Merge branch 'dev'

docs/conf.py

@@ -15,6 +15,7 @@
 import sys
 import os
 import shlex
+import json
 
 on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
 
@@ -61,10 +62,12 @@
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
+with open('../package.json') as f:
+    pkg = json.load(f)
 # The short X.Y version.
-version = '0.1.1'
+version = pkg['version']
 # The full version, including alpha/beta/rc tags.
-release = '0.1.1'
+release = pkg['version']
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/development/npm.rst

@@ -4,6 +4,7 @@ Updating the npm package
 1. Update version in dev::
 
     git checkout dev
+    git pull
     npm version patch  // or minor, or major; prints new version number
 
 2. Merge into master::

docs/development/workflow.rst

@@ -12,6 +12,17 @@ To only run the tests, do::
 
     npm test
 
+To reproduce intermittent test failures run the inference tests with
+the random seed displayed in the test output. For example::
+
+    RANDOM_SEED=2344512342 nodeunit tests/test-inference.js
+
+nodeunit can also run individual tests or test groups. For example::
+
+    nodeunit tests/test-inference.js -t Enumerate
+
+See the `nodeunit documentation`_ for details.
+
 To only run the linter::
 
     grunt gjslint
@@ -39,3 +50,4 @@ Multiple ``--require`` arguments can be used to include multiple
 packages.
 
 .. _continuous integration tests: https://travis-ci.org/probmods/webppl
+.. _nodeunit documentation: https://github.com/caolan/nodeunit#command-line-options

docs/usage.rst

@@ -5,6 +5,10 @@ Running webppl programs::
 
     webppl examples/geometric.wppl
 
+Seeding the random number generator::
+
+    RANDOM_SEED=2344512342 webppl examples/lda.wppl
+
 Compiling webppl programs to Javascript::
 
     webppl examples/geometric.wppl --compile --out geometric.js

package.json

@@ -1,7 +1,7 @@
 {
   "name": "webppl",
   "description": "Probabilistic programming for the web",
-  "version": "0.2.0",
+  "version": "0.2.1",
   "author": "webppl contributors",
   "main": "src/main.js",
   "repository": {
@@ -36,6 +36,7 @@
     "grunt-contrib-nodeunit": "^0.4.1",
     "grunt-gjslint": "^0.1.6",
     "nodeunit": "^0.9.1",
+    "seedrandom": "^2.4.2",
     "through2": "^2.0.0"
   },
   "scripts": {

src/erp.js

@@ -128,7 +128,7 @@ var deserializeERP = function(JSONString) {
 
 var uniformERP = new ERP({
   sample: function(params) {
-    var u = Math.random();
+    var u = util.random();
     return (1 - u) * params[0] + u * params[1];
   },
   score: function(params, val) {
@@ -142,7 +142,7 @@ var uniformERP = new ERP({
 var bernoulliERP = new ERP({
   sample: function(params) {
     var weight = params[0];
-    var val = Math.random() < weight;
+    var val = util.random() < weight;
     return val;
   },
   score: function(params, val) {
@@ -166,7 +166,7 @@ var bernoulliERP = new ERP({
 
