mirror of
https://github.com/saymrwulf/KnowledgeRefinery.git
synced 2026-06-04 23:39:41 +00:00
38a99476d6
macOS app for corpus ingestion, semantic search, and concept universe visualization powered by local LLMs via LM Studio. Architecture: - Go daemon (17MB single binary, zero dependencies) - chi router, pure-Go SQLite, tiktoken tokenizer - 6-stage pipeline: scan → extract → chunk → embed → annotate → conceptualize - Brute-force cosine vector search in memory - 89 tests across 8 packages - SwiftUI app (macOS 15+) - Multi-workspace management with auto-start daemons - Live pipeline progress, search, concept browser - WebGPU 3D universe renderer with Canvas2D fallback - Custom crystal app icon
69 lines
1.7 KiB
Go
69 lines
1.7 KiB
Go
package mathutil
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import (
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"testing"
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)
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func TestKMeansEmpty(t *testing.T) {
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labels, centroids := KMeans(nil, 3, 100)
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if labels != nil || centroids != nil {
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t.Error("expected nil for empty input")
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}
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}
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func TestKMeansFewerThanK(t *testing.T) {
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vecs := [][]float32{
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{1, 0}, {0, 1},
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}
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labels, centroids := KMeans(vecs, 5, 100)
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if len(labels) != 2 {
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t.Fatalf("expected 2 labels, got %d", len(labels))
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}
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if len(centroids) != 2 {
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t.Fatalf("expected 2 centroids, got %d", len(centroids))
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}
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// Each point should be its own cluster
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if labels[0] != 0 || labels[1] != 1 {
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t.Errorf("expected labels [0,1], got %v", labels)
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}
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}
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func TestKMeansTwoClusters(t *testing.T) {
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// Two well-separated clusters
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vecs := [][]float32{
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{0, 0}, {0.1, 0.1}, {-0.1, 0.1},
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{10, 10}, {10.1, 10.1}, {9.9, 10.1},
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}
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labels, centroids := KMeans(vecs, 2, 100)
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if len(labels) != 6 {
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t.Fatalf("expected 6 labels, got %d", len(labels))
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}
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if len(centroids) != 2 {
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t.Fatalf("expected 2 centroids, got %d", len(centroids))
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}
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// First 3 should be in the same cluster, last 3 in another
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if labels[0] != labels[1] || labels[1] != labels[2] {
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t.Errorf("first cluster not consistent: %v", labels)
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}
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if labels[3] != labels[4] || labels[4] != labels[5] {
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t.Errorf("second cluster not consistent: %v", labels)
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}
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if labels[0] == labels[3] {
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t.Errorf("clusters should be different: %v", labels)
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}
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}
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func TestKMeansOneClusters(t *testing.T) {
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vecs := [][]float32{
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{1, 1}, {1.1, 1}, {1, 1.1},
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}
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labels, centroids := KMeans(vecs, 1, 100)
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if len(labels) != 3 || len(centroids) != 1 {
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t.Fatalf("expected 3 labels and 1 centroid")
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}
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for _, l := range labels {
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if l != 0 {
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t.Errorf("all labels should be 0 with k=1, got %d", l)
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}
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}
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}
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