vega-statistics - npm Package Compare versions

Comparing version 1.8.1 to 1.9.0

build/vega-statistics.js

@@ -823,2 +823,17 @@ (function (global, factory) {
 
+  function constant (data, x, y) {
+    let mean = 0,
+      n = 0;
+    for (const d of data) {
+      const val = y(d);
+      if (x(d) == null || val == null || isNaN(val)) continue;
+      mean += (val - mean) / ++n;
+    }
+    return {
+      coef: [mean],
+      predict: () => mean,
+      rSquared: 0
+    };
+  }
+
   // Ordinary Least Squares
@@ -1048,2 +1063,3 @@ function ols (uX, uY, uXY, uX2) {
     // use more efficient methods for lower orders
+    if (order === 0) return constant(data, x, y);
     if (order === 1) return linear(data, x, y);
@@ -1342,2 +1358,3 @@ if (order === 2) return quad(data, x, y);
   exports.randomUniform = uniform;
+  exports.regressionConstant = constant;
   exports.regressionExp = exp;
@@ -1344,0 +1361,0 @@ exports.regressionLinear = linear;

build/vega-statistics.min.js

@@ -1,2 +0,2 @@
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u,f=0,a=1,i=r(t[0]),s=i,c=i+n;for(;a<o;++a){if(u=r(t[a]),u>=c){for(s=(i+s)/2;f<a;++f)l[f]=s;c=u+n,i=u}s=u}for(s=(i+s)/2;f<a;++f)l[f]=s;return e?function(t,n){const e=t.length;let r,o,l=0,u=1;for(;t[l]===t[u];)++u;for(;u<e;){for(r=u+1;t[u]===t[r];)++r;if(t[u]-t[u-1]<n){for(o=u+(l+r-u-u>>1);o<u;)t[o++]=t[u];for(;o>u;)t[o--]=t[l]}l=u,u=r}return t}(l,n+n/4):l},t.quantileLogNormal=q,t.quantileNormal=b,t.quantileUniform=S,t.quantiles=s,t.quartiles=c,t.randomInteger=function(n,e){let r,o,l;null==e&&(e=n,n=0);const u={min(t){return arguments.length?(r=t||0,l=o-r,u):r},max(t){return arguments.length?(o=t||0,l=o-r,u):o},sample:()=>r+Math.floor(l*t.random()),pdf:t=>t===Math.floor(t)&&t>=r&&t<o?1/l:0,cdf(t){const n=Math.floor(t);return n<r?0:n>=o?1:(n-r+1)/l},icdf:t=>t>=0&&t<=1?r-1+Math.floor(t*l):NaN};return u.min(n).max(e)},t.randomKDE=function(n,e){const r=v();let o=0;const l={data(t){return arguments.length?(n=t,o=t?t.length:0,l.bandwidth(e)):n},bandwidth(t){return arguments.length?(!(e=t)&&n&&(e=h(n)),l):e},sample:()=>n[~~(t.random()*o)]+e*r.sample(),pdf(t){let l=0,u=0;for(;u<o;++u)l+=r.pdf((t-n[u])/e);return l/e/o},cdf(t){let l=0,u=0;for(;u<o;++u)l+=r.cdf((t-n[u])/e);return l/o},icdf(){throw Error("KDE icdf not supported.")}};return l.data(n)},t.randomLCG=function(t){return function(){return(t=(1103515245*t+12345)%2147483647)/2147483647}},t.randomLogNormal=function(t,n){let e,r;const o={mean(t){return arguments.length?(e=t||0,o):e},stdev(t){return arguments.length?(r=null==t?1:t,o):r},sample:()=>x(e,r),pdf:t=>y(t,e,r),cdf:t=>w(t,e,r),icdf:t=>q(t,e,r)};return o.mean(t).stdev(n)},t.randomMixture=function(n,e){let r,o=0;const l={weights(t){return arguments.length?(r=function(t){const n=[];let e,r=0;for(e=0;e<o;++e)r+=n[e]=null==t[e]?1:+t[e];for(e=0;e<o;++e)n[e]/=r;return n}(e=t||[]),l):e},distributions(t){return arguments.length?(t?(o=t.length,n=t):(o=0,n=[]),l.weights(e)):n},sample(){const e=t.random();let l=n[o-1],u=r[0],f=0;for(;f<o-1;u+=r[++f])if(e<u){l=n[f];break}return l.sample()},pdf(t){let e=0,l=0;for(;l<o;++l)e+=r[l]*n[l].pdf(t);return e},cdf(t){let e=0,l=0;for(;l<o;++l)e+=r[l]*n[l].cdf(t);return e},icdf(){throw Error("Mixture icdf not supported.")}};return l.distributions(n).weights(e)},t.randomNormal=v,t.randomUniform=function(t,n){let e,r;const o={min(t){return arguments.length?(e=t||0,o):e},max(t){return arguments.length?