func (wm *writtenMemory) backwardWErase() {
n := wm.N
m := len(wm.TopVal) / n
mgrad := make([]float64, n*m)
mGradG := blas64.General{Rows: n, Cols: m, Stride: m, Data: mgrad}
hEraseGrad := blas64.Vector{Inc: 1, Data: make([]float64, m)}
for i, weights := range wm.Ws {
erase := wm.erase[i]
add := wm.add[i]
eraseV := blas64.Vector{Inc: 1, Data: erase}
addV := blas64.Vector{Inc: 1, Data: add}
weightsVal := blas64.Vector{Inc: 1, Data: weights.TopVal}
for j := range mgrad {
mgrad[j] = 0
}
blas64.Ger(1, weightsVal, eraseV, mGradG)
wm.div1MWE(mgrad)
floats.Mul(mgrad, wm.TopGrad)
weightsV := blas64.Vector{Inc: 1, Data: weights.TopGrad}
blas64.Gemv(blas.NoTrans, -1, mGradG, eraseV, 1, weightsV)
blas64.Gemv(blas.NoTrans, 1, blas64.General{Rows: n, Cols: m, Stride: m, Data: wm.TopGrad}, addV, 1, weightsV)
hErase := wm.Heads[i].EraseGrad()
for j := range hEraseGrad.Data {
hEraseGrad.Data[j] = 0
}
blas64.Gemv(blas.Trans, -1, mGradG, weightsVal, 1, hEraseGrad)
for j, e := range erase {
hErase[j] += hEraseGrad.Data[j] * e * (1 - e)
}
}
}
// isLeftEigenvectorOf returns whether the vector yRe+i*yIm, where i is the
// imaginary unit, is the left eigenvector of A corresponding to the eigenvalue
// lambda.
//
// A left eigenvector corresponding to a complex eigenvalue λ is a complex
// non-zero vector y such that
// y^H A = λ y^H,
// which is equivalent for real A to
// A^T y = conj(λ) y,
func isLeftEigenvectorOf(a blas64.General, yRe, yIm []float64, lambda complex128, tol float64) bool {
if a.Rows != a.Cols {
panic("matrix not square")
}
if imag(lambda) != 0 && yIm == nil {
// Complex eigenvalue of a real matrix cannot have a real
// eigenvector.
return false
}
n := a.Rows
// 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})
if imag(lambda) == 0 && yIm == nil {
// Real eigenvalue and eigenvector.
// Compute λy and store the result into lambday.
lambday := make([]float64, n)
floats.AddScaled(lambday, real(lambda), yRe)
if floats.Distance(yReAns, lambday, math.Inf(1)) > tol {
return false
}
return true
}
// Complex eigenvector, and real or complex eigenvalue.
// 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})
// Compute conj(λ)y and store the result into lambday.
