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matx.go
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matx.go
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package neuralNetwork
import (
"fmt"
"math"
"github.com/gcla/sklearn/base"
"gonum.org/v1/gonum/blas/blas64"
"gonum.org/v1/gonum/mat"
)
type matx struct{ Dense *mat.Dense }
func (m matx) RemoveFirstRow() matx {
r, c := m.Dense.Dims()
return matx{Dense: base.MatDenseSlice(m.Dense, 1, r, 0, c)}
}
// AddScaledApplied adds scale*B to m
func (m matx) AddScaled(scale float64, B mat.RawMatrixer) {
amat, bmat := m.RawMatrix(), B.RawMatrix()
for ja, jb, jm := 0, 0, 0; ja < amat.Rows*amat.Stride; ja, jb, jm = ja+amat.Stride, jb+bmat.Stride, jm+amat.Stride {
for i, v := range amat.Data[ja : ja+amat.Cols] {
amat.Data[i+jm] = v + scale*bmat.Data[i+jb]
}
}
}
// CopyPrependOnes copy B to m with a column of ones prepended
func (m matx) CopyPrependOnes(B mat.RawMatrixer) {
mmat, bmat := m.RawMatrix(), B.RawMatrix()
for jb, jm := 0, 0; jb < bmat.Rows*bmat.Stride; jb, jm = jb+bmat.Stride, jm+mmat.Stride {
mmat.Data[jm] = 1.
copy(mmat.Data[jm+1:jm+1+bmat.Cols], bmat.Data[jb:jb+bmat.Cols])
}
}
// AddScaledApplied adds scale*f(B) to m
func (m matx) AddScaledApplied(scale float64, B mat.RawMatrixer, f func(float64) float64) {
amat, bmat := m.RawMatrix(), B.RawMatrix()
for ja, jb, jm := 0, 0, 0; ja < amat.Rows*amat.Stride; ja, jb, jm = ja+amat.Stride, jb+bmat.Stride, jm+amat.Stride {
for i, v := range amat.Data[ja : ja+amat.Cols] {
amat.Data[i+jm] = v + scale*f(bmat.Data[i+jb])
}
}
}
// CopyApplied copy f(B) to m
func (m matx) CopyApplied(B mat.RawMatrixer, f func(float64) float64) {
amat, bmat := m.RawMatrix(), B.RawMatrix()
if amat.Rows != bmat.Rows || amat.Cols != bmat.Cols {
panic(fmt.Errorf("%d,%d != %d,%d", amat.Rows, amat.Cols, bmat.Rows, bmat.Cols))
}
for ja, jb := 0, 0; ja < amat.Rows*amat.Stride; ja, jb = ja+amat.Stride, jb+bmat.Stride {
for i, vb := range bmat.Data[jb : jb+bmat.Cols] {
amat.Data[i+ja] = f(vb)
}
}
}
// CopyScaledApplied2 copy scale*f(B,C) to m
func (m matx) CopyScaledApplied2(B, C mat.RawMatrixer, scale float64, f func(float64, float64) float64) {
amat, bmat, cmat := m.RawMatrix(), B.RawMatrix(), C.RawMatrix()
if amat.Rows != bmat.Rows || amat.Cols != bmat.Cols {
panic(fmt.Errorf("%d,%d != %d,%d", amat.Rows, amat.Cols, bmat.Rows, bmat.Cols))
}
for ja, jb, jc := 0, 0, 0; ja < amat.Rows*amat.Stride; ja, jb, jc = ja+amat.Stride, jb+bmat.Stride, jc+cmat.Stride {
for i := range amat.Data[ja : ja+amat.Cols] {
amat.Data[i+ja] = scale * f(bmat.Data[i+jb], cmat.Data[i+jc])
}
}
}
// SumApplied2 returns sum of f(B,C) t
func (matx) SumApplied2(B, C mat.RawMatrixer, f func(float64, float64) float64) float64 {
bmat, cmat := B.RawMatrix(), C.RawMatrix()
if cmat.Rows != bmat.Rows || cmat.Cols != bmat.Cols {
panic(fmt.Errorf("%d,%d != %d,%d", bmat.Rows, bmat.Cols, cmat.Rows, cmat.Cols))
}
sum := 0.
for jb, jc := 0, 0; jb < bmat.Rows*bmat.Stride; jb, jc = jb+bmat.Stride, jc+cmat.Stride {
for i := range bmat.Data[jb : jb+bmat.Cols] {
sum += f(bmat.Data[i+jb], cmat.Data[i+jc])
}
}
return sum
}
func (m matx) SumSquares() float64 {
amat := m.RawMatrix()
sum := 0.
for ja := 0; ja < amat.Rows*amat.Stride; ja = ja + amat.Stride {
for _, v := range amat.Data[ja : ja+amat.Cols] {
sum += v * v
}
}
return sum
}
func (m matx) SumAbs() float64 {
amat := m.RawMatrix()
sum := 0.
for ja := 0; ja < amat.Rows*amat.Stride; ja = ja + amat.Stride {
for _, v := range amat.Data[ja : ja+amat.Cols] {
sum += math.Abs(v)
}
}
return sum
}
func (m matx) RawMatrix() blas64.General {
return m.Dense.RawMatrix()
}
func (m matx) Orthonormalize() {
feats, outs := m.Dense.Dims()
if mat.Norm(m.Dense, 1) == 0. {
return
}
var vec func(i int) *mat.Dense
var n int
if feats < outs {
vec = func(i int) *mat.Dense { return base.MatDenseSlice(m.Dense, i, i+1, 0, outs) }
n = feats
} else {
vec = func(i int) *mat.Dense { return base.MatDenseSlice(m.Dense, 0, feats, i, i+1) }
n = outs
}
for i := 0; i < n; i++ {
row := vec(i)
nrm := mat.Norm(row, 2)
if nrm == 0. {
break
}
row.Scale(1./nrm, row)
for i1 := 0; i1 < i; i1++ {
r1 := vec(i1)
cos := matx{}.SumApplied2(row, r1, func(r, r1 float64) float64 { return r * r1 })
if cos == 1. {
//fmt.Println("Orthonormalize: colin")
continue
}
// splain := "\nbefore:" + base.MatStr(row)
// splain += "\nadd :" + base.MatStr(r1) + fmt.Sprintf("* %g", -cos)
matx{Dense: row}.AddScaled(-cos, r1)
// splain += "\nresult:" + base.MatStr(row)
nrm = mat.Norm(row, 2)
// splain += fmt.Sprintf("\nnorme2: %g\ni:%d, i1:%d\n", nrm, i, i1)
if nrm == 0. {
panic("can't orthonormalize")
// return
}
row.Scale(1./nrm, row)
}
}
}