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blas/blas64: reorder struct fields
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Reorder vector and matrix struct fields to match the order in which BLAS
routines accept them. This makes calls with inline declarations with
unnamed fields consistent and easier to understand.
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@vladimir-ch
vladimir-ch committed Aug 16, 2018
1 parent f4b55da commit 10913d8
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Showing 10 changed files with 52 additions and 52 deletions.
32 changes: 16 additions & 16 deletions blas/blas64/blas64.go
View file
Expand Up @@ -28,72 +28,72 @@ func Implementation() blas.Float64 {

// Vector represents a vector with an associated element increment.
type Vector struct {
Inc int
Data []float64
Inc int
}

// General represents a matrix using the conventional storage scheme.
type General struct {
Rows, Cols int
Stride int
Data []float64
Stride int
}

// Band represents a band matrix using the band storage scheme.
type Band struct {
Rows, Cols int
KL, KU int
Stride int
Data []float64
Stride int
}

// Triangular represents a triangular matrix using the conventional storage scheme.
type Triangular struct {
N int
Stride int
Data []float64
Uplo blas.Uplo
Diag blas.Diag
N int
Data []float64
Stride int
}

// TriangularBand represents a triangular matrix using the band storage scheme.
type TriangularBand struct {
N, K int
Stride int
Data []float64
Uplo blas.Uplo
Diag blas.Diag
N, K int
Data []float64
Stride int
}

// TriangularPacked represents a triangular matrix using the packed storage scheme.
type TriangularPacked struct {
N int
Data []float64
Uplo blas.Uplo
Diag blas.Diag
N int
Data []float64
}

// Symmetric represents a symmetric matrix using the conventional storage scheme.
type Symmetric struct {
Uplo blas.Uplo
N int
Stride int
Data []float64
Uplo blas.Uplo
Stride int
}

// SymmetricBand represents a symmetric matrix using the band storage scheme.
type SymmetricBand struct {
Uplo blas.Uplo
N, K int
Stride int
Data []float64
Uplo blas.Uplo
Stride int
}

// SymmetricPacked represents a symmetric matrix using the packed storage scheme.
type SymmetricPacked struct {
Uplo blas.Uplo
N int
Data []float64
Uplo blas.Uplo
}

// Level 1
Expand Down
8 changes: 4 additions & 4 deletions lapack/testlapack/dgebak.go
View file
Expand Up @@ -73,14 +73,14 @@ func testDgebak(t *testing.T, impl Dgebaker, job lapack.Job, side lapack.EVSide,
for i := n - 1; i > ihi; i-- {
scale[i] = float64(rnd.Intn(i + 1))
blas64.Swap(n,
blas64.Vector{p.Stride, p.Data[i:]},
blas64.Vector{p.Stride, p.Data[int(scale[i]):]})
blas64.Vector{p.Data[i:], p.Stride},
blas64.Vector{p.Data[int(scale[i]):], p.Stride})
}
for i := 0; i < ilo; i++ {
scale[i] = float64(i + rnd.Intn(ihi-i+1))
blas64.Swap(n,
blas64.Vector{p.Stride, p.Data[i:]},
blas64.Vector{p.Stride, p.Data[int(scale[i]):]})
blas64.Vector{p.Data[i:], p.Stride},
blas64.Vector{p.Data[int(scale[i]):], p.Stride})
}
}

Expand Down
12 changes: 6 additions & 6 deletions lapack/testlapack/dgebal.go
View file
Expand Up @@ -92,12 +92,12 @@ func testDgebal(t *testing.T, impl Dgebaler, job lapack.Job, a blas64.General) {
t.Errorf("%v: invalid ordering of ilo=%v and ihi=%v", prefix, ilo, ihi)
}

if ilo >= 2 && !isUpperTriangular(blas64.General{ilo - 1, ilo - 1, a.Stride, a.Data}) {
if ilo >= 2 && !isUpperTriangular(blas64.General{ilo - 1, ilo - 1, a.Data, a.Stride}) {
t.Errorf("%v: T1 is not upper triangular", prefix)
}
m := n - ihi - 1 // Order of T2.
k := ihi + 1
if m >= 2 && !isUpperTriangular(blas64.General{m, m, a.Stride, a.Data[k*a.Stride+k:]}) {
if m >= 2 && !isUpperTriangular(blas64.General{m, m, a.Data[k*a.Stride+k:], a.Stride}) {
t.Errorf("%v: T2 is not upper triangular", prefix)
}