(r=null==t?1:t,o):r},sample:()=>A(e,r),pdf:t=>F(t,e,r),cdf:t=>L(t,e,r),icdf:t=>S(t,e,r)};return null==n&&(n=null==t?1:t,t=0),o.min(t).max(n)},t.regressionConstant=E,t.regressionExp=function(t,n,e){const[r,o,l,u]=C(t,n,e);let f,a,i,s=0,c=0,h=0,d=0,p=0;I(t,n,e,((t,n)=>{f=r[p++],a=Math.log(n),i=f*n,s+=(n*a-s)/p,c+=(i-c)/p,h+=(i*a-h)/p,d+=(f*i-d)/p}));const[M,m]=U(c/u,s/u,h/u,d/u),g=t=>Math.exp(M+m*(t-l));return{coef:[Math.exp(M-m*l),m],predict:g,rSquared:P(t,n,e,u,g)}},t.regressionLinear=T,t.regressionLoess=function(t,n,e,r){const[o,l,u,f]=C(t,n,e,!0),a=o.length,s=Math.max(2,~~(r*a)),c=new Float64Array(a),h=new Float64Array(a),d=new Float64Array(a).fill(1);for(let t=-1;++t<=R;){const n=[0,s-1];for(let t=0;t<a;++t){const e=o[t],r=n[0],u=n[1],f=e-o[r]>o[u]-e?r:u;let a=0,i=0,s=0,p=0,M=0;const m=1/Math.abs(o[f]-e||1);for(let t=r;t<=u;++t){const n=o[t],r=l[t],u=Q(Math.abs(e-n)*m)*d[t],f=n*u;a+=u,i+=f,s+=r*u,p+=r*f,M+=n*f}const[g,N]=U(i/a,s/a,p/a,M/a);c[t]=g+N*e,h[t]=Math.abs(l[t]-c[t]),j(o,t+1,n)}if(t===R)break;const e=i(h,.5,void 0);if(Math.abs(e)<K)break;for(let t,n,r=0;r<a;++r)t=h[r]/(6*e),d[r]=t>=1?K:(n=1-t*t)*n}return function(t,n,e,r){const o=t.length,l=[];let u,f=0,a=0,i=[];for(;f<o;++f)u=t[f]+e,i[0]===u?i[1]+=(n[f]-i[1])/++a:(a=0,i[1]+=r,i=[u,n[f]],l.push(i));return i[1]+=r,l}(o,c,u,f)},t.regressionLog=function(t,n,e){let r=0,o=0,l=0,u=0,f=0;I(t,n,e,((t,n)=>{++f,t=Math.log(t),r+=(t-r)/f,o+=(n-o)/f,l+=(t*n-l)/f,u+=(t*t-u)/f}));const a=U(r,o,l,u),i=t=>a[0]+a[1]*Math.log(t);return{coef:a,predict:i,rSquared:P(t,n,e,o,i)}},t.regressionPoly=function(t,n,e,r){if(0===r)return E(t,n,e);if(1===r)return T(t,n,e);if(2===r)return k(t,n,e);const[o,l,u,f]=C(t,n,e),a=o.length,i=[],s=[],c=r+1;let h,d,p,M,m;for(h=0;h<c;++h){for(p=0,M=0;p<a;++p)M+=Math.pow(o[p],h)*l[p];for(i.push(M),m=new Float64Array(c),d=0;d<c;++d){for(p=0,M=0;p<a;++p)M+=Math.pow(o[p],h+d);m[d]=M}s.push(m)}s.push(i);const g=function(t){const n=t.length-1,e=[];let r,o,l,u,f;for(r=0;r<n;++r){for(u=r,o=r+1;o<n;++o)Math.abs(t[r][o])>Math.abs(t[r][u])&&(u=o);for(l=r;l<n+1;++l)f=t[l][r],t[l][r]=t[l][u],t[l][u]=f;for(o=r+1;o<n;++o)for(l=n;l>=r;l--)t[l][o]-=t[l][r]*t[r][o]/t[r][r]}for(o=n-1;o>=0;--o){for(f=0,l=o+1;l<n;++l)f+=t[l][o]*e[l];e[o]=(t[n][o]-f)/t[o][o]}return e}(s),N=t=>{t-=u;let n=f+g[0]+g[1]*t+g[2]*t*t;for(h=3;h<c;++h)n+=g[h]*Math.pow(t,h);return n};return{coef:D(c,g,-u,f),predict:N,rSquared:P(t,n,e,f,N)}},t.regressionPow=function(t,n,e){let r=0,o=0,l=0,u=0,f=0,a=0;I(t,n,e,((t,n)=>{const e=Math.log(t),i=Math.log(n);++a,r+=(e-r)/a,o+=(i-o)/a,l+=(e*i-l)/a,u+=(e*e-u)/a,f+=(n-f)/a}));const i=U(r,o,l,u),s=t=>i[0]*Math.pow(t,i[1]);return i[0]=Math.exp(i[0]),{coef:i,predict:s,rSquared:P(t,n,e,f,s)}},t.regressionQuad=k,t.sampleCurve=function(t,n,e,r){e=e||25,r=Math.max(e,r||200);const o=n=>[n,t(n)],l=n[0],u=n[1],f=u-l,a=f/r,i=[o(l)],s=[];if(e===r){for(let t=1;t<r;++t)i.push(o(l+t/e*f));return i.push(o(u)),i}s.push(o(u));for(let t=e;--t>0;)s.push(o(l+t/e*f));let c=i[0],h=s[s.length-1];const d=1/f,p=function(t,n){let e=t,r=t;const o=n.length;for(let t=0;t<o;++t){const o=n[t][1];o<e&&(e=o),o>r&&(r=o)}return 1/(r-e)}(c[1],s);for(;h;){const t=o((c[0]+h[0])/2);t[0]-c[0]>=a&&z(c,t,h,d,p)>G?s.push(t):(c=h,i.push(h),s.pop()),h=s[s.length-1]}return i},t.sampleLogNormal=x,t.sampleNormal=m,t.sampleUniform=A,t.setRandom=function(n){t.random=n}}));
 //# sourceMappingURL=vega-statistics.min.js.map