lambda = cmplx.Conj(lambda)
lambday := make([]complex128, n)
for i := range lambday {
lambday[i] = lambda * complex(yRe[i], yIm[i])
}
for i, v := range lambday {
ay := complex(yReAns[i], yImAns[i])
if cmplx.Abs(v-ay) > tol {
return false
}
}
return true
}
func (old *controller1) Forward(reads []*memRead, x []float64) Controller {
c := controller1{
weightsVal: old.weightsVal,
weightsGrad: old.weightsGrad,
Reads: reads,
X: x,
H1Val: make([]float64, old.h1Size+1),
H1Grad: make([]float64, old.h1Size+1),
heads: make([]*Head, len(reads)),
outVal: make([]float64, old.wyRows()),
outGrad: make([]float64, old.wyRows()),
numHeads: old.numHeads,
memoryM: old.memoryM,
memoryN: old.memoryN,
xSize: old.xSize,
h1Size: old.h1Size,
ySize: old.ySize,
}
ud := make([]float64, c.wh1Cols())
for i, read := range reads {
copy(ud[i*c.memoryM:], read.TopVal)
}
copy(ud[c.numHeads*c.memoryM:], c.X)
ud[c.numHeads*c.memoryM+c.xSize] = 1
c.ReadsXVal = blas64.Vector{Inc: 1, Data: ud}
h1 := blas64.Vector{Inc: 1, Data: c.H1Val[0:c.h1Size]}
blas64.Gemv(blas.NoTrans, 1, c.wh1Val(), c.ReadsXVal, 1, h1)
for i, h := range c.H1Val[0:c.h1Size] {
c.H1Val[i] = Sigmoid(h)
}
c.H1Val[c.h1Size] = 1
h1 = blas64.Vector{Inc: 1, Data: c.H1Val}
outV := blas64.Vector{Inc: 1, Data: c.outVal}
blas64.Gemv(blas.NoTrans, 1, c.wyVal(), h1, 1, outV)
hul := headUnitsLen(c.memoryM)
for i := range c.heads {
head := NewHead(c.memoryM)
c.heads[i] = head
start := c.ySize + i*hul
head.vals = c.outVal[start : start+hul]
head.grads = c.outGrad[start : start+hul]
}
return &c
}
// isRightEigenvectorOf returns whether the vector xRe+i*xIm, where i is the
// imaginary unit, is the right eigenvector of A corresponding to the eigenvalue
// lambda.
//
// A right eigenvector corresponding to a complex eigenvalue λ is a complex
// non-zero vector x such that
// A x = λ x.
func isRightEigenvectorOf(a blas64.General, xRe, xIm []float64, lambda complex128, tol float64) bool {
if a.Rows != a.Cols {
panic("matrix not square")
}
if imag(lambda) != 0 && xIm == nil {
// Complex eigenvalue of a real matrix cannot have a real
// eigenvector.
return false
}
n := a.Rows
// 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})
if imag(lambda) == 0 && xIm == nil {
// Real eigenvalue and eigenvector.
// Compute λx and store the result into lambdax.
lambdax := make([]float64, n)
floats.AddScaled(lambdax, real(lambda), xRe)
if floats.Distance(xReAns, lambdax, math.Inf(1)) > tol {
return false
}
return true
}
// Complex eigenvector, and real or complex eigenvalue.
// 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})
// Compute λx and store the result into lambdax.
lambdax := make([]complex128, n)
for i := range lambdax {
lambdax[i] = lambda * complex(xRe[i], xIm[i])
}
for i, v := range lambdax {
ax := complex(xReAns[i], xImAns[i])
if cmplx.Abs(v-ax) > tol {
return false
}
}
return true
}
// replaces x with Q.x
func (f LQFactor) applyQTo(x *Dense, trans bool) {
nh, nc := f.LQ.Dims()
m, n := x.Dims()
if m != nc {
panic(ErrShape)
}
proj := make([]float64, n)
if trans {
for k := nh - 1; k >= 0; k-- {
hh := f.LQ.RawRowView(k)[k:]
