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free-simulator.js
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free-simulator.js
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/*
CryptoJS v3.1.2
code.google.com/p/crypto-js
(c) 2009-2013 by Jeff Mott. All rights reserved.
code.google.com/p/crypto-js/wiki/License
*/
var CryptoJS=CryptoJS||function(a,m){var r={},f=r.lib={},g=function(){},l=f.Base={extend:function(a){g.prototype=this;var b=new g;a&&b.mixIn(a);b.hasOwnProperty("init")||(b.init=function(){b.$super.init.apply(this,arguments)});b.init.prototype=b;b.$super=this;return b},create:function(){var a=this.extend();a.init.apply(a,arguments);return a},init:function(){},mixIn:function(a){for(var b in a)a.hasOwnProperty(b)&&(this[b]=a[b]);a.hasOwnProperty("toString")&&(this.toString=a.toString)},clone:function(){return this.init.prototype.extend(this)}},
p=f.WordArray=l.extend({init:function(a,b){a=this.words=a||[];this.sigBytes=b!=m?b:4*a.length},toString:function(a){return(a||q).stringify(this)},concat:function(a){var b=this.words,d=a.words,c=this.sigBytes;a=a.sigBytes;this.clamp();if(c%4)for(var j=0;j<a;j++)b[c+j>>>2]|=(d[j>>>2]>>>24-8*(j%4)&255)<<24-8*((c+j)%4);else if(65535<d.length)for(j=0;j<a;j+=4)b[c+j>>>2]=d[j>>>2];else b.push.apply(b,d);this.sigBytes+=a;return this},clamp:function(){var n=this.words,b=this.sigBytes;n[b>>>2]&=4294967295<<
32-8*(b%4);n.length=a.ceil(b/4)},clone:function(){var a=l.clone.call(this);a.words=this.words.slice(0);return a},random:function(n){for(var b=[],d=0;d<n;d+=4)b.push(4294967296*a.random()|0);return new p.init(b,n)}}),y=r.enc={},q=y.Hex={stringify:function(a){var b=a.words;a=a.sigBytes;for(var d=[],c=0;c<a;c++){var j=b[c>>>2]>>>24-8*(c%4)&255;d.push((j>>>4).toString(16));d.push((j&15).toString(16))}return d.join("")},parse:function(a){for(var b=a.length,d=[],c=0;c<b;c+=2)d[c>>>3]|=parseInt(a.substr(c,
2),16)<<24-4*(c%8);return new p.init(d,b/2)}},G=y.Latin1={stringify:function(a){var b=a.words;a=a.sigBytes;for(var d=[],c=0;c<a;c++)d.push(String.fromCharCode(b[c>>>2]>>>24-8*(c%4)&255));return d.join("")},parse:function(a){for(var b=a.length,d=[],c=0;c<b;c++)d[c>>>2]|=(a.charCodeAt(c)&255)<<24-8*(c%4);return new p.init(d,b)}},fa=y.Utf8={stringify:function(a){try{return decodeURIComponent(escape(G.stringify(a)))}catch(b){throw Error("Malformed UTF-8 data");}},parse:function(a){return G.parse(unescape(encodeURIComponent(a)))}},
h=f.BufferedBlockAlgorithm=l.extend({reset:function(){this._data=new p.init;this._nDataBytes=0},_append:function(a){"string"==typeof a&&(a=fa.parse(a));this._data.concat(a);this._nDataBytes+=a.sigBytes},_process:function(n){var b=this._data,d=b.words,c=b.sigBytes,j=this.blockSize,l=c/(4*j),l=n?a.ceil(l):a.max((l|0)-this._minBufferSize,0);n=l*j;c=a.min(4*n,c);if(n){for(var h=0;h<n;h+=j)this._doProcessBlock(d,h);h=d.splice(0,n);b.sigBytes-=c}return new p.init(h,c)},clone:function(){var a=l.clone.call(this);
a._data=this._data.clone();return a},_minBufferSize:0});f.Hasher=h.extend({cfg:l.extend(),init:function(a){this.cfg=this.cfg.extend(a);this.reset()},reset:function(){h.reset.call(this);this._doReset()},update:function(a){this._append(a);this._process();return this},finalize:function(a){a&&this._append(a);return this._doFinalize()},blockSize:16,_createHelper:function(a){return function(b,d){return(new a.init(d)).finalize(b)}},_createHmacHelper:function(a){return function(b,d){return(new ga.HMAC.init(a,
d)).finalize(b)}}});var ga=r.algo={};return r}(Math);
(function(a){var m=CryptoJS,r=m.lib,f=r.Base,g=r.WordArray,m=m.x64={};m.Word=f.extend({init:function(a,p){this.high=a;this.low=p}});m.WordArray=f.extend({init:function(l,p){l=this.words=l||[];this.sigBytes=p!=a?p:8*l.length},toX32:function(){for(var a=this.words,p=a.length,f=[],q=0;q<p;q++){var G=a[q];f.push(G.high);f.push(G.low)}return g.create(f,this.sigBytes)},clone:function(){for(var a=f.clone.call(this),p=a.words=this.words.slice(0),g=p.length,q=0;q<g;q++)p[q]=p[q].clone();return a}})})();
(function(){function a(){return g.create.apply(g,arguments)}for(var m=CryptoJS,r=m.lib.Hasher,f=m.x64,g=f.Word,l=f.WordArray,f=m.algo,p=[a(1116352408,3609767458),a(1899447441,602891725),a(3049323471,3964484399),a(3921009573,2173295548),a(961987163,4081628472),a(1508970993,3053834265),a(2453635748,2937671579),a(2870763221,3664609560),a(3624381080,2734883394),a(310598401,1164996542),a(607225278,1323610764),a(1426881987,3590304994),a(1925078388,4068182383),a(2162078206,991336113),a(2614888103,633803317),
