spencermountain--compromise
wehub-resource-sync
89 行
2.5 KiB
JavaScript
89 行
2.5 KiB
JavaScript
import test from 'tape'
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import nlp from '../_lib.js'
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const here = '[one/hyphen-matrix] '
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test('hyphen-input', (t) => {
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const doc = nlp(`before follow-up after`)
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t.equal(doc.has('follow-up'), true, here + 'hyphen -> follow-up')
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t.equal(doc.has('follow up'), true, here + 'hyphen -> follow up')
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t.equal(doc.has('followup'), false, here + 'hyphen -> followup')//would be nice
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t.end()
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})
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test('no-hyphen-input', (t) => {
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const doc = nlp(`before follow up after`)
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t.equal(doc.has('follow-up'), true, here + 'no-hyphen -> follow-up')
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t.equal(doc.has('follow up'), true, here + 'no-hyphen ->follow up')
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t.equal(doc.has('followup'), false, here + 'no-hyphen ->followup')
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t.end()
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})
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test('compound-hyphen-input', (t) => {
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const doc = nlp(`before followup after`)
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t.equal(doc.has('follow-up'), false, here + 'compound -> follow-up')//maybe?
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t.equal(doc.has('follow up'), false, here + 'compound -> follow up')
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t.equal(doc.has('followup'), true, here + 'compound -> followup')
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t.end()
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})
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// ==================
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test('hyphen-skipping', (t) => {
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const doc = nlp(`before super-cool after`)
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t.equal(doc.has('before super'), true, here + '[hyphen] before')
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t.equal(doc.has('before super cool'), true, here + '[hyphen] before-mid')
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t.equal(doc.has('cool after'), true, here + '[hyphen] after')
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t.equal(doc.has('super cool after'), true, here + '[hyphen] after-mid')
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//
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t.equal(doc.has('before after'), false, here + '[hyphen] no-jump')
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t.equal(doc.has('before super after'), false, here + '[hyphen] no-mid-jump')
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t.equal(doc.has('before cool after'), false, here + '[hyphen] no-mid-2-jump')
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t.end()
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})
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test('match-dash', function (t) {
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const arr = [
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're-purpose',
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'co-opting',
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'mis-information',
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'proto-plasmic',
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'counter-argument',
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'soft-sell',
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'big-news',
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'do-over',
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'over-the-top',
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'larger-than-life',
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're-zoning-laws',
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'inter-sectional',
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'counter-argument',
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're-purpose itself',
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'full-enough tank',
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'the size-difference',
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'counter-balance',
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'score was 10-2',
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'Nobel Prize–winning',
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'take-down ',
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'the non-player-character',
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'load-bearing walls',
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'this clearly-impossible story',
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'beautiful-looking flowers ',
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'community-based education',
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'Mother-in-law',
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'Master-at-arms',
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'Editor-in-chief',
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'Ten-year-old',
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'Factory-made',
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'Twelve-pack',
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'fifty-six bottles',
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'a 10-minute speech',
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'self-serve',
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'non-sequitur'
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]
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arr.forEach(str => {
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t.equal(nlp(str).has(str), true, here + '[dash] ' + str)
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})
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t.end()
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})
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