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prabhatdev
2020-07-28 00:48:25 +05:30
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commit 194b41124d
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212
node_modules/vega-regression/build/vega-regression.js generated vendored Normal file
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(function (global, factory) {
typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('vega-statistics'), require('vega-dataflow'), require('vega-util')) :
typeof define === 'function' && define.amd ? define(['exports', 'vega-statistics', 'vega-dataflow', 'vega-util'], factory) :
(global = global || self, factory((global.vega = global.vega || {}, global.vega.transforms = {}), global.vega, global.vega, global.vega));
}(this, (function (exports, vegaStatistics, vegaDataflow, vegaUtil) { 'use strict';
function partition(data, groupby) {
var groups = [],
get = function(f) { return f(t); },
map, i, n, t, k, g;
// partition data points into stack groups
if (groupby == null) {
groups.push(data);
} else {
for (map={}, i=0, n=data.length; i<n; ++i) {
t = data[i];
k = groupby.map(get);
g = map[k];
if (!g) {
map[k] = (g = []);
g.dims = k;
groups.push(g);
}
g.push(t);
}
}
return groups;
}
/**
* Compute locally-weighted regression fits for one or more data groups.
* @constructor
* @param {object} params - The parameters for this operator.
* @param {function(object): *} params.x - An accessor for the predictor data field.
* @param {function(object): *} params.y - An accessor for the predicted data field.
* @param {Array<function(object): *>} [params.groupby] - An array of accessors to groupby.
* @param {number} [params.bandwidth=0.3] - The loess bandwidth.
*/
function Loess(params) {
vegaDataflow.Transform.call(this, null, params);
}
Loess.Definition = {
'type': 'Loess',
'metadata': {'generates': true},
'params': [
{ 'name': 'x', 'type': 'field', 'required': true },
{ 'name': 'y', 'type': 'field', 'required': true },
{ 'name': 'groupby', 'type': 'field', 'array': true },
{ 'name': 'bandwidth', 'type': 'number', 'default': 0.3 },
{ 'name': 'as', 'type': 'string', 'array': true }
]
};
var prototype = vegaUtil.inherits(Loess, vegaDataflow.Transform);
prototype.transform = function(_, pulse) {
var 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.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];
t[as[1]] = p[1];
values.push(vegaDataflow.ingest(t));
});
});
if (this.value) out.rem = this.value;
this.value = out.add = out.source = values;
}
return out;
};
const Methods = {
linear: vegaStatistics.regressionLinear,
log: vegaStatistics.regressionLog,
exp: vegaStatistics.regressionExp,
pow: vegaStatistics.regressionPow,
quad: vegaStatistics.regressionQuad,
poly: vegaStatistics.regressionPoly
};
function degreesOfFreedom(method, order) {
return method === 'poly' ? order : method === 'quad' ? 2 : 1;
}
/**
* Compute regression fits for one or more data groups.
* @constructor
* @param {object} params - The parameters for this operator.
* @param {function(object): *} params.x - An accessor for the predictor data field.
* @param {function(object): *} params.y - An accessor for the predicted data field.
* @param {string} [params.method='linear'] - The regression method to apply.
* @param {Array<function(object): *>} [params.groupby] - An array of accessors to groupby.
* @param {Array<number>} [params.extent] - The domain extent over which to plot the regression line.
* @param {number} [params.order=3] - The polynomial order. Only applies to the 'poly' method.
