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