Expand Down Expand Up @@ -145,13 +145,13 @@ func testDgebal(t *testing.T, impl Dgebaler, job lapack.Job, a blas64.General) {
p := eye(n, n)
for j := n - 1; j > ihi; j-- {
blas64.Swap(n,
blas64.Vector{p.Stride, p.Data[j:]},
blas64.Vector{p.Stride, p.Data[int(scale[j]):]})
blas64.Vector{p.Data[j:], p.Stride},
blas64.Vector{p.Data[int(scale[j]):], p.Stride})
}
for j := 0; j < ilo; j++ {
blas64.Swap(n,
blas64.Vector{p.Stride, p.Data[j:]},
blas64.Vector{p.Stride, p.Data[int(scale[j]):]})
blas64.Vector{p.Data[j:], p.Stride},
blas64.Vector{p.Data[int(scale[j]):], p.Stride})
}
// Compute P^T*A*P and store into want.
ap := zeros(n, n, n)
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4 changes: 2 additions & 2 deletions lapack/testlapack/dlacn2.go
View file
Expand Up @@ -55,10 +55,10 @@ func Dlacn2Test(t *testing.T, impl Dlacn2er) {
case 0:
break loop
case 1:
blas64.Gemv(blas.NoTrans, 1, a, blas64.Vector{1, x}, 0, blas64.Vector{1, work})
blas64.Gemv(blas.NoTrans, 1, a, blas64.Vector{x, 1}, 0, blas64.Vector{work, 1})
copy(x, work)
case 2:
blas64.Gemv(blas.Trans, 1, a, blas64.Vector{1, x}, 0, blas64.Vector{1, work})
blas64.Gemv(blas.Trans, 1, a, blas64.Vector{x, 1}, 0, blas64.Vector{work, 1})
copy(x, work)
}
}
Expand Down
2 changes: 1 addition & 1 deletion lapack/testlapack/dorg2l.go
View file
Expand Up @@ -48,7 +48,7 @@ func Dorg2lTest(t *testing.T, impl Dorg2ler) {
impl.Dgeql2(m, n, a, lda, tau, work)

impl.Dorg2l(m, n, k, a, lda, tau[n-k:], work)
if !hasOrthonormalColumns(blas64.General{m, n, lda, a}) {
if !hasOrthonormalColumns(blas64.General{m, n, a, lda}) {
t.Errorf("Case m=%v, n=%v, k=%v: columns of Q not orthonormal", m, n, k)
}
}
Expand Down
2 changes: 1 addition & 1 deletion lapack/testlapack/dorgql.go
View file
Expand Up @@ -71,7 +71,7 @@ func DorgqlTest(t *testing.T, impl Dorgqler) {
h := eye(m, m)
jj := m - k + l
j := n - k + l
v := blas64.Vector{1, make([]float64, m)}
v := blas64.Vector{make([]float64, m), 1}
for i := 0; i < jj; i++ {
v.Data[i] = a.Data[i*a.Stride+j]
}
Expand Down
8 changes: 4 additions & 4 deletions lapack/testlapack/general.go
View file
Expand Up @@ -1290,7 +1290,7 @@ func isRightEigenvectorOf(a blas64.General, xRe, xIm []float64, lambda complex12

// Compute A real(x) and store the result into xReAns.
xReAns := make([]float64, n)
blas64.Gemv(blas.NoTrans, 1, a, blas64.Vector{1, xRe}, 0, blas64.Vector{1, xReAns})
blas64.Gemv(blas.NoTrans, 1, a, blas64.Vector{xRe, 1}, 0, blas64.Vector{xReAns, 1})

if imag(lambda) == 0 && xIm == nil {
// Real eigenvalue and eigenvector.
Expand All @@ -1308,7 +1308,7 @@ func isRightEigenvectorOf(a blas64.General, xRe, xIm []float64, lambda complex12

// Compute A imag(x) and store the result into xImAns.
xImAns := make([]float64, n)
blas64.Gemv(blas.NoTrans, 1, a, blas64.Vector{1, xIm}, 0, blas64.Vector{1, xImAns})
blas64.Gemv(blas.NoTrans, 1, a, blas64.Vector{xIm, 1}, 0, blas64.Vector{xImAns, 1})

// Compute λx and store the result into lambdax.
lambdax := make([]complex128, n)
Expand Down Expand Up @@ -1349,7 +1349,7 @@ func isLeftEigenvectorOf(a blas64.General, yRe, yIm []float64, lambda complex128

// Compute A^T real(y) and store the result into yReAns.
yReAns := make([]float64, n)
blas64.Gemv(blas.Trans, 1, a, blas64.Vector{1, yRe}, 0, blas64.Vector{1, yReAns})
blas64.Gemv(blas.Trans, 1, a, blas64.Vector{yRe, 1}, 0, blas64.Vector{yReAns, 1})

if imag(lambda) == 0 && yIm == nil {
// Real eigenvalue and eigenvector.
Expand All @@ -1367,7 +1367,7 @@ func isLeftEigenvectorOf(a blas64.General, yRe, yIm []float64, lambda complex128