sub := x.View(k, 0, m-k, n).(*Dense)
blas64.Gemv(blas.Trans,
1, sub.Mat, blas64.Vector{Inc: 1, Data: hh},
0, blas64.Vector{Inc: 1, Data: proj},
)
for i := k; i < m; i++ {
row := x.RawRowView(i)
blas64.Axpy(n, -hh[i-k],
blas64.Vector{Inc: 1, Data: proj},
blas64.Vector{Inc: 1, Data: row},
)
}
}
} else {
for k := 0; k < nh; k++ {
hh := f.LQ.RawRowView(k)[k:]
sub := x.View(k, 0, m-k, n).(*Dense)
blas64.Gemv(blas.Trans,
1, sub.Mat, blas64.Vector{Inc: 1, Data: hh},
0, blas64.Vector{Inc: 1, Data: proj},
)
for i := k; i < m; i++ {
row := x.RawRowView(i)
blas64.Axpy(n, -hh[i-k],
blas64.Vector{Inc: 1, Data: proj},
blas64.Vector{Inc: 1, Data: row},
)
}
}
}
}
func (c *controller1) Backward() {
out := blas64.Vector{Inc: 1, Data: c.outGrad}
h1Val := blas64.Vector{Inc: 1, Data: c.H1Val}
h1Grad := blas64.Vector{Inc: 1, Data: c.H1Grad}
blas64.Gemv(blas.Trans, 1, c.wyVal(), out, 1, h1Grad)
blas64.Ger(1, out, h1Val, c.wyGrad())
h1Val = blas64.Vector{Inc: 1, Data: c.H1Val[0:c.h1Size]}
h1Grad = blas64.Vector{Inc: 1, Data: c.H1Grad[0:c.h1Size]}
for i, v := range h1Val.Data {
h1Grad.Data[i] *= v * (1 - v)
}
u := blas64.Vector{Inc: 1, Data: make([]float64, c.wh1Cols())}
blas64.Gemv(blas.Trans, 1, c.wh1Val(), h1Grad, 1, u)
blas64.Ger(1, h1Grad, c.ReadsXVal, c.wh1Grad())
for i, read := range c.Reads {
copy(read.TopGrad, u.Data[i*c.memoryM:(i+1)*c.memoryM])
}
}
func (r *memRead) Backward() {
n := r.Memory.N
m := len(r.Memory.TopVal) / n
grad := blas64.Vector{Inc: 1, Data: r.TopGrad}
memVal := blas64.General{Rows: n, Cols: m, Stride: m, Data: r.Memory.TopVal}
weightsGrad := blas64.Vector{Inc: 1, Data: r.W.TopGrad}
blas64.Gemv(blas.NoTrans, 1, memVal, grad, 1, weightsGrad)
memGrad := blas64.General{Rows: n, Cols: m, Stride: m, Data: r.Memory.TopGrad}
weights := blas64.Vector{Inc: 1, Data: r.W.TopVal}
blas64.Ger(1, weights, grad, memGrad)
}
// BestClassifier returns the classifier in the static
// pool whose output correlates the most highly with
// the given weight vector, as measured by absolute
// cosine distance.
//
// The list argument is ignored, since a StaticPool
// always uses the set of samples it was given when
// it was initialized.
func (s *StaticPool) BestClassifier(list SampleList, weights linalg.Vector) Classifier {
vec := blas64.Vector{
Inc: 1,
Data: weights,
}
output := blas64.Vector{
Inc: 1,
Data: make([]float64, len(s.classifiers)),
}
blas64.Gemv(blas.NoTrans, 1, s.outputMatrix, vec, 0, output)
largest := blas64.Iamax(len(s.classifiers), output)
return s.classifiers[largest]
}
func newMemRead(w *refocus, memory *writtenMemory) *memRead {
m := len(memory.TopVal) / memory.N
r := memRead{
W: w,
Memory: memory,
TopVal: make([]float64, m),
TopGrad: make([]float64, m),
}
weights := blas64.Vector{Inc: 1, Data: w.TopVal}
mem := blas64.General{Rows: memory.N, Cols: m, Stride: m, Data: memory.TopVal}
top := blas64.Vector{Inc: 1, Data: r.TopVal}
blas64.Gemv(blas.Trans, 1, mem, weights, 1, top)
return &r
}
// MulVec computes a * b. The result is stored into the receiver.
// MulVec panics if the number of columns in a does not equal the number of rows in b.