*/
function Regression(params) {
vegaDataflow.Transform.call(this, null, params);
}
Regression.Definition = {
'type': 'Regression',
'metadata': {'generates': true},
'params': [
{ 'name': 'x', 'type': 'field', 'required': true },
{ 'name': 'y', 'type': 'field', 'required': true },
{ 'name': 'groupby', 'type': 'field', 'array': true },
{ 'name': 'method', 'type': 'string', 'default': 'linear', 'values': Object.keys(Methods) },
{ 'name': 'order', 'type': 'number', 'default': 3 },
{ 'name': 'extent', 'type': 'number', 'array': true, 'length': 2 },
{ 'name': 'params', 'type': 'boolean', 'default': false },
{ 'name': 'as', 'type': 'string', 'array': true }
]
};
var prototype$1 = vegaUtil.inherits(Regression, vegaDataflow.Transform);
prototype$1.transform = function(_, pulse) {
var 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 = [];
let domain = _.extent;
if (!vegaUtil.hasOwnProperty(Methods, method)) {
vegaUtil.error('Invalid regression method: ' + method);
}
if (domain != null) {
if (method === 'log' && domain[0] <= 0) {
pulse.dataflow.warn('Ignoring extent with values <= 0 for log regression.');
domain = null;
}
}
groups.forEach(g => {
const n = g.length;
if (n <= dof) {
pulse.dataflow.warn('Skipping regression with more parameters than data points.');
return;
}
const model = fit(g, _.x, _.y, order);
if (_.params) {
// if parameter vectors requested return those
values.push(vegaDataflow.ingest({
keys: g.dims,
coef: model.coef,
rSquared: model.rSquared
}));
return;
}
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));
};
if (method === 'linear') {
// for linear regression we only need the end points
dom.forEach(x => add([x, model.predict(x)]));
} else {
// otherwise return trend line sample points
vegaStatistics.sampleCurve(model.predict, dom, 25, 200).forEach(add);
}
});
if (this.value) out.rem = this.value;
this.value = out.add = out.source = values;
}
return out;
};
exports.loess = Loess;
exports.regression = Regression;
Object.defineProperty(exports, '__esModule', { value: true });
})));

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node_modules/vega-regression/build/vega-regression.min.js generated vendored Normal file
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!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=e||self).vega=e.vega||{},e.vega.transforms={}),e.vega,e.vega,e.vega)}(this,(function(e,r,a,t){"use strict";function n(e,r){var a,t,n,s,i,o,u=[],l=function(e){return e(s)};if(null==r)u.push(e);else for(a={},t=0,n=e.length;t<n;++t)s=e[t],(o=a[i=r.map(l)])||(a[i]=o=[],o.dims=i,u.push(o)),o.push(s);return u}function s(e){a.Transform.call(this,null,e)}s.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(s,a.Transform).transform=function(e,s){var i=s.fork(s.NO_SOURCE|s.NO_FIELDS);if(!this.value||s.changed()||e.modified()){const o=n(s.materialize(s.SOURCE).source,e.groupby),u=(e.groupby||[]).map(t.accessorName),l=u.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<l;++e)r[u[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=function(e,s){var o=s.fork(s.NO_SOURCE|s.NO_FIELDS);if(!this.value||s.changed()||e.modified()){const u=n(s.materialize(s.SOURCE).source,e.groupby),l=(e.groupby||[]).map(t.accessorName),d=e.method||"linear",f=e.order||3,m=function(e,r){return"poly"===e?r:"quad"===e?2:1}(d,f),p=e.as||[t.accessorName(e.x),t.accessorName(e.y)],c=i[d],g=[];let y=e.extent;t.hasOwnProperty(i,d)||t.error("Invalid regression method: "+d),null!=y&&"log"===d&&y[0]<=0&&(s.dataflow.warn("Ignoring extent with values <= 0 for log regression."),y=null),u.forEach(n=>{if(n.length<=m)return void s.dataflow.warn("Skipping regression with more parameters than data points.");const i=c(n,e.x,e.y,f);if(e.params)return void g.push(a.ingest({keys:n.dims,coef:i.coef,rSquared:i.rSquared}));const o=y||t.extent(n,e.x),u=e=>{const r={};for(let e=0;e<l.length;++e)r[l[e]]=n.dims[e];r[p[0]]=e[0],r[p[1]]=e[1],g.push(a.ingest(r))};"linear"===d?o.forEach(e=>u([e,i.predict(e)])):r.sampleCurve(i.predict,o,25,200).forEach(u)}),this.value&&(o.rem=this.value),this.value=o.add=o.source=g}return o},e.loess=s,e.regression=o,Object.defineProperty(e,"__esModule",{value:!0})}));