29:null 2a:["$","title",null,{"children":"KNN Classifier with DTW | Maddie Bendele | Observable"}] 2f:[] 2d:{"id":"fa4c6566a8448283","slug":null,"title":"KNN Classifier with DTW","thumbnail":"49e3b8e7b5c25c76853ac31b4ed41e895e2facb91573182f4e68e995d0ccb899","access_level":"public","create_time":"$D2023-10-20T00:10:43.997Z","update_time":"$D2023-10-20T00:42:39.778Z","view_time":null,"first_public_time":"$D2023-10-20T00:10:43.997Z","fork":{"id":"cfc42e0e6dcaabe0","slug":null,"title":"KNN Classifier with DTW","owner":{"id":"6e5394990b36f277","login":"aman-gautam","name":"Aman Gautam"}},"stars":0,"starred":false,"owner":{"type":"team","id":"f5a60deb983d2ac1","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/eda08aa21264f41360de6456790ae57ec6fa4ab04010c83e854f643516c54538","tier":"starter_2024"},"version":133,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"24c6369a6fc83150","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/df056cb5c50bb95442f5984e912a3fdad13fb13fc423a0b8e54da82de553579a","tier":null}],"members":[{"type":"individual","id":"24c6369a6fc83150","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/df056cb5c50bb95442f5984e912a3fdad13fb13fc423a0b8e54da82de553579a","tier":null,"role":"editor"},{"type":"individual","id":"9f316fa2aba52cf9","login":"scresawn","name":"Steven Cresawn","avatar_url":"https://avatars-next.observableusercontent.com/e2727956835daa3b5e9995b66c1111489c2e403c176b4fb761ec6ad64b97786b","tier":null,"role":"editor"}],"comments":[{"id":"ca4b0123b134c2ea","reply_to_id":null,"content":"does this need a return statment?","create_time":"$D2023-10-20T00:28:48.000Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"24c6369a6fc83150","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/df056cb5c50bb95442f5984e912a3fdad13fb13fc423a0b8e54da82de553579a","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"KNN Classifier with DTW","cells":[{"id":0,"value":"# KNN Classifier with DTW","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":18,"value":"// dtw implementation\n\nfunction dtw(s1, s2) {\n const n = s1.length;\n const m = s2.length;\n const d = new Array(n + 1);\n for (let i = 0; i <= n; i++) {\n d[i] = new Array(m + 1);\n }\n for (let i = 0; i <= n; i++) {\n for (let j = 0; j <= m; j++) {\n d[i][j] = Infinity;\n }\n }\n d[0][0] = 0;\n for (let i = 1; i <= n; i++) {\n for (let j = 1; j <= m; j++) {\n const cost = Math.abs(s1[i - 1] - s2[j - 1]);\n d[i][j] = cost + Math.min(d[i - 1][j], d[i][j - 1], d[i - 1][j - 1]);\n }\n }\n return d[n][m];\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"// Writing the KNNClassifier which supports custom distance function\nclass KNNClassifier {\n constructor(k, distanceFunc) {\n this.k = k;\n this.distanceFunc = distanceFunc;\n this.data = [];\n this.labels = [];\n }\n\n fit(data, labels) {\n this.data = data;\n this.labels = labels;\n }\n\n predict(x) {\n const distances = this.data.map((d, i) => [this.distanceFunc(x, d), i]);\n distances.sort((a, b) => a[0] - b[0]);\n const closest = distances.slice(0, this.k);\n const votes = closest.reduce((acc, val) => {\n acc[this.labels[val[1]]] = (acc[this.labels[val[1]]] || 0) + 1;\n return acc;\n }, {});\n return Object.keys(votes).sort((a, b) => votes[b] - votes[a])[0];\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"We will need another function to normalize the array to be in the range 0-1 which can be done with the following function...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":11,"value":"function normalize(arr) {\n const min = Math.min(...arr);\n const max = Math.max(...arr);\n return arr.map(x => (x - min) / (max - min));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"Now let us try out a few combinations of DTW","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"// arrays which are very similar...\narr1 = [1, 2, 3, 4, 5]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"arr2 = [10, 20, 30, 40, 60]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"arr3 = [11, 12, 13, 14, 15]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"d12 = dtw(normalize(arr1), normalize(arr2))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"d13 = dtw(normalize(arr1), normalize(arr3))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"d23 = dtw(normalize(arr2), normalize(arr3))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"dotplot = Plot.plot({\n marks: [Plot.dot(data, { x: \"gap\", y: \"midpoint\", stroke: \"genefunction\" })]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"dotplot.legend('color')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":61,"value":"Inputs.table(cars)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"function euclideanDistance(a, b) {\n let sum = 0;\n for (let i = 0; i < a.length; i++) {\n sum += (a[i] - b[i]) ** 2;\n }\n return Math.sqrt(sum);\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"Training the KNN Classifier","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":71,"value":"Data = data.map((x) => [x[\"start\"], x[\"stop\"], x[\"gap\"], x[\"midpoint\"]])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"genefunction = data.map((x) => x.genefunction)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"dataKNNClassifier = new KNNClassifier(7, euclideanDistance)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"knn = {\n let dog = dataKNNClassifier.fit(Data, genefunction);\n return dog;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"knn","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"dataKNNClassifier.predict([200, 40])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"viewof dataset = Inputs.select(user.datasets.sort(), {\n label: \"Select a data set\",\n value: \"Actino_Draft\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"data = {\n let data = await getPhameratorData(\n dataset,\n \"/genesbycluster/G\",\n user.email,\n user.password\n );\n return data.map((d) => {\n d.phamName = +d.phamName;\n return d;\n });\n // .slice(0, 400)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"import {\n terms,\n getPhameratorData,\n user,\n formWithSubmit,\n styles\n} with { formData } from \"@scresawn/phamerator-api-utilities\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"import { text, select, slider, button } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"styles","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"viewof formData = {\n let formData = \"Signed In\";\n formData = formWithSubmit(html`\n \n`);\n return formData;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2e:"$2d:body"

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