func (v *Vector) MulVec(a Matrix, b *Vector) {
r, c := a.Dims()
br := b.Len()
if c != br {
panic(ErrShape)
}
a, trans := untranspose(a)
ar, ac := a.Dims()
v.reuseAs(r)
var restore func()
if v == a {
v, restore = v.isolatedWorkspace(a.(*Vector))
defer restore()
} else if v == b {
v, restore = v.isolatedWorkspace(b)
defer restore()
}
switch a := a.(type) {
case *Vector:
if a.Len() == 1 {
// {1,1} x {1,n}
av := a.At(0, 0)
for i := 0; i < b.Len(); i++ {
v.mat.Data[i*v.mat.Inc] = av * b.mat.Data[i*b.mat.Inc]
}
return
}
if b.Len() == 1 {
// {1,n} x {1,1}
bv := b.At(0, 0)
for i := 0; i < a.Len(); i++ {
v.mat.Data[i*v.mat.Inc] = bv * a.mat.Data[i*a.mat.Inc]
}
return
}
// {n,1} x {1,n}
var sum float64
for i := 0; i < c; i++ {
sum += a.At(i, 0) * b.At(i, 0)
}
v.SetVec(0, sum)
return
case RawSymmetricer:
amat := a.RawSymmetric()
blas64.Symv(1, amat, b.mat, 0, v.mat)
case RawTriangular:
v.CopyVec(b)
amat := a.RawTriangular()
ta := blas.NoTrans
if trans {
ta = blas.Trans
}
blas64.Trmv(ta, amat, v.mat)
case RawMatrixer:
amat := a.RawMatrix()
t := blas.NoTrans
if trans {
t = blas.Trans
}
blas64.Gemv(t, 1, amat, b.mat, 0, v.mat)
case Vectorer:
if trans {
col := make([]float64, ar)
for c := 0; c < ac; c++ {
v.mat.Data[c*v.mat.Inc] = blas64.Dot(ar,
blas64.Vector{Inc: 1, Data: a.Col(col, c)},
b.mat,
)
}
} else {
row := make([]float64, ac)
for r := 0; r < ar; r++ {
v.mat.Data[r*v.mat.Inc] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: a.Row(row, r)},
b.mat,
)
}
}
default:
if trans {
col := make([]float64, ar)
for c := 0; c < ac; c++ {
for i := range col {
col[i] = a.At(i, c)
}
var f float64
for i, e := range col {
f += e * b.mat.Data[i*b.mat.Inc]
}
v.mat.Data[c*v.mat.Inc] = f
}
} else {
row := make([]float64, ac)
for r := 0; r < ar; r++ {
for i := range row {
row[i] = a.At(r, i)
}
var f float64
for i, e := range row {
f += e * b.mat.Data[i*b.mat.Inc]
}
v.mat.Data[r*v.mat.Inc] = f
}
}
}
}
// Mul takes the matrix product of a and b, placing the result in the receiver.
//
// See the Muler interface for more information.
func (m *Dense) Mul(a, b Matrix) {
ar, ac := a.Dims()
br, bc := b.Dims()
if ac != br {
panic(ErrShape)
}
aU, aTrans := untranspose(a)
bU, bTrans := untranspose(b)
m.reuseAs(ar, bc)
var restore func()
if m == aU {
m, restore = m.isolatedWorkspace(aU)
defer restore()
} else if m == bU {
m, restore = m.isolatedWorkspace(bU)
defer restore()
}
aT := blas.NoTrans
if aTrans {
aT = blas.Trans
}
bT := blas.NoTrans
if bTrans {
bT = blas.Trans
}
// Some of the cases do not have a transpose option, so create
// temporary memory.
// C = A^T * B = (B^T * A)^T
// C^T = B^T * A.
if aU, ok := aU.(RawMatrixer); ok {
amat := aU.RawMatrix()
if bU, ok := bU.(RawMatrixer); ok {
bmat := bU.RawMatrix()
blas64.Gemm(aT, bT, 1, amat, bmat, 0, m.mat)
return
}
if bU, ok := bU.(RawSymmetricer); ok {
bmat := bU.RawSymmetric()
if aTrans {
c := getWorkspace(ac, ar, false)
blas64.Symm(blas.Left, 1, bmat, amat, 0, c.mat)
strictCopy(m, c.T())
putWorkspace(c)
return
}
blas64.Symm(blas.Right, 1, bmat, amat, 0, m.mat)
return
}
if bU, ok := bU.(RawTriangular); ok {
// Trmm updates in place, so copy aU first.
