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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66 lines
1.6 KiB
66 lines
1.6 KiB
'use strict'
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var interp = require('ndarray-linear-interpolate')
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var cwise = require('cwise')
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var do_warp = cwise({
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args: ['index', 'array', 'scalar', 'scalar', 'scalar'],
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pre: function(idx, dest, func, interp, n) {
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this.warped = new Array(n)
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},
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body: function warpND(idx, dest, func, interp, n) {
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func(this.warped, idx)
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dest = interp.apply(undefined, this.warped)
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}
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})
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var do_warp_1 = cwise({
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args: ['index', 'array', 'scalar', 'scalar', 'scalar'],
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pre: function() {
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this.warped = [0]
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},
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body: function warp1D(idx, dest, func, interp, src) {
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func(this.warped, idx)
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dest = interp(src, this.warped[0])
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}
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})
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var do_warp_2 = cwise({
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args: ['index', 'array', 'scalar', 'scalar', 'scalar'],
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pre: function() {
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this.warped = [0, 0]
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},
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body: function warp2D(idx, dest, func, interp, src) {
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func(this.warped, idx)
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dest = interp(src, this.warped[0], this.warped[1])
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}
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})
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var do_warp_3 = cwise({
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args: ['index', 'array', 'scalar', 'scalar', 'scalar'],
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pre: function() {
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this.warped = [0, 0, 0]
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},
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body: function warp3D(idx, dest, func, interp, src) {
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func(this.warped, idx)
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dest = interp(src, this.warped[0], this.warped[1], this.warped[2])
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}
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})
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module.exports = function warp(dest, src, func) {
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switch(src.shape.length) {
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case 1:
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do_warp_1(dest, func, interp.d1, src)
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break
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case 2:
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do_warp_2(dest, func, interp.d2, src)
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break
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case 3:
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do_warp_3(dest, func, interp.d3, src)
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break
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default:
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do_warp(dest, func, interp.bind(undefined, src), src.shape.length)
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break
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}
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return dest
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}
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