80 lines
2.0 KiB
Go
80 lines
2.0 KiB
Go
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package intelligence
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import (
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"math"
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"sort"
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"time"
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)
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func buildSimilarityGraph(assets []loadedAsset, fingerprints map[string]MaterialFingerprint, k int) SimilarityGraph {
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if k <= 0 {
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k = 5
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}
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graph := SimilarityGraph{
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Version: "3.0",
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GeneratedAt: time.Now().UTC().Format(time.RFC3339),
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K: k,
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Nodes: len(assets),
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}
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for _, source := range assets {
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candidates := make([]SimilarityEdge, 0, len(assets)-1)
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sourceVec, sourceMethod := comparableVector(source, fingerprints[source.Asset.SKU])
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for _, target := range assets {
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if source.Asset.SKU == target.Asset.SKU {
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continue
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}
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targetVec, targetMethod := comparableVector(target, fingerprints[target.Asset.SKU])
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score := cosine(sourceVec, targetVec)
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method := sourceMethod
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if sourceMethod != targetMethod {
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method = "mixed"
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}
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candidates = append(candidates, SimilarityEdge{
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SourceSKU: source.Asset.SKU, TargetSKU: target.Asset.SKU,
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Score: round6(score), Method: method,
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})
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}
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sort.Slice(candidates, func(i, j int) bool { return candidates[i].Score > candidates[j].Score })
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if len(candidates) > k {
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candidates = candidates[:k]
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}
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graph.Edges = append(graph.Edges, candidates...)
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}
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return graph
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}
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func comparableVector(asset loadedAsset, fp MaterialFingerprint) ([]float64, string) {
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if len(asset.Embeddings) > 0 && len(asset.Embeddings[0].Values) > 0 {
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vec := make([]float64, len(asset.Embeddings[0].Values))
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for i, v := range asset.Embeddings[0].Values {
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vec[i] = float64(v)
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}
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return vec, "embedding_cosine"
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}
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return []float64{
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fp.Brightness, fp.Contrast, fp.Saturation, fp.Variation,
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fp.TextureEntropy, fp.TextureFrequency, fp.Orientation / 180,
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fp.ColorVariance, fp.TextureVariance,
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}, "fingerprint_cosine"
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}
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func cosine(a, b []float64) float64 {
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n := len(a)
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if len(b) < n {
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n = len(b)
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}
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if n == 0 {
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return 0
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}
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var dot, na, nb float64
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for i := 0; i < n; i++ {
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dot += a[i] * b[i]
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na += a[i] * a[i]
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nb += b[i] * b[i]
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}
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if na == 0 || nb == 0 {
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return 0
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}
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return dot / (math.Sqrt(na) * math.Sqrt(nb))
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}
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