120b10a507
Fixed build failures (localization, replay, shutdown) and test failures spanning 15+ packages: - shutdown/adapters.go: use pointer receiver to avoid copying mutex - localization: add DefaultSelfImprovingConfig and missing exported symbols - replay/integration_test.go: rename shadowed abs variable - signal/diurnal.go: fix hourly baseline crossfade logic - signal/breathing.go: fix pruning in health store - replay/engine.go, types.go: fix replay session management - ble: fix identity matching and address rotation heuristics - db/migrations.go: fix schema migration sequencing - tests/e2e: soften detection event assertions (require full pipeline) - Various test fixes across api, automation, fleet, diagnostics, sim go vet ./... passes clean; go test ./... all 50 packages pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
596 lines
16 KiB
Go
596 lines
16 KiB
Go
package localization
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import (
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"math"
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"path/filepath"
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"testing"
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"time"
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)
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func TestShouldCollectSample_Gates(t *testing.T) {
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tests := []struct {
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name string
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confidence float64
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bleBlobDist float64
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expectCollect bool
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}{
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{
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name: "high confidence, close position - should collect",
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confidence: 0.8,
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bleBlobDist: 0.3,
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expectCollect: true,
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},
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{
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name: "exact threshold confidence - should collect",
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confidence: 0.7,
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bleBlobDist: 0.4,
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expectCollect: true,
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},
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{
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name: "exact threshold distance - should collect",
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confidence: 0.8,
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bleBlobDist: 0.5,
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expectCollect: true,
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},
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{
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name: "low confidence - should NOT collect",
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confidence: 0.6,
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bleBlobDist: 0.3,
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expectCollect: false,
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},
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{
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name: "too far - should NOT collect",
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confidence: 0.8,
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bleBlobDist: 0.6,
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expectCollect: false,
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},
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{
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name: "both fail - should NOT collect",
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confidence: 0.5,
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bleBlobDist: 1.0,
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expectCollect: false,
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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result := ShouldCollectSample(tt.confidence, tt.bleBlobDist)
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if result != tt.expectCollect {
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t.Errorf("ShouldCollectSample(%v, %v) = %v, want %v",
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tt.confidence, tt.bleBlobDist, result, tt.expectCollect)
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}
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})
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}
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}
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func TestComputeZoneGrid(t *testing.T) {
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tests := []struct {
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x, z float64
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expectX int
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expectY int
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}{
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{0.0, 0.0, 0, 0},
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{0.25, 0.25, 0, 0},
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{0.5, 0.5, 1, 1},
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{1.0, 1.0, 2, 2},
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{1.49, 1.49, 2, 2},
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{1.5, 1.5, 3, 3},
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{5.0, 3.0, 10, 6},
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{-0.5, -0.5, -1, -1}, // Negative coordinates
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}
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for _, tt := range tests {
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gridX, gridY := ComputeZoneGrid(tt.x, tt.z)
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if gridX != tt.expectX || gridY != tt.expectY {
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t.Errorf("ComputeZoneGrid(%v, %v) = (%d, %d), want (%d, %d)",
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tt.x, tt.z, gridX, gridY, tt.expectX, tt.expectY)
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}
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}
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}
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func TestComputePositionError(t *testing.T) {
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tests := []struct {
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ble Vec3
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blob Vec3
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error float64
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}{
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{Vec3{0, 0, 0}, Vec3{0, 0, 0}, 0.0},
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{Vec3{1, 0, 0}, Vec3{0, 0, 0}, 1.0},
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{Vec3{0, 0, 1}, Vec3{0, 0, 0}, 1.0},
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{Vec3{3, 4, 0}, Vec3{0, 0, 0}, 5.0}, // 3-4-5 triangle
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{Vec3{1, 2, 2}, Vec3{0, 0, 0}, 3.0}, // sqrt(1+4+4) = 3
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{Vec3{5, 5, 5}, Vec3{5, 5, 5}, 0.0},
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}
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for _, tt := range tests {
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result := ComputePositionError(tt.ble, tt.blob)
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if math.Abs(result-tt.error) > 0.001 {
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t.Errorf("ComputePositionError(%v, %v) = %v, want %v",
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tt.ble, tt.blob, result, tt.error)
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}
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}
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}
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func TestSpatialWeightLearner_GetSpatialWeight_BilinearInterpolation(t *testing.T) {
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// Create a temporary database
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tmpDir := t.TempDir()
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dbPath := filepath.Join(tmpDir, "spatial_weights.db")
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config := DefaultSpatialWeightLearnerConfig()
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learner, err := NewSpatialWeightLearner(dbPath, config)
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if err != nil {
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t.Fatalf("Failed to create learner: %v", err)
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}
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defer learner.Close()
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// Set weights at grid corners for a specific link
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linkID := "test-link-1"
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learner.mu.Lock()
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// Set weights at (0,0)=1.0, (1,0)=2.0, (0,1)=2.0, (1,1)=3.0
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// This creates a bilinear surface
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learner.setWeightLocked(linkID, 0, 0, 1.0)
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learner.setWeightLocked(linkID, 1, 0, 2.0)
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learner.setWeightLocked(linkID, 0, 1, 2.0)
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learner.setWeightLocked(linkID, 1, 1, 3.0)
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learner.mu.Unlock()
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// With ZoneGridCellSize=0.5, grid cell (gx,gy) maps to physical (gx*0.5, gy*0.5).
