Updated README file for new features.

README.md

@@ -17,11 +17,23 @@ where $e$ is an edge, $w_e$ is the weight of an edge (the distance between the t
 Implementation:
 * Run using Javascript, node.js (v8 or above)
 * Needs the following modules installed: jsnetworkx, lodash-clonedeep, command-line-args, mocha (for testing), assert (for testing), istanbul (for coverage), and coveralls mocha-lcov-reporter (for coverage)
-* Run: npm install wfunkenbusch-markov-chain-monte-carlo
-* Create a .js file which calls the main function and sets inputs
-* Run: node *file name*.js
 
-Ex. .js file
+1. npm install wfunkenbusch-markov-chain-monte-carlo
+2. Create a .js file which calls the main function and sets inputs
+3. node *file name*.js
+
+Alternatively,
+
+1. git clone https://github.com/wfunkenbusch/Markov-Chain-Monte-Carlo
+2. Enter repository
+3. node ./lib/index.js/ --nodes *nodes* --T *T* --r *r* --N *N*
+
+To run unit tests:
+
+1. Enter repository
+2. npm test
+
+Example .js file:
 
 const mcmc = require('wfunkenbusch-markov-chain-monte-carlo')
 var options = {};
@@ -52,8 +64,24 @@ Outputs:
 
 All outputs are printed to the console.
 
-* Current iteration number, for keeping track of code progress.
-* Each adjacency matrix, in order of appearance, followed by the number of times it appeared.
+* Current iteration number, for keeping track of code progress. To suppress, comment out console.log(i + 1) in main(). To reduce the number of outputs, console.log(i + 1) may be replaced with:
+
+if ((i + 1) % n === 0) {
+    console.log(i + 1)
+}
+
+where n is the fraction of outputs to print.
+
+* The top 1% of adjacency matrices, followed by the number of times they appeared. Prioritizes graphs which appeared earlier. To change the percentage of top graphs printed, replace the line
+
+var nBestGraphs = Math.floor(Fun.AList.length / 100 + 1);
+
+with
+
+var nBestGraphs = Math.floor(Fun.AList.length * (*fraction to print*) + 1);
+
 * The expected number of edges connected to the source node.
 * The expected number of edges in the graph.
-* The expected value of the maximum shortest path from the source node to all other nodes.
+* The expected value of the maximum shortest path from the source node to all other nodes.
+
+Note: the total number of adjacency matrices is N + 1.

lib/index.js

@@ -577,9 +577,6 @@ function main(options) {
   Fun.generate();
   Fun.storeA(Fun.G);
 
-  // Prints current iteration
-  console.log(1);
-
   // Finds longest shortest path length from source node to a node
   var maxPathLength = 0;
 
@@ -597,13 +594,13 @@ function main(options) {
   var totalMaxPathLength = maxPathLength;
 
   // Metropolis-Hastings algorithm, storing A at each iteration
-  for (var i = 0; i < N - 1; i++) {
+  for (var i = 0; i < N; i++) {
     Fun.iterate();
     Fun.acceptOrReject(Fun.G, Fun.H, T, r);
     Fun.storeA(Fun.G);
 
     // Prints current iteration
-    console.log(i + 2);
+    console.log(i + 1);
 
     // Overwrites current longest path if path length for tested node is larger
     maxPathLength = 0;

package.json

@@ -1,6 +1,6 @@
 {
   "name": "wfunkenbusch-markov-chain-monte-carlo",
-  "version": "0.0.1",
+  "version": "0.0.2",
   "description": "Markov Chain Monte-Carlo",
   "homepage": "",
   "author": {