a(3248222580,3479774868),a(3835390401,2666613458),a(4022224774,944711139),a(264347078,2341262773),a(604807628,2007800933),a(770255983,1495990901),a(1249150122,1856431235),a(1555081692,3175218132),a(1996064986,2198950837),a(2554220882,3999719339),a(2821834349,766784016),a(2952996808,2566594879),a(3210313671,3203337956),a(3336571891,1034457026),a(3584528711,2466948901),a(113926993,3758326383),a(338241895,168717936),a(666307205,1188179964),a(773529912,1546045734),a(1294757372,1522805485),a(1396182291,
2643833823),a(1695183700,2343527390),a(1986661051,1014477480),a(2177026350,1206759142),a(2456956037,344077627),a(2730485921,1290863460),a(2820302411,3158454273),a(3259730800,3505952657),a(3345764771,106217008),a(3516065817,3606008344),a(3600352804,1432725776),a(4094571909,1467031594),a(275423344,851169720),a(430227734,3100823752),a(506948616,1363258195),a(659060556,3750685593),a(883997877,3785050280),a(958139571,3318307427),a(1322822218,3812723403),a(1537002063,2003034995),a(1747873779,3602036899),
a(1955562222,1575990012),a(2024104815,1125592928),a(2227730452,2716904306),a(2361852424,442776044),a(2428436474,593698344),a(2756734187,3733110249),a(3204031479,2999351573),a(3329325298,3815920427),a(3391569614,3928383900),a(3515267271,566280711),a(3940187606,3454069534),a(4118630271,4000239992),a(116418474,1914138554),a(174292421,2731055270),a(289380356,3203993006),a(460393269,320620315),a(685471733,587496836),a(852142971,1086792851),a(1017036298,365543100),a(1126000580,2618297676),a(1288033470,
3409855158),a(1501505948,4234509866),a(1607167915,987167468),a(1816402316,1246189591)],y=[],q=0;80>q;q++)y[q]=a();f=f.SHA512=r.extend({_doReset:function(){this._hash=new l.init([new g.init(1779033703,4089235720),new g.init(3144134277,2227873595),new g.init(1013904242,4271175723),new g.init(2773480762,1595750129),new g.init(1359893119,2917565137),new g.init(2600822924,725511199),new g.init(528734635,4215389547),new g.init(1541459225,327033209)])},_doProcessBlock:function(a,f){for(var h=this._hash.words,
g=h[0],n=h[1],b=h[2],d=h[3],c=h[4],j=h[5],l=h[6],h=h[7],q=g.high,m=g.low,r=n.high,N=n.low,Z=b.high,O=b.low,$=d.high,P=d.low,aa=c.high,Q=c.low,ba=j.high,R=j.low,ca=l.high,S=l.low,da=h.high,T=h.low,v=q,s=m,H=r,E=N,I=Z,F=O,W=$,J=P,w=aa,t=Q,U=ba,K=R,V=ca,L=S,X=da,M=T,x=0;80>x;x++){var B=y[x];if(16>x)var u=B.high=a[f+2*x]|0,e=B.low=a[f+2*x+1]|0;else{var u=y[x-15],e=u.high,z=u.low,u=(e>>>1|z<<31)^(e>>>8|z<<24)^e>>>7,z=(z>>>1|e<<31)^(z>>>8|e<<24)^(z>>>7|e<<25),D=y[x-2],e=D.high,k=D.low,D=(e>>>19|k<<13)^
(e<<3|k>>>29)^e>>>6,k=(k>>>19|e<<13)^(k<<3|e>>>29)^(k>>>6|e<<26),e=y[x-7],Y=e.high,C=y[x-16],A=C.high,C=C.low,e=z+e.low,u=u+Y+(e>>>0<z>>>0?1:0),e=e+k,u=u+D+(e>>>0<k>>>0?1:0),e=e+C,u=u+A+(e>>>0<C>>>0?1:0);B.high=u;B.low=e}var Y=w&U^~w&V,C=t&K^~t&L,B=v&H^v&I^H&I,ha=s&E^s&F^E&F,z=(v>>>28|s<<4)^(v<<30|s>>>2)^(v<<25|s>>>7),D=(s>>>28|v<<4)^(s<<30|v>>>2)^(s<<25|v>>>7),k=p[x],ia=k.high,ea=k.low,k=M+((t>>>14|w<<18)^(t>>>18|w<<14)^(t<<23|w>>>9)),A=X+((w>>>14|t<<18)^(w>>>18|t<<14)^(w<<23|t>>>9))+(k>>>0<M>>>
0?1:0),k=k+C,A=A+Y+(k>>>0<C>>>0?1:0),k=k+ea,A=A+ia+(k>>>0<ea>>>0?1:0),k=k+e,A=A+u+(k>>>0<e>>>0?1:0),e=D+ha,B=z+B+(e>>>0<D>>>0?1:0),X=V,M=L,V=U,L=K,U=w,K=t,t=J+k|0,w=W+A+(t>>>0<J>>>0?1:0)|0,W=I,J=F,I=H,F=E,H=v,E=s,s=k+e|0,v=A+B+(s>>>0<k>>>0?1:0)|0}m=g.low=m+s;g.high=q+v+(m>>>0<s>>>0?1:0);N=n.low=N+E;n.high=r+H+(N>>>0<E>>>0?1:0);O=b.low=O+F;b.high=Z+I+(O>>>0<F>>>0?1:0);P=d.low=P+J;d.high=$+W+(P>>>0<J>>>0?1:0);Q=c.low=Q+t;c.high=aa+w+(Q>>>0<t>>>0?1:0);R=j.low=R+K;j.high=ba+U+(R>>>0<K>>>0?1:0);S=l.low=
S+L;l.high=ca+V+(S>>>0<L>>>0?1:0);T=h.low=T+M;h.high=da+X+(T>>>0<M>>>0?1:0)},_doFinalize:function(){var a=this._data,f=a.words,h=8*this._nDataBytes,g=8*a.sigBytes;f[g>>>5]|=128<<24-g%32;f[(g+128>>>10<<5)+30]=Math.floor(h/4294967296);f[(g+128>>>10<<5)+31]=h;a.sigBytes=4*f.length;this._process();return this._hash.toX32()},clone:function(){var a=r.clone.call(this);a._hash=this._hash.clone();return a},blockSize:32});m.SHA512=r._createHelper(f);m.HmacSHA512=r._createHmacHelper(f)})();
/*
CryptoJS v3.1.2
code.google.com/p/crypto-js
(c) 2009-2013 by Jeff Mott. All rights reserved.
code.google.com/p/crypto-js/wiki/License
*/
(function(){var h=CryptoJS,j=h.lib.WordArray;h.enc.Base64={stringify:function(b){var e=b.words,f=b.sigBytes,c=this._map;b.clamp();b=[];for(var a=0;a<f;a+=3)for(var d=(e[a>>>2]>>>24-8*(a%4)&255)<<16|(e[a+1>>>2]>>>24-8*((a+1)%4)&255)<<8|e[a+2>>>2]>>>24-8*((a+2)%4)&255,g=0;4>g&&a+0.75*g<f;g++)b.push(c.charAt(d>>>6*(3-g)&63));if(e=c.charAt(64))for(;b.length%4;)b.push(e);return b.join("")},parse:function(b){var e=b.length,f=this._map,c=f.charAt(64);c&&(c=b.indexOf(c),-1!=c&&(e=c));for(var c=[],a=0,d=0;d<
e;d++)if(d%4){var g=f.indexOf(b.charAt(d-1))<<2*(d%4),h=f.indexOf(b.charAt(d))>>>6-2*(d%4);c[a>>>2]|=(g|h)<<24-8*(a%4);a++}return j.create(c,a)},_map:"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/="}})();
/*
CryptoJS v3.1.2
code.google.com/p/crypto-js
(c) 2009-2013 by Jeff Mott. All rights reserved.
