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L2 Distance kernel is a slight modification of the L2 Norm kernel, taking two vectors and calculating the difference before summing squares. Same overflow protections as the netlib form of the norm calculation.
93 lines
1.8 KiB
Go
93 lines
1.8 KiB
Go
// Copyright ©2019 The Gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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// +build !amd64 noasm appengine safe
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package f64
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import "math"
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// L2NormUnitary returns the L2-norm of x.
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func L2NormUnitary(x []float64) (norm float64) {
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var scale float64
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sumSquares := 1.0
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for _, v := range x {
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if v == 0 {
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continue
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}
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absxi := math.Abs(v)
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if math.IsNaN(absxi) {
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return math.NaN()
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}
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if scale < absxi {
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s := scale / absxi
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sumSquares = 1 + sumSquares*s*s
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scale = absxi
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} else {
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s := absxi / scale
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sumSquares += s * s
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}
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}
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if math.IsInf(scale, 1) {
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return math.Inf(1)
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}
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return scale * math.Sqrt(sumSquares)
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}
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// L2NormInc returns the L2-norm of x.
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func L2NormInc(x []float64, n, incX uintptr) (norm float64) {
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var scale float64
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sumSquares := 1.0
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for ix := uintptr(0); ix < n*incX; ix += incX {
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val := x[ix]
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if val == 0 {
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continue
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}
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absxi := math.Abs(val)
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if math.IsNaN(absxi) {
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return math.NaN()
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}
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if scale < absxi {
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s := scale / absxi
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sumSquares = 1 + sumSquares*s*s
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scale = absxi
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} else {
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s := absxi / scale
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sumSquares += s * s
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}
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}
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if math.IsInf(scale, 1) {
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return math.Inf(1)
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}
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return scale * math.Sqrt(sumSquares)
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}
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// L2DistanceUnitary returns the L2-norm of x-y.
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func L2DistanceUnitary(x, y []float64) (norm float64) {
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var scale float64
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sumSquares := 1.0
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for i, v := range x {
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v -= y[i]
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if v == 0 {
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continue
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}
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absxi := math.Abs(v)
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if math.IsNaN(absxi) {
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return math.NaN()
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}
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if scale < absxi {
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s := scale / absxi
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sumSquares = 1 + sumSquares*s*s
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scale = absxi
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} else {
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s := absxi / scale
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sumSquares += s * s
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}
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}
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if math.IsInf(scale, 1) {
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return math.Inf(1)
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}
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return scale * math.Sqrt(sumSquares)
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}
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