(2, 7, 0.34));
g.addEdge(new jsgraphs.Edge(4, 5, 0.35));
g.addEdge(new jsgraphs.Edge(1, 2, 0.36));
g.addEdge(new jsgraphs.Edge(4, 7, 0.37));
g.addEdge(new jsgraphs.Edge(0, 4, 0.38));
g.addEdge(new jsgraphs.Edge(6, 2, 0.4));
g.addEdge(new jsgraphs.Edge(3, 6, 0.52));
g.addEdge(new jsgraphs.Edge(6, 0, 0.58));
g.addEdge(new jsgraphs.Edge(6, 4, 0.93));

var kruskal = new jsgraphs.KruskalMST(g); 
var mst = kruskal.mst;
for(var i=0; i < mst.length; ++i) {
    var e = mst[i];
    var v = e.either();
    var w = e.other(v);
    console.log('(' + v + ', ' + w + '): ' + e.weight);
}

Use Lazy Prim algorithm to find the minimum spanning tree of a weighted graph

The sample code below show how to obtain the minimum spanning tree from a weighted graph using Lazy Prim algorithm (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedGraph(8);

g.addEdge(new jsgraphs.Edge(0, 7, 0.16));
g.addEdge(new jsgraphs.Edge(2, 3, 0.17));
g.addEdge(new jsgraphs.Edge(1, 7, 0.19));
g.addEdge(new jsgraphs.Edge(0, 2, 0.26));
g.addEdge(new jsgraphs.Edge(5, 7, 0.28));
g.addEdge(new jsgraphs.Edge(1, 3, 0.29));
g.addEdge(new jsgraphs.Edge(1, 5, 0.32));
g.addEdge(new jsgraphs.Edge(2, 7, 0.34));
g.addEdge(new jsgraphs.Edge(4, 5, 0.35));
g.addEdge(new jsgraphs.Edge(1, 2, 0.36));
g.addEdge(new jsgraphs.Edge(4, 7, 0.37));
g.addEdge(new jsgraphs.Edge(0, 4, 0.38));
g.addEdge(new jsgraphs.Edge(6, 2, 0.4));
g.addEdge(new jsgraphs.Edge(3, 6, 0.52));
g.addEdge(new jsgraphs.Edge(6, 0, 0.58));
g.addEdge(new jsgraphs.Edge(6, 4, 0.93));

var prim = new jsgraphs.LazyPrimMST(g); 
var mst = prim.mst;
for(var i=0; i < mst.length; ++i) {
    var e = mst[i];
    var v = e.either();
    var w = e.other(v);
    console.log('(' + v + ', ' + w + '): ' + e.weight);
}

Use Eager Prim algorithm to find the minimum spanning tree of a weighted graph

The sample code below show how to obtain the minimum spanning tree from a weighted graph using Eager Prim algorithm (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedGraph(8);

g.addEdge(new jsgraphs.Edge(0, 7, 0.16));
g.addEdge(new jsgraphs.Edge(2, 3, 0.17));
g.addEdge(new jsgraphs.Edge(1, 7, 0.19));
g.addEdge(new jsgraphs.Edge(0, 2, 0.26));
g.addEdge(new jsgraphs.Edge(5, 7, 0.28));
g.addEdge(new jsgraphs.Edge(1, 3, 0.29));
g.addEdge(new jsgraphs.Edge(1, 5, 0.32));
g.addEdge(new jsgraphs.Edge(2, 7, 0.34));
g.addEdge(new jsgraphs.Edge(4, 5, 0.35));
g.addEdge(new jsgraphs.Edge(1, 2, 0.36));
g.addEdge(new jsgraphs.Edge(4, 7, 0.37));
g.addEdge(new jsgraphs.Edge(0, 4, 0.38));
g.addEdge(new jsgraphs.Edge(6, 2, 0.4));
g.addEdge(new jsgraphs.Edge(3, 6, 0.52));
g.addEdge(new jsgraphs.Edge(6, 0, 0.58));
g.addEdge(new jsgraphs.Edge(6, 4, 0.93));

var prim = new jsgraphs.EagerPrimMST(g); 
var mst = prim.mst;
for(var i=0; i < mst.length; ++i) {
    var e = mst[i];
    var v = e.either();
    var w = e.other(v);
    console.log('(' + v + ', ' + w + '): ' + e.weight);
}

