383 lines
8.9 KiB
Go
383 lines
8.9 KiB
Go
package producer
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import (
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"fmt"
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"math"
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log "github.com/sirupsen/logrus"
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"gonum.org/v1/gonum/mat"
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)
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func normalize(v *mat.VecDense) (*mat.VecDense, float64, float64) {
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var vOff, vScale float64
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vOff = mat.Sum(v) / float64(v.Len())
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vScale = math.Max(math.Abs(mat.Max(v)-vOff), math.Abs(mat.Min(v)-vOff))
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for i := 0; i < v.Len(); i++ {
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v.SetVec(i, (v.AtVec(i)-vOff)/vScale)
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}
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return v, vOff, vScale
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}
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func normalize2(v *mat.VecDense, vOff, vScale float64) *mat.VecDense {
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for i := 0; i < v.Len(); i++ {
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v.SetVec(i, (v.AtVec(i)-vOff)/vScale)
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}
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return v
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}
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func solveCoefficients(f, latVec, lonVec, heightVec *mat.VecDense) ([]float64, error) {
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M := setupSystemOfEquations(f, latVec, lonVec, heightVec)
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n := f.Len()
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weights := mat.NewDiagDense(n, nil)
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for i := 0; i < n; i++ {
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weights.SetDiag(i, 1.0)
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}
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w2 := mat.NewDiagDense(n, nil)
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iterations := 0
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var x mat.VecDense
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// var e0 float64
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for iterations < 20 {
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iterations++
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// w2 = weights^2
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for i := 0; i < n; i++ {
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w2.SetDiag(i, weights.At(i, i)*weights.At(i, i))
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}
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// x = (M^T * w2 * M)^-1 * M^T * w2 * R
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var MtW2 mat.Dense
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MtW2.Mul(M.T(), w2)
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var MtW2M mat.Dense
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MtW2M.Mul(&MtW2, M)
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invMtW2M, err := invertRPCMatrix(&MtW2M)
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if err != nil {
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return nil, err
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}
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var MtW2F mat.VecDense
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MtW2F.MulVec(&MtW2, f)
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x.MulVec(invMtW2M, &MtW2F)
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numerator := mat.NewVecDense(20, nil)
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denominator := mat.NewVecDense(20, nil)
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numerator.SetVec(0, x.AtVec(0))
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denominator.SetVec(0, 1.0)
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for idx := 1; idx < 20; idx++ {
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numerator.SetVec(idx, x.AtVec(idx))
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denominator.SetVec(idx, x.AtVec(idx+19))
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}
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weights = setupWeightMatrix(denominator, latVec, lonVec, heightVec)
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// residual = m.t()*w2*(m*tempCoeff-r);
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// var temp0, temp1, residual mat.VecDense
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// temp0.MulVec(M, &x)
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// temp1.SubVec(&temp0, f)
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// residual.MulVec(&MtW2, &temp1)
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// var square mat.Dense
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// square.Mul(residual.T(), &residual)
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// residualValue := math.Sqrt(square.At(0, 0))
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// if residualValue < 0.0001 {
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// break
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// }
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// fmt.Printf("residual value: %.16f\n", residualValue)
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// fmt.Printf("iterations: %d\n", iterations)
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e := 0.0
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for i := 0; i < n; i++ {
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Rcal := project(numerator, denominator, latVec.AtVec(i), lonVec.AtVec(i), heightVec.AtVec(i))
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e += math.Pow(Rcal-f.AtVec(i), 2)
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}
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e = math.Sqrt(e / float64(n))
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fmt.Println("iterations:", iterations, "r error:", e)
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if e < 1e-5 {
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break
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}
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// dnum := mat.NewVecDense(20, nil)
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// b0, b1 := true, true
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// dden := mat.NewVecDense(19, nil)
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// for i := 0; i < n; i++ {
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// dnum.SetVec(i, math.Abs(numerator.AtVec(i)-numerator0.AtVec(i)))
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// numerator0.SetVec(i, numerator.AtVec(i))
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// fmt.Println("dnum:", i, dnum.AtVec(i))
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// if dnum.AtVec(i) > 0.000001 {
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// b0 = false
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// break
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// }
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// }
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// for i := 1; i < n; i++ {
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// dden.SetVec(i, math.Abs(denominator.AtVec(i)-denominator0.AtVec(i)))
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// denominator0.SetVec(i, denominator.AtVec(i))
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// if dden.AtVec(i) > 0.000001 {
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// b1 = false
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// break
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// }
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// }
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// if b0 && b1 {
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// break
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// }
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}