 var randomIntegerERP = new ERP({
   sample: function(params) {
-    return Math.floor(Math.random() * params[0]);
+    return Math.floor(util.random() * params[0]);
   },
   score: function(params, val) {
     var stop = params[0];
@@ -183,8 +183,8 @@ function gaussianSample(params) {
   var sigma = params[1];
   var u, v, x, y, q;
   do {
-    u = 1 - Math.random();
-    v = 1.7156 * (Math.random() - 0.5);
+    u = 1 - util.random();
+    v = 1.7156 * (util.random() - 0.5);
     x = u - 0.449871;
     y = Math.abs(v) + 0.386595;
     q = x * x + y * (0.196 * y - 0.25472 * x);
@@ -266,7 +266,7 @@ function gammaSample(params) {
   var a = params[0];
   var b = params[1];
   if (a < 1) {
-    return gammaSample([1 + a, b]) * Math.pow(Math.random(), 1 / a);
+    return gammaSample([1 + a, b]) * Math.pow(util.random(), 1 / a);
   }
   var x, v, u;
   var d = a - 1 / 3;
@@ -277,7 +277,7 @@ function gammaSample(params) {
       v = 1 + c * x;
     } while (v <= 0);
     v = v * v * v;
-    u = Math.random();
+    u = util.random();
     if ((u < 1 - 0.331 * x * x * x * x) || (Math.log(u) < 0.5 * x * x + d * (1 - v + Math.log(v)))) {
       return b * d * v;
     }
@@ -298,7 +298,7 @@ var gammaERP = new ERP({
 var exponentialERP = new ERP({
   sample: function(params) {
     var a = params[0];
-    var u = Math.random();
+    var u = util.random();
     return Math.log(u) / (-1 * a);
   },
   score: function(params, val) {
@@ -363,7 +363,7 @@ function binomialSample(params) {
   }
   var u;
   for (var i = 0; i < n; i++) {
-    u = Math.random();
+    u = util.random();
     if (u < p) {
       k++;
     }
@@ -452,7 +452,7 @@ var poissonERP = new ERP({
     var emu = Math.exp(-mu);
     var p = 1;
     do {
-      p *= Math.random();
+      p *= util.random();
       k++;
     } while (p > emu);
     return (k - 1) || 0;
@@ -499,16 +499,16 @@ var dirichletERP = new ERP({ sample: dirichletSample, score: dirichletScore });
 
 function multinomialSample(theta) {
   var thetaSum = util.sum(theta);
-  var x = Math.random() * thetaSum;
+  var x = util.random() * thetaSum;
   var k = theta.length;
   var probAccum = 0;
   for (var i = 0; i < k; i++) {
     probAccum += theta[i];
-    if (probAccum >= x) {
+    if (x < probAccum) {
       return i;
-    } //FIXME: if x=0 returns i=0, but this isn't right if theta[0]==0...
+    }
   }
-  return k;
+  return k - 1;
 }
 
 // Make a discrete ERP from a {val: prob, etc.} object (unormalized).
@@ -535,14 +535,16 @@ function makeMarginalERP(marginal) {
   // Make an ERP from marginal:
   var dist = new ERP({
     sample: function(params) {
-      var x = Math.random();
+      var x = util.random();
       var probAccum = 0;
-      for (var i in marginal) {if (marginal.hasOwnProperty(i)) {
-        probAccum += marginal[i].prob;
-        // FIXME: if x=0 returns i=0, but this isn't right if theta[0]==0...
-        if (probAccum >= x)
-          return marginal[i].val;
-      }}
+      for (var i in marginal) {
+        if (marginal.hasOwnProperty(i)) {
+          probAccum += marginal[i].prob;
+          if (x < probAccum) {
+            return marginal[i].val;
+          }
+        }
+      }
       return marginal[i].val;
     },
     score: function(params, val) {
@@ -620,7 +622,7 @@ function gaussianProposalParams(params, prevVal) {
 }
 
 function dirichletProposalParams(params, prevVal) {
-  var concentration = 0.1; // TODO: choose the right parameters.
+  var concentration = 0.1;
   var driftParams = params.map(function(x) {return concentration * x});
   return driftParams;
 }

src/hashtable.js

@@ -376,9 +376,9 @@ function Hashtable() {
     // console.log("SIZE: " + size);
     var L = biggestBucket.entries.length;
     while (true) {
-      var bi = Math.floor(Math.random() * buckets.length);
+      var bi = Math.floor(util.random() * buckets.length);
       var bucket = buckets[bi];
-      var p = Math.floor(Math.random() * L);
+      var p = Math.floor(util.random() * L);
       if (p < bucket.entries.length)
         return bucket.entries[p][1];
     }

src/headerUtils.js

@@ -51,7 +51,7 @@ module.exports = function(env) {
       var args = Array.prototype.slice.call(arguments, 3);
       var stringedArgs = JSON.stringify(args);
       var foundInCache = stringedArgs in c;
-      var recomp = Math.random() < recompProb;
+      var recomp = util.random() < recompProb;
       if (foundInCache && !recomp) {      // return stored value
         return k(s, c[stringedArgs]);
       } else {                           // recompute

src/inference/asyncpf.js

@@ -66,7 +66,7 @@ module.exports = function(env) {
 
     // launch a new particle OR continue an existing one
     var p, launchP;
-    var i = Math.floor((this.buffer.length + 1) * Math.random());
+    var i = Math.floor((this.buffer.length + 1) * util.random());
     if (i == this.buffer.length) { // generate new particle
       p = initParticle(this.store, this.exitK);
     } else {                    // launch particle in queue
@@ -122,7 +122,7 @@ module.exports = function(env) {
 