bmat := bU.RawTriangular()
if aTrans {
c := getWorkspace(ac, ar, false)
var tmp Dense
tmp.SetRawMatrix(aU.RawMatrix())
c.Copy(&tmp)
bT := blas.Trans
if bTrans {
bT = blas.NoTrans
}
blas64.Trmm(blas.Left, bT, 1, bmat, c.mat)
strictCopy(m, c.T())
putWorkspace(c)
return
}
m.Copy(a)
blas64.Trmm(blas.Right, bT, 1, bmat, m.mat)
return
}
if bU, ok := bU.(*Vector); ok {
bvec := bU.RawVector()
if bTrans {
// {ar,1} x {1,bc}, which is not a vector.
// Instead, construct B as a General.
bmat := blas64.General{
Rows: bc,
Cols: 1,
Stride: bvec.Inc,
Data: bvec.Data,
}
blas64.Gemm(aT, bT, 1, amat, bmat, 0, m.mat)
return
}
cvec := blas64.Vector{
Inc: m.mat.Stride,
Data: m.mat.Data,
}
blas64.Gemv(aT, 1, amat, bvec, 0, cvec)
return
}
}
if bU, ok := bU.(RawMatrixer); ok {
bmat := bU.RawMatrix()
if aU, ok := aU.(RawSymmetricer); ok {
amat := aU.RawSymmetric()
if bTrans {
c := getWorkspace(bc, br, false)
blas64.Symm(blas.Right, 1, amat, bmat, 0, c.mat)
strictCopy(m, c.T())
putWorkspace(c)
return
}
blas64.Symm(blas.Left, 1, amat, bmat, 0, m.mat)
return
}
if aU, ok := aU.(RawTriangular); ok {
// Trmm updates in place, so copy bU first.
amat := aU.RawTriangular()
if bTrans {
c := getWorkspace(bc, br, false)
var tmp Dense
tmp.SetRawMatrix(bU.RawMatrix())
c.Copy(&tmp)
aT := blas.Trans
if aTrans {
aT = blas.NoTrans
}
blas64.Trmm(blas.Right, aT, 1, amat, c.mat)
strictCopy(m, c.T())
putWorkspace(c)
return
}
m.Copy(b)
blas64.Trmm(blas.Left, aT, 1, amat, m.mat)
return
}
if aU, ok := aU.(*Vector); ok {
avec := aU.RawVector()
if aTrans {
// {1,ac} x {ac, bc}
// Transpose B so that the vector is on the right.
cvec := blas64.Vector{
Inc: 1,
Data: m.mat.Data,
}
bT := blas.Trans
if bTrans {
bT = blas.NoTrans
}
blas64.Gemv(bT, 1, bmat, avec, 0, cvec)
return
}
// {ar,1} x {1,bc} which is not a vector result.
// Instead, construct A as a General.
amat := blas64.General{
Rows: ar,
Cols: 1,
Stride: avec.Inc,
Data: avec.Data,
}
blas64.Gemm(aT, bT, 1, amat, bmat, 0, m.mat)
return
}
}
if aU, ok := aU.(Vectorer); ok {
if bU, ok := bU.(Vectorer); ok {
row := make([]float64, ac)
col := make([]float64, br)
if aTrans {
if bTrans {
for r := 0; r < ar; r++ {
dataTmp := m.mat.Data[r*m.mat.Stride : r*m.mat.Stride+bc]
for c := 0; c < bc; c++ {
dataTmp[c] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: aU.Col(row, r)},
blas64.Vector{Inc: 1, Data: bU.Row(col, c)},
)
}
}
return
}
// TODO(jonlawlor): determine if (b*a)' is more efficient
for r := 0; r < ar; r++ {
dataTmp := m.mat.Data[r*m.mat.Stride : r*m.mat.Stride+bc]
for c := 0; c < bc; c++ {
dataTmp[c] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: aU.Col(row, r)},
blas64.Vector{Inc: 1, Data: bU.Col(col, c)},
)
}
}
return
}
if bTrans {
for r := 0; r < ar; r++ {
dataTmp := m.mat.Data[r*m.mat.Stride : r*m.mat.Stride+bc]
for c := 0; c < bc; c++ {
dataTmp[c] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: aU.Row(row, r)},
blas64.Vector{Inc: 1, Data: bU.Row(col, c)},
)
}
}
return
}
for r := 0; r < ar; r++ {
dataTmp := m.mat.Data[r*m.mat.Stride : r*m.mat.Stride+bc]
for c := 0; c < bc; c++ {
dataTmp[c] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: aU.Row(row, r)},
blas64.Vector{Inc: 1, Data: bU.Col(col, c)},
)
}
}
return
}
}
row := make([]float64, ac)
for r := 0; r < ar; r++ {
for i := range row {
row[i] = a.At(r, i)
}
for c := 0; c < bc; c++ {
var v float64
for i, e := range row {
v += e * b.At(i, c)
}
m.mat.Data[r*m.mat.Stride+c] = v
}
}
}
// MulVec computes a * b if trans == false and a^T * b if trans == true. The
// result is stored into the receiver. MulVec panics if the number of columns in
// a does not equal the number of rows in b.