// Compute A^T imag(y) and store the result into yImAns.
yImAns := make([]float64, n)
blas64.Gemv(blas.Trans, 1, a, blas64.Vector{1, yIm}, 0, blas64.Vector{1, yImAns})
blas64.Gemv(blas.Trans, 1, a, blas64.Vector{yIm, 1}, 0, blas64.Vector{yImAns, 1})

// Compute conj(λ)y and store the result into lambday.
lambda = cmplx.Conj(lambda)
Expand Down
2 changes: 1 addition & 1 deletion lapack/testlapack/matgen.go
View file
Expand Up @@ -716,7 +716,7 @@ func applyReflector(qh blas64.General, q blas64.General, v []float64) {
panic("bad size of q")
}
qv := make([]float64, n)
blas64.Gemv(blas.NoTrans, 1, q, blas64.Vector{1, v}, 0, blas64.Vector{1, qv})
blas64.Gemv(blas.NoTrans, 1, q, blas64.Vector{v, 1}, 0, blas64.Vector{qv, 1})
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
qh.Data[i*qh.Stride+j] = q.Data[i*q.Stride+j]
Expand Down
16 changes: 8 additions & 8 deletions mat/cholesky.go
View file
Expand Up @@ -478,12 +478,12 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x Vector) (ok bool)
tmp.CopyVec(x)
xmat = tmp.RawVector()
}
blas64.Copy(n, xmat, blas64.Vector{1, work})
blas64.Copy(n, xmat, blas64.Vector{work, 1})

