31:T488,embeddedData = data .filter((d, i) => oneHots[i].some((e) => e)) .map((d) => ({ ...d, colors: { "Match Term Count": d.total_matches, "Term Fingerprint": allTerms .map((t) => d.all_matched_terms.has(t)) .map((d) => Number(d)) .join(""), //"Selected Term" "Matched Roles": parseInt( includeRoles .map((r) => includeCategories .map((c) => [c, r].join("_") + "_matches") .some((k) => d[k].length > 0) ) .map((d) => Number(d)) .join(""), 2 ), "Matched Categories": parseInt( includeCategories .map((c) => includeRoles .map((r) => [c, r].join("_") + "_matches") .some((k) => d[k].length > 0) ) .map((d) => Number(d)) .join(""), 2 ), "Matched Role+Category": parseInt( includeCategories .map((c) => includeRoles.map((r) => [[c, r].join("_") + "_matches"])) .flat() .map((k) => Number(d[k].length > 0)) .join(""), 2 ) } }))32:T4c5,colorLUT = ({ "Match Term Count": (d) => d, "Matched Roles": (d) => includeRoles .reduce( (acc, val) => acc.concat(acc.map((combo) => [...combo, val])), [[]] ) .reduce((acc, combo) => { const binaryIndex = includeRoles.reduce( (sum, role, i) => sum + (combo.includes(role) ? 1 << (includeRoles.length - 1 - i) : 0), 0 ); acc[binaryIndex] = combo.length ? combo.join("+") : "none"; return acc; }, {})[d], "Matched Categories": (d) => includeCategories .reduce( (acc, val) => acc.concat(acc.map((combo) => [...combo, val])), [[]] ) .reduce((acc, combo) => { const binaryIndex = includeCategories.reduce( (sum, role, i) => sum + (combo.includes(role) ? 1 << (includeCategories.length - 1 - i) : 0), 0 ); acc[binaryIndex] = combo.length ? combo.join("+") : "none"; return acc; }, {})[d], // "K-Means Clusters": (d) => // d3.range(clusterCount).reduce((a, v) => { // a[v] = v; // return a; // }, {})[d] // "Term Fingerprint" })33:T12f5,// Copyright 2021, Observable Inc. // Released under the ISC license. // https://observablehq.com/@d3/color-legend function Legend( color, { title, tickSize = 6, width = 320, height = 44 + tickSize, marginTop = 18, marginRight = 0, marginBottom = 16 + tickSize, marginLeft = 0, ticks = width / 64, tickFormat, tickValues } = {} ) { function ramp(color, n = 256) { const canvas = document.createElement("canvas"); canvas.width = n; canvas.height = 1; const context = canvas.getContext("2d"); for (let i = 0; i < n; ++i) { context.fillStyle = color(i / (n - 1)); context.fillRect(i, 0, 1, 1); } return canvas; } const svg = d3 .create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .style("overflow", "visible") .style("display", "block"); let tickAdjust = (g) => g.selectAll(".tick line").attr("y1", marginTop + marginBottom - height); let x; // Continuous if (color.interpolate) { const n = Math.min(color.domain().length, color.range().length); x = color .copy() .rangeRound( d3.quantize(d3.interpolate(marginLeft, width - marginRight), n) ); svg .append("image") .attr("x", marginLeft) .attr("y", marginTop) .attr("width", width - marginLeft - marginRight) .attr("height", height - marginTop - marginBottom) .attr("preserveAspectRatio", "none") .attr( "xlink:href", ramp( color.copy().domain(d3.quantize(d3.interpolate(0, 1), n)) ).toDataURL() ); } // Sequential else if (color.interpolator) { x = Object.assign( color .copy() .interpolator(d3.interpolateRound(marginLeft, width - marginRight)), { range() { return [marginLeft, width - marginRight]; } } ); svg .append("image") .attr("x", marginLeft) .attr("y", marginTop) .attr("width", width - marginLeft - marginRight) .attr("height", height - marginTop - marginBottom) .attr("preserveAspectRatio", "none") .attr("xlink:href", ramp(color.interpolator()).toDataURL()); // scaleSequentialQuantile doesn’t implement ticks or tickFormat. if (!x.ticks) { if (tickValues === undefined) { const n = Math.round(ticks + 1); tickValues = d3 .range(n) .map((i) => d3.quantile(color.domain(), i / (n - 1))); } if (typeof tickFormat !