Find the shortest paths using Dijkstra

The sample code below show how to obtain the shortest paths from a starting point 0 on a weighted directed graph using Dijkstra (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedDiGraph(8);
g.addEdge(new jsgraphs.Edge(0, 1, 5.0));
g.addEdge(new jsgraphs.Edge(0, 4, 9.0));
g.addEdge(new jsgraphs.Edge(0, 7, 8.0));
g.addEdge(new jsgraphs.Edge(1, 2, 12.0));
g.addEdge(new jsgraphs.Edge(1, 3, 15.0));
g.addEdge(new jsgraphs.Edge(1, 7, 4.0));
g.addEdge(new jsgraphs.Edge(2, 3, 3.0));
g.addEdge(new jsgraphs.Edge(2, 6, 11.0));
g.addEdge(new jsgraphs.Edge(3, 6, 9.0));
g.addEdge(new jsgraphs.Edge(4, 5, 5.0));
g.addEdge(new jsgraphs.Edge(4, 6, 20.0));
g.addEdge(new jsgraphs.Edge(4, 7, 5.0));
g.addEdge(new jsgraphs.Edge(5, 2, 1.0));
g.addEdge(new jsgraphs.Edge(5, 6, 13.0));
g.addEdge(new jsgraphs.Edge(7, 5, 6.0));
g.addEdge(new jsgraphs.Edge(7, 2, 7.0));  


var dijkstra = new jsgraphs.Dijkstra(g, 0);

for(var v = 1; v < g.V; ++v){
    if(dijkstra.hasPathTo(v)){
        var path = dijkstra.pathTo(v);
        console.log('=====path from 0 to ' + v + ' start==========');
        for(var i = 0; i < path.length; ++i) {
            var e = path[i];
            console.log(e.from() + ' => ' + e.to() + ': ' + e.weight);
        }
        console.log('=====path from 0 to ' + v + ' end==========');
        console.log('=====distance: '  + dijkstra.distanceTo(v) + '=========');
    }
}

Find the shortest paths using Bellman-Ford

The sample code below show how to obtain the shortest paths from a starting point 0 on a weighted directed graph using Bellman-Ford:

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedDiGraph(8);
g.addEdge(new jsgraphs.Edge(0, 1, 5.0));
g.addEdge(new jsgraphs.Edge(0, 4, 9.0));
g.addEdge(new jsgraphs.Edge(0, 7, 8.0));
g.addEdge(new jsgraphs.Edge(1, 2, 12.0));
g.addEdge(new jsgraphs.Edge(1, 3, 15.0));
g.addEdge(new jsgraphs.Edge(1, 7, 4.0));
g.addEdge(new jsgraphs.Edge(2, 3, 3.0));
g.addEdge(new jsgraphs.Edge(2, 6, 11.0));
g.addEdge(new jsgraphs.Edge(3, 6, 9.0));
g.addEdge(new jsgraphs.Edge(4, 5, 5.0));
g.addEdge(new jsgraphs.Edge(4, 6, 20.0));
g.addEdge(new jsgraphs.Edge(4, 7, 5.0));
g.addEdge(new jsgraphs.Edge(5, 2, 1.0));
g.addEdge(new jsgraphs.Edge(5, 6, 13.0));
g.addEdge(new jsgraphs.Edge(7, 5, 6.0));
g.addEdge(new jsgraphs.Edge(7, 2, 7.0));  


var bf = new jsgraphs.BellmanFord(g, 0);

for(var v = 1; v < g.V; ++v){
    if(bf.hasPathTo(v)){
        var path = bf.pathTo(v);
        console.log('=====path from 0 to ' + v + ' start==========');
        for(var i = 0; i < path.length; ++i) {
            var e = path[i];
            console.log(e.from() + ' => ' + e.to() + ': ' + e.weight);
        }
        console.log('=====path from 0 to ' + v + ' end==========');
        console.log('=====distance: '  + bf.distanceTo(v) + '=========');
    }
}

Find the shortest paths using Topological Sort Shortest Paths

The sample code below show how to obtain the shortest paths from a starting point 0 on a weighted directed acylic graph using Topological Sort:

