StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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72 lines
1.7 KiB
72 lines
1.7 KiB
import {ticks, tickIncrement} from "d3-array";
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import continuous, {copy, identity} from "./continuous";
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import {initRange} from "./init";
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import tickFormat from "./tickFormat";
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export function linearish(scale) {
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var domain = scale.domain;
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scale.ticks = function(count) {
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var d = domain();
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return ticks(d[0], d[d.length - 1], count == null ? 10 : count);
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};
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scale.tickFormat = function(count, specifier) {
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var d = domain();
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return tickFormat(d[0], d[d.length - 1], count == null ? 10 : count, specifier);
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};
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scale.nice = function(count) {
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if (count == null) count = 10;
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var d = domain(),
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i0 = 0,
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i1 = d.length - 1,
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start = d[i0],
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stop = d[i1],
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step;
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if (stop < start) {
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step = start, start = stop, stop = step;
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step = i0, i0 = i1, i1 = step;
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}
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step = tickIncrement(start, stop, count);
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if (step > 0) {
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start = Math.floor(start / step) * step;
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stop = Math.ceil(stop / step) * step;
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step = tickIncrement(start, stop, count);
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} else if (step < 0) {
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start = Math.ceil(start * step) / step;
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stop = Math.floor(stop * step) / step;
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step = tickIncrement(start, stop, count);
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}
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if (step > 0) {
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d[i0] = Math.floor(start / step) * step;
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d[i1] = Math.ceil(stop / step) * step;
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domain(d);
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} else if (step < 0) {
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d[i0] = Math.ceil(start * step) / step;
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d[i1] = Math.floor(stop * step) / step;
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domain(d);
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}
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return scale;
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};
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return scale;
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}
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export default function linear() {
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var scale = continuous(identity, identity);
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scale.copy = function() {
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return copy(scale, linear());
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};
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initRange.apply(scale, arguments);
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return linearish(scale);
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}
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