== "function") { tickFormat = d3.format(tickFormat === undefined ? ",f" : tickFormat); } } } // Threshold else if (color.invertExtent) { const thresholds = color.thresholds ? color.thresholds() // scaleQuantize : color.quantiles ? color.quantiles() // scaleQuantile : color.domain(); // scaleThreshold const thresholdFormat = tickFormat === undefined ? (d) => d : typeof tickFormat === "string" ? d3.format(tickFormat) : tickFormat; x = d3 .scaleLinear() .domain([-1, color.range().length - 1]) .rangeRound([marginLeft, width - marginRight]); svg .append("g") .selectAll("rect") .data(color.range()) .join("rect") .attr("x", (d, i) => x(i - 1)) .attr("y", marginTop) .attr("width", (d, i) => x(i) - x(i - 1)) .attr("height", height - marginTop - marginBottom) .attr("fill", (d) => d); tickValues = d3.range(thresholds.length); tickFormat = (i) => thresholdFormat(thresholds[i], i); } // Ordinal else { x = d3 .scaleBand() .domain(color.domain()) .rangeRound([marginLeft, width - marginRight]); svg .append("g") .selectAll("rect") .data(color.domain()) .join("rect") .attr("x", x) .attr("y", marginTop) .attr("width", Math.max(0, x.bandwidth() - 1)) .attr("height", height - marginTop - marginBottom) .attr("fill", color); tickAdjust = () => {}; } svg .append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call( d3 .axisBottom(x) .ticks(ticks, typeof tickFormat === "string" ? tickFormat : undefined) .tickFormat(typeof tickFormat === "function" ? tickFormat : undefined) .tickSize(tickSize) .tickValues(tickValues) ) .call(tickAdjust) .call((g) => g.select(".domain").remove()) .call((g) => g .append("text") .attr("x", marginLeft) .attr("y", marginTop + marginBottom - height - 6) .attr("fill", "currentColor") .attr("text-anchor", "start") .attr("font-weight", "bold") .attr("class", "title") .text(title) ); return svg.node(); }34:T87e,// Copyright 2021, Observable Inc. // Released under the ISC license. // https://observablehq.com/@d3/color-legend function Swatches( color, { columns = null, format, unknown: formatUnknown, swatchSize = 15, swatchWidth = swatchSize, swatchHeight = swatchSize, marginLeft = 0 } = {} ) { const id = `-swatches-${Math.random().toString(16).slice(2)}`; const unknown = formatUnknown == null ? undefined : color.unknown(); const unknowns = unknown == null || unknown === d3.scaleImplicit ? [] : [unknown]; const domain = color.domain().concat(unknowns); if (format === undefined) format = (x) => (x === unknown ? formatUnknown : x); function entity(character) { return `&#${character.charCodeAt(0).toString()};`; } if (columns !== null) return htl.html`

${domain.map((value) => { const label = `${format(value)}`; return htl.html`

${label}

`; })}

`; return htl.html`

${domain.map( (value) => htl.html`${format( value )}` )}

`; }35:T5fa,{ if (draw2D) { const canvas = DOM.canvas(width, width); const ctx = canvas.getContext("2d"); const margin = 30; ctx.fillStyle = "#000033"; ctx.fillRect(0, 0, width, width); const xSpread = d3.extent(umap2.points, (d) => d[0]); const ySpread = d3.extent(umap2.points, (d) => d[1]); const xAspect = xSpread[1] - xSpread[0]; const yAspect = ySpread[1] - ySpread[0]; // Use the larger dimension to normalize both axes const maxAspect = Math.max(xAspect, yAspect); // Calculate offsets to center the smaller dimensions const xOffset = (maxAspect - xAspect) / 2; const yOffset = (maxAspect - yAspect) / 2; // Create separate scales for x and y, both using the same normalization const xScale = d3 .scaleLinear() .domain([xSpread[0] - xOffset, xSpread[0] - xOffset + maxAspect]) .range([margin, width - margin]); const yScale = d3 .scaleLinear() .domain([ySpread[0] - yOffset, ySpread[0] - yOffset + maxAspect]) .range([margin, width - margin]); const colorScale = d3 .scaleOrdinal() .domain(d3.range(clusterCount)) .range( d3 .range(clusterCount) .map((e) => d3.interpolateSpectral(e / (clusterCount - 1))) ); umap2.points.forEach((d, i) => { ctx.beginPath(); ctx.arc(xScale(d[0]), yScale(d[1]), 2, 0, 2 * Math.PI); ctx.fillStyle = umap2.converged ? colorScale(umap2DClusters[i]) : "white"; ctx.fill(); }); return canvas; } }36:[{"id":"68dbcaee472fc698df8f624995e27539357a758d4d468834a7cbf1edc7eb79e914edc6e99524cb5194b747deba04a95bbe1e9e932c7bc809ba2c347e45eabaaf","href":"https://static.observableusercontent.com/files/68dbcaee472fc698df8f624995e27539357a758d4d468834a7cbf1edc7eb79e914edc6e99524cb5194b747deba04a95bbe1e9e932c7bc809ba2c347e45eabaaf","name":"job_analysis_results.csv.zip","type":"application/zip","lastModified":1754793789928.984,"size":15027529}] 2f:{"title":"Traditional Design, Strategic Design, and Product Management