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vega-regression
Advanced tools
vega-regression - npm Package Compare versions
Comparing version 1.1.0 to 1.1.1
@@ -9,13 +9,13 @@ (function (global, factory) { | ||
| var groups = [], | ||
| get = function (f) { | ||
| return f(t); | ||
| }, | ||
| map, | ||
| i, | ||
| n, | ||
| t, | ||
| k, | ||
| g; // partition data points into stack groups | ||
| get = function (f) { | ||
| return f(t); | ||
| }, | ||
| map, | ||
| i, | ||
| n, | ||
| t, | ||
| k, | ||
| g; | ||
| // partition data points into stack groups | ||
| if (groupby == null) { | ||
@@ -28,3 +28,2 @@ groups.push(data); | ||
| g = map[k]; | ||
| if (!g) { | ||
@@ -35,7 +34,5 @@ map[k] = g = []; | ||
| } | ||
| g.push(t); | ||
| } | ||
| } | ||
| return groups; | ||
@@ -53,3 +50,2 @@ } | ||
| */ | ||
| function Loess(params) { | ||
@@ -88,18 +84,15 @@ vegaDataflow.Transform.call(this, null, params); | ||
| const out = pulse.fork(pulse.NO_SOURCE | pulse.NO_FIELDS); | ||
| if (!this.value || pulse.changed() || _.modified()) { | ||
| const source = pulse.materialize(pulse.SOURCE).source, | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(vegaUtil.accessorName), | ||
| m = names.length, | ||
| as = _.as || [vegaUtil.accessorName(_.x), vegaUtil.accessorName(_.y)], | ||
| values = []; | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(vegaUtil.accessorName), | ||
| m = names.length, | ||
| as = _.as || [vegaUtil.accessorName(_.x), vegaUtil.accessorName(_.y)], | ||
| values = []; | ||
| groups.forEach(g => { | ||
| vegaStatistics.regressionLoess(g, _.x, _.y, _.bandwidth || 0.3).forEach(p => { | ||
| const t = {}; | ||
| for (let i = 0; i < m; ++i) { | ||
| t[names[i]] = g.dims[i]; | ||
| } | ||
| t[as[0]] = p[0]; | ||
@@ -113,6 +106,4 @@ t[as[1]] = p[1]; | ||
| } | ||
| return out; | ||
| } | ||
| }); | ||
@@ -128,4 +119,4 @@ | ||
| }; | ||
| const degreesOfFreedom = (method, order) => method === 'poly' ? order : method === 'quad' ? 2 : 1; | ||
| const degreesOfFreedom = (method, order) => method === 'poly' ? order : method === 'quad' ? 2 : 1; | ||
| /** | ||
@@ -142,4 +133,2 @@ * Compute regression fits for one or more data groups. | ||
| */ | ||
| function Regression(params) { | ||
@@ -192,19 +181,16 @@ vegaDataflow.Transform.call(this, null, params); | ||
| const out = pulse.fork(pulse.NO_SOURCE | pulse.NO_FIELDS); | ||
| if (!this.value || pulse.changed() || _.modified()) { | ||
| const source = pulse.materialize(pulse.SOURCE).source, | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(vegaUtil.accessorName), | ||
| method = _.method || 'linear', | ||
| order = _.order || 3, | ||
| dof = degreesOfFreedom(method, order), | ||
| as = _.as || [vegaUtil.accessorName(_.x), vegaUtil.accessorName(_.y)], | ||
| fit = Methods[method], | ||
| values = []; | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(vegaUtil.accessorName), | ||
| method = _.method || 'linear', | ||
| order = _.order || 3, | ||
| dof = degreesOfFreedom(method, order), | ||
| as = _.as || [vegaUtil.accessorName(_.x), vegaUtil.accessorName(_.y)], | ||
| fit = Methods[method], | ||
| values = []; | ||
| let domain = _.extent; | ||
