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feat: implement comprehensive neural clustering system

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- Add 7 advanced clustering algorithms (semantic, k-means, DBSCAN, hierarchical, graph, multi-modal, sampling)
- Integrate with existing 31 NounTypes + 40 VerbTypes taxonomy for semantic clustering
- Leverage HNSW index for O(n) hierarchical clustering performance
- Add graph community detection using verb relationships and Louvain modularity
- Implement multi-modal fusion combining vector + graph + semantic signals
- Add Triple Intelligence integration for intelligent cluster labeling
- Support adaptive sampling strategies for large datasets
- Include 150+ utility methods for advanced clustering operations
- Add comprehensive TypeScript definitions and error handling
- Optimize for graph-explorer integration with LOD patterns

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
David Snelling 2025-09-01 15:37:56 -07:00
parent 6c62bc4e9d
commit 29ccc5846b
4 changed files with 3476 additions and 36 deletions
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24
tests/neural-clustering.test.ts
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@ -1,10 +1,10 @@
import { describe, it, expect, beforeEach, afterEach } from 'vitest'
import { BrainyData } from '../src/index.js'
import { NeuralAPI } from '../src/neural/neuralAPI.js'
import { ImprovedNeuralAPI } from '../src/neural/improvedNeuralAPI.js'
describe('Neural Clustering and Analysis', () => {
let db: BrainyData | null = null
let neural: NeuralAPI | null = null
let neural: ImprovedNeuralAPI | null = null
// Helper to create test vectors with semantic meaning
const createTestVector = (seed: number = 0, category: 'tech' | 'food' | 'travel' = 'tech') => {
@ -17,7 +17,7 @@ describe('Neural Clustering and Analysis', () => {
beforeEach(async () => {
db = new BrainyData()
await db.init()
neural = new NeuralAPI(db)
neural = new ImprovedNeuralAPI(db)
})
afterEach(async () => {
@ -44,19 +44,19 @@ describe('Neural Clustering and Analysis', () => {
})
it('should calculate similarity between IDs', async () => {
const similarity = await neural!.similarity('tech1', 'tech2')
const similarity = await neural!.similar('tech1', 'tech2')
expect(typeof similarity).toBe('number')
expect(similarity).toBeGreaterThan(0)
expect(similarity).toBeLessThanOrEqual(1)
// Tech items should be more similar to each other
const crossCategorySim = await neural!.similarity('tech1', 'food1')
const crossCategorySim = await neural!.similar('tech1', 'food1')
expect(similarity).toBeGreaterThan(crossCategorySim)
})
it('should calculate similarity between text strings', async () => {
const similarity = await neural!.similarity(
const similarity = await neural!.similar(
'JavaScript programming',
'TypeScript development'
)
@ -69,14 +69,14 @@ describe('Neural Clustering and Analysis', () => {
const vector1 = createTestVector(10, 'tech')
const vector2 = createTestVector(11, 'tech')
const similarity = await neural!.similarity(vector1, vector2)
const similarity = await neural!.similar(vector1, vector2)
expect(typeof similarity).toBe('number')
expect(similarity).toBeGreaterThan(0.8) // Similar vectors
})
it('should return detailed similarity result when requested', async () => {
const result = await neural!.similarity('tech1', 'tech2', { detailed: true })
const result = await neural!.similar('tech1', 'tech2', { detailed: true })
expect(typeof result).toBe('object')
if (typeof result === 'object') {
@ -333,12 +333,12 @@ describe('Neural Clustering and Analysis', () => {
// First calculation
const start1 = performance.now()
const sim1 = await neural!.similarity('item1', 'item2')
const sim1 = await neural!.similar('item1', 'item2')
const time1 = performance.now() - start1
// Second calculation (should be cached)
const start2 = performance.now()
const sim2 = await neural!.similarity('item1', 'item2')
const sim2 = await neural!.similar('item1', 'item2')
const time2 = performance.now() - start2
expect(sim1).toBe(sim2)
@ -368,12 +368,12 @@ describe('Neural Clustering and Analysis', () => {
describe('Error Handling', () => {
it('should handle invalid IDs gracefully', async () => {
const similarity = await neural!.similarity('nonexistent1', 'nonexistent2')
const similarity = await neural!.similar('nonexistent1', 'nonexistent2')
expect(similarity).toBe(0) // Should return 0 for non-existent items
})
it('should handle empty clustering gracefully', async () => {
const emptyNeural = new NeuralAPI(db!)
const emptyNeural = new ImprovedNeuralAPI(db!)
const clusters = await emptyNeural.clusters()
expect(Array.isArray(clusters)).toBe(true)