build/vega-statistics.module.js

@@ -644,2 +644,17 @@ import { ascending, quantileSorted, deviation, quantile, median } from 'd3-array';
 
+function constant (data, x, y) {
+  let mean = 0,
+    n = 0;
+  for (const d of data) {
+    const val = y(d);
+    if (x(d) == null || val == null || isNaN(val)) continue;
+    mean += (val - mean) / ++n;
+  }
+  return {
+    coef: [mean],
+    predict: () => mean,
+    rSquared: 0
+  };
+}
+
 // Ordinary Least Squares
@@ -869,2 +884,3 @@ function ols (uX, uY, uXY, uX2) {
   // use more efficient methods for lower orders
+  if (order === 0) return constant(data, x, y);
   if (order === 1) return linear(data, x, y);
@@ -1141,2 +1157,2 @@ if (order === 2) return quad(data, x, y);
 
-export { estimateBandwidth as bandwidthNRD, bin, bootstrapCI, cumulativeLogNormal, cumulativeNormal, cumulativeUniform, densityLogNormal, densityNormal, densityUniform, dotbin, quantileLogNormal, quantileNormal, quantileUniform, quantiles, quartiles, random, integer as randomInteger, kde as randomKDE, lcg as randomLCG, lognormal as randomLogNormal, mixture as randomMixture, gaussian as randomNormal, uniform as randomUniform, exp as regressionExp, linear as regressionLinear, loess as regressionLoess, log as regressionLog, poly as regressionPoly, pow as regressionPow, quad as regressionQuad, sampleCurve, sampleLogNormal, sampleNormal, sampleUniform, setRandom };
+export { estimateBandwidth as bandwidthNRD, bin, bootstrapCI, cumulativeLogNormal, cumulativeNormal, cumulativeUniform, densityLogNormal, densityNormal, densityUniform, dotbin, quantileLogNormal, quantileNormal, quantileUniform, quantiles, quartiles, random, integer as randomInteger, kde as randomKDE, lcg as randomLCG, lognormal as randomLogNormal, mixture as randomMixture, gaussian as randomNormal, uniform as randomUniform, constant as regressionConstant, exp as regressionExp, linear as regressionLinear, loess as regressionLoess, log as regressionLog, poly as regressionPoly, pow as regressionPow, quad as regressionQuad, sampleCurve, sampleLogNormal, sampleNormal, sampleUniform, setRandom };

index.js

@@ -33,2 +33,3 @@ export {default as bandwidthNRD} from './src/bandwidth';
 } from './src/uniform';
+export {default as regressionConstant} from './src/regression/constant';
 export {default as regressionLinear} from './src/regression/linear';
@@ -35,0 +36,0 @@ export {default as regressionLog} from './src/regression/log';

package.json

 {
   "name": "vega-statistics",
-  "version": "1.8.1",
+  "version": "1.9.0",
   "description": "Statistical routines and probability distributions.",
@@ -27,3 +27,3 @@ "keywords": [
   },
-  "gitHead": "fb1092f6b931d450f9c210b67ae4752bd3dd461b"
+  "gitHead": "a7a312ebe9db675ae03bd354f193ed34a976d21f"
 }

src/regression/poly.js

@@ -0,1 +1,2 @@
+import constant from './constant';
 import linear from './linear';
@@ -13,2 +14,3 @@ import {points} from './points';
   // use more efficient methods for lower orders
+  if (order === 0) return constant(data, x, y);
   if (order === 1) return linear(data, x, y);
@@ -15,0 +17,0 @@ if (order === 2) return quad(data, x, y);

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Number of package files

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increased by2.78%

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3578

increased by1.36%

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