code.google.com/p/crypto-js/wiki/License
*/
var CryptoJS=CryptoJS||function(a,j){var c={},b=c.lib={},f=function(){},l=b.Base={extend:function(a){f.prototype=this;var d=new f;a&&d.mixIn(a);d.hasOwnProperty("init")||(d.init=function(){d.$super.init.apply(this,arguments)});d.init.prototype=d;d.$super=this;return d},create:function(){var a=this.extend();a.init.apply(a,arguments);return a},init:function(){},mixIn:function(a){for(var d in a)a.hasOwnProperty(d)&&(this[d]=a[d]);a.hasOwnProperty("toString")&&(this.toString=a.toString)},clone:function(){return this.init.prototype.extend(this)}},
u=b.WordArray=l.extend({init:function(a,d){a=this.words=a||[];this.sigBytes=d!=j?d:4*a.length},toString:function(a){return(a||m).stringify(this)},concat:function(a){var d=this.words,M=a.words,e=this.sigBytes;a=a.sigBytes;this.clamp();if(e%4)for(var b=0;b<a;b++)d[e+b>>>2]|=(M[b>>>2]>>>24-8*(b%4)&255)<<24-8*((e+b)%4);else if(65535<M.length)for(b=0;b<a;b+=4)d[e+b>>>2]=M[b>>>2];else d.push.apply(d,M);this.sigBytes+=a;return this},clamp:function(){var D=this.words,d=this.sigBytes;D[d>>>2]&=4294967295<<
32-8*(d%4);D.length=a.ceil(d/4)},clone:function(){var a=l.clone.call(this);a.words=this.words.slice(0);return a},random:function(D){for(var d=[],b=0;b<D;b+=4)d.push(4294967296*a.random()|0);return new u.init(d,D)}}),k=c.enc={},m=k.Hex={stringify:function(a){var d=a.words;a=a.sigBytes;for(var b=[],e=0;e<a;e++){var c=d[e>>>2]>>>24-8*(e%4)&255;b.push((c>>>4).toString(16));b.push((c&15).toString(16))}return b.join("")},parse:function(a){for(var d=a.length,b=[],e=0;e<d;e+=2)b[e>>>3]|=parseInt(a.substr(e,
2),16)<<24-4*(e%8);return new u.init(b,d/2)}},y=k.Latin1={stringify:function(a){var b=a.words;a=a.sigBytes;for(var c=[],e=0;e<a;e++)c.push(String.fromCharCode(b[e>>>2]>>>24-8*(e%4)&255));return c.join("")},parse:function(a){for(var b=a.length,c=[],e=0;e<b;e++)c[e>>>2]|=(a.charCodeAt(e)&255)<<24-8*(e%4);return new u.init(c,b)}},z=k.Utf8={stringify:function(a){try{return decodeURIComponent(escape(y.stringify(a)))}catch(b){throw Error("Malformed UTF-8 data");}},parse:function(a){return y.parse(unescape(encodeURIComponent(a)))}},
x=b.BufferedBlockAlgorithm=l.extend({reset:function(){this._data=new u.init;this._nDataBytes=0},_append:function(a){"string"==typeof a&&(a=z.parse(a));this._data.concat(a);this._nDataBytes+=a.sigBytes},_process:function(b){var d=this._data,c=d.words,e=d.sigBytes,l=this.blockSize,k=e/(4*l),k=b?a.ceil(k):a.max((k|0)-this._minBufferSize,0);b=k*l;e=a.min(4*b,e);if(b){for(var x=0;x<b;x+=l)this._doProcessBlock(c,x);x=c.splice(0,b);d.sigBytes-=e}return new u.init(x,e)},clone:function(){var a=l.clone.call(this);
a._data=this._data.clone();return a},_minBufferSize:0});b.Hasher=x.extend({cfg:l.extend(),init:function(a){this.cfg=this.cfg.extend(a);this.reset()},reset:function(){x.reset.call(this);this._doReset()},update:function(a){this._append(a);this._process();return this},finalize:function(a){a&&this._append(a);return this._doFinalize()},blockSize:16,_createHelper:function(a){return function(b,c){return(new a.init(c)).finalize(b)}},_createHmacHelper:function(a){return function(b,c){return(new ja.HMAC.init(a,
c)).finalize(b)}}});var ja=c.algo={};return c}(Math);
(function(a){var j=CryptoJS,c=j.lib,b=c.Base,f=c.WordArray,j=j.x64={};j.Word=b.extend({init:function(a,b){this.high=a;this.low=b}});j.WordArray=b.extend({init:function(b,c){b=this.words=b||[];this.sigBytes=c!=a?c:8*b.length},toX32:function(){for(var a=this.words,b=a.length,c=[],m=0;m<b;m++){var y=a[m];c.push(y.high);c.push(y.low)}return f.create(c,this.sigBytes)},clone:function(){for(var a=b.clone.call(this),c=a.words=this.words.slice(0),k=c.length,f=0;f<k;f++)c[f]=c[f].clone();return a}})})();
(function(){function a(){return f.create.apply(f,arguments)}for(var j=CryptoJS,c=j.lib.Hasher,b=j.x64,f=b.Word,l=b.WordArray,b=j.algo,u=[a(1116352408,3609767458),a(1899447441,602891725),a(3049323471,3964484399),a(3921009573,2173295548),a(961987163,4081628472),a(1508970993,3053834265),a(2453635748,2937671579),a(2870763221,3664609560),a(3624381080,2734883394),a(310598401,1164996542),a(607225278,1323610764),a(1426881987,3590304994),a(1925078388,4068182383),a(2162078206,991336113),a(2614888103,633803317),
a(3248222580,3479774868),a(3835390401,2666613458),a(4022224774,944711139),a(264347078,2341262773),a(604807628,2007800933),a(770255983,1495990901),a(1249150122,1856431235),a(1555081692,3175218132),a(1996064986,2198950837),a(2554220882,3999719339),a(2821834349,766784016),a(2952996808,2566594879),a(3210313671,3203337956),a(3336571891,1034457026),a(3584528711,2466948901),a(113926993,3758326383),a(338241895,168717936),a(666307205,1188179964),a(773529912,1546045734),a(1294757372,1522805485),a(1396182291,
2643833823),a(1695183700,2343527390),a(1986661051,1014477480),a(2177026350,1206759142),a(2456956037,344077627),a(2730485921,1290863460),a(2820302411,3158454273),a(3259730800,3505952657),a(3345764771,106217008),a(3516065817,3606008344),a(3600352804,1432725776),a(4094571909,1467031594),a(275423344,851169720),a(430227734,3100823752),a(506948616,1363258195),a(659060556,3750685593),a(883997877,3785050280),a(958139571,3318307427),a(1322822218,3812723403),a(1537002063,2003034995),a(1747873779,3602036899),
a(1955562222,1575990012),a(2024104815,1125592928),a(2227730452,2716904306),a(2361852424,442776044),a(2428436474,593698344),a(2756734187,3733110249),a(3204031479,2999351573),a(3329325298,3815920427),a(3391569614,3928383900),a(3515267271,566280711),a(3940187606,3454069534),a(4118630271,4000239992),a(116418474,1914138554),a(174292421,2731055270),a(289380356,3203993006),a(460393269,320620315),a(685471733,587496836),a(852142971,1086792851),a(1017036298,365543100),a(1126000580,2618297676),a(1288033470,
3409855158),a(1501505948,4234509866),a(1607167915,987167468),a(1816402316,1246189591)],k=[],m=0;80>m;m++)k[m]=a();b=b.SHA512=c.extend({_doReset:function(){this._hash=new l.init([new f.init(1779033703,4089235720),new f.init(3144134277,2227873595),new f.init(1013904242,4271175723),new f.init(2773480762,1595750129),new f.init(1359893119,2917565137),new f.init(2600822924,725511199),new f.init(528734635,4215389547),new f.init(1541459225,327033209)])},_doProcessBlock:function(a,b){for(var c=this._hash.words,