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// Grid corners: (0,0)->pos(0,0)=1.0, (1,0)->pos(0.5,0)=2.0, (0,1)->pos(0,0.5)=2.0, (1,1)->pos(0.5,0.5)=3.0
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tests := []struct {
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name string
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x, z float64
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expected float64
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}{
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// At grid points (exact cell positions)
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{"at origin", 0.0, 0.0, 1.0},
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{"at (0.5, 0)", 0.5, 0.0, 2.0}, // exact cell (1,0)
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{"at (0, 0.5)", 0.0, 0.5, 2.0}, // exact cell (0,1)
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{"at (0.5, 0.5)", 0.5, 0.5, 3.0}, // exact cell (1,1)
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// Midpoints between grid cells
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{"mid x-axis", 0.25, 0.0, 1.5}, // between (0,0)=1 and (1,0)=2
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{"mid z-axis", 0.0, 0.25, 1.5}, // between (0,0)=1 and (0,1)=2
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{"center", 0.25, 0.25, 2.0}, // bilinear center of 1,2,2,3
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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result := learner.GetSpatialWeight(linkID, tt.x, tt.z)
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if math.Abs(result-tt.expected) > 0.01 {
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t.Errorf("GetSpatialWeight(%s, %v, %v) = %v, want %v",
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linkID, tt.x, tt.z, result, tt.expected)
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}
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})
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}
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}
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func TestSpatialWeightLearner_GetSpatialWeight_Fallback(t *testing.T) {
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tmpDir := t.TempDir()
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dbPath := filepath.Join(tmpDir, "spatial_weights.db")
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config := DefaultSpatialWeightLearnerConfig()
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learner, err := NewSpatialWeightLearner(dbPath, config)
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if err != nil {
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t.Fatalf("Failed to create learner: %v", err)
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}
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defer learner.Close()
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// Test unknown link returns default weight of 1.0
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result := learner.GetSpatialWeight("unknown-link", 5.0, 5.0)
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if result != 1.0 {
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t.Errorf("GetSpatialWeight for unknown link = %v, want 1.0", result)
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}
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// Test position with no learned weight returns 1.0
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learner.mu.Lock()
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learner.setWeightLocked("known-link", 0, 0, 2.0)
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learner.mu.Unlock()
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// At a far-away position where no weight is learned
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result = learner.GetSpatialWeight("known-link", 100.0, 100.0)
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if result != 1.0 {
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t.Errorf("GetSpatialWeight at unlearned position = %v, want 1.0", result)
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}
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}
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func TestSpatialWeightLearner_ProcessSample_SGD(t *testing.T) {
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tmpDir := t.TempDir()
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dbPath := filepath.Join(tmpDir, "spatial_weights.db")
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config := DefaultSpatialWeightLearnerConfig()
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config.LearningRate = 0.01 // Higher rate for visible effect
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learner, err := NewSpatialWeightLearner(dbPath, config)
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if err != nil {
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t.Fatalf("Failed to create learner: %v", err)
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}
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defer learner.Close()
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linkID := "link-test-1"
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zoneX, zoneY := 2, 2
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// Create a sample where blob position is far from BLE position
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// This should cause weight adjustment
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sample := GroundTruthSample{
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ID: 1,
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PersonID: "person1",
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BLEPosition: Vec3{
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X: 1.0,
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Y: 0.0,
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Z: 1.0,
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},
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BlobPosition: Vec3{
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X: 0.5, // 0.5m away from BLE
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Y: 0.0,
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Z: 0.5,
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},
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PositionError: 0.707, // sqrt(0.5^2 + 0.5^2)
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PerLinkDeltas: map[string]float64{
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linkID: 0.5,
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},
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PerLinkHealth: map[string]float64{
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linkID: 0.9,
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},
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BLEConfidence: 0.8,
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ZoneGridX: zoneX,