| if (!vegaUtil.hasOwnProperty(Methods, method)) { | ||
| vegaUtil.error('Invalid regression method: ' + method); | ||
| } | ||
| if (domain != null) { | ||
@@ -216,6 +202,4 @@ if (method === 'log' && domain[0] <= 0) { | ||
| } | ||
| groups.forEach(g => { | ||
| const n = g.length; | ||
| if (n <= dof) { | ||
@@ -225,5 +209,3 @@ pulse.dataflow.warn('Skipping regression with more parameters than data points.'); | ||
| } | ||
| const model = fit(g, _.x, _.y, order); | ||
| if (_.params) { | ||
@@ -238,16 +220,12 @@ // if parameter vectors requested return those | ||
| } | ||
| const dom = domain || vegaUtil.extent(g, _.x), | ||
| add = p => { | ||
| const t = {}; | ||
| for (let i = 0; i < names.length; ++i) { | ||
| t[names[i]] = g.dims[i]; | ||
| } | ||
| t[as[0]] = p[0]; | ||
| t[as[1]] = p[1]; | ||
| values.push(vegaDataflow.ingest(t)); | ||
| }; | ||
| add = p => { | ||
| const t = {}; | ||
| for (let i = 0; i < names.length; ++i) { | ||
| t[names[i]] = g.dims[i]; | ||
| } | ||
| t[as[0]] = p[0]; | ||
| t[as[1]] = p[1]; | ||
| values.push(vegaDataflow.ingest(t)); | ||
| }; | ||
| if (method === 'linear') { | ||
@@ -264,6 +242,4 @@ // for linear regression we only need the end points | ||
| } | ||
| return out; | ||
| } | ||
| }); | ||
@@ -274,4 +250,2 @@ | ||
| Object.defineProperty(exports, '__esModule', { value: true }); | ||
| })); |
@@ -1,2 +0,2 @@ | ||
| !function(e,r){"object"==typeof exports&&"undefined"!=typeof module?r(exports,require("vega-statistics"),require("vega-dataflow"),require("vega-util")):"function"==typeof define&&define.amd?define(["exports","vega-statistics","vega-dataflow","vega-util"],r):r((e="undefined"!=typeof globalThis?globalThis:e||self).vega={},e.vega,e.vega,e.vega)}(this,(function(e,r,a,t){"use strict";function s(e,r){var a,t,s,n,o,i,l=[],u=function(e){return e(n)};if(null==r)l.push(e);else for(a={},t=0,s=e.length;t<s;++t)n=e[t],(i=a[o=r.map(u)])||(a[o]=i=[],i.dims=o,l.push(i)),i.push(n);return l}function n(e){a.Transform.call(this,null,e)}n.Definition={type:"Loess",metadata:{generates:!0},params:[{name:"x",type:"field",required:!0},{name:"y",type:"field",required:!0},{name:"groupby",type:"field",array:!0},{name:"bandwidth",type:"number",default:.3},{name:"as",type:"string",array:!0}]},t.inherits(n,a.Transform,{transform(e,n){const o=n.fork(n.NO_SOURCE|n.NO_FIELDS);if(!this.value||n.changed()||e.modified()){const i=s(n.materialize(n.SOURCE).source,e.groupby),l=(e.groupby||[]).map(t.accessorName),u=l.length,d=e.as||[t.accessorName(e.x),t.accessorName(e.y)],f=[];i.forEach((t=>{r.regressionLoess(t,e.x,e.y,e.bandwidth||.3).forEach((e=>{const r={};for(let e=0;e<u;++e)r[l[e]]=t.dims[e];r[d[0]]=e[0],r[d[1]]=e[1],f.push(a.ingest(r))}))})),this.value&&(o.rem=this.value),this.value=o.add=o.source=f}return o}});const o={linear:r.regressionLinear,log:r.regressionLog,exp:r.regressionExp,pow:r.regressionPow,quad:r.regressionQuad,poly:r.regressionPoly};function i(e){a.Transform.call(this,null,e)}i.Definition={type:"Regression",metadata:{generates:!0},params:[{name:"x",type:"field",required:!0},{name:"y",type:"field",required:!0},{name:"groupby",type:"field",array:!0},{name:"method",type:"string",default:"linear",values:Object.keys(o)},{name:"order",type:"number",default:3},{name:"extent",type:"number",array:!0,length:2},{name:"params",type:"boolean",default:!1},{name:"as",type:"string",array:!0}]},t.inherits(i,a.Transform,{transform(e,n){const