f=c[0],j=c[1],d=c[2],l=c[3],e=c[4],m=c[5],N=c[6],c=c[7],aa=f.high,O=f.low,ba=j.high,P=j.low,ca=d.high,Q=d.low,da=l.high,R=l.low,ea=e.high,S=e.low,fa=m.high,T=m.low,ga=N.high,U=N.low,ha=c.high,V=c.low,r=aa,n=O,G=ba,E=P,H=ca,F=Q,Y=da,I=R,s=ea,p=S,W=fa,J=T,X=ga,K=U,Z=ha,L=V,t=0;80>t;t++){var A=k[t];if(16>t)var q=A.high=a[b+2*t]|0,g=A.low=a[b+2*t+1]|0;else{var q=k[t-15],g=q.high,v=q.low,q=(g>>>1|v<<31)^(g>>>8|v<<24)^g>>>7,v=(v>>>1|g<<31)^(v>>>8|g<<24)^(v>>>7|g<<25),C=k[t-2],g=C.high,h=C.low,C=(g>>>19|
h<<13)^(g<<3|h>>>29)^g>>>6,h=(h>>>19|g<<13)^(h<<3|g>>>29)^(h>>>6|g<<26),g=k[t-7],$=g.high,B=k[t-16],w=B.high,B=B.low,g=v+g.low,q=q+$+(g>>>0<v>>>0?1:0),g=g+h,q=q+C+(g>>>0<h>>>0?1:0),g=g+B,q=q+w+(g>>>0<B>>>0?1:0);A.high=q;A.low=g}var $=s&W^~s&X,B=p&J^~p&K,A=r&G^r&H^G&H,ka=n&E^n&F^E&F,v=(r>>>28|n<<4)^(r<<30|n>>>2)^(r<<25|n>>>7),C=(n>>>28|r<<4)^(n<<30|r>>>2)^(n<<25|r>>>7),h=u[t],la=h.high,ia=h.low,h=L+((p>>>14|s<<18)^(p>>>18|s<<14)^(p<<23|s>>>9)),w=Z+((s>>>14|p<<18)^(s>>>18|p<<14)^(s<<23|p>>>9))+(h>>>
0<L>>>0?1:0),h=h+B,w=w+$+(h>>>0<B>>>0?1:0),h=h+ia,w=w+la+(h>>>0<ia>>>0?1:0),h=h+g,w=w+q+(h>>>0<g>>>0?1:0),g=C+ka,A=v+A+(g>>>0<C>>>0?1:0),Z=X,L=K,X=W,K=J,W=s,J=p,p=I+h|0,s=Y+w+(p>>>0<I>>>0?1:0)|0,Y=H,I=F,H=G,F=E,G=r,E=n,n=h+g|0,r=w+A+(n>>>0<h>>>0?1:0)|0}O=f.low=O+n;f.high=aa+r+(O>>>0<n>>>0?1:0);P=j.low=P+E;j.high=ba+G+(P>>>0<E>>>0?1:0);Q=d.low=Q+F;d.high=ca+H+(Q>>>0<F>>>0?1:0);R=l.low=R+I;l.high=da+Y+(R>>>0<I>>>0?1:0);S=e.low=S+p;e.high=ea+s+(S>>>0<p>>>0?1:0);T=m.low=T+J;m.high=fa+W+(T>>>0<J>>>0?1:
0);U=N.low=U+K;N.high=ga+X+(U>>>0<K>>>0?1:0);V=c.low=V+L;c.high=ha+Z+(V>>>0<L>>>0?1:0)},_doFinalize:function(){var a=this._data,b=a.words,c=8*this._nDataBytes,f=8*a.sigBytes;b[f>>>5]|=128<<24-f%32;b[(f+128>>>10<<5)+30]=Math.floor(c/4294967296);b[(f+128>>>10<<5)+31]=c;a.sigBytes=4*b.length;this._process();return this._hash.toX32()},clone:function(){var a=c.clone.call(this);a._hash=this._hash.clone();return a},blockSize:32});j.SHA512=c._createHelper(b);j.HmacSHA512=c._createHmacHelper(b)})();
(function(){var a=CryptoJS,j=a.enc.Utf8;a.algo.HMAC=a.lib.Base.extend({init:function(a,b){a=this._hasher=new a.init;"string"==typeof b&&(b=j.parse(b));var f=a.blockSize,l=4*f;b.sigBytes>l&&(b=a.finalize(b));b.clamp();for(var u=this._oKey=b.clone(),k=this._iKey=b.clone(),m=u.words,y=k.words,z=0;z<f;z++)m[z]^=1549556828,y[z]^=909522486;u.sigBytes=k.sigBytes=l;this.reset()},reset:function(){var a=this._hasher;a.reset();a.update(this._iKey)},update:function(a){this._hasher.update(a);return this},finalize:function(a){var b=
this._hasher;a=b.finalize(a);b.reset();return b.finalize(this._oKey.clone().concat(a))}})})();
/*
CryptoJS v3.1.2
code.google.com/p/crypto-js
(c) 2009-2013 by Jeff Mott. All rights reserved.
code.google.com/p/crypto-js/wiki/License
*/
var CryptoJS=CryptoJS||function(h,s){var f={},t=f.lib={},g=function(){},j=t.Base={extend:function(a){g.prototype=this;var c=new g;a&&c.mixIn(a);c.hasOwnProperty("init")||(c.init=function(){c.$super.init.apply(this,arguments)});c.init.prototype=c;c.$super=this;return c},create:function(){var a=this.extend();a.init.apply(a,arguments);return a},init:function(){},mixIn:function(a){for(var c in a)a.hasOwnProperty(c)&&(this[c]=a[c]);a.hasOwnProperty("toString")&&(this.toString=a.toString)},clone:function(){return this.init.prototype.extend(this)}},
q=t.WordArray=j.extend({init:function(a,c){a=this.words=a||[];this.sigBytes=c!=s?c:4*a.length},toString:function(a){return(a||u).stringify(this)},concat:function(a){var c=this.words,d=a.words,b=this.sigBytes;a=a.sigBytes;this.clamp();if(b%4)for(var e=0;e<a;e++)c[b+e>>>2]|=(d[e>>>2]>>>24-8*(e%4)&255)<<24-8*((b+e)%4);else if(65535<d.length)for(e=0;e<a;e+=4)c[b+e>>>2]=d[e>>>2];else c.push.apply(c,d);this.sigBytes+=a;return this},clamp:function(){var a=this.words,c=this.sigBytes;a[c>>>2]&=4294967295<<
32-8*(c%4);a.length=h.ceil(c/4)},clone:function(){var a=j.clone.call(this);a.words=this.words.slice(0);return a},random:function(a){for(var c=[],d=0;d<a;d+=4)c.push(4294967296*h.random()|0);return new q.init(c,a)}}),v=f.enc={},u=v.Hex={stringify:function(a){var c=a.words;a=a.sigBytes;for(var d=[],b=0;b<a;b++){var e=c[b>>>2]>>>24-8*(b%4)&255;d.push((e>>>4).toString(16));d.push((e&15).toString(16))}return d.join("")},parse:function(a){for(var c=a.length,d=[],b=0;b<c;b+=2)d[b>>>3]|=parseInt(a.substr(b,
2),16)<<24-4*(b%8);return new q.init(d,c/2)}},k=v.Latin1={stringify:function(a){var c=a.words;a=a.sigBytes;for(var d=[],b=0;b<a;b++)d.push(String.fromCharCode(c[b>>>2]>>>24-8*(b%4)&255));return d.join("")},parse:function(a){for(var c=a.length,d=[],b=0;b<c;b++)d[b>>>2]|=(a.charCodeAt(b)&255)<<24-8*(b%4);return new q.init(d,c)}},l=v.Utf8={stringify:function(a){try{return decodeURIComponent(escape(k.stringify(a)))}catch(c){throw Error("Malformed UTF-8 data");}},parse:function(a){return k.parse(unescape(encodeURIComponent(a)))}},
x=t.BufferedBlockAlgorithm=j.extend({reset:function(){this._data=new q.init;this._nDataBytes=0},_append:function(a){"string"==typeof a&&(a=l.parse(a));this._data.concat(a);this._nDataBytes+=a.sigBytes},_process:function(a){var c=this._data,d=c.words,b=c.sigBytes,e=this.blockSize,f=b/(4*e),f=a?h.ceil(f):h.max((f|0)-this._minBufferSize,0);a=f*e;b=h.min(4*a,b);if(a){for(var m=0;m<a;m+=e)this._doProcessBlock(d,m);m=d.splice(0,a);c.sigBytes-=b}return new q.init(m,b)},clone:function(){var a=j.clone.call(this);
a._data=this._data.clone();return a},_minBufferSize:0});t.Hasher=x.extend({cfg:j.extend(),init:function(a){this.cfg=this.cfg.extend(a);this.reset()},reset:function(){x.reset.call(this);this._doReset()},update:function(a){this._append(a);this._process();return this},finalize:function(a){a&&this._append(a);return this._doFinalize()},blockSize:16,_createHelper:function(a){return function(c,d){return(new a.init(d)).finalize(c)}},_createHmacHelper:function(a){return function(c,d){return(new w.HMAC.init(a,
d)).finalize(c)}}});var w=f.algo={};return f}(Math);
