@@ -1,26 +1,3 @@
-function NormalDistribution(sigma, mu) {
- return new Object({
- sigma: sigma,
- mu: mu,
- sample: function() {
- var res;
- if (this.storedDeviate) {
- res = this.storedDeviate * this.sigma + this.mu;
- this.storedDeviate = null;
- } else {
- var dist = Math.sqrt(-1 * Math.log(Math.random()));
- var angle = 2 * Math.PI * Math.random();
- this.storedDeviate = dist*Math.cos(angle);
- res = dist*Math.sin(angle) * this.sigma + this.mu;
- }
- return res;
- },
- sampleInt : function() {
- return Math.round(this.sample());
- }
- });
-}
-
/*
* weight: number
* QQQ NOTE: what do the weights mean?
@@ -64,9 +41,17 @@ function TwoDDistribution(weight, mus, variances) {
},
sample_full: function(n) {
- var x = this.randn(n);
- var cho = cholesky($M(this.variances));
- return cho;
+ var rands = randn(n);
+ var mva = $M(this.variances);
+ var mmus = $M(this.mus);
+ var l = cholesky(mva);
+ var r = l.x(rands.transpose());
+ for (i=0; i < n; i++) {
+ r.elements[0][i] += mmus.elements[0][0];
+ r.elements[1][i] += mmus.elements[1][0];
+ }
+
+ return r.transpose();
},
});
gmm/gmm.js
@@ -3,17 +3,71 @@
<script type="text/javascript" src="../linalg/cholesky.js"></script>
<script type="text/javascript" src="../g.raphael/raphael.js"></script>
<script type="text/javascript" src="../g.raphael/g.raphael.js"></script>
-<script type="text/javascript" src="../g.raphael/g.line.js"></script>
+<script type="text/javascript" src="../g.raphael/g.scatter.js"></script>
<script type="text/javascript" src="gmm.js"></script>
<script type="text/javascript" src="jquery-1.3.2.min.js"></script>
<script type="text/javascript">
r = "";
$(document).ready(function(){
+ //TODO: test with non-positive-symmetric matrices
r = Raphael("gaussian", 500, 500);
//normal_dist_sanity_check();
- gaussian_dist_sanity_check(r);
+ //gaussian_dist_sanity_check(r);
+ full_gaussian_dist_sanity_check(r);
});
+function full_gaussian_dist_sanity_check(canvas) {
+ var weight = eval($("#weight").val());
+ var mus = eval($("#mus").val());
+ var vars = eval($("#vars").val());
+ var t = TwoDDistribution(weight, mus, vars);
+
+ var ss = t.sample_full(1000);
+ var xs = [];
+ var ys = [];
+ for (s=0; s<ss.elements.length; s++) {
+ xs.push(ss.elements[s][0]);
+ ys.push(ss.elements[s][1]);
+ }
+ //XXX TODO debugging deleteme
+ _xs = xs;
+ _ys = ys;
+
+ //TODO could do a scatter chart that accepts x,y pairs
+ r.remove();
+ r = Raphael("gaussian", 500, 500);
+ var min = Math.min(Math.min.apply(null, xs), Math.min.apply(null, ys))
+ var max = Math.max(Math.max.apply(null, xs), Math.max.apply(null, ys))
+ var shim = max-min;
+ min -= shim * .2;
+ max += shim * .2;
+ var opts = {nostroke: true, axis: "0 0 1 1", symbol: "o",
+ axminx:min, axmaxx: max, axminy: min, axmaxy: max};
+ r.g.scatterplot(40,0,400,400, xs, ys, opts);
+}
+
+/* TODO DELETEME DEBUGGING XXX */
+function sh(values, dim) {
+ var k = values.length / dim,
+ l = k,
+ sum = 0,
+ res = [];
+ for (j=0; j < values.length; j++) {
+ console.log("k, l, sum, res, j: ", k, l, sum, res, j);
+ l--;
+ if (l < 0) {
+ sum += values[j] * (1 + l);
+ res.push(sum / k);
+ sum = values[j] * -l;
+ l += k;
+ } else {
+ sum += values[j];
+ }
+ }
+ return res;
+}
+/* TODO DELETEME DEBUGGING XXX */
+
function gaussian_dist_sanity_check(canvas) {
var weight = eval($("#weight").val());
var mus = eval($("#mus").val());
@@ -28,12 +82,11 @@ function gaussian_dist_sanity_check(canvas) {
ys.push(ss[s][1]);
}
- //TODO could do a scatter chart that accepts x,y pairs
r.remove();
r = Raphael("gaussian", 500, 500);
opts = {nostroke: true, axis: "0 0 1 1", symbol: "o"}
//axminx: .8, axmaxx: 1.2, axminy: .8, axmaxy: 1.2};
- r.g.linechart(40,0,400,400, xs, ys, opts);
+ r.g.scatterplot(40,0,400,400, xs, ys, opts);
}
//TODO: compare to numpy; does it converge on a bell curve at the same n?
@@ -96,8 +149,9 @@ function normal_dist_sanity_check() {
<div id="gaussian" style="float:left"> </div>
weight:<input type="text" id="weight" value="1"></input><br>
mus: <input type="text" id="mus" value="[1,1]"></input><br>
-vars: <input type="text" id="vars" value="[1,1]"></input><br>
-<input type="submit" onclick="gaussian_dist_sanity_check(r)">
+vars: <input type="text" id="vars" value="[[1,0],[0,1]]"></input><br>
+<!-- vars: <input type="text" id="vars" value="[1,1]"></input><br> -->
+<input type="submit" onclick="full_gaussian_dist_sanity_check(r)">
</div>
<div id="chart"> </div>
</body>
gmm/test.html
92e1fc28c09117329293c292623cd353ab447bb3
Bill Mill
bill.mill@gmail.com
http://github.com/llimllib/grabcut_js/commit/13c3a41585e37348fde004b739fb20567d943cd8
13c3a41585e37348fde004b739fb20567d943cd8
2009-11-04T05:59:36-08:00
2009-11-04T05:59:36-08:00
lots of work on on the full gaussian distribution
281ea80c3ef441d2f8e6bb97f209c8549d9f5d25
Bill Mill
bill.mill@gmail.com