Analysis v3","cells":[{"id":0,"value":"# Traditional Design, Strategic Design, and Product Management Analysis v3\nPlease be patient, this page takes some time to load and process all the data.","pinned":false,"hidden":false,"mode":"md"},{"id":84,"value":"---\n### Load File","pinned":false,"hidden":false,"mode":"md"},{"id":109,"value":"zippedFile = FileAttachment(\"job_analysis_results.csv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"---\n### Parse Data","pinned":false,"hidden":false,"mode":"md"},{"id":24,"value":"rawData = (await zippedFile.file(zippedFile.filenames[0])).csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"keys = Object.keys(rawData[0])","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"---\n### Filter Data","pinned":false,"hidden":false,"mode":"md"},{"id":78,"value":"viewof minMatchCount = Inputs.range([0, 20], {\n label: \"Minimum Match Count\",\n value: 7,\n step: 1\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"data = rawData\n .filter((d) => parseInt(d.total_matches) >= minMatchCount)\n .map((d) =>\n keys.reduce(\n (a, k) => {\n if (k == \"total_matches\") {\n a[k] = parseInt(d[k]);\n } else if (k.includes(\"matches\")) {\n let terms = d[k].split(\", \").filter((d) => d);\n a[k] = terms;\n terms.forEach((t) => a.all_matched_terms.add(t));\n } else {\n a[k] = d[k];\n }\n\n return a;\n },\n { all_matched_terms: new Set() }\n )\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1672,"value":"*The visualizations below will include **${data.length.toLocaleString()}** of the filtered **${rawData.length.toLocaleString()}** postings, from an original **1,348,455** posting dataset.*","pinned":false,"hidden":false,"mode":"md"},{"id":158,"value":"---\n### Schema","pinned":false,"hidden":false,"mode":"md"},{"id":122,"value":"categories = [\"titles\", \"skills\", \"phrases\"].sort((a, b) => d3.ascending(a, b))","pinned":false,"hidden":false,"mode":"ojs"},{"id":126,"value":"roles = [\"traditional_design\", \"strategic_design\", \"product_management\"].sort(\n (a, b) => d3.ascending(a, b)\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":130,"value":"viewof includeCategories = Inputs.checkbox(categories, {\n label: \"Categories to Include\",\n value: categories\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"viewof includeRoles = Inputs.checkbox(roles, {\n label: \"Roles to Include\",\n value: roles\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":119,"value":"intersections = categories\n .map((c) =>\n roles.map((r) => ({\n category: c,\n role: r,\n set: new Set(data.map((d) => d[c + \"_\" + r + \"_matches\"]).flat())\n }))\n )\n .flat()","pinned":false,"hidden":false,"mode":"ojs"},{"id":160,"value":"includeTerms = intersections.filter(\n (i) => includeCategories.includes(i.category) && includeRoles.includes(i.role)\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":805,"value":"allTerms = includeTerms.map((d) => [...d.set]).flat()","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"oneHots = data.map((d) =>\n includeTerms\n .map((t) => [...t.set].map((s) => Number(d.all_matched_terms.has(s))))\n .flat()\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":289,"value":"embeddedOneHots = oneHots.filter((d, i) => oneHots[i].some((e) => e))","pinned":false,"hidden":false,"mode":"ojs"},{"id":281,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"---\n### PCA","pinned":false,"hidden":false,"mode":"md"},{"id":188,"value":"components = {\n const pca = new PCA.PCA(embeddedOneHots);\n return pca.predict(embeddedOneHots).data;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":92,"value":"---\n### Dimensionality Reduction","pinned":false,"hidden":false,"mode":"md"},{"id":95,"value":"umap = {\n refresh;\n const umap = new UMAP({\n nComponents: 3,\n minDist: 0.5,\n spread: 1,\n nNeighbors: 30\n // repulsionStrength: 100.0, // Much higher repulsion\n //negativeSampleRate: 10 // More repulsion updates per epoch\n }),\n nEpochs = umap.initializeFit(components);\n\n for (let i = 0; i < nEpochs; i++) {\n umap.step();\n\n if (i % 2 == 0) {\n yield {\n points: umap.getEmbedding(),\n converged: false\n };\n }\n }\n\n yield {\n points: umap.getEmbedding(),\n converged: true\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"---\n### K-Means