func (v *Vector) MulVec(a Matrix, trans bool, b *Vector) {
ar, ac := a.Dims()
br := b.Len()
if trans {
if ar != br {
panic(ErrShape)
}
} else {
if ac != br {
panic(ErrShape)
}
}
var w Vector
if v != a && v != b {
w = *v
}
if w.n == 0 {
if trans {
w.mat.Data = use(w.mat.Data, ac)
} else {
w.mat.Data = use(w.mat.Data, ar)
}
w.mat.Inc = 1
w.n = ar
if trans {
w.n = ac
}
} else {
if trans {
if ac != w.n {
panic(ErrShape)
}
} else {
if ar != w.n {
panic(ErrShape)
}
}
}
switch a := a.(type) {
case RawSymmetricer:
amat := a.RawSymmetric()
blas64.Symv(1, amat, b.mat, 0, w.mat)
case RawTriangular:
w.CopyVec(b)
amat := a.RawTriangular()
ta := blas.NoTrans
if trans {
ta = blas.Trans
}
blas64.Trmv(ta, amat, w.mat)
case RawMatrixer:
amat := a.RawMatrix()
t := blas.NoTrans
if trans {
t = blas.Trans
}
blas64.Gemv(t, 1, amat, b.mat, 0, w.mat)
case Vectorer:
if trans {
col := make([]float64, ar)
for c := 0; c < ac; c++ {
w.mat.Data[c*w.mat.Inc] = blas64.Dot(ar,
blas64.Vector{Inc: 1, Data: a.Col(col, c)},
b.mat,
)
}
} else {
row := make([]float64, ac)
for r := 0; r < ar; r++ {
w.mat.Data[r*w.mat.Inc] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: a.Row(row, r)},
b.mat,
)
}
}
default:
if trans {
col := make([]float64, ar)
for c := 0; c < ac; c++ {
for i := range col {
col[i] = a.At(i, c)
}
var f float64
for i, e := range col {
f += e * b.mat.Data[i*b.mat.Inc]
}
w.mat.Data[c*w.mat.Inc] = f
}
} else {
row := make([]float64, ac)
for r := 0; r < ar; r++ {
for i := range row {
row[i] = a.At(r, i)
}
var f float64
for i, e := range row {
f += e * b.mat.Data[i*b.mat.Inc]
}
w.mat.Data[r*w.mat.Inc] = f
}
}
}
*v = w
}
func DlarfgTest(t *testing.T, impl Dlarfger) {
for i, test := range []struct {
alpha float64
n int
x []float64
}{
{
alpha: 4,
n: 3,
},
{
alpha: -2,
n: 3,
},
{
alpha: 0,
n: 3,
},
{
alpha: 1,
n: 1,
},
{
alpha: 1,
n: 2,
x: []float64{4, 5, 6},
},
} {
n := test.n
incX := 1
var x []float64
if test.x == nil {
x = make([]float64, n-1)
for i := range x {
x[i] = rand.Float64()
}
} else {
x = make([]float64, n-1)
copy(x, test.x)
}
xcopy := make([]float64, n-1)
copy(xcopy, x)
alpha := test.alpha
beta, tau := impl.Dlarfg(n, alpha, x, incX)
// Verify the returns and the values in v. Construct h and perform
// the explicit multiplication.