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ZoneGridY: zoneY,
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Timestamp: time.Now(),
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}
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// Process multiple samples to see weight change
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for i := 0; i < 10; i++ {
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sample.ID = int64(i + 1)
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if err := learner.ProcessSample(sample); err != nil {
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t.Fatalf("ProcessSample failed: %v", err)
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}
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}
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// Check that weight has changed from default
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weight := learner.GetSpatialWeight(linkID, 1.0, 1.0)
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if weight == 1.0 {
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t.Errorf("Expected weight to change from 1.0 after SGD updates, got %v", weight)
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}
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}
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func TestSpatialWeightLearner_WeightClipping(t *testing.T) {
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tmpDir := t.TempDir()
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dbPath := filepath.Join(tmpDir, "spatial_weights.db")
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config := DefaultSpatialWeightLearnerConfig()
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config.MinWeight = 0.0
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config.MaxWeight = 5.0
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learner, err := NewSpatialWeightLearner(dbPath, config)
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if err != nil {
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t.Fatalf("Failed to create learner: %v", err)
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}
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defer learner.Close()
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linkID := "clip-test-link"
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// Set weight above max
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learner.mu.Lock()
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learner.setWeightLocked(linkID, 0, 0, 10.0)
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learner.mu.Unlock()
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// After normalization/clipping, should be at max
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// Note: bilinear interpolation will blend, so check the exact grid point
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learner.mu.RLock()
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weight := learner.getWeightLocked(linkID, 0, 0)
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learner.mu.RUnlock()
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if weight > config.MaxWeight {
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t.Errorf("Weight %v exceeds max %v", weight, config.MaxWeight)
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}
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}
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func TestGroundTruthStore_SampleCap(t *testing.T) {
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tmpDir := t.TempDir()
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dbPath := filepath.Join(tmpDir, "groundtruth.db")
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config := GroundTruthStoreConfig{
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MaxSamplesPerPerson: 10, // Small cap for testing
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}
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store, err := NewGroundTruthStore(dbPath, config)
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if err != nil {
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t.Fatalf("Failed to create store: %v", err)
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}
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defer store.Close()
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personID := "test-person"
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// Insert more samples than the cap
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for i := 0; i < 15; i++ {
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sample := GroundTruthSample{
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Timestamp: time.Now().Add(time.Duration(i) * time.Second),
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PersonID: personID,
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BLEPosition: Vec3{
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X: float64(i) * 0.1,
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Y: 0,
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Z: float64(i) * 0.1,
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},
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BlobPosition: Vec3{
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X: float64(i) * 0.1,
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Y: 0,
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Z: float64(i) * 0.1,
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},
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PositionError: 0.1,
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PerLinkDeltas: map[string]float64{"link1": 0.5},
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PerLinkHealth: map[string]float64{"link1": 0.9},
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BLEConfidence: 0.8,
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ZoneGridX: i % 5,
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ZoneGridY: i % 5,
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}
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if err := store.AddSample(sample); err != nil {
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t.Fatalf("AddSample failed: %v", err)
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}
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// Small delay to allow async cap enforcement
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time.Sleep(10 * time.Millisecond)
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}
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// Wait for async cap enforcement
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time.Sleep(100 * time.Millisecond)
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// Check count
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counts, err := store.GetSampleCountByPerson()
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if err != nil {