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log.Println("iterations:", iterations)
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return mat.Col(nil, 0, &x), nil
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}
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func setupSystemOfEquations(Rn, latVec, lonVec, heightVec *mat.VecDense) *mat.Dense {
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n := latVec.Len()
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// 设计矩阵 B = [ 20个分子系数 19个分母系数 ]
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B := mat.NewDense(n, 39, nil)
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for i := 0; i < n; i++ {
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P := latVec.AtVec(i)
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L := lonVec.AtVec(i)
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H := heightVec.AtVec(i)
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r := Rn.AtVec(i)
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B.Set(i, 0, 1)
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B.Set(i, 1, L)
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B.Set(i, 2, P)
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B.Set(i, 3, H)
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B.Set(i, 4, L*P)
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B.Set(i, 5, L*H)
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B.Set(i, 6, P*H)
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B.Set(i, 7, L*L)
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B.Set(i, 8, P*P)
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B.Set(i, 9, H*H)
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B.Set(i, 10, P*L*H)
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B.Set(i, 11, L*L*L)
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B.Set(i, 12, L*P*P)
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B.Set(i, 13, L*H*H)
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B.Set(i, 14, L*L*P)
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B.Set(i, 15, P*P*P)
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B.Set(i, 16, P*H*H)
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B.Set(i, 17, L*L*H)
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B.Set(i, 18, P*P*H)
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B.Set(i, 19, H*H*H)
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B.Set(i, 20, -L*r)
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B.Set(i, 21, -P*r)
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B.Set(i, 22, -H*r)
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B.Set(i, 23, -L*P*r)
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B.Set(i, 24, -L*H*r)
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B.Set(i, 25, -P*H*r)
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B.Set(i, 26, -L*L*r)
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B.Set(i, 27, -P*P*r)
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B.Set(i, 28, -H*H*r)
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B.Set(i, 29, -P*L*H*r)
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B.Set(i, 30, -L*L*L*r)
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B.Set(i, 31, -L*P*P*r)
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B.Set(i, 32, -L*H*H*r)
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B.Set(i, 33, -L*L*P*r)
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B.Set(i, 34, -P*P*P*r)
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B.Set(i, 35, -P*H*H*r)
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B.Set(i, 36, -L*L*H*r)
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B.Set(i, 37, -P*P*H*r)
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B.Set(i, 38, -H*H*H*r)
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}
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return B
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}
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// 构建权矩阵 [ 1/B ]
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func setupWeightMatrix(coeffs, latVec, lonVec, heightVec *mat.VecDense) *mat.DiagDense {
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n := latVec.Len()
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row := mat.NewDense(n, 20, nil)
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result := mat.NewDiagDense(n, nil)
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for i := 0; i < n; i++ {
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P := latVec.AtVec(i)
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L := lonVec.AtVec(i)
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H := heightVec.AtVec(i)
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row.Set(i, 0, 1)
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row.Set(i, 1, L)
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row.Set(i, 2, P)
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row.Set(i, 3, H)
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row.Set(i, 4, L*P)
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row.Set(i, 5, L*H)
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row.Set(i, 6, P*H)
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row.Set(i, 7, L*L)
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row.Set(i, 8, P*P)
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row.Set(i, 9, H*H)
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row.Set(i, 10, P*L*H)
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row.Set(i, 11, L*L*L)
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row.Set(i, 12, L*P*P)
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row.Set(i, 13, L*H*H)
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row.Set(i, 14, L*L*P)
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row.Set(i, 15, P*P*P)
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row.Set(i, 16, P*H*H)
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row.Set(i, 17, L*L*H)
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row.Set(i, 18, P*P*H)
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row.Set(i, 19, H*H*H)
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var B float64
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for idx2 := 0; idx2 < 20; idx2++ {
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B += coeffs.AtVec(idx2) * row.At(i, idx2)
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}
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result.SetDiag(i, 1/B)
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}
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return result
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}
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func invertRPCMatrix(At *mat.Dense) (*mat.Dense, error) {
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var AtInv mat.Dense
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err := AtInv.Inverse(At)
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if err != nil {
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// 岭估计方法调整法方程状态，使得矩阵非奇异，最小二乘平差可以收敛
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r, c := At.Dims()
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log.Infof("cannot inverse matrix(%d*%d): %v", r, c, err)
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k := 0.0000001 // [0.00000005, 0.000005]
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log.Infof("try to adjust matrix with +kI, k=%.8f", k)
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I := mat.NewDiagDense(r, nil)
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for i := 0; i < r; i++ {
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I.SetDiag(i, k)
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}
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At.Add(At, I)
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err = AtInv.Inverse(At)
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}
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if err != nil {
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log.Infof("cannot inverse matrix: %v, try SVD method", err)
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// 计算矩阵的 SVD 分解
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var svd mat.SVD
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ok := svd.Factorize(At, mat.SVDThin)