h := make([]float64, n*n)
for i := 0; i < n; i++ {
h[i*n+i] = 1
}
hmat := blas64.General{
Rows: n,
Cols: n,
Stride: n,
Data: h,
}
v := make([]float64, n)
copy(v[1:], x)
v[0] = 1
vVec := blas64.Vector{
Inc: 1,
Data: v,
}
blas64.Ger(-tau, vVec, vVec, hmat)
eye := blas64.General{
Rows: n,
Cols: n,
Stride: n,
Data: make([]float64, n*n),
}
blas64.Gemm(blas.Trans, blas.NoTrans, 1, hmat, hmat, 0, eye)
iseye := true
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
if i == j {
if math.Abs(eye.Data[i*n+j]-1) > 1e-14 {
iseye = false
}
} else {
if math.Abs(eye.Data[i*n+j]) > 1e-14 {
iseye = false
}
}
}
}
if !iseye {
t.Errorf("H^T * H is not I %V", eye)
}
xVec := blas64.Vector{
Inc: 1,
Data: make([]float64, n),
}
xVec.Data[0] = test.alpha
copy(xVec.Data[1:], xcopy)
ans := make([]float64, n)
ansVec := blas64.Vector{
Inc: 1,
Data: ans,
}
blas64.Gemv(blas.NoTrans, 1, hmat, xVec, 0, ansVec)
if math.Abs(ans[0]-beta) > 1e-14 {
t.Errorf("Case %v, beta mismatch. Want %v, got %v", i, ans[0], beta)
}
for i := 1; i < n; i++ {
if math.Abs(ans[i]) > 1e-14 {
t.Errorf("Case %v, nonzero answer %v", i, ans)
break
}
}
}
}
// MulVec computes a * b if trans == false and a^T * b if trans == true. The
// result is stored into the reciever. MulVec panics if the number of columns in
// a does not equal the number of rows in b.
func (m *Vector) MulVec(a Matrix, trans bool, b *Vector) {
ar, ac := a.Dims()
br, _ := b.Dims()
if trans {
if ar != br {
panic(ErrShape)
}
} else {
if ac != br {
panic(ErrShape)
}
}
var w Vector
if m != a && m != b {
w = *m
}
if w.n == 0 {
if trans {
w.mat.Data = use(w.mat.Data, ac)
} else {
w.mat.Data = use(w.mat.Data, ar)
}
w.mat.Inc = 1
w.n = ar
} else {
if trans {
if ac != w.n {
panic(ErrShape)
}
} else {
if ar != w.n {
panic(ErrShape)
}
}
}
switch a := a.(type) {
case RawSymmetricer:
amat := a.RawSymmetric()
blas64.Symv(1, amat, b.mat, 0, w.mat)
*m = w
return
case RawMatrixer:
amat := a.RawMatrix()
t := blas.NoTrans
if trans {
t = blas.Trans
}
blas64.Gemv(t, 1, amat, b.mat, 0, w.mat)
*m = w
return
case Vectorer:
row := make([]float64, ac)
for r := 0; r < ar; r++ {
w.mat.Data[r*m.mat.Inc] = blas64.Dot(ac,
blas64.Vector{Inc: 1, Data: a.Row(row, r)},
b.mat,
)
}
*m = w
return
default:
row := make([]float64, ac)
for r := 0; r < ar; r++ {
for i := range row {
row[i] = a.At(r, i)
}
var v float64
for i, e := range row {
v += e * b.mat.Data[i*b.mat.Inc]
}
w.mat.Data[r*m.mat.Inc] = v
}
*m = w
return
}
}