i=n.fork(n.NO_SOURCE|n.NO_FIELDS);if(!this.value||n.changed()||e.modified()){const l=s(n.materialize(n.SOURCE).source,e.groupby),u=(e.groupby||[]).map(t.accessorName),d=e.method||"linear",f=e.order||3,m=((e,r)=>"poly"===e?r:"quad"===e?2:1)(d,f),p=e.as||[t.accessorName(e.x),t.accessorName(e.y)],g=o[d],c=[];let y=e.extent;t.hasOwnProperty(o,d)||t.error("Invalid regression method: "+d),null!=y&&"log"===d&&y[0]<=0&&(n.dataflow.warn("Ignoring extent with values <= 0 for log regression."),y=null),l.forEach((s=>{if(s.length<=m)return void n.dataflow.warn("Skipping regression with more parameters than data points.");const o=g(s,e.x,e.y,f);if(e.params)return void c.push(a.ingest({keys:s.dims,coef:o.coef,rSquared:o.rSquared}));const i=y||t.extent(s,e.x),l=e=>{const r={};for(let e=0;e<u.length;++e)r[u[e]]=s.dims[e];r[p[0]]=e[0],r[p[1]]=e[1],c.push(a.ingest(r))};"linear"===d?i.forEach((e=>l([e,o.predict(e)]))):r.sampleCurve(o.predict,i,25,200).forEach(l)})),this.value&&(i.rem=this.value),this.value=i.add=i.source=c}return i}}),e.loess=n,e.regression=i,Object.defineProperty(e,"__esModule",{value:!0})})); | ||
| !function(e,r){"object"==typeof exports&&"undefined"!=typeof module?r(exports,require("vega-statistics"),require("vega-dataflow"),require("vega-util")):"function"==typeof define&&define.amd?define(["exports","vega-statistics","vega-dataflow","vega-util"],r):r((e="undefined"!=typeof globalThis?globalThis:e||self).vega={},e.vega,e.vega,e.vega)}(this,(function(e,r,a,t){"use strict";function s(e,r){var a,t,s,n,i,o,l=[],u=function(e){return e(n)};if(null==r)l.push(e);else for(a={},t=0,s=e.length;t<s;++t)n=e[t],(o=a[i=r.map(u)])||(a[i]=o=[],o.dims=i,l.push(o)),o.push(n);return l}function n(e){a.Transform.call(this,null,e)}n.Definition={type:"Loess",metadata:{generates:!0},params:[{name:"x",type:"field",required:!0},{name:"y",type:"field",required:!0},{name:"groupby",type:"field",array:!0},{name:"bandwidth",type:"number",default:.3},{name:"as",type:"string",array:!0}]},t.inherits(n,a.Transform,{transform(e,n){const i=n.fork(n.NO_SOURCE|n.NO_FIELDS);if(!this.value||n.changed()||e.modified()){const o=s(n.materialize(n.SOURCE).source,e.groupby),l=(e.groupby||[]).map(t.accessorName),u=l.length,d=e.as||[t.accessorName(e.x),t.accessorName(e.y)],f=[];o.forEach((t=>{r.regressionLoess(t,e.x,e.y,e.bandwidth||.3).forEach((e=>{const r={};for(let e=0;e<u;++e)r[l[e]]=t.dims[e];r[d[0]]=e[0],r[d[1]]=e[1],f.push(a.ingest(r))}))})),this.value&&(i.rem=this.value),this.value=i.add=i.source=f}return i}});const i={linear:r.regressionLinear,log:r.regressionLog,exp:r.regressionExp,pow:r.regressionPow,quad:r.regressionQuad,poly:r.regressionPoly};function o(e){a.Transform.call(this,null,e)}o.Definition={type:"Regression",metadata:{generates:!0},params:[{name:"x",type:"field",required:!0},{name:"y",type:"field",required:!0},{name:"groupby",type:"field",array:!0},{name:"method",type:"string",default:"linear",values:Object.keys(i)},{name:"order",type:"number",default:3},{name:"extent",type:"number",array:!0,length:2},{name:"params",type:"boolean",default:!1},{name:"as",type:"string",array:!0}]},t.inherits(o,a.Transform,{transform(e,n){const