(function(h){for(var s=CryptoJS,f=s.lib,t=f.WordArray,g=f.Hasher,f=s.algo,j=[],q=[],v=function(a){return 4294967296*(a-(a|0))|0},u=2,k=0;64>k;){var l;a:{l=u;for(var x=h.sqrt(l),w=2;w<=x;w++)if(!(l%w)){l=!1;break a}l=!0}l&&(8>k&&(j[k]=v(h.pow(u,0.5))),q[k]=v(h.pow(u,1/3)),k++);u++}var a=[],f=f.SHA256=g.extend({_doReset:function(){this._hash=new t.init(j.slice(0))},_doProcessBlock:function(c,d){for(var b=this._hash.words,e=b[0],f=b[1],m=b[2],h=b[3],p=b[4],j=b[5],k=b[6],l=b[7],n=0;64>n;n++){if(16>n)a[n]=
c[d+n]|0;else{var r=a[n-15],g=a[n-2];a[n]=((r<<25|r>>>7)^(r<<14|r>>>18)^r>>>3)+a[n-7]+((g<<15|g>>>17)^(g<<13|g>>>19)^g>>>10)+a[n-16]}r=l+((p<<26|p>>>6)^(p<<21|p>>>11)^(p<<7|p>>>25))+(p&j^~p&k)+q[n]+a[n];g=((e<<30|e>>>2)^(e<<19|e>>>13)^(e<<10|e>>>22))+(e&f^e&m^f&m);l=k;k=j;j=p;p=h+r|0;h=m;m=f;f=e;e=r+g|0}b[0]=b[0]+e|0;b[1]=b[1]+f|0;b[2]=b[2]+m|0;b[3]=b[3]+h|0;b[4]=b[4]+p|0;b[5]=b[5]+j|0;b[6]=b[6]+k|0;b[7]=b[7]+l|0},_doFinalize:function(){var a=this._data,d=a.words,b=8*this._nDataBytes,e=8*a.sigBytes;
d[e>>>5]|=128<<24-e%32;d[(e+64>>>9<<4)+14]=h.floor(b/4294967296);d[(e+64>>>9<<4)+15]=b;a.sigBytes=4*d.length;this._process();return this._hash},clone:function(){var a=g.clone.call(this);a._hash=this._hash.clone();return a}});s.SHA256=g._createHelper(f);s.HmacSHA256=g._createHmacHelper(f)})(Math);
var R = {}; // the Recurrent library
(function(global) {
"use strict";
// Utility fun
function assert(condition, message) {
// from http://stackoverflow.com/questions/15313418/javascript-assert
if (!condition) {
message = message || "Assertion failed";
if (typeof Error !== "undefined") {
throw new Error(message);
}
throw message; // Fallback
}
}
// Random numbers utils
var return_v = false;
var v_val = 0.0;
var gaussRandom = function() {
if (return_v) {
return_v = false;
return v_val;
}
var u = 2 * Math.random() - 1;
var v = 2 * Math.random() - 1;
var r = u * u + v * v;
if (r == 0 || r > 1) return gaussRandom();
var c = Math.sqrt(-2 * Math.log(r) / r);
v_val = v * c; // cache this
return_v = true;
return u * c;
}
var randf = function(a, b) {
return Math.random() * (b - a) + a;
}
var randi = function(a, b) {
return Math.floor(Math.random() * (b - a) + a);
}
var randn = function(mu, std) {
return mu + gaussRandom() * std;
}
// helper function returns array of zeros of length n
// and uses typed arrays if available
var zeros = function(n) {
if (typeof(n) === 'undefined' || isNaN(n)) {
return [];
}
if (typeof ArrayBuffer === 'undefined') {
// lacking browser support
var arr = new Array(n);
for (var i = 0; i < n; i++) {
arr[i] = 0;
}
return arr;
} else {
return new Float64Array(n);
}
}
// Mat holds a matrix
var Mat = function(n, d) {
// n is number of rows d is number of columns
this.n = n;
this.d = d;
this.w = zeros(n * d);
this.dw = zeros(n * d);
}
Mat.prototype = {
get: function(row, col) {
// slow but careful accessor function
// we want row-major order
var ix = (this.d * row) + col;
assert(ix >= 0 && ix < this.w.length);
return this.w[ix];
},
set: function(row, col, v) {
// slow but careful accessor function
var ix = (this.d * row) + col;
assert(ix >= 0 && ix < this.w.length);
this.w[ix] = v;
},
setFrom: function(arr) {
for (var i = 0, n = arr.length; i < n; i++) {
this.w[i] = arr[i];
}
},
setColumn: function(m, i) {
for (var q = 0, n = m.w.length; q < n; q++) {
this.w[(this.d * q) + i] = m.w[q];
}
},
toJSON: function() {
var json = {};
json['n'] = this.n;
json['d'] = this.d;
json['w'] = this.w;
return json;
},
fromJSON: function(json) {
this.n = json.n;
this.d = json.d;
this.w = zeros(this.n * this.d);
this.dw = zeros(this.n * this.d);
for (var i = 0, n = this.n * this.d; i < n; i++) {
this.w[i] = json.w[i]; // copy over weights
}
}
}
var copyMat = function(b) {
var a = new Mat(b.n, b.d);
a.setFrom(b.w);
return a;
}
var copyNet = function(net) {
// nets are (k,v) pairs with k = string key, v = Mat()
var new_net = {};
for (var p in net) {
if (net.hasOwnProperty(p)) {
new_net[p] = copyMat(net[p]);
}
}
return new_net;
}
var updateMat = function(m, alpha) {
// updates in place
for (var i = 0, n = m.n * m.d; i < n; i++) {
if (m.dw[i] !== 0) {
m.w[i] += -alpha * m.dw[i];
m.dw[i] = 0;
}
}
}
var updateNet = function(net, alpha) {
for (var p in net) {
if (net.hasOwnProperty(p)) {
updateMat(net[p], alpha);
}
}
}
var netToJSON = function(net) {
var j = {};
for (var p in net) {
if (net.hasOwnProperty(p)) {
j[p] = net[p].toJSON();
}
}
return j;
}
var netFromJSON = function(j) {
var net = {};
for (var p in j) {
if (j.hasOwnProperty(p)) {
net[p] = new Mat(1, 1); // not proud of this
net[p].fromJSON(j[p]);
}
}
return net;
}
var netZeroGrads = function(net) {
for (var p in net) {
if (net.hasOwnProperty(p)) {
var mat = net[p];
gradFillConst(mat, 0);
}
}
}
var netFlattenGrads = function(net) {
var n = 0;
for (var p in net) {
if (net.hasOwnProperty(p)) {
var mat = net[p];
n += mat.dw.length;
}
}
var g = new Mat(n, 1);
var ix = 0;
for (var p in net) {
if (net.hasOwnProperty(p)) {
var mat = net[p];
for (var i = 0, m = mat.dw.length; i < m; i++) {
g.w[ix] = mat.dw[i];
ix++;
}
}
}
return g;
}
// return Mat but filled with random numbers from gaussian
var RandMat = function(n, d, mu, std) {
var m = new Mat(n, d);
fillRandn(m, mu, std);
//fillRand(m,-std,std); // kind of :P
return m;
}
// Mat utils
// fill matrix with random gaussian numbers
var fillRandn = function(m, mu, std) {
for (var i = 0, n = m.w.length; i < n; i++) {
m.w[i] = randn(mu, std);
}
}
var fillRand = function(m, lo, hi) {
for (var i = 0, n = m.w.length; i < n; i++) {
m.w[i] = randf(lo, hi);
}
}
var gradFillConst = function(m, c) {
for (var i = 0, n = m.dw.length; i < n; i++) {
m.dw[i] = c
}
}
// Transformer definitions
var Graph = function(needs_backprop) {
if (typeof needs_backprop === 'undefined') {
needs_backprop = true;
}
this.needs_backprop = needs_backprop;
// this will store a list of functions that perform backprop,
// in their forward pass order. So in backprop we will go
// backwards and evoke each one
this.backprop = [];
}
Graph.prototype = {
backward: function() {
for (var i = this.backprop.length - 1; i >= 0; i--) {
this.backprop[i](); // tick!