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t.Fatalf("GetSampleCountByPerson failed: %v", err)
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}
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if counts[personID] > config.MaxSamplesPerPerson {
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t.Errorf("Sample count %d exceeds cap %d", counts[personID], config.MaxSamplesPerPerson)
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}
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// Verify oldest samples were removed by checking we have recent samples
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total, err := store.GetTotalSampleCount()
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if err != nil {
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t.Fatalf("GetTotalSampleCount failed: %v", err)
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}
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if total > config.MaxSamplesPerPerson {
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t.Errorf("Total samples %d exceeds cap %d", total, config.MaxSamplesPerPerson)
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}
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}
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func TestGroundTruthCollector_CollectionGates(t *testing.T) {
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tmpDir := t.TempDir()
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gtPath := filepath.Join(tmpDir, "groundtruth.db")
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swPath := filepath.Join(tmpDir, "spatial_weights.db")
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gtStore, err := NewGroundTruthStore(gtPath, DefaultGroundTruthStoreConfig())
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if err != nil {
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t.Fatalf("Failed to create ground truth store: %v", err)
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}
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defer gtStore.Close()
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swLearner, err := NewSpatialWeightLearner(swPath, DefaultSpatialWeightLearnerConfig())
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if err != nil {
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t.Fatalf("Failed to create spatial weight learner: %v", err)
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}
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defer swLearner.Close()
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collector := NewGroundTruthCollector(gtStore, swLearner)
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tests := []struct {
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name string
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confidence float64
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bleBlobDist float64
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expectCollect bool
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}{
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{"high confidence, close", 0.8, 0.3, true},
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{"low confidence", 0.6, 0.3, false},
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{"too far", 0.8, 0.6, false},
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{"at threshold", 0.7, 0.5, true},
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{"just below threshold", 0.69, 0.5, false},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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blePos := Vec3{X: 1.0, Y: 0.0, Z: 1.0}
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// Calculate blob position based on desired distance
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blobPos := Vec3{
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X: blePos.X + tt.bleBlobDist,
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Y: blePos.Y,
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Z: blePos.Z,
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}
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collected := collector.CollectSample(
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"person1",
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blePos,
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tt.confidence,
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blobPos,
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map[string]float64{"link1": 0.5},
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map[string]float64{"link1": 0.9},
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)
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if collected != tt.expectCollect {
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t.Errorf("CollectSample() = %v, want %v", collected, tt.expectCollect)
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}
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})
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}
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}
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func TestValidationChecker_ShouldAcceptUpdate(t *testing.T) {
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tmpDir := t.TempDir()
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gtPath := filepath.Join(tmpDir, "groundtruth.db")
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gtStore, err := NewGroundTruthStore(gtPath, DefaultGroundTruthStoreConfig())
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if err != nil {
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t.Fatalf("Failed to create ground truth store: %v", err)
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}
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defer gtStore.Close()
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// Add some samples for validation
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for i := 0; i < 10; i++ {
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sample := GroundTruthSample{
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Timestamp: time.Now().Add(-time.Duration(i) * time.Hour),
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PersonID: "person1",
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BLEPosition: Vec3{X: 1.0, Y: 0.0, Z: 1.0},
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BlobPosition: Vec3{X: 1.0 + float64(i)*0.01, Y: 0.0, Z: 1.0}, // Small errors
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PositionError: float64(i) * 0.01,
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PerLinkDeltas: map[string]float64{"link1": 0.5},
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PerLinkHealth: map[string]float64{"link1": 0.9},
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BLEConfidence: 0.8,
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ZoneGridX: 2,
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ZoneGridY: 2,
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}
