project setup

Author: chen0040

.gitignore

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+
+.DS_Store
+thumbs.db
+*.log
+node_modules/
+.idea

.travis.yml

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+language: node_js
+node_js:
+- "stable"
+
+before_script:
+  - npm install -g grunt-cli
+
+after_success:
+- npm run coveralls

Gruntfile.js

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+module.exports = function (grunt) {
+  // Load the plugin that provides the "uglify" task.
+  grunt.loadNpmTasks('grunt-contrib-uglify');
+  grunt.loadNpmTasks('grunt-mocha-test');
+  grunt.loadNpmTasks('grunt-eslint');
+
+  grunt.initConfig({
+    pkg: grunt.file.readJSON('package.json'),
+    uglify: {
+      options: {
+        preserveComments: 'some',
+      },
+      build: {
+        src: 'src/jsgraphs.js',
+        dest: 'build/jsgraphs.min.js',
+      },
+    },
+    mochaTest: {
+      test: {
+        options: {
+          reporter: 'spec',
+        },
+        src: ['tests/**/*.js'],
+      },
+    },
+  });
+
+  // Default task(s).
+  grunt.registerTask('default', ['uglify']);
+  grunt.registerTask('test', ['mochaTest']);
+};

index.js

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+module.exports = require('./src/jsgraphs');

package.json

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+{
+  "name": "js-graph-algorithms",
+  "version": "1.0.5",
+  "description": "Package implements data structures and algorithms for processing various types of graphs",
+  "author": "Xianshun Chen",
+  "contributors": [
+    "Xianshun Chen <[email protected]>"
+  ],
+  "license": "MIT",
+  "main": "index.js",
+  "directories": {
+    "test": "test"
+  },
+  "scripts": {
+    "test": "mocha test",
+    "cover": "istanbul cover _mocha",
+    "coveralls": "npm run cover -- --report lcovonly && cat ./coverage/lcov.info | coveralls"
+  },
+  "bin": {
+    "js-graphs": "./src/jsgraphs.js"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/chen0040/js-graph-algorithms.git"
+  },
+  "keywords": [
+    "graph", 
+    "weighted graph", 
+    "digraph", 
+    "directed graph", 
+    "connected components", 
+    "strongly connected components", 
+    "min cut", 
+    "max flow", 
+    "dijkstra", 
+    "bellman-ford",
+    "minimum spanning tree",
+    "depth first search",
+    "breadth first search",
+    "directed cycles",
+    "topological sort",
+    "ford-fulkerson",
+    "prim",
+    "kruskal"
+  ],
+  "dependencies": {
+    
+  },
+  "devDependencies": {
+    "chai": "^3.5.0",
+    "coveralls": "^2.13.1",
+    "grunt": "^1.0.1",
+    "grunt-contrib-uglify": "^3.0.0",
+    "grunt-eslint": "^19.0.0",
+    "grunt-mocha-test": "^0.13.2",
+    "istanbul": "^0.4.5",
+    "mocha": "^3.4.1"
+  },
+  "bugs": {
+    "url": "https://github.com/chen0040/js-graph-algorithms/issues"
+  },
+  "homepage": "https://github.com/chen0040/js-graph-algorithms#readme"
+}

src/jsgraphs.js

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+var jsgraphs = jsgraphs || {};
+
+(function(jss){
+	var Graph = function () {
+        
+    };
+    
+    jss.Graph = Graph;
+
+})(jsgraphs);
+
+if(module) {
+	module.exports = jsgraphs;
+}

test/chi-square-distribution-spec.js

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+var expect    = require("chai").expect;
+var jsgraphs = require("../src/jsgraphs");
+
+describe("Create chi square distribution", function() {
+  describe("default constructor", function() {
+    var distribution = new jsgraphs.ChiSquareDistribution(20);
+    it("has df of 10.00", function() {
+    	expect(distribution.df).to.equal(20); 
+      
+    });
+  });
+
+  describe('run cumulative probability', function(){
+    var distribution = new jsgraphs.ChiSquareDistribution(20);
+    it('has cumulativeProbability working', function(){
+      for(var X=0.2; X < 10.0; X += 0.5){
+        console.log(X + ': ' + distribution.cumulativeProbability(X));
+      }
+    });
+  });
+
+
+});

test/f-distribution-spec.js

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+var expect    = require("chai").expect;
+var jsgraphs = require("../src/jsgraphs");
+
+describe("Create f distribution", function() {
+  describe("default constructor", function() {
+    var distribution = new jsgraphs.FDistribution(10.0, 15.0);
+    it("has df1 of 10.0 and df2 of 15.0", function() {
+    	expect(distribution.df1).to.equal(10.0); 
+      expect(distribution.df2).to.equal(15.0); 
+      
+    });
+  });
+
+  describe('run cumulative probability', function(){
+    var distribution = new jsgraphs.FDistribution(10.0, 15.0);
+    it('has cumulativeProbability working', function(){
+      for(var F=0.2; F < 10.0; F += 0.5){
+        console.log(F + ': ' + distribution.cumulativeProbability(F));
+      }
+    });
+  });
+
+
+});

test/normal-distribution-spec.js

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+var expect    = require("chai").expect;
+var jsgraphs = require("../src/jsgraphs");
+
+describe("Create normal distribution", function() {
+  describe("default constructor", function() {
+    var distribution = new jsgraphs.NormalDistribution();
+    it("has mean of 0.0 and sd of 1.0", function() {
+    	
+      console.log('lnconstant: ' + distribution.lnconstant);
+    	expect(distribution.mean).to.equal(0.0);
+    	expect(distribution.sd).to.equal(1.0);
+    });
+
+    it("has 50% cumulative area at Z = 0", function(){
+      expect(distribution.cumulativeProbability(0.0)).to.above(0.4999);
+      expect(distribution.cumulativeProbability(0.0)).to.below(0.5001);
+    });
+
+    it("has Z = 0 at 50%", function(){
+      expect(distribution.invCumulativeProbability(0.5)).to.equal(0.0);
+    });
+  });
+
+  describe("Constructor with arguments", function() {
+    it("has user-defined mean and sd", function() {
+    	var distribution = new jsgraphs.NormalDistribution(5.0, 12.0);
+    	expect(distribution.mean).to.equal(5.0);
+    	expect(distribution.sd).to.equal(12.0);
+    });
+  });
+});

test/t-distribution-spec.js

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+var expect    = require("chai").expect;
+var jsgraphs = require("../src/jsgraphs");
+
+describe("Create t distribution", function() {
+  describe("default constructor", function() {
+    var distribution = new jsgraphs.TDistribution(10.0);
+    it("has df of 10.0", function() {
+    	expect(distribution.df).to.equal(10.0); 
+      
+    });
+  });
+
+  describe('run cumulative probability', function(){
+    var distribution = new jsgraphs.TDistribution(10);
+    it('has probability of 0.5 at t_df = 0', function(){
+      expect(distribution.cumulativeProbability(0.0)).to.equal(0.5);
+    });
+    it('has t_df = 0 with probability of 0.5', function(){
+      expect(distribution.invCumulativeProbability(0.5)).to.above(-0.001);
+      expect(distribution.invCumulativeProbability(0.5)).to.below(+0.001);
+    });
+    it('should run correctly and ascendingly for Z on range p = 0 to p = 1.0', function(){
+      var prevZ = -10000000;
+      for(var p = 0.0; p < 1.0; p += 0.01) {
+        var Z = distribution.invCumulativeProbability(p);
+        expect(Z).to.above(prevZ);
+        prevZ = Z;
+      }
+    })
+  });
+
+
+});