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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50 lines
1.1 KiB
50 lines
1.1 KiB
4 years ago
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var test = require('tape');
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var rat = require('big-rat')
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var lerp = require('../lerp')
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var rv = require('../index')
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var toFloat = require('../to-float')
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var equal = require('../equals')
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test('lerp to 0', function(t) {
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var start = rv([-2.5, -2.5])
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var end = rv([2.5, 2.5])
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var r = lerp(start, end, rat(0))
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t.ok(equal(start, r), 'at t=0 the result is same as start [-2.5, -2.5]')
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t.end();
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})
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test('lerp to 1/2', function(t) {
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var start = rv([-2.5, -2.5])
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var end = rv([2.5, 2.5])
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var exp = rv([0, 0])
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var r = lerp(start, end, rat(1, 2))
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t.ok(equal(r, exp), 'at t=0.5 the result is [0, 0]')
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t.end()
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})
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test('lerp to 1', function(t) {
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var start = rv([-2.5, -2.5])
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var end = rv([2.5, 2.5])
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var r = lerp(start, end, rat(1))
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t.ok(equal(r, end), 'at t=1 the result is end [2.5, 2.5]')
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t.end()
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})
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test('lerp down a 2d line', function(t) {
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var start = rv([0, 0]);
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var end = rv([100, 0]);
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for (var i=0; i<=10; i++) {
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var r = lerp(start, end, rat(i, 10))
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var expected = rv([i*10, 0])
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t.ok(equal(r, expected), 'at t=' + (i/10))
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}
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t.end()
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})
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