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all: run gofmt and generate all packages

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Changes made in dsp/fourier/internal/fftpack break the formatting used
there, so these are reverted. There will be complaints in CI.

[git-generate]
gofmt -w .
go generate gonum.org/v1/gonum/blas
go generate gonum.org/v1/gonum/blas/gonum
go generate gonum.org/v1/gonum/unit
go generate gonum.org/v1/gonum/unit/constant
go generate gonum.org/v1/gonum/graph/formats/dot
go generate gonum.org/v1/gonum/graph/formats/rdf
go generate gonum.org/v1/gonum/stat/card

git checkout -- dsp/fourier/internal/fftpack
This commit is contained in:
Dan Kortschak
2022-08-05 20:57:59 +09:30
parent fee5019b48
commit 5f0141ca4c
308 changed files with 5004 additions and 3064 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
mat/svd.go
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@@ -57,7 +57,9 @@ func (svd *SVD) succFact() bool {
//
// The full singular value decomposition (kind == SVDFull) is a factorization
// of an m×n matrix A of the form
// A = U * Σ * Vᵀ
//
// A = U * Σ * Vᵀ
//
// where Σ is an m×n diagonal matrix, U is an m×m orthogonal matrix, and V is an
// n×n orthogonal matrix. The diagonal elements of Σ are the singular values of A.
// The first min(m,n) columns of U and V are, respectively, the left and right
@@ -66,7 +68,9 @@ func (svd *SVD) succFact() bool {
// Significant storage space can be saved by using the thin representation of
// the SVD (kind == SVDThin) instead of the full SVD, especially if
// m >> n or m << n. The thin SVD finds
// A = U~ * Σ * V~ᵀ
//
// A = U~ * Σ * V~ᵀ
//
// where U~ is of size m×min(m,n), Σ is a diagonal matrix of size min(m,n)×min(m,n)
// and V~ is of size n×min(m,n).
//
@@ -273,10 +277,14 @@ func (svd *SVD) VTo(dst *Dense) {
}
// SolveTo calculates the minimum-norm solution to a linear least squares problem
// minimize over n-element vectors x: |b - A*x|_2 and |x|_2
//
// minimize over n-element vectors x: |b - A*x|_2 and |x|_2
//
// where b is a given m-element vector, using the SVD of m×n matrix A stored in
// the receiver. A may be rank-deficient, that is, the given effective rank can be
// rank ≤ min(m,n)
//
// rank ≤ min(m,n)
//
// The rank can be computed using SVD.Rank.
//
// Several right-hand side vectors b and solution vectors x can be handled in a
@@ -353,10 +361,14 @@ func (m repVector) At(i, j int) float64 {
func (m repVector) T() Matrix { return Transpose{m} }
// SolveVecTo calculates the minimum-norm solution to a linear least squares problem
// minimize over n-element vectors x: |b - A*x|_2 and |x|_2
//
// minimize over n-element vectors x: |b - A*x|_2 and |x|_2
//
// where b is a given m-element vector, using the SVD of m×n matrix A stored in
// the receiver. A may be rank-deficient, that is, the given effective rank can be
// rank ≤ min(m,n)
//
// rank ≤ min(m,n)
//
// The rank can be computed using SVD.Rank.
//
// The resulting vector x will be stored in dst. dst must be either empty or