Clustering","pinned":false,"hidden":false,"mode":"md"},{"id":992,"value":"viewof clusterCount = Inputs.range([1, 20], {\n label: \"Cluster Count\",\n value: 6,\n step: 1\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1389,"value":"clusters = {\n refreshKMeans;\n return umap.converged\n ? skmeans(umap.points, clusterCount)\n : { idxs: d3.range(embeddedData.length) };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":256,"value":"---\n### Scatter GL","pinned":false,"hidden":false,"mode":"md"},{"id":736,"value":"dataset = new ScatterGL.Dataset(umap.points)","pinned":false,"hidden":false,"mode":"ojs"},{"id":733,"value":"{\n scatterGL.render(dataset);\n return \"Rendering...\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1379,"value":"pointColorer = scatterGL.setPointColorer((i) => {\n if (colorBy == \"K-Means Clusters\" && clusters.idxs) {\n return d3\n .color(clusterScale(clusters.idxs[i]))\n .copy({ opacity: 0.5 })\n .formatRgb();\n } else if (colorBy == \"Selected Term\") {\n return d3\n .color(\n d3.interpolatePlasma(\n embeddedData[i].all_matched_terms.has(selectedTerm) ? 0.85 : 0.15\n )\n )\n .copy({\n opacity: embeddedData[i].all_matched_terms.has(selectedTerm)\n ? 0.85\n : 0.5\n })\n .formatRgb();\n } else {\n return d3\n .color(colorScale(embeddedData[i].colors[colorBy]))\n .copy({ opacity: 0.5 })\n .formatRgb();\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":243,"value":"mutable point = ({ click: null, hover: null })","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"{\n scatterGL.startOrbitAnimation();\n scatterGL.scatterPlot.orbitCameraControls.rotateSpeed = 10;\n scatterGL.scatterPlot.updateOrbitAnimation();\n scatterGL.resize();\n\n viewof scatterGL.addEventListener(\"dblclick\", (_) => {\n scatterGL.scatterPlot.setCameraPositionAndTarget([1, 1, 1], [0, 0, 0]);\n });\n return \"Updating...\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":972,"value":"---\n### Visualization","pinned":false,"hidden":false,"mode":"md"},{"id":339,"value":"viewof colorBy = Inputs.radio(\n [\n \"Match Term Count\",\n \"Matched Roles\",\n \"Matched Categories\",\n \"Matched Role+Category\",\n \"Term Fingerprint\",\n \"K-Means Clusters\",\n \"Selected Term\"\n ],\n {\n label: \"Color by\",\n value: \"Matched Roles\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1414,"value":"viewof selectedTerm = Inputs.select(\n new Set(allTerms.sort((a, b) => d3.ascending(a, b))),\n {\n label: \"Select Term to Highlight\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":248,"value":"viewof refresh = Inputs.button(\"Refresh UMAP\", {\n label: \"If the visualization is not showing...\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1550,"value":"viewof refreshKMeans = Inputs.button(\"Refresh K-Means\", {\n label: \"Generate new clusters...\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":275,"value":"point.hover\n ? `${embeddedData[point.hover].job_title} @ ${\n embeddedData[point.hover].company\n }`\n : \"\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":241,"value":"viewof scatterGL = {\n const containerElement = DOM.element(\"div\");\n containerElement.style.height = `${width / 1.5}px`;\n containerElement.style.position = \"relative\";\n yield containerElement;\n\n const scatterGL = new ScatterGL(containerElement, {\n onHover: (point) => {\n mutable point = Object.assign(mutable point, { hover: point });\n },\n onClick: (point) => {\n mutable point = Object.assign(mutable point, { click: point });\n },\n renderMode: \"POINT\",\n styles: {\n backgroundColor: \"rgba(0,10,25,1)\",\n\n point: {\n colorUnselected: \"rgba(120, 120, 120, 0.9)\",\n colorNoSelection: \"rgba(51, 51, 51, 0.9)\",\n colorSelected: \"rgba(255, 10, 10, 0.9)\",\n colorHover: \"rgba(255, 11, 79, 0.7)\",\n scaleDefault: 0.5,\n scaleSelected: 1.2,\n scaleHover: 2\n }\n }\n });\n\n yield Object.assign(containerElement, { value: scatterGL });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":709,"value":"colorLUT[colorBy]\n ? Swatches(colorScale, {\n columns: `${width}px`,\n\n format: (d) =>\n capitalizeFirstLetter(\n colorLUT[colorBy](d).split(\"_\").join(\" \").split(\"+\").join(\" + \")\n )\n })\n : \"\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1704,"value":"function capitalizeFirstLetter(str) {\n var splitStr = str.toLowerCase().split(\" \");\n for (var i = 0; i < splitStr.length; i++) {\n // You do not need to check if i is larger than splitStr length, as your for does that for you\n // Assign it back to the array\n splitStr[i] =\n splitStr[i].charAt(0).toUpperCase() + splitStr[i].substring(1);\n }\n // Directly return the joined string\n return splitStr.join(\" \");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1527,"value":"---\n### Export Data\n*Explore the data in the sortable table below. Alternatively, mouseover the table, click on the vertical ellipsis in the left sidebar, and click \"Download CSV\".*","pinned":false,"hidden":false,"mode":"md"},{"id":1444,"value":"viewof jobStudyData = Inputs.table(exportData, {\n columns,\n layout: \"auto\",\n rows: 20\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":960,"value":"---\n### Colors","pinned":false,"hidden":false,"mode":"md"},{"id":438,"value":"colorScale = d3\n .scaleOrdinal()\n .domain(\n [...new Set(embeddedData.map((d) => d.colors[colorBy]))]\n // .sort((a, b) =>\n // d3.ascending(a, b)\n // )\n )\n .range(d3.schemeObservable10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":874,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1396,"value":"clusterScale = d3\n .scaleOrdinal()\n .domain(d3.range(clusterCount))\n .range(\n d3\n .range(clusterCount)\n .map((i) => d3.interpolateSpectral(i / (clusterCount - 1)))\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1538,"value":"--- \n### Utilities","pinned":false,"hidden":false,"mode":"md"},{"id":1503,"value":"exportData = embeddedData.map((d, i) => ({\n ...columns.reduce((a, v) => {\n a[v] = Array.isArray(d[v]) ? d[v].join(\" | \") : d[v];\n return a;\n }, {}),\n cluster: clusters.idxs[i]\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1510,"value":"columns = [\n \"job_title\",\n \"company\",\n \"titles_traditional_design_matches\",\n \"titles_strategic_design_matches\",\n \"titles_product_management_matches\",\n \"skills_traditional_design_matches\",\n \"skills_strategic_design_matches\",\n \"skills_product_management_matches\",\n \"phrases_traditional_design_matches\",\n \"phrases_strategic_design_matches\",\n \"phrases_product_management_matches\",\n \"cluster\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"---\n### Imports","pinned":false,"hidden":false,"mode":"md"},{"id":97,"value":"UMAP = (await require(\"umap-js@1.3.1\")).UMAP","pinned":false,"hidden":false,"mode":"ojs"},{"id":102,"value":"ScatterGL = {\n const { ScatterGL } = await require.alias({\n THREE: \"three@0.126/build/three.min.js\"\n })(\"scatter-gl@0.0.12\");\n return ScatterGL;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"PCA = import(\"https://unpkg.com/ml-pca@4.1.1/lib-esm/pca.js?module\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":711,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":994,"value":"skmeans = (await import(\"https://cdn.skypack.dev/skmeans@0.11.3?min\")).default","pinned":false,"hidden":false,"mode":"ojs"},{"id":1034,"value":"hull = (await import(\"https://cdn.skypack.dev/convex-hull@1.0.3?min\")).default","pinned":false,"hidden":false,"mode":"ojs"},{"id":1573,"value":"---\n### 2D Fallback","pinned":false,"hidden":false,"mode":"md"},{"id":1626,"value":"viewof draw2D = Inputs.toggle({ label: \"Draw 2D?\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1567,"value":"umap2 = {\n if (draw2D) {\n const umap = new UMAP({\n nComponents: 2,\n minDist: 0.5,\n spread: 1,\n nNeighbors: 30\n // repulsionStrength: 100.0, // Much higher repulsion\n //negativeSampleRate: 10 // More repulsion updates per epoch\n }),\n nEpochs = umap.initializeFit(components);\n\n for (let i = 0; i < nEpochs; i++) {\n umap.step();\n\n if (i % 2 == 0) {\n yield {\n points: umap.getEmbedding(),\n converged: false\n };\n }\n }\n\n yield {\n points: umap.getEmbedding(),\n converged: true\n };\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1660,"value":"umap2DClusters = {\n if (draw2D) {\n return skmeans(umap2.points, clusterCount).idxs;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1577,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}