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.WeightedDiGraph(8);
g.addEdge(new jsgraphs.Edge(0, 1, 5.0));
g.addEdge(new jsgraphs.Edge(0, 4, 9.0));
g.addEdge(new jsgraphs.Edge(0, 7, 8.0));
g.addEdge(new jsgraphs.Edge(1, 2, 12.0));
g.addEdge(new jsgraphs.Edge(1, 3, 15.0));
g.addEdge(new jsgraphs.Edge(1, 7, 4.0));
g.addEdge(new jsgraphs.Edge(2, 3, 3.0));
g.addEdge(new jsgraphs.Edge(2, 6, 11.0));
g.addEdge(new jsgraphs.Edge(3, 6, 9.0));
g.addEdge(new jsgraphs.Edge(4, 5, 5.0));
g.addEdge(new jsgraphs.Edge(4, 6, 20.0));
g.addEdge(new jsgraphs.Edge(4, 7, 5.0));
g.addEdge(new jsgraphs.Edge(5, 2, 1.0));
g.addEdge(new jsgraphs.Edge(5, 6, 13.0));
g.addEdge(new jsgraphs.Edge(7, 5, 6.0));
g.addEdge(new jsgraphs.Edge(7, 2, 7.0));  


var bf = new jsgraphs.TopologicalSortShortestPaths(g, 0);

for(var v = 1; v < g.V; ++v){
    if(bf.hasPathTo(v)){
        var path = bf.pathTo(v);
        console.log('=====path from 0 to ' + v + ' start==========');
        for(var i = 0; i < path.length; ++i) {
            var e = path[i];
            console.log(e.from() + ' => ' + e.to() + ': ' + e.weight);
        }
        console.log('=====path from 0 to ' + v + ' end==========');
        console.log('=====distance: '  + bf.distanceTo(v) + '=========');
    }
}

Find the MaxFlow-MinCut using Ford-Fulkerson algorithm

The sample code below show how to obtain the MaxFlow-MinCut of a directed weighted graph using ford-fulkerson algorithm (Link: HTML DEMO):

var jsgraphs = require('js-graph-algorithms');
var g = new jsgraphs.FlowNetwork(8);
g.addEdge(new jsgraphs.FlowEdge(0, 1, 10));
g.addEdge(new jsgraphs.FlowEdge(0, 2, 5));
g.addEdge(new jsgraphs.FlowEdge(0, 3, 15));
g.addEdge(new jsgraphs.FlowEdge(1, 4, 9));
g.addEdge(new jsgraphs.FlowEdge(1, 5, 15));
g.addEdge(new jsgraphs.FlowEdge(1, 2, 4));
g.addEdge(new jsgraphs.FlowEdge(2, 5, 8));
g.addEdge(new jsgraphs.FlowEdge(2, 3, 4));
g.addEdge(new jsgraphs.FlowEdge(3, 6, 16));
g.addEdge(new jsgraphs.FlowEdge(4, 5, 15));
g.addEdge(new jsgraphs.FlowEdge(4, 7, 10));
g.addEdge(new jsgraphs.FlowEdge(5, 7, 10));
g.addEdge(new jsgraphs.FlowEdge(5, 6, 15));
g.addEdge(new jsgraphs.FlowEdge(6, 2, 6));
g.addEdge(new jsgraphs.FlowEdge(6, 7, 10)); 

g.node(2).label = 'Hello';
g.edge(0, 1).label = 'World';

var source = 0;
var target = 7;
var ff = new jsgraphs.FordFulkerson(g, source, target);
console.log('max-flow: ' + ff.value);

var minCut = ff.minCut(g);

for(var i = 0; i < minCut.length; ++i) {
    var e = minCut[i];
    console.log('min-cut: (' + e.from() + ", " + e.to() + ')');
}

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keras-sentiment-analysis-web-api