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if !ok {
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fmt.Println("SVD 分解失败")
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return nil, fmt.Errorf("设计矩阵不可逆, SVD 分解失败: %v", err)
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}
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// 获取 U、Σ 和 V^T
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var u, v mat.Dense
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svd.UTo(&u)
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svd.VTo(&v)
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sigma := svd.Values(nil)
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// 计算 Σ^+ (Sigma pseudo-inverse)
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m, n := u.Dims()
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sigmaInv := mat.NewDense(n, m, nil)
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for i := 0; i < len(sigma); i++ {
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if sigma[i] > 1e-10 { // 避免除以零
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sigmaInv.Set(i, i, 1/sigma[i])
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}
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}
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// 计算 V * Σ^+ * U^T
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var temp mat.Dense
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temp.Mul(&v, sigmaInv)
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AtInv.Mul(&temp, u.T())
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}
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return &AtInv, nil
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}
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// SolveNormalEquation 使用正规方程法求解最小二乘问题
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func SolveNormalEquation(A *mat.Dense, b *mat.VecDense) ([]float64, error) {
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var At mat.Dense
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At.Mul(A.T(), A) // At = A^T * A
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// 求解 (A^T * A)^-1 * (A^T * b)
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var AtInv mat.Dense
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err := AtInv.Inverse(&At)
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if err != nil {
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// 岭估计方法调整法方程状态，使得矩阵非奇异，最小二乘平差可以收敛
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r, c := At.Dims()
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log.Infof("cannot inverse design matrix(%d*%d): %v", r, c, err)
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log.Info("try to adjust design matrix with +kI, k=0.0000001")
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k := 0.0000001 // [0.00000005, 0.000005]
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I := mat.NewDiagDense(r, nil)
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for i := 0; i < r; i++ {
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I.SetDiag(i, k)
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}
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At.Add(&At, I)
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err = AtInv.Inverse(&At)
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}
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if err != nil {
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log.Infof("cannot inverse design matrix: %v, try SVD method", err)
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// 计算矩阵的 SVD 分解
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var svd mat.SVD
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ok := svd.Factorize(&At, mat.SVDThin)
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if !ok {
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fmt.Println("SVD 分解失败")
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return nil, fmt.Errorf("设计矩阵不可逆, SVD 分解失败: %v", err)
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}
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// 获取 U、Σ 和 V^T
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var u, v mat.Dense
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svd.UTo(&u)
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svd.VTo(&v)
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sigma := svd.Values(nil)
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// 计算 Σ^+ (Sigma pseudo-inverse)
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m, n := u.Dims()
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sigmaInv := mat.NewDense(n, m, nil)
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for i := 0; i < len(sigma); i++ {
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if sigma[i] > 1e-10 { // 避免除以零
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sigmaInv.Set(i, i, 1/sigma[i])
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}
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}
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// 计算 V * Σ^+ * U^T
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var temp mat.Dense
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temp.Mul(&v, sigmaInv)
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AtInv.Mul(&temp, u.T())
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}
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var Atb mat.VecDense
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Atb.MulVec(A.T(), b) // Atb = A^T * b
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var x mat.VecDense
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x.MulVec(&AtInv, &Atb) // x = (A^T * A)^-1 * (A^T * b)
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return mat.Col(nil, 0, &x), nil
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}
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func localize(num, den *mat.VecDense, row, col float64) (P, L, H float64) {
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return
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}
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func project(num, den *mat.VecDense, P, L, H float64) (v float64) {
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v = applyPoly(num, P, L, H) / applyPoly(den, P, L, H)
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return v
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}
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func applyPoly(poly *mat.VecDense,
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P, L, H float64) (v float64) {
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v = 0.0
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v += poly.AtVec(0)
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v += poly.AtVec(1) * L
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v += poly.AtVec(2) * P
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v += poly.AtVec(3) * H
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v += poly.AtVec(4) * L * P
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v += poly.AtVec(5) * L * H
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v += poly.AtVec(6) * P * H
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v += poly.AtVec(7) * L * L
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v += poly.AtVec(8) * P * P
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v += poly.AtVec(9) * H * H
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v += poly.AtVec(10) * P * L * H
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v += poly.AtVec(11) * L * L * L
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v += poly.AtVec(12) * L * P * P
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v += poly.AtVec(13) * L * H * H
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v += poly.AtVec(14) * L * L * P
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v += poly.AtVec(15) * P * P * P
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v += poly.AtVec(16) * P * H * H
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v += poly.AtVec(17) * L * L * H
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v += poly.AtVec(18) * P * P * H
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v += poly.AtVec(19) * H * H * H
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return v
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}
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