o=n.fork(n.NO_SOURCE|n.NO_FIELDS);if(!this.value||n.changed()||e.modified()){const l=s(n.materialize(n.SOURCE).source,e.groupby),u=(e.groupby||[]).map(t.accessorName),d=e.method||"linear",f=e.order||3,m=((e,r)=>"poly"===e?r:"quad"===e?2:1)(d,f),p=e.as||[t.accessorName(e.x),t.accessorName(e.y)],g=i[d],c=[];let h=e.extent;t.hasOwnProperty(i,d)||t.error("Invalid regression method: "+d),null!=h&&"log"===d&&h[0]<=0&&(n.dataflow.warn("Ignoring extent with values <= 0 for log regression."),h=null),l.forEach((s=>{if(s.length<=m)return void n.dataflow.warn("Skipping regression with more parameters than data points.");const i=g(s,e.x,e.y,f);if(e.params)return void c.push(a.ingest({keys:s.dims,coef:i.coef,rSquared:i.rSquared}));const o=h||t.extent(s,e.x),l=e=>{const r={};for(let e=0;e<u.length;++e)r[u[e]]=s.dims[e];r[p[0]]=e[0],r[p[1]]=e[1],c.push(a.ingest(r))};"linear"===d?o.forEach((e=>l([e,i.predict(e)]))):r.sampleCurve(i.predict,o,25,200).forEach(l)})),this.value&&(o.rem=this.value),this.value=o.add=o.source=c}return o}}),e.loess=n,e.regression=o})); | ||
| //# sourceMappingURL=vega-regression.min.js.map |
@@ -7,13 +7,13 @@ import { regressionLoess, sampleCurve, regressionLinear, regressionLog, regressionExp, regressionPow, regressionQuad, regressionPoly } from 'vega-statistics'; | ||
| var groups = [], | ||
| get = function (f) { | ||
| return f(t); | ||
| }, | ||
| map, | ||
| i, | ||
| n, | ||
| t, | ||
| k, | ||
| g; // partition data points into stack groups | ||
| get = function (f) { | ||
| return f(t); | ||
| }, | ||
| map, | ||
| i, | ||
| n, | ||
| t, | ||
| k, | ||
| g; | ||
| // partition data points into stack groups | ||
| if (groupby == null) { | ||
@@ -26,3 +26,2 @@ groups.push(data); | ||
| g = map[k]; | ||
| if (!g) { | ||
@@ -33,7 +32,5 @@ map[k] = g = []; | ||
| } | ||
| g.push(t); | ||
| } | ||
| } | ||
| return groups; | ||
@@ -51,3 +48,2 @@ } | ||
| */ | ||
| function Loess(params) { | ||
@@ -86,18 +82,15 @@ Transform.call(this, null, params); | ||
| const out = pulse.fork(pulse.NO_SOURCE | pulse.NO_FIELDS); | ||
| if (!this.value || pulse.changed() || _.modified()) { | ||
| const source = pulse.materialize(pulse.SOURCE).source, | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(accessorName), | ||
| m = names.length, | ||
| as = _.as || [accessorName(_.x), accessorName(_.y)], | ||
| values = []; | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(accessorName), | ||
| m = names.length, | ||
| as = _.as || [accessorName(_.x), accessorName(_.y)], | ||
| values = []; | ||
| groups.forEach(g => { | ||
| regressionLoess(g, _.x, _.y, _.bandwidth || 0.3).forEach(p => { | ||
| const t = {}; | ||
| for (let i = 0; i < m; ++i) { | ||
| t[names[i]] = g.dims[i]; | ||
| } | ||
| t[as[0]] = p[0]; | ||
@@ -111,6 +104,4 @@ t[as[1]] = p[1]; | ||
| } | ||
| return out; | ||
| } | ||
| }); | ||
@@ -126,4 +117,4 @@ | ||
| }; | ||
| const degreesOfFreedom = (method, order) => method === 'poly' ? order : method === 'quad' ? 2 : 1; | ||
