vega-statistics - npm Package Compare versions

Comparing version 1.7.2 to 1.7.3

build/vega-statistics.js

@@ -796,24 +796,24 @@ (function (global, factory) {
   function exp(data, x, y) {
-    let Y = 0, YL = 0, XY = 0, XYL = 0, X2Y = 0, n = 0;
+    // eslint-disable-next-line no-unused-vars
+    const [xv, yv, ux, uy] = points(data, x, y);
+    let YL = 0, XY = 0, XYL = 0, X2Y = 0, n = 0, dx, ly, xy;
 
-    visitPoints(data, x, y, (dx, dy) => {
-      const ly = Math.log(dy),
-            xy = dx * dy;
-      ++n;
-      Y += (dy - Y) / n;
+    visitPoints(data, x, y, (_, dy) => {
+      dx = xv[n++];
+      ly = Math.log(dy);
+      xy = dx * dy;
+
+      YL += (dy * ly - YL) / n;
       XY += (xy - XY) / n;
+      XYL += (xy * ly - XYL) / n;
       X2Y += (dx * xy - X2Y) / n;
-      YL += (dy * ly - YL) / n;
-      XYL += (xy * ly - XYL) / n;
     });
 
-    const coef = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y),
-          predict = x => coef[0] * Math.exp(coef[1] * x);
+    const [c0, c1] = ols(XY / uy, YL / uy, XYL / uy, X2Y / uy),
+          predict = x => Math.exp(c0 + c1 * (x - ux));
 
-    coef[0] = Math.exp(coef[0]);
-
     return {
-      coef: coef,
+      coef: [Math.exp(c0 - c1 * ux), c1],
       predict: predict,
-      rSquared: rSquared(data, x, y, Y, predict)
+      rSquared: rSquared(data, x, y, uy, predict)
     };
@@ -851,3 +851,2 @@ }
   function quad(data, x, y) {
-
     const [xv, yv, ux, uy] = points(data, x, y),
@@ -854,0 +853,0 @@ n = xv.length;

build/vega-statistics.min.js

@@ -1,1 +0,1 @@
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a,f=0,l=0,i=[];for(;f<o;++f)a=t[f]+r,i[0]===a?i[1]+=(n[f]-i[1])/++l:(l=0,i[1]+=e,i=[a,n[f]],u.push(i));return i[1]+=e,u}(u,s,f,l)},t.regressionLog=function(t,n,r){let e=0,o=0,u=0,a=0,f=0;q(t,n,r,(t,n)=>{++f,t=Math.log(t),e+=(t-e)/f,o+=(n-o)/f,u+=(t*n-u)/f,a+=(t*t-a)/f});const l=y(e,o,u,a),i=t=>l[0]+l[1]*Math.log(t);return{coef:l,predict:i,rSquared:A(t,n,r,o,i)}},t.regressionPoly=function(t,n,r,e){if(1===e)return F(t,n,r);if(2===e)return L(t,n,r);const[o,u,a,f]=N(t,n,r),l=o.length,i=[],c=[],s=e+1;let h,d,p,m,M;for(h=0;h<s;++h){for(p=0,m=0;p<l;++p)m+=Math.pow(o[p],h)*u[p];for(i.push(m),M=new Float64Array(s),d=0;d<s;++d){for(p=0,m=0;p<l;++p)m+=Math.pow(o[p],h+d);M[d]=m}c.push(M)}c.push(i);const g=function(t){const n=t.length-1,r=[];let e,o,u,a,f;for(e=0;e<n;++e){for(a=e,o=e+1;o<n;++o)Math.abs(t[e][o])>Math.abs(t[e][a])&&(a=o);for(u=e;u<n+1;++u)f=t[u][e],t[u][e]=t[u][a],t[u][a]=f;for(o=e+1;o<n;++o)for(u=n;u>=e;u--)t[u][o]-=t[u][e]*t[e][o]/t[e][e]}for(o=n-1;o>=0;--o){for(f=0,u=o+1;u<n;++u)f+=t[u][o]*r[u];r[o]=(t[n][o]-f)/t[o][o]}return r}(c),v=t=>{t-=a;let n=f+g[0]+g[1]*t+g[2]*t*t;for(h=3;h<s;++h)n+=g[h]*Math.pow(t,h);return n};return{coef:S(s,g,-a,f),predict:v,rSquared:A(t,n,r,f,v)}},t.regressionPow=function(t,n,r){let e=0,o=0,u=0,a=0,f=0,l=0;q(t,n,r,(t,n)=>{const r=Math.log(t),i=Math.log(n);++l,e+=(r-e)/l,o+=(i-o)/l,u+=(r*i-u)/l,a+=(r*r-a)/l,f+=(n-f)/l});const i=y(e,o,u,a),c=t=>i[0]*Math.pow(t,i[1]);return i[0]=Math.exp(i[0]),{coef:i,predict:c,rSquared:A(t,n,r,f,c)}},t.regressionQuad=L,t.sampleCurve=function(t,n,r,e){r=r||25,e=Math.max(r,e||200);const o=n=>[n,t(n)],u=n[0],a=n[1],f=a-u,l=f/e,i=[o(u)],c=[];if(r===e){for(let t=1;t<e;++t)i.push(o(u+t/r*f));return i.push(o(a)),i}c.push(o(a));for(let t=r;--t>0;)c.push(o(u+t/r*f));let s=i[0],h=c[c.length-1];for(;h;){const t=o((s[0]+h[0])/2);t[0]-s[0]>=l&&I(s,t,h)>U?c.push(t):(s=h,i.push(h),c.pop()),h=c[c.length-1]}return i},t.sampleLogNormal=p,t.sampleNormal=i,t.sampleUniform=v,t.setRandom=function(n){t.random=n},Object.defineProperty(t,"__esModule",{value:!0})}));

