wehub-resource-sync
107 lines
2.9 KiB
JavaScript
107 lines
2.9 KiB
JavaScript
import test from 'tape'
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import nlp from '../_lib.js'
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const here = '[one/sort] '
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test('sortAlpha:', function (t) {
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const str = 'John xoo, John fredman, John davis, John fredman,'
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let r = nlp(str)
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r = r.split('@hasComma')
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r = r.sort('alpha')
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const want = ['John davis,', 'John fredman,', 'John fredman,', 'John xoo,']
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t.deepEqual(r.out('array'), want, here + 'sort-alpha')
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t.end()
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})
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test('sortSequential:', function (t) {
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const str = 'John xoo, John fredman, John davis'
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let r = nlp(str)
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r = r.split('@hasComma')
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r = r.sort('alphabetical')
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r = r.sort('seq')
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const want = ['John xoo,', 'John fredman,', 'John davis']
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t.deepEqual(r.out('array'), want, here + 'sort-chron')
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t.end()
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})
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test('reverse:', function (t) {
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const str = 'John xoo, John fredman, John davis'
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let r = nlp(str)
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r = r.split('@hasComma')
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r = r.sort('alphabetical')
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r = r.reverse()
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const want = ['John xoo,', 'John fredman,', 'John davis']
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t.deepEqual(r.out('array'), want, here + 'alpha-reverse')
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t.end()
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})
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test('freq:', function (t) {
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const str = 'John xoo, John fredman, John davis'
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let r = nlp(str)
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r = r.terms()
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r = r.sort('freq')
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const want = ['John', 'John', 'John', 'xoo,', 'fredman,', 'davis']
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t.deepEqual(r.out('array'), want, here + 'freq-reverse')
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t.end()
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})
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test('length:', function (t) {
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const str = 'Amy, John Fredman, Dr. Bill, Alexis Smithsonian'
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let r = nlp(str)
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r = r.split('@hasComma')
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r = r.sort('length')
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r = r.reverse()
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const want = ['Amy,', 'Dr. Bill,', 'John Fredman,', 'Alexis Smithsonian']
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t.deepEqual(r.out('array'), want, here + 'sort length')
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t.end()
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})
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test('wordCount:', function (t) {
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const str = 'John Fredman, Amy, Dr. Bill G. Gates'
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let r = nlp(str)
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r = r.split('@hasComma')
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r = r.sort('wordCount')
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r.reverse()
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const want = ['Dr. Bill G. Gates', 'John Fredman,', 'Amy,']
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t.deepEqual(r.out('array'), want, here + 'sort-wordcount')
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t.end()
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})
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test('unique:', function (t) {
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const str = 'John xoo, John fredman, john xoo, John davis'
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let r = nlp(str)
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r = r.split('@hasComma')
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r = r.unique()
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const want = ['John xoo,', 'John fredman,', 'John davis']
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t.deepEqual(r.out('array'), want, here + 'sort-unique')
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t.end()
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})
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test('custom-sort:', function (t) {
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const doc = nlp('Eeny, meeny, miny, moe')
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const terms = doc.terms()
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terms.sort((a, b) => {
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a = a.text('normal')
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b = b.text('normal')
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if (a.length > b.length) {
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return -1
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}
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if (a.length < b.length) {
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return 1
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}
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return 0
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})
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const arr = terms.map(d => d.text('normal'))
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t.deepEqual(arr, ['meeny', 'eeny', 'miny', 'moe'], here + 'custom sort output')
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t.end()
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})
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test('frequency:', function (t) {
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const str = 'John xoo, John fredman, john xoo, John davis'
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let r = nlp(str)
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r = r.split('@hasComma')
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const a = r.out('frequency')
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t.equal(a[0].normal, 'john xoo', here + 'topk is sorted')
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t.equal(a[0].count, 2, here + 'topk finds two')
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t.end()
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})
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