| const degreesOfFreedom = (method, order) => method === 'poly' ? order : method === 'quad' ? 2 : 1; | ||
| /** | ||
@@ -140,4 +131,2 @@ * Compute regression fits for one or more data groups. | ||
| */ | ||
| function Regression(params) { | ||
@@ -190,19 +179,16 @@ Transform.call(this, null, params); | ||
| const out = pulse.fork(pulse.NO_SOURCE | pulse.NO_FIELDS); | ||
| if (!this.value || pulse.changed() || _.modified()) { | ||
| const source = pulse.materialize(pulse.SOURCE).source, | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(accessorName), | ||
| method = _.method || 'linear', | ||
| order = _.order || 3, | ||
| dof = degreesOfFreedom(method, order), | ||
| as = _.as || [accessorName(_.x), accessorName(_.y)], | ||
| fit = Methods[method], | ||
| values = []; | ||
| groups = partition(source, _.groupby), | ||
| names = (_.groupby || []).map(accessorName), | ||
| method = _.method || 'linear', | ||
| order = _.order || 3, | ||
| dof = degreesOfFreedom(method, order), | ||
| as = _.as || [accessorName(_.x), accessorName(_.y)], | ||
| fit = Methods[method], | ||
| values = []; | ||
| let domain = _.extent; | ||
| if (!hasOwnProperty(Methods, method)) { | ||
| error('Invalid regression method: ' + method); | ||
| } | ||
| if (domain != null) { | ||
@@ -214,6 +200,4 @@ if (method === 'log' && domain[0] <= 0) { | ||
| } | ||
| groups.forEach(g => { | ||
| const n = g.length; | ||
| if (n <= dof) { | ||
@@ -223,5 +207,3 @@ pulse.dataflow.warn('Skipping regression with more parameters than data points.'); | ||
| } | ||
| const model = fit(g, _.x, _.y, order); | ||
| if (_.params) { | ||
@@ -236,16 +218,12 @@ // if parameter vectors requested return those | ||
| } | ||
| const dom = domain || extent(g, _.x), | ||
| add = p => { | ||
| const t = {}; | ||
| for (let i = 0; i < names.length; ++i) { | ||
| t[names[i]] = g.dims[i]; | ||
| } | ||
| t[as[0]] = p[0]; | ||
| t[as[1]] = p[1]; | ||
| values.push(ingest(t)); | ||
| }; | ||
| add = p => { | ||
| const t = {}; | ||
| for (let i = 0; i < names.length; ++i) { | ||
| t[names[i]] = g.dims[i]; | ||
| } | ||
| t[as[0]] = p[0]; | ||
| t[as[1]] = p[1]; | ||
| values.push(ingest(t)); | ||
| }; | ||
| if (method === 'linear') { | ||
@@ -262,8 +240,6 @@ // for linear regression we only need the end points | ||
| } | ||
| return out; | ||
| } | ||
| }); | ||
| export { Loess as loess, Regression as regression }; |
| { | ||
| "name": "vega-regression", | ||
| "version": "1.1.0", | ||
| "version": "1.1.1", | ||
| "description": "Regression transform for Vega dataflows.", | ||
@@ -18,3 +18,3 @@ "keywords": [ | ||
| "prebuild": "rimraf build", | ||
| "build": "rollup -c", | ||
| "build": "rollup -c rollup.config.mjs", | ||
| "pretest": "yarn build --config-test", | ||
@@ -25,3 +25,3 @@ "test": "tape 'test/**/*-test.js'", | ||
| "dependencies": { | ||
| "d3-array": "^3.1.1", | ||
| "d3-array": "^3.2.2", | ||
| "vega-dataflow": "^5.7.3", | ||
@@ -34,3 +34,3 @@ "vega-statistics": "^1.7.9", | ||
| }, | ||
| "gitHead": "9a3faca4395cade9ecdfde90af98f1c53e9916b2" | ||
| "gitHead": "fb1092f6b931d450f9c210b67ae4752bd3dd461b" | ||
| } |
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