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https://github.com/photoprism/photoprism.git
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03129c9129
Split the catch-all values.go into one file per concept, each mirrored by its test: distance.go, norm.go, stats.go, product.go, centroid.go, plus the mean methods folded into mean.go and Copy/Dim/Sum into vector.go. Remove values.go, values_test.go, and values_more_test.go so functionality and tests live where developers expect them. Hoist the two 512-dimensional face embeddings shared by the distance, norm, and cosine tests into fixtures_test.go, removing the previous triplication, and decompose the monolithic TestVector into per-concept tests. Close pre-existing coverage gaps in the integer converters and the GeometricMean/HarmonicMean method wrappers, bringing the package to 100% statement coverage. Pure code movement; no behavior change.
82 lines
1.8 KiB
Go
82 lines
1.8 KiB
Go
package vector
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import "math"
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// EuclideanDist returns the Euclidean distance between the vectors,
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func (v Vector) EuclideanDist(w Vector) float64 {
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return EuclideanDist(v, w)
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}
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// CosineSimilarity returns the cosine similarity between two vectors,
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// ranging from -1 (opposite) to 1 (identical).
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func (v Vector) CosineSimilarity(w Vector) float64 {
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return CosineSimilarity(v, w)
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}
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// CosineDist returns the cosine distance between two vectors (1 - cosine similarity).
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func (v Vector) CosineDist(w Vector) float64 {
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return CosineDist(v, w)
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}
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// EuclideanDist returns the Euclidean distance between multiple vectors.
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func EuclideanDist(a, b Vector) float64 {
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if a.Dim() != b.Dim() {
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return NaN()
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}
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var (
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s, t float64
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)
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for i := range a {
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t = a[i] - b[i]
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s += t * t
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}
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return math.Sqrt(s)
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}
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// CosineSimilarity returns the cosine similarity between two vectors, ranging
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// from -1 (opposite) to 1 (identical). It returns NaN when the dimensions
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// differ and 0 when either operand is a zero vector.
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func CosineSimilarity(a, b Vector) float64 {
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if a.Dim() != b.Dim() {
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return NaN()
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}
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var sum, s1, s2 float64
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for i := range a {
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sum += a[i] * b[i]
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s1 += a[i] * a[i]
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s2 += b[i] * b[i]
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}
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if s1 == 0 || s2 == 0 {
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return 0.0
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}
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return sum / (math.Sqrt(s1) * math.Sqrt(s2))
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}
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// CosineDist returns the cosine distance between two vectors, defined as
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// 1 - CosineSimilarity. Identical vectors yield 0; it returns NaN when the
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// dimensions differ.
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func CosineDist(a, b Vector) float64 {
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return 1.0 - CosineSimilarity(a, b)
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}
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// CosineDists returns the cosine distances between two sets of vectors.
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func CosineDists(x, y Vectors) Vectors {
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result := make(Vectors, len(x))
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for i, a := range x {
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result[i] = make([]float64, len(y))
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for j, b := range y {
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result[i][j] = CosineDist(a, b)
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}
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}
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return result
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}
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