package.json

 {
   "name": "vega-statistics",
-  "version": "1.7.2",
+  "version": "1.7.3",
   "description": "Statistical routines and probability distributions.",
@@ -29,3 +29,3 @@ "keywords": [
   },
-  "gitHead": "178cab5429841aaf02d164753a0621e89d1dce9a"
+  "gitHead": "49a57c10b309addc513a52a38e1959ee169d4758"
 }

src/regression/exp.js

 import ols from './ols';
-import {visitPoints} from './points';
+import {points, visitPoints} from './points';
 import rSquared from './r-squared';
 
 export default function(data, x, y) {
-  let Y = 0, YL = 0, XY = 0, XYL = 0, X2Y = 0, n = 0;
+  // eslint-disable-next-line no-unused-vars
+  const [xv, yv, ux, uy] = points(data, x, y);
+  let YL = 0, XY = 0, XYL = 0, X2Y = 0, n = 0, dx, ly, xy;
 
-  visitPoints(data, x, y, (dx, dy) => {
-    const ly = Math.log(dy),
-          xy = dx * dy;
-    ++n;
-    Y += (dy - Y) / n;
+  visitPoints(data, x, y, (_, dy) => {
+    dx = xv[n++];
+    ly = Math.log(dy);
+    xy = dx * dy;
+
+    YL += (dy * ly - YL) / n;
     XY += (xy - XY) / n;
+    XYL += (xy * ly - XYL) / n;
     X2Y += (dx * xy - X2Y) / n;
-    YL += (dy * ly - YL) / n;
-    XYL += (xy * ly - XYL) / n;
   });
 
-  const coef = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y),
-        predict = x => coef[0] * Math.exp(coef[1] * x);
+  const [c0, c1] = ols(XY / uy, YL / uy, XYL / uy, X2Y / uy),
+        predict = x => Math.exp(c0 + c1 * (x - ux));
 
-  coef[0] = Math.exp(coef[0]);
-
   return {
-    coef: coef,
+    coef: [Math.exp(c0 - c1 * ux), c1],
     predict: predict,
-    rSquared: rSquared(data, x, y, Y, predict)
+    rSquared: rSquared(data, x, y, uy, predict)
   };
 }

src/regression/quad.js

@@ -5,3 +5,2 @@ import {points} from './points';
 export default function(data, x, y) {
-
   const [xv, yv, ux, uy] = points(data, x, y),
@@ -8,0 +7,0 @@ n = xv.length;

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