Web api built on flask for keras-based sentiment analysis using Word Embedding, RNN and CNN
Python
26
star
21

java-ssd-object-detection

Image SSD object detection in Java using Tensorrflow
Java
25
star
22

spring-websocket-android-client-demo

Demo on how to integrate spring websocket on the server with android client
Java
23
star
23

keras-question-and-answering-web-api

Question answering system developed using seq2seq and memory network model in Keras
Python
22
star
24

keras-fake-news-generator-and-detector

Fake news generator and detector using keras
Python
21
star
25

java-magento-client

Java client for communicating with Magento site
Java
21
star
26

spring-boot-spark-integration-demo

Demo on how to integrate Spring Data JPA, Apache Spark and GraphX with Java and Scala mixed codes
Java
18
star
27

keras-video-object-detector

Object detector in videos using keras and YOLO
Python
17
star
28

java-reinforcement-learning-flappy-bird

Demo of java-reinforcement-learning library using flappy bird
Java
16
star
29

keras-language-translator-web-api

A simple language translator implemented in Keras with Flask serving web
Python
15
star
30

cs-hidden-markov-models

HIdden Markov Models using C#
C#
14
star
31

keras-chinese-resume-parser-and-analyzer

keras project that parses and analyze chinese resumes
Python
13
star
32

java-dynamic-programming

Solving dynamic programming problems in Java
Java
13
star
33

spring-boot-excel-upload-demo

Demo project on how upload and process csv and excel file in the spring boot
Java
12
star
34

java-decision-forest

Package implements decision tree and isolation forest
Java
12
star
35

java-tensorflow-samples

Java sample codes on how to integrate with tensorflow
Java
12
star
36

mxnet-sentiment-analysis

Sentiment Analysis implemented using Gluon and MXNet
Python
11
star
37

keras-search-engine

A simple document and image search engine implemented in keras
Python
11
star
38

mxnet-recommender

Collaborative Filtering NN and CNN based recommender implemented with MXNet
Python
11
star
39

unity-tensorflow-samples

Unity project that loads pretrained tensorflow pb model files and use them to predict
Python
11
star
40

java-clustering

Package provides java implementation of various clustering algorithms
Java
11
star
41

java-audio-embedding

Audio classifier, encoder, and search engine in Java
Java
10
star
42

js-recommender

Package provides java implementation of content collaborative filtering for recommend-er system
JavaScript
10
star
43

pyalgs

Package pyalgs implements algorithms in Robert Sedgwick's Algorithms using Python
Python
10
star
44

spring-websocket-csharp-client-demo

Demo of connecting C# client to spring web application via websocket
Java
10
star
45

java-lda

Package provides java implementation of the latent dirichlet allocation (LDA) for topic modelling
Java
9
star
46

scrapy-projects

Projects using selenium, requests, bs4, and scrapy for web scraping on google images, google trends and others
Python
9
star
47

cs-moea

Multi-Objective Evolutionary Algorithms implemented in .NET
C#
9
star
48

java-local-outlier-factor

Package implements a number local outlier factor algorithms for outlier detection and finding anomalous data
Java
9
star
49

js-stats

Package provides the javascript implementation of various statistics and distribution
JavaScript
8
star
50

java-genetic-programming

Genetic-programming framework for various genetic programming paradigms such as linear genetic programming, tree genetic programming, gene expression programming, etc
Java
8
star
51

java-adaptive-resonance-theory

Package provides java implementation of algorithms in the field of adaptive resonance theory (ART)
Java
7
star
52

java-outliers

Package provide java implementation of outlier detection using normal distribution for multi-variate datasets
Java
7
star
53

java-basic-blockchain

Proof-of-concept blockchain implementation in Java
Java
7
star
54

cs-fuzzy-logic

Package provides C# implementation of fuzzy logic system
C#
6
star
55

java-libsvm

Package provides the direct java conversion of the origin libsvm C codes as well as a number of adapter to make it easier to program with libsvm on Java
Java
6
star
56

java-reinforcement-learning-tic-tac-toe

Demo of reinforcement learning using tic-tac-toe
Java
6
star
57

spring-security-csrf-android-demo

Demo on how to communicate android with spring security and CSRF enabled
Java
6
star
58

spark-ml-genetic-programming

Package provides java implementation of big-data genetic programming for Apache Spark
Java
6
star
59

java-ann-mlp

Package provides java implementation of multi-layer perceptron neural network with back-propagation learning algorithm
Java
6
star
60

java-text-embedding

Word embedding in Java
Java
5
star
61

java-data-frame

Package provides the core data frame implementation for numerical computation
Java
5
star
62

