feat: k-means algorithm #75
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Author
Yanyan-Wang
reviewed
Oct 8, 2023
packages/graph/src/utils.ts
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| let distance = 0; | ||
| switch (distanceType) { | ||
| case DistanceType.EuclideanDistance: | ||
| distance = new Vector(item).euclideanDistance(new Vector(otherItem)); |
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感觉 new 两个 vector 有点浪费性能，还不如直接数值计算一下
packages/graph/src/k-means.ts
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| // When the number of nodes or the length of the attribute set is less than k, k will be adjusted to the smallest of them | ||
| const finalK = Math.min(k, nodes.length, allPropertiesWeightUniq.length); | ||
| for (let i = 0; i < nodes.length; i++) { | ||
| nodes[i].data.originIndex = i; |
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现在做了数据隔离，这种写到数据里面的方式不太好了，是不是可以另外存到一个 map 里面
packages/graph/src/k-means.ts
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| id: `${i}`, | ||
| nodes: [nodes[maxDistanceNodeIndex]] | ||
| }; | ||
| nodes[maxDistanceNodeIndex].data.clusterId = String(i); |
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同上，可以用一个 Map<ID, string> 存储这些信息，key 是节点 id， value 是聚类 id
Contributor
|
感谢 PR ～ |
…nd return it from k-means func
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Description
propertiesKeyis a param to detect whether the relevant property used in k-means is in nodes.dataproperty collects all of the properties of nodes,propertiesKeyparam have been removed in v5 algorithm.