if alpha > 0 {
// Compute rank-1 update.
if alpha != 1 {
blas64.Scal(n, math.Sqrt(alpha), blas64.Vector{1, work})
blas64.Scal(n, math.Sqrt(alpha), blas64.Vector{work, 1})
}
umat := c.chol.mat
stride := umat.Stride
Expand All @@ -504,8 +504,8 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x Vector) (ok bool)
// the Givens matrix from the left. Only
// the i-th row and x are modified.
blas64.Rot(n-i-1,
blas64.Vector{1, umat.Data[i*stride+i+1 : i*stride+n]},
blas64.Vector{1, work[i+1 : n]},
blas64.Vector{umat.Data[i*stride+i+1 : i*stride+n], 1},
blas64.Vector{work[i+1 : n], 1},
c, s)
}
}
Expand All @@ -516,7 +516,7 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x Vector) (ok bool)
// Compute rank-1 downdate.
alpha = math.Sqrt(-alpha)
if alpha != 1 {
blas64.Scal(n, alpha, blas64.Vector{1, work})
blas64.Scal(n, alpha, blas64.Vector{work, 1})
}
// Solve U^T * p = x storing the result into work.
ok = lapack64.Trtrs(blas.Trans, c.chol.RawTriangular(), blas64.General{
Expand All @@ -530,7 +530,7 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x Vector) (ok bool)
// the factorization is valid.
panic(badCholesky)
}
norm := blas64.Nrm2(n, blas64.Vector{1, work})
norm := blas64.Nrm2(n, blas64.Vector{work, 1})
if norm >= 1 {
// The updated matrix is not positive definite.
return false
Expand All @@ -557,7 +557,7 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x Vector) (ok bool)
// Apply Givens matrices to U.
// TODO(vladimir-ch): Use workspace to avoid modifying the
// receiver in case an invalid factorization is created.
blas64.Rot(n-i, blas64.Vector{1, work[i:n]}, blas64.Vector{1, umat.Data[i*stride+i : i*stride+n]}, cos[i], sin[i])
blas64.Rot(n-i, blas64.Vector{work[i:n], 1}, blas64.Vector{umat.Data[i*stride+i : i*stride+n], 1}, cos[i], sin[i])
if umat.Data[i*stride+i] == 0 {
// The matrix is singular (may rarely happen due to
// floating-point effects?).
Expand All @@ -567,7 +567,7 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x Vector) (ok bool)
// that on the i-th row the diagonal is negative,
// multiply U from the left by an identity matrix that
// has -1 on the i-th row.
blas64.Scal(n-i, -1, blas64.Vector{1, umat.Data[i*stride+i : i*stride+n]})
blas64.Scal(n-i, -1, blas64.Vector{umat.Data[i*stride+i : i*stride+n], 1})
}
}
if ok {
Expand Down
18 changes: 9 additions & 9 deletions mat/format_test.go
View file
Expand Up @@ -26,7 +26,7 @@ func TestFormat(t *testing.T) {
[]rp{
{"%v", "⎡0 0 0⎤\n⎢0 0 0⎥\n⎣0 0 0⎦"},
{"% f", "⎡. . .⎤\n⎢. . .⎥\n⎣. . .⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{0, 0, 0, 0, 0, 0, 0, 0, 0}}, capRows:3, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Data:[]float64{0, 0, 0, 0, 0, 0, 0, 0, 0}, Stride:3}, capRows:3, capCols:3}"},
{"%s", "%!s(*mat.Dense=Dims(3, 3))"},
},
},
Expand All @@ -35,39 +35,39 @@ func TestFormat(t *testing.T) {
[]rp{
{"%v", "⎡1 1 1⎤\n⎢1 1 1⎥\n⎣1 1 1⎦"},
{"% f", "⎡1 1 1⎤\n⎢1 1 1⎥\n⎣1 1 1⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}}, capRows:3, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}, Stride:3}, capRows:3, capCols:3}"},
},
},
{
Formatted(NewDense(3, 3, []float64{1, 1, 1, 1, 1, 1, 1, 1, 1}), Prefix("\t")),
[]rp{
{"%v", "⎡1 1 1⎤\n\t⎢1 1 1⎥\n\t⎣1 1 1⎦"},
{"% f", "⎡1 1 1⎤\n\t⎢1 1 1⎥\n\t⎣1 1 1⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}}, capRows:3, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}, Stride:3}, capRows:3, capCols:3}"},
},
},
{
Formatted(NewDense(3, 3, []float64{1, 0, 0, 0, 1, 0, 0, 0, 1})),
[]rp{
{"%v", "⎡1 0 0⎤\n⎢0 1 0⎥\n⎣0 0 1⎦"},
{"% f", "⎡1 . .⎤\n⎢. 1 .⎥\n⎣. . 1⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 0, 0, 0, 1, 0, 0, 0, 1}}, capRows:3, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Data:[]float64{1, 0, 0, 0, 1, 0, 0, 0, 1}, Stride:3}, capRows:3, capCols:3}"},
},
},
{
Formatted(NewDense(2, 3, []float64{1, 2, 3, 4, 5, 6})),
[]rp{
{"%v", "⎡1 2 3⎤\n⎣4 5 6⎦"},
{"% f", "⎡1 2 3⎤\n⎣4 5 6⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{1, 2, 3, 4, 5, 6}}, capRows:2, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Data:[]float64{1, 2, 3, 4, 5, 6}, Stride:3}, capRows:2, capCols:3}"},
},
},
{
Formatted(NewDense(3, 2, []float64{1, 2, 3, 4, 5, 6})),
[]rp{
{"%v", "⎡1 2⎤\n⎢3 4⎥\n⎣5 6⎦"},
{"% f", "⎡1 2⎤\n⎢3 4⎥\n⎣5 6⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:2, Stride:2, Data:[]float64{1, 2, 3, 4, 5, 6}}, capRows:3, capCols:2}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:2, Data:[]float64{1, 2, 3, 4, 5, 6}, Stride:2}, capRows:3, capCols:2}"},
},
},
{
Expand All @@ -80,7 +80,7 @@ func TestFormat(t *testing.T) {
{"%v", "⎡ 0 1 1.4142135623730951⎤\n⎣1.7320508075688772 2 2.23606797749979⎦"},
{"%.2f", "⎡0.00 1.00 1.41⎤\n⎣1.73 2.00 2.24⎦"},
{"% f", "⎡ . 1 1.4142135623730951⎤\n⎣1.7320508075688772 2 2.23606797749979⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:2, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}, Stride:3}, capRows:2, capCols:3}"},
},
},
{
Expand All @@ -93,7 +93,7 @@ func TestFormat(t *testing.T) {
{"%v", "⎡ 0 1⎤\n⎢1.4142135623730951 1.7320508075688772⎥\n⎣ 2 2.23606797749979⎦"},
{"%.2f", "⎡0.00 1.00⎤\n⎢1.41 1.73⎥\n⎣2.00 2.24⎦"},
{"% f", "⎡ . 1⎤\n⎢1.4142135623730951 1.7320508075688772⎥\n⎣ 2 2.23606797749979⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:2, Stride:2, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:3, capCols:2}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:2, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}, Stride:2}, capRows:3, capCols:2}"},
},
},
{
Expand All @@ -106,7 +106,7 @@ func TestFormat(t *testing.T) {
{"%v", "⎡ 0 1 1.4142135623730951⎤\n⎣1.7320508075688772 2 2.23606797749979⎦"},
{"%.2f", "⎡0.00 1.00 1.41⎤\n⎣1.73 2.00 2.24⎦"},
{"% f", "⎡ . 1 1.4142135623730951⎤\n⎣1.7320508075688772 2 2.23606797749979⎦"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:2, capCols:3}"},
{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}, Stride:3}, capRows:2, capCols:3}"},
},
},
{
Expand Down

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