}
},
rowPluck: function(m, ix) {
// pluck a row of m with index ix and return it as col vector
assert(ix >= 0 && ix < m.n);
var d = m.d;
var out = new Mat(d, 1);
for (var i = 0, n = d; i < n; i++) {
out.w[i] = m.w[d * ix + i];
} // copy over the data
if (this.needs_backprop) {
var backward = function() {
for (var i = 0, n = d; i < n; i++) {
m.dw[d * ix + i] += out.dw[i];
}
}
this.backprop.push(backward);
}
return out;
},
tanh: function(m) {
// tanh nonlinearity
var out = new Mat(m.n, m.d);
var n = m.w.length;
for (var i = 0; i < n; i++) {
out.w[i] = Math.tanh(m.w[i]);
}
if (this.needs_backprop) {
var backward = function() {
for (var i = 0; i < n; i++) {
// grad for z = tanh(x) is (1 - z^2)
var mwi = out.w[i];
m.dw[i] += (1.0 - mwi * mwi) * out.dw[i];
}
}
this.backprop.push(backward);
}
return out;
},
sigmoid: function(m) {
// sigmoid nonlinearity
var out = new Mat(m.n, m.d);
var n = m.w.length;
for (var i = 0; i < n; i++) {
out.w[i] = sig(m.w[i]);
}
if (this.needs_backprop) {
var backward = function() {
for (var i = 0; i < n; i++) {
// grad for z = tanh(x) is (1 - z^2)
var mwi = out.w[i];
m.dw[i] += mwi * (1.0 - mwi) * out.dw[i];
}
}
this.backprop.push(backward);
}
return out;
},
relu: function(m) {
var out = new Mat(m.n, m.d);
var n = m.w.length;
for (var i = 0; i < n; i++) {
out.w[i] = Math.max(0, m.w[i]); // relu
}
if (this.needs_backprop) {
var backward = function() {
for (var i = 0; i < n; i++) {
m.dw[i] += m.w[i] > 0 ? out.dw[i] : 0.0;
}
}
this.backprop.push(backward);
}
return out;
},
mul: function(m1, m2) {
// multiply matrices m1 * m2
assert(m1.d === m2.n, 'matmul dimensions misaligned');
var n = m1.n;
var d = m2.d;
var out = new Mat(n, d);
for (var i = 0; i < m1.n; i++) { // loop over rows of m1
for (var j = 0; j < m2.d; j++) { // loop over cols of m2
var dot = 0.0;
for (var k = 0; k < m1.d; k++) { // dot product loop
dot += m1.w[m1.d * i + k] * m2.w[m2.d * k + j];
}
out.w[d * i + j] = dot;
}
}
if (this.needs_backprop) {
var backward = function() {
for (var i = 0; i < m1.n; i++) { // loop over rows of m1
for (var j = 0; j < m2.d; j++) { // loop over cols of m2
for (var k = 0; k < m1.d; k++) { // dot product loop
var b = out.dw[d * i + j];
m1.dw[m1.d * i + k] += m2.w[m2.d * k + j] * b;
m2.dw[m2.d * k + j] += m1.w[m1.d * i + k] * b;
}
}
}
}
this.backprop.push(backward);
}
return out;
},
add: function(m1, m2) {
assert(m1.w.length === m2.w.length);
var out = new Mat(m1.n, m1.d);
for (var i = 0, n = m1.w.length; i < n; i++) {
out.w[i] = m1.w[i] + m2.w[i];
}
if (this.needs_backprop) {
var backward = function() {
for (var i = 0, n = m1.w.length; i < n; i++) {
m1.dw[i] += out.dw[i];
m2.dw[i] += out.dw[i];
}
}
this.backprop.push(backward);
}
return out;
},
dot: function(m1, m2) {
// m1 m2 are both column vectors
assert(m1.w.length === m2.w.length);
var out = new Mat(1, 1);
var dot = 0.0;
for (var i = 0, n = m1.w.length; i < n; i++) {
dot += m1.w[i] * m2.w[i];
}
out.w[0] = dot;
if (this.needs_backprop) {
var backward = function() {
for (var i = 0, n = m1.w.length; i < n; i++) {
m1.dw[i] += m2.w[i] * out.dw[0];
m2.dw[i] += m1.w[i] * out.dw[0];
}
}
this.backprop.push(backward);
}
return out;
},
eltmul: function(m1, m2) {
assert(m1.w.length === m2.w.length);
var out = new Mat(m1.n, m1.d);
for (var i = 0, n = m1.w.length; i < n; i++) {
out.w[i] = m1.w[i] * m2.w[i];
}
if (this.needs_backprop) {
var backward = function() {
for (var i = 0, n = m1.w.length; i < n; i++) {
m1.dw[i] += m2.w[i] * out.dw[i];
m2.dw[i] += m1.w[i] * out.dw[i];
}
}
this.backprop.push(backward);
}
return out;
},
}
var softmax = function(m) {
var out = new Mat(m.n, m.d); // probability volume
var maxval = -999999;
for (var i = 0, n = m.w.length; i < n; i++) {
if (m.w[i] > maxval) maxval = m.w[i];
}
var s = 0.0;
for (var i = 0, n = m.w.length; i < n; i++) {
out.w[i] = Math.exp(m.w[i] - maxval);
s += out.w[i];
}
for (var i = 0, n = m.w.length; i < n; i++) {
out.w[i] /= s;
}
// no backward pass here needed
// since we will use the computed probabilities outside
// to set gradients directly on m
return out;
}
var Solver = function() {
this.decay_rate = 0.999;
this.smooth_eps = 1e-8;
this.step_cache = {};
}
Solver.prototype = {
step: function(model, step_size, regc, clipval) {
// perform parameter update
var solver_stats = {};
var num_clipped = 0;
var num_tot = 0;
for (var k in model) {
if (model.hasOwnProperty(k)) {
var m = model[k]; // mat ref
if (!(k in this.step_cache)) {
this.step_cache[k] = new Mat(m.n, m.d);
}
var s = this.step_cache[k];
for (var i = 0, n = m.w.length; i < n; i++) {
// rmsprop adaptive learning rate
var mdwi = m.dw[i];
s.w[i] = s.w[i] * this.decay_rate + (1.0 - this.decay_rate) * mdwi * mdwi;
// gradient clip
if (mdwi > clipval) {
mdwi = clipval;
num_clipped++;
}
if (mdwi < -clipval) {
mdwi = -clipval;
num_clipped++;
}
num_tot++;
// update (and regularize)
m.w[i] += -step_size * mdwi / Math.sqrt(s.w[i] + this.smooth_eps) - regc * m.w[i];
m.dw[i] = 0; // reset gradients for next iteration
}
}
}
solver_stats['ratio_clipped'] = num_clipped * 1.0 / num_tot;
return solver_stats;
}
}
var initLSTM = function(input_size, hidden_sizes, output_size) {
// hidden size should be a list
var model = {};
for (var d = 0; d < hidden_sizes.length; d++) { // loop over depths
var prev_size = d === 0 ? input_size : hidden_sizes[d - 1];
var hidden_size = hidden_sizes[d];
// gates parameters
model['Wix' + d] = new RandMat(hidden_size, prev_size, 0, 0.08);
model['Wih' + d] = new RandMat(hidden_size, hidden_size, 0, 0.08);
model['bi' + d] = new Mat(hidden_size, 1);
model['Wfx' + d] = new RandMat(hidden_size, prev_size, 0, 0.08);
model['Wfh' + d] = new RandMat(hidden_size, hidden_size, 0, 0.08);
model['bf' + d] = new Mat(hidden_size, 1);
model['Wox' + d] = new RandMat(hidden_size, prev_size, 0, 0.08);
model['Woh' + d] = new RandMat(hidden_size, hidden_size, 0, 0.08);
model['bo' + d] = new Mat(hidden_size, 1);
// cell write params
model['Wcx' + d] = new RandMat(hidden_size, prev_size, 0, 0.08);
model['Wch' + d] = new RandMat(hidden_size, hidden_size, 0, 0.08);
model['bc' + d] = new Mat(hidden_size, 1);
}
// decoder params
model['Whd'] = new RandMat(output_size, hidden_size, 0, 0.08);
model['bd'] = new Mat(output_size, 1);
return model;
}
var forwardLSTM = function(G, model, hidden_sizes, x, prev) {
// forward prop for a single tick of LSTM
// G is graph to append ops to
// model contains LSTM parameters
// x is 1D column vector with observation
// prev is a struct containing hidden and cell
// from previous iteration
if (prev == null || typeof prev.h === 'undefined') {
var hidden_prevs = [];