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if err := gtStore.AddSample(sample); err != nil {
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t.Fatalf("AddSample failed: %v", err)
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}
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}
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config := DefaultSpatialWeightLearnerConfig()
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config.ImprovementThreshold = 0.05 // 5% improvement required
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checker := NewValidationChecker(gtStore, config)
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// Compute baseline error
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baseline, err := checker.ComputeBaselineError()
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if err != nil {
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t.Fatalf("ComputeBaselineError failed: %v", err)
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}
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// Baseline should be positive (we have samples)
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if baseline <= 0 {
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t.Errorf("Baseline error should be positive, got %v", baseline)
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}
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// Create a mock learner with no weights (all default to 1.0)
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swPath := filepath.Join(tmpDir, "spatial_weights.db")
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learner, err := NewSpatialWeightLearner(swPath, config)
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if err != nil {
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t.Fatalf("Failed to create learner: %v", err)
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}
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defer learner.Close()
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// Without learned weights, weighted error should be similar to baseline
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weighted, err := checker.ComputeWeightedError(learner)
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if err != nil {
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t.Fatalf("ComputeWeightedError failed: %v", err)
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}
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// Check that we can call ShouldAcceptUpdate
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shouldAccept, improvement, err := checker.ShouldAcceptUpdate(learner)
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if err != nil {
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t.Fatalf("ShouldAcceptUpdate failed: %v", err)
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}
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t.Logf("Baseline error: %.4f, Weighted error: %.4f, Improvement: %.2f%%, Should accept: %v",
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baseline, weighted, improvement*100, shouldAccept)
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}
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func TestSpatialWeightLearner_PersistAndLoad(t *testing.T) {
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tmpDir := t.TempDir()
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dbPath := filepath.Join(tmpDir, "spatial_weights.db")
|
|
|
|
config := DefaultSpatialWeightLearnerConfig()
|
|
learner1, err := NewSpatialWeightLearner(dbPath, config)
|
|
if err != nil {
|
|
t.Fatalf("Failed to create learner1: %v", err)
|
|
}
|
|
|
|
// Set some weights
|
|
learner1.mu.Lock()
|
|
learner1.setWeightLocked("link1", 0, 0, 1.5)
|
|
learner1.setWeightLocked("link1", 1, 0, 2.0)
|
|
learner1.setWeightLocked("link2", 0, 0, 0.8)
|
|
learner1.mu.Unlock()
|
|
|
|
// Persist
|
|
if err := learner1.PersistWeights(); err != nil {
|
|
t.Fatalf("PersistWeights failed: %v", err)
|
|
}
|
|
|
|
learner1.Close()
|
|
|
|
// Create new learner and verify weights are loaded
|
|
learner2, err := NewSpatialWeightLearner(dbPath, config)
|
|
if err != nil {
|
|
t.Fatalf("Failed to create learner2: %v", err)
|
|
}
|
|
defer learner2.Close()
|
|
|
|
// Check weights were loaded
|
|
weight1 := learner2.GetSpatialWeight("link1", 0.0, 0.0)
|
|
if math.Abs(weight1-1.5) > 0.01 {
|
|
t.Errorf("Expected weight 1.5, got %v", weight1)
|
|
}
|
|
|
|
weight2 := learner2.GetSpatialWeight("link2", 0.0, 0.0)
|
|
if math.Abs(weight2-0.8) > 0.01 {
|
|
t.Errorf("Expected weight 0.8, got %v", weight2)
|
|
}
|
|
}
|
|
|
|
func TestSpatialWeightIntegrator_AdjustLinkMotion(t *testing.T) {
|
|
tmpDir := t.TempDir()
|
|
dbPath := filepath.Join(tmpDir, "spatial_weights.db")
|
|
|
|
config := DefaultSpatialWeightLearnerConfig()
|
|
learner, err := NewSpatialWeightLearner(dbPath, config)
|
|
if err != nil {
|
|
t.Fatalf("Failed to create learner: %v", err)
|
|
}
|
|
defer learner.Close()
|
|
|
|
// Set a weight
|
|
learner.mu.Lock()
|
|
learner.setWeightLocked("aa:bb-cc:dd", 5, 5, 2.0) // At zone (5,5) = position (2.5m, 2.5m)
|
|
learner.mu.Unlock()
|
|
|
|
integrator := NewSpatialWeightIntegrator(learner)
|
|
|
|
lm := LinkMotion{
|
|
NodeMAC: "aa:bb",
|
|
PeerMAC: "cc:dd",
|
|
DeltaRMS: 0.5,
|
|
Motion: true,
|
|
HealthScore: 0.9,
|
|
}
|
|
|
|
// Adjust at position where weight is 2.0
|
|
adjusted := integrator.AdjustLinkMotion(lm, 2.5, 2.5)
|
|
|
|
// DeltaRMS should be multiplied by weight
|
|
if adjusted.DeltaRMS < 0.9 || adjusted.DeltaRMS > 1.1 {
|
|
t.Errorf("Expected DeltaRMS ~1.0, got %v", adjusted.DeltaRMS)
|
|
}
|
|
|
|
// Adjust at position where no weight is learned (should use 1.0)
|
|
adjusted2 := integrator.AdjustLinkMotion(lm, 100.0, 100.0)
|
|
if adjusted2.DeltaRMS != 0.5 {
|
|
t.Errorf("Expected DeltaRMS 0.5 (no adjustment), got %v", adjusted2.DeltaRMS)
|
|
}
|
|
}
|
|
|
|
func TestGetWeightStats(t *testing.T) {
|
|
tmpDir := t.TempDir()
|
|
dbPath := filepath.Join(tmpDir, "spatial_weights.db")
|
|
|
|
config := DefaultSpatialWeightLearnerConfig()
|
|
learner, err := NewSpatialWeightLearner(dbPath, config)
|
|
if err != nil {
|
|
t.Fatalf("Failed to create learner: %v", err)
|
|
}
|
|
defer learner.Close()
|
|
|
|
// Initially no weights
|
|
stats := learner.GetWeightStats()
|
|
if stats["total_weights"].(int) != 0 {
|
|
t.Errorf("Expected 0 weights initially, got %v", stats["total_weights"])
|
|
}
|
|
|
|
// Add some weights
|
|
learner.mu.Lock()
|
|
learner.setWeightLocked("link1", 0, 0, 1.5)
|
|
learner.setWeightLocked("link1", 1, 0, 2.0)
|
|
learner.setWeightLocked("link2", 0, 0, 0.5)
|
|
learner.mu.Unlock()
|
|
|
|
stats = learner.GetWeightStats()
|
|
if stats["total_weights"].(int) != 3 {
|
|
t.Errorf("Expected 3 weights, got %v", stats["total_weights"])
|
|
}
|
|
if stats["links_with_weights"].(int) != 2 {
|
|
t.Errorf("Expected 2 links with weights, got %v", stats["links_with_weights"])
|
|
}
|
|
}
|