       // compute number of children and their weights
       if (logRatio < 0) {
-        numChildrenAndWeight = Math.log(Math.random()) < logRatio ?
+        numChildrenAndWeight = Math.log(util.random()) < logRatio ?
             [1, wbar] :
             [0, -Infinity];
       } else {

src/inference/incrementalmh.js

@@ -564,7 +564,7 @@ module.exports = function(env) {
   };
 
   ArrayERPMasterList.prototype.getRandom = function() {
-    var idx = Math.floor(Math.random() * this.erpNodes.length);
+    var idx = Math.floor(util.random() * this.erpNodes.length);
     return this.erpNodes[idx];
   };
 
@@ -862,7 +862,7 @@ module.exports = function(env) {
                                     this.rvsPropLP, this.fwdPropLP);
         debuglog(1, 'num vars:', this.erpMasterList.size(), 'old num vars:', this.erpMasterList.oldSize());
         debuglog(1, 'acceptance prob:', acceptance);
-        if (Math.random() >= acceptance) {
+        if (util.random() >= acceptance) {
           debuglog(1, 'REJECT');
           this.score = this.oldScore;
           var n = this.touchedNodes.length;
@@ -934,11 +934,14 @@ module.exports = function(env) {
         }
       }
     } else {
-      var dist;
-      if (this.returnHist)
-        dist = erp.makeMarginalERP(util.logHist(this.returnHist));
-      else
-        dist = erp.makeMarginalERP({});
+      var hist;
+      if (this.returnSamps || this.onlyMAP) {
+        hist = {};
+        hist[JSON.stringify(this.MAP.value)] = { prob: 1, val: this.MAP.value };
+      } else {
+        hist = this.returnHist;
+      }
+      var dist = erp.makeMarginalERP(util.logHist(hist));
       if (this.returnSamps) {
         if (this.onlyMAP)
           this.returnSamps.push(this.MAP);

src/inference/mhkernel.js

@@ -34,7 +34,7 @@ module.exports = function(env) {
     }
     // Make a new proposal.
     env.query.clear();
-    this.regenFrom = this.proposalBoundary + Math.floor(Math.random() * numERP);
+    this.regenFrom = this.proposalBoundary + Math.floor(util.random() * numERP);
     this.trace = this.oldTrace.upto(this.regenFrom);
     var regen = this.oldTrace.choiceAtIndex(this.regenFrom);
     return this.sample(_.clone(regen.store), regen.k, regen.address, regen.erp, regen.params, true);
@@ -90,7 +90,7 @@ module.exports = function(env) {
       assert(!this.trace.isComplete(), 'Particle missed exit address during rejuvenation.');
     }
     var prob = acceptProb(this.trace, this.oldTrace, this.regenFrom, this.reused, this.proposalBoundary);
-    var accept = Math.random() < prob;
+    var accept = util.random() < prob;
     return this.cont(accept ? this.trace : this.oldTrace, accept);
   };
 

src/inference/pmcmc.js

@@ -69,7 +69,7 @@ module.exports = function(env) {
 
   PMCMC.prototype.activeContinuationWithStore = function() {
     var k = last(this.activeParticle().continuations);
-    var s = _.clone(last(this.activeParticle().stores)); // FIXME: why is cloning here necessary?
+    var s = _.clone(last(this.activeParticle().stores));
     return function() {
       return k(s);
     };

src/inference/rejection.js

@@ -32,7 +32,7 @@ module.exports = function(env) {
 
   Rejection.prototype.run = function() {
     this.scoreSoFar = 0;
-    this.threshold = this.maxScore + Math.log(Math.random());
+    this.threshold = this.maxScore + Math.log(util.random());
     return this.wpplFn(_.clone(this.s), env.exit, this.a);
   }
 

src/transforms/caching.js

@@ -8,8 +8,6 @@ var build = require('ast-types').builders;
 var types = require('ast-types').types;
 var isPrimitive = require('../syntax').isPrimitive;
 
-
-// TODO: Auto-extract this list, somehow?
 var cacheExempt = [
   'flip',
   'randomInteger',