java-tensorflow-music

Music classification, music search, music recommender and music encoder implemented in Tensorflow and Java
Java
5
star
63

cs-feedback-control

A simple control system framework that provide tools for feedback controllers such as PID controller, kalman filters, fuzzy controller
C#
5
star
64

java-statistical-inference

Opinionated statistical inference engine with fluent api to make it easier for conducting statistical inference with little or no knowledge of statistical inference principles involved
Java
5
star
65

keras-image-to-image

Transform one image to another image in Keras using GAN
Python
4
star
66

cs-tree-genetic-programming

tree-based genetic programming implemented using C#
C#
4
star
67

keras-gan-models

Some generative adversarial network models that I studied
Python
4
star
68

java-glm

Generalized linear models for regression and classification problems
Java
4
star
69

cs-ffmpeg-mp3-converter

Convert audio file of other formats to mp3 using ffmpeg in .NET
C#
4
star
70

php-magento2-api-extensions

Some useful Magento2 API extensions
PHP
4
star
71

cs-grammatical-evolution

Grammatical evolution implemented using C#
C#
4
star
72

java-leetcode

My daily LeetCode solutions
Java
4
star
73

android-code-view

A code viewer with code syntax highlight for Android
Java
4
star
74

java-machine-learning-web-api

A simple machine learning web server that caters for small datasets
Java
4
star
75

java-regex-cultivator

Regex generator which use genetic programming evolve grok and and to automatically discover regex given a set of texts having similar structure
Java
4
star
76

spring-websocket-angular-4-demo

Demo on how to integrate spring websocket with angular 4 application
TypeScript
3
star
77

android-magento-client

android client for communicating with magento
Java
3
star
78

keras-timeseries-web-api

recurrent neural networks for timeseries prediction in Keras
Python
3
star
79

java-naive-bayes-classifier

Package provides java implementation of naive bayes classifier
Java
3
star
80

vagrant-magento-2.16

Vagrantfile for magento 2 and Ubuntu
ApacheConf
3
star
81

spring-boot-auth2-slingshot

The original spring-boot-slingshot project that is extended with Auth2 for login using Facebook and Google
Java
3
star
82

spark-opt-moea

Distributed Multi-Objective Evolutionary Computation Framework for Spark
Java
3
star
83

cpp-steering-behaviors

OpenGL Demo for Game Agent Steering + Flocking + Swarm Behaviors
C
3
star
84

js-svm

Package provides javascript implementation of support vector machines
JavaScript
3
star
85

cs-linear-genetic-programming

Linear Genetic Programming implemented in C#
C#
3
star
86

java-som

Package provides java implementation of self-organizing feature map (Kohonen map)
Java
3
star
87

spring-security-csrf-angular-4-demo

Demo on how to integrate angular 4 application with spring application that has spring security and CSRF enabled
Java
3
star
88

spring-websocket-java-client-demo

Demo on how to integrate spring websocket on the server with java client
Java
3
star
89

unity-magento-client

Magento client implemented in Unity3D
C#
3
star
90

cpp-mfc-fractal-art-iec-lgp

Interactive Evolutionary Computation for Fractal Arts using Linear Genetic Programming and MFC
C
2
star
91

cs-swarm-intelligence

Swam intelligence for numerical optimization implemented in .NET
C#
2
star
92

mxnet-text-to-image

Text to Image translation using Generative Adversarial Network and MXNet
Python
2
star
93

mxnet-vqa

Yet Another Visual Question Answering in MXNet
Python
2
star
94

keras-image-captioning

Image captioning using recurrent network and convolutional network in Keras
Python
2
star
95

cs-optimization-continuous-solutions

Local searches for continuous optimization implemented in C#
C#
2
star
96

spring-security-csrf-unity-client-demo

Java
2
star
97

cs-optimization-binary-solutions

Local search optimization for binary-coded solutions implemented in C#
C#
2
star
98

spring-boot-slingshot

slingshot project with spring boot and spring security and spring data jpa
Java
2
star
99

mxnet-image-to-image

Image to Image translation using MXNet and GAN
Python
2
star
100

cs-ipico-reader

C# IPICO Reader
C#
2
star