var cell_prevs = [];
for (var d = 0; d < hidden_sizes.length; d++) {
hidden_prevs.push(new R.Mat(hidden_sizes[d], 1));
cell_prevs.push(new R.Mat(hidden_sizes[d], 1));
}
} else {
var hidden_prevs = prev.h;
var cell_prevs = prev.c;
}
var hidden = [];
var cell = [];
for (var d = 0; d < hidden_sizes.length; d++) {
var input_vector = d === 0 ? x : hidden[d - 1];
var hidden_prev = hidden_prevs[d];
var cell_prev = cell_prevs[d];
// input gate
var h0 = G.mul(model['Wix' + d], input_vector);
var h1 = G.mul(model['Wih' + d], hidden_prev);
var input_gate = G.sigmoid(G.add(G.add(h0, h1), model['bi' + d]));
// forget gate
var h2 = G.mul(model['Wfx' + d], input_vector);
var h3 = G.mul(model['Wfh' + d], hidden_prev);
var forget_gate = G.sigmoid(G.add(G.add(h2, h3), model['bf' + d]));
// output gate
var h4 = G.mul(model['Wox' + d], input_vector);
var h5 = G.mul(model['Woh' + d], hidden_prev);
var output_gate = G.sigmoid(G.add(G.add(h4, h5), model['bo' + d]));
// write operation on cells
var h6 = G.mul(model['Wcx' + d], input_vector);
var h7 = G.mul(model['Wch' + d], hidden_prev);
var cell_write = G.tanh(G.add(G.add(h6, h7), model['bc' + d]));
// compute new cell activation
var retain_cell = G.eltmul(forget_gate, cell_prev); // what do we keep from cell
var write_cell = G.eltmul(input_gate, cell_write); // what do we write to cell
var cell_d = G.add(retain_cell, write_cell); // new cell contents
// compute hidden state as gated, saturated cell activations
var hidden_d = G.eltmul(output_gate, G.tanh(cell_d));
hidden.push(hidden_d);
cell.push(cell_d);
}
// one decoder to outputs at end
var output = G.add(G.mul(model['Whd'], hidden[hidden.length - 1]), model['bd']);
// return cell memory, hidden representation and output
return {
'h': hidden,
'c': cell,
'o': output
};
}
var sig = function(x) {
// helper function for computing sigmoid
return 1.0 / (1 + Math.exp(-x));
}
var maxi = function(w) {
// argmax of array w
var maxv = w[0];
var maxix = 0;
for (var i = 1, n = w.length; i < n; i++) {
var v = w[i];
if (v > maxv) {
maxix = i;
maxv = v;
}
}
return maxix;
}
var samplei = function(w) {
// sample argmax from w, assuming w are
// probabilities that sum to one
var r = randf(0, 1);
var x = 0.0;
var i = 0;
while (true) {
x += w[i];
if (x > r) {
return i;
}
i++;
}
return w.length - 1; // pretty sure we should never get here?
}
// various utils
global.assert = assert;
global.zeros = zeros;
global.maxi = maxi;
global.samplei = samplei;
global.randi = randi;
global.randn = randn;
global.softmax = softmax;
// classes
global.Mat = Mat;
global.RandMat = RandMat;
global.forwardLSTM = forwardLSTM;
global.initLSTM = initLSTM;
// more utils
global.updateMat = updateMat;
global.updateNet = updateNet;
global.copyMat = copyMat;
global.copyNet = copyNet;
global.netToJSON = netToJSON;
global.netFromJSON = netFromJSON;
global.netZeroGrads = netZeroGrads;
global.netFlattenGrads = netFlattenGrads;
// optimization
global.Solver = Solver;
global.Graph = Graph;
})(R);
// END OF RECURRENTJS
var RL = {};
(function(global) {
"use strict";
// syntactic sugar function for getting default parameter values
var getopt = function(opt, field_name, default_value) {
if (typeof opt === 'undefined') {
return default_value;
}
return (typeof opt[field_name] !== 'undefined') ? opt[field_name] : default_value;
}
var zeros = R.zeros; // inherit these
var assert = R.assert;
var randi = R.randi;
var randf = R.randf;
var setConst = function(arr, c) {
for (var i = 0, n = arr.length; i < n; i++) {
arr[i] = c;
}
}
var sampleWeighted = function(p) {
var r = Math.random();
var c = 0.0;
for (var i = 0, n = p.length; i < n; i++) {
c += p[i];
if (c >= r) {
return i;
}
}
assert(false, 'wtf');
}
// ------
// AGENTS
// ------
// DPAgent performs Value Iteration
// - can also be used for Policy Iteration if you really wanted to
// - requires model of the environment :(
// - does not learn from experience :(
// - assumes finite MDP :(
var DPAgent = function(env, opt) {
this.V = null; // state value function
this.P = null; // policy distribution \pi(s,a)
this.env = env; // store pointer to environment
this.gamma = getopt(opt, 'gamma', 0.75); // future reward discount factor
this.reset();
}
DPAgent.prototype = {
reset: function() {
// reset the agent's policy and value function
this.ns = this.env.getNumStates();
this.na = this.env.getMaxNumActions();
this.V = zeros(this.ns);
this.P = zeros(this.ns * this.na);
// initialize uniform random policy
for (var s = 0; s < this.ns; s++) {
var poss = this.env.allowedActions(s);
for (var i = 0, n = poss.length; i < n; i++) {
this.P[poss[i] * this.ns + s] = 1.0 / poss.length;
}
}
},
act: function(s) {
// behave according to the learned policy
var poss = this.env.allowedActions(s);
var ps = [];
for (var i = 0, n = poss.length; i < n; i++) {
var a = poss[i];
var prob = this.P[a * this.ns + s];
ps.push(prob);
}
var maxi = sampleWeighted(ps);
return poss[maxi];
},
learn: function() {
// perform a single round of value iteration
self.evaluatePolicy(); // writes this.V
self.updatePolicy(); // writes this.P
},
evaluatePolicy: function() {
// perform a synchronous update of the value function
var Vnew = zeros(this.ns);
for (var s = 0; s < this.ns; s++) {
// integrate over actions in a stochastic policy
// note that we assume that policy probability mass over allowed actions sums to one
var v = 0.0;
var poss = this.env.allowedActions(s);
for (var i = 0, n = poss.length; i < n; i++) {
var a = poss[i];
var prob = this.P[a * this.ns + s]; // probability of taking action under policy
if (prob === 0) {
continue;
} // no contribution, skip for speed
var ns = this.env.nextStateDistribution(s, a);
var rs = this.env.reward(s, a, ns); // reward for s->a->ns transition
v += prob * (rs + this.gamma * this.V[ns]);
}
Vnew[s] = v;
}
this.V = Vnew; // swap
},
updatePolicy: function() {
// update policy to be greedy w.r.t. learned Value function
for (var s = 0; s < this.ns; s++) {
var poss = this.env.allowedActions(s);
// compute value of taking each allowed action
var vmax, nmax;
var vs = [];
for (var i = 0, n = poss.length; i < n; i++) {
var a = poss[i];
var ns = this.env.nextStateDistribution(s, a);
var rs = this.env.reward(s, a, ns);
var v = rs + this.gamma * this.V[ns];
vs.push(v);
if (i === 0 || v > vmax) {
vmax = v;
nmax = 1;
} else if (v === vmax) {
nmax += 1;
}
}
// update policy smoothly across all argmaxy actions
for (var i = 0, n = poss.length; i < n; i++) {
var a = poss[i];
this.P[a * this.ns + s] = (vs[i] === vmax) ? 1.0 / nmax : 0.0;
}
}
},
}
// QAgent uses TD (Q-Learning, SARSA)
// - does not require environment model :)
