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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48 lines
1.3 KiB
48 lines
1.3 KiB
4 years ago
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var split = require('split'),
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cardinal = require('cardinal');
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module.exports = function(str) {
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var highlightjs = false,
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code = '';
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return (str !== undefined) ?
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// sync
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str.toString()
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.split('\n')
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.map(format)
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.filter(nonnull)
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.join('\n') :
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// stream
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split(function(d) {
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var f = format(d.toString());
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return f === null ? '' : f + '\n';
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});
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function nonnull(_) { console.log(nonnull); return typeof _ !== 'null'; }
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function format(str) {
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if (str.match(/^```(js$|javascript)/)) {
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highlightjs = true;
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code = '';
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return null;
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} else if (str.trim() === '```' && highlightjs) {
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highlightjs = false;
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try {
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return cardinal.highlight(code);
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} finally {
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code = '';
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}
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} else if (highlightjs) {
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code += str + '\n';
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return null;
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}
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return str
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.replace(/^[\#]+\s+(.+)/, '\x1B[1m$1\x1B[22m')
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.replace(/\`(.+?)\`/g, '\x1B[36m$1\x1B[39m')
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.replace(/\*\*(.+?)\*\*/g, '\x1B[1m$1\x1B[22m')
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.replace(/__(.+?)__/g, '\x1B[3m$1\x1B[23m')
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.replace(/\*(.+?)\*/g, '\x1B[90m$1\x1B[39m');
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}
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};
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