59 lines
2.4 KiB
Java
59 lines
2.4 KiB
Java
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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var tslib_1 = require("tslib");
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var regression_1 = tslib_1.__importDefault(require("regression"));
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var util_1 = require("@antv/util");
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var get_series_values_1 = tslib_1.__importDefault(require("../util/get-series-values"));
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var data_set_1 = require("../data-set");
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var option_parser_1 = require("../util/option-parser");
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var bandwidth_1 = require("../util/bandwidth");
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var DEFAULT_OPTIONS = {
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as: ['x', 'y'],
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// fields: [ 'x', 'y' ], // required two fields
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method: 'linear',
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// extent: [], // extent to execute regression function, default: [ min(x), max(x) ]
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// bandwidth: 1, // bandWidth to execute regression function
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order: 2,
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precision: 2,
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};
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var REGRESSION_METHODS = ['linear', 'exponential', 'logarithmic', 'power', 'polynomial'];
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function transform(dataView, options) {
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options = util_1.assign({}, DEFAULT_OPTIONS, options);
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var fields = option_parser_1.getFields(options);
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if (!util_1.isArray(fields) || fields.length !== 2) {
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throw new TypeError('invalid fields: must be an array of 2 strings.');
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}
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var _a = tslib_1.__read(fields, 2), xField = _a[0], yField = _a[1];
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var method = options.method;
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if (REGRESSION_METHODS.indexOf(method) === -1) {
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throw new TypeError("invalid method: " + method + ". Must be one of " + REGRESSION_METHODS.join(', '));
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}
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var points = dataView.rows.map(function (row) { return [row[xField], row[yField]]; });
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var regressionResult = regression_1.default[method](points, options);
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var extent = options.extent;
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if (!util_1.isArray(extent) || extent.length !== 2) {
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extent = dataView.range(xField);
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}
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var bandwidth = options.bandwidth;
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if (!util_1.isNumber(bandwidth) || bandwidth <= 0) {
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bandwidth = bandwidth_1.silverman(dataView.getColumn(xField));
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}
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var valuesToPredict = get_series_values_1.default(extent, bandwidth);
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var result = [];
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var _b = tslib_1.__read(options.as, 2), asX = _b[0], asY = _b[1];
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valuesToPredict.forEach(function (value) {
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var row = {};
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var _a = tslib_1.__read(regressionResult.predict(value), 2), x = _a[0], y = _a[1];
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row[asX] = x;
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row[asY] = y;
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if (isFinite(y)) {
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result.push(row);
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}
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});
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dataView.rows = result;
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}
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data_set_1.DataSet.registerTransform('regression', transform);
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exports.default = {
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REGRESSION_METHODS: REGRESSION_METHODS,
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};
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