// - learns from experience :)
var TDAgent = function(env, opt) {
this.update = getopt(opt, 'update', 'qlearn'); // qlearn | sarsa
this.gamma = getopt(opt, 'gamma', 0.75); // future reward discount factor
this.epsilon = getopt(opt, 'epsilon', 0.1); // for epsilon-greedy policy
this.alpha = getopt(opt, 'alpha', 0.01); // value function learning rate
// class allows non-deterministic policy, and smoothly regressing towards the optimal policy based on Q
this.smooth_policy_update = getopt(opt, 'smooth_policy_update', false);
this.beta = getopt(opt, 'beta', 0.01); // learning rate for policy, if smooth updates are on
// eligibility traces
this.lambda = getopt(opt, 'lambda', 0); // eligibility trace decay. 0 = no eligibility traces used
this.replacing_traces = getopt(opt, 'replacing_traces', true);
// optional optimistic initial values
this.q_init_val = getopt(opt, 'q_init_val', 0);
this.planN = getopt(opt, 'planN', 0); // number of planning steps per learning iteration (0 = no planning)
this.Q = null; // state action value function
this.P = null; // policy distribution \pi(s,a)
this.e = null; // eligibility trace
this.env_model_s = null;; // environment model (s,a) -> (s',r)
this.env_model_r = null;; // environment model (s,a) -> (s',r)
this.env = env; // store pointer to environment
this.reset();
}
TDAgent.prototype = {
reset: function() {
// reset the agent's policy and value function
this.ns = this.env.getNumStates();
this.na = this.env.getMaxNumActions();
this.Q = zeros(this.ns * this.na);
if (this.q_init_val !== 0) {
setConst(this.Q, this.q_init_val);
}
this.P = zeros(this.ns * this.na);
this.e = zeros(this.ns * this.na);
// model/planning vars
this.env_model_s = zeros(this.ns * this.na);
setConst(this.env_model_s, -1); // init to -1 so we can test if we saw the state before
this.env_model_r = zeros(this.ns * this.na);
this.sa_seen = [];
this.pq = zeros(this.ns * this.na);
// initialize uniform random policy
for (var s = 0; s < this.ns; s++) {
var poss = this.env.allowedActions(s);
for (var i = 0, n = poss.length; i < n; i++) {
this.P[poss[i] * this.ns + s] = 1.0 / poss.length;
}
}
// agent memory, needed for streaming updates
// (s0,a0,r0,s1,a1,r1,...)
this.r0 = null;
this.s0 = null;
this.s1 = null;
this.a0 = null;
this.a1 = null;
},
resetEpisode: function() {
// an episode finished
},
act: function(s) {
// act according to epsilon greedy policy
var poss = this.env.allowedActions(s);
var probs = [];
for (var i = 0, n = poss.length; i < n; i++) {
probs.push(this.P[poss[i] * this.ns + s]);
}
// epsilon greedy policy
if (Math.random() < this.epsilon) {
var a = poss[randi(0, poss.length)]; // random available action
this.explored = true;
} else {
var a = poss[sampleWeighted(probs)];
this.explored = false;
}
// shift state memory
this.s0 = this.s1;
this.a0 = this.a1;
this.s1 = s;
this.a1 = a;
return a;
},
learn: function(r1) {
// takes reward for previous action, which came from a call to act()
if (!(this.r0 == null)) {
this.learnFromTuple(this.s0, this.a0, this.r0, this.s1, this.a1, this.lambda);
if (this.planN > 0) {
this.updateModel(this.s0, this.a0, this.r0, this.s1);
this.plan();
}
}
this.r0 = r1; // store this for next update
},
updateModel: function(s0, a0, r0, s1) {
// transition (s0,a0) -> (r0,s1) was observed. Update environment model
var sa = a0 * this.ns + s0;
if (this.env_model_s[sa] === -1) {
// first time we see this state action
this.sa_seen.push(a0 * this.ns + s0); // add as seen state
}
this.env_model_s[sa] = s1;
this.env_model_r[sa] = r0;
},
plan: function() {
// order the states based on current priority queue information
var spq = [];
for (var i = 0, n = this.sa_seen.length; i < n; i++) {
var sa = this.sa_seen[i];
var sap = this.pq[sa];
if (sap > 1e-5) { // gain a bit of efficiency
spq.push({
sa: sa,
p: sap
});
}
}
spq.sort(function(a, b) {
return a.p < b.p ? 1 : -1
});
// perform the updates
var nsteps = Math.min(this.planN, spq.length);
for (var k = 0; k < nsteps; k++) {
// random exploration
//var i = randi(0, this.sa_seen.length); // pick random prev seen state action
//var s0a0 = this.sa_seen[i];
var s0a0 = spq[k].sa;
this.pq[s0a0] = 0; // erase priority, since we're backing up this state
var s0 = s0a0 % this.ns;
var a0 = Math.floor(s0a0 / this.ns);
var r0 = this.env_model_r[s0a0];
var s1 = this.env_model_s[s0a0];
var a1 = -1; // not used for Q learning
if (this.update === 'sarsa') {
// generate random action?...
var poss = this.env.allowedActions(s1);
var a1 = poss[randi(0, poss.length)];
}
this.learnFromTuple(s0, a0, r0, s1, a1, 0); // note lambda = 0 - shouldnt use eligibility trace here
}
},
learnFromTuple: function(s0, a0, r0, s1, a1, lambda) {
var sa = a0 * this.ns + s0;
// calculate the target for Q(s,a)
if (this.update === 'qlearn') {
// Q learning target is Q(s0,a0) = r0 + gamma * max_a Q[s1,a]
var poss = this.env.allowedActions(s1);
var qmax = 0;
for (var i = 0, n = poss.length; i < n; i++) {
var s1a = poss[i] * this.ns + s1;
var qval = this.Q[s1a];
if (i === 0 || qval > qmax) {
qmax = qval;
}
}
var target = r0 + this.gamma * qmax;
} else if (this.update === 'sarsa') {
// SARSA target is Q(s0,a0) = r0 + gamma * Q[s1,a1]
var s1a1 = a1 * this.ns + s1;
var target = r0 + this.gamma * this.Q[s1a1];
}
if (lambda > 0) {
// perform an eligibility trace update
if (this.replacing_traces) {
this.e[sa] = 1;
} else {
this.e[sa] += 1;
}
var edecay = lambda * this.gamma;
var state_update = zeros(this.ns);
for (var s = 0; s < this.ns; s++) {
var poss = this.env.allowedActions(s);
for (var i = 0; i < poss.length; i++) {
var a = poss[i];
var saloop = a * this.ns + s;
var esa = this.e[saloop];
var update = this.alpha * esa * (target - this.Q[saloop]);
this.Q[saloop] += update;
this.updatePriority(s, a, update);
this.e[saloop] *= edecay;
var u = Math.abs(update);
if (u > state_update[s]) {
state_update[s] = u;
}
}
}
for (var s = 0; s < this.ns; s++) {
if (state_update[s] > 1e-5) { // save efficiency here
this.updatePolicy(s);
}
}
if (this.explored && this.update === 'qlearn') {
// have to wipe the trace since q learning is off-policy :(
this.e = zeros(this.ns * this.na);
}
} else {
// simpler and faster update without eligibility trace
// update Q[sa] towards it with some step size
var update = this.alpha * (target - this.Q[sa]);
this.Q[sa] += update;
this.updatePriority(s0, a0, update);
// update the policy to reflect the change (if appropriate)
this.updatePolicy(s0);
}
},
updatePriority: function(s, a, u) {
// used in planning. Invoked when Q[sa] += update
// we should find all states that lead to (s,a) and upgrade their priority