Merge pull request #201 from Bytom/v0.1

[bytom/vapor.git] / vendor / gonum.org / v1 / gonum / lapack / gonum / dsyev.go

diff --git a/vendor/gonum.org/v1/gonum/lapack/gonum/dsyev.go b/vendor/gonum.org/v1/gonum/lapack/gonum/dsyev.go
deleted file mode 100644 (file)
index b4c20c7..0000000
--- a/vendor/gonum.org/v1/gonum/lapack/gonum/dsyev.go
+++ /dev/null
@@ -1,113 +0,0 @@
-// Copyright ©2016 The Gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-package gonum
-
-import (
-       "math"
-
-       "gonum.org/v1/gonum/blas"
-       "gonum.org/v1/gonum/blas/blas64"
-       "gonum.org/v1/gonum/lapack"
-)
-
-// Dsyev computes all eigenvalues and, optionally, the eigenvectors of a real
-// symmetric matrix A.
-//
-// w contains the eigenvalues in ascending order upon return. w must have length
-// at least n, and Dsyev will panic otherwise.
-//
-// On entry, a contains the elements of the symmetric matrix A in the triangular
-// portion specified by uplo. If jobz == lapack.ComputeEV a contains the
-// orthonormal eigenvectors of A on exit, otherwise on exit the specified
-// triangular region is overwritten.
-//
-// work is temporary storage, and lwork specifies the usable memory length. At minimum,
-// lwork >= 3*n-1, and Dsyev will panic otherwise. The amount of blocking is
-// limited by the usable length. If lwork == -1, instead of computing Dsyev the
-// optimal work length is stored into work[0].
-func (impl Implementation) Dsyev(jobz lapack.EVJob, uplo blas.Uplo, n int, a []float64, lda int, w, work []float64, lwork int) (ok bool) {
-       checkMatrix(n, n, a, lda)
-       upper := uplo == blas.Upper
-       wantz := jobz == lapack.ComputeEV
-       var opts string
-       if upper {
-               opts = "U"
-       } else {
-               opts = "L"
-       }
-       nb := impl.Ilaenv(1, "DSYTRD", opts, n, -1, -1, -1)
-       lworkopt := max(1, (nb+2)*n)
-       work[0] = float64(lworkopt)
-       if lwork == -1 {
-               return
-       }
-       if len(work) < lwork {
-               panic(badWork)
-       }
-       if lwork < 3*n-1 {
-               panic(badWork)
-       }
-       if n == 0 {
-               return true
-       }
-       if n == 1 {
-               w[0] = a[0]
-               work[0] = 2
-               if wantz {
-                       a[0] = 1
-               }
-               return true
-       }
-       safmin := dlamchS
-       eps := dlamchP
-       smlnum := safmin / eps
-       bignum := 1 / smlnum
-       rmin := math.Sqrt(smlnum)
-       rmax := math.Sqrt(bignum)
-
-       // Scale matrix to allowable range, if necessary.
-       anrm := impl.Dlansy(lapack.MaxAbs, uplo, n, a, lda, work)
-       scaled := false
-       var sigma float64
-       if anrm > 0 && anrm < rmin {
-               scaled = true
-               sigma = rmin / anrm
-       } else if anrm > rmax {
-               scaled = true
-               sigma = rmax / anrm
-       }
-       if scaled {
-               kind := lapack.LowerTri
-               if upper {
-                       kind = lapack.UpperTri
-               }
-               impl.Dlascl(kind, 0, 0, 1, sigma, n, n, a, lda)
-       }
-       var inde int
-       indtau := inde + n
-       indwork := indtau + n
-       llwork := lwork - indwork
-       impl.Dsytrd(uplo, n, a, lda, w, work[inde:], work[indtau:], work[indwork:], llwork)
-
-       // For eigenvalues only, call Dsterf. For eigenvectors, first call Dorgtr
-       // to generate the orthogonal matrix, then call Dsteqr.
-       if !wantz {
-               ok = impl.Dsterf(n, w, work[inde:])
-       } else {
-               impl.Dorgtr(uplo, n, a, lda, work[indtau:], work[indwork:], llwork)
-               ok = impl.Dsteqr(lapack.EVComp(jobz), n, w, work[inde:], a, lda, work[indtau:])
-       }
-       if !ok {
-               return false
-       }
-
-       // If the matrix was scaled, then rescale eigenvalues appropriately.
-       if scaled {
-               bi := blas64.Implementation()
-               bi.Dscal(n, 1/sigma, w, 1)
-       }
-       work[0] = float64(lworkopt)
-       return true
-}