func (this *TrainingSet) Variance_sum(i0, i1 int, mean *Matrix.Matrix, res **Matrix.Matrix, sustract *Matrix.Matrix, done chan<- bool) {
di := i1 - i0
if di >= THRESHOLD {
mi := i0 + di/2
done2 := make(chan bool, THRESHOLD)
res1 := Matrix.NullMatrixP(1, this.Xs.GetNColumns())
res2 := Matrix.NullMatrixP(1, this.Xs.GetNColumns())
go this.Variance_sum(i0, mi, mean, &res1, sustract, done2)
go this.Variance_sum(mi, i1, mean, &res1, sustract, done2)
<-done2
<-done2
SP, _ := Matrix.Sum(res1, res2)
*res = SP
} else {
for i := i0; i <= i1; i++ {
xsi := this.Xs.GetRow(i)
Sustract, _ := Matrix.Sustract(mean, xsi)
Square := Matrix.DotMultiplication(Sustract, Sustract)
sustract.SetRow(i, Sustract)
SP, _ := Matrix.Sum(Square, *res)
*res = SP
}
}
done <- true
}
func DSoftmax(X *Matrix.Matrix) *Matrix.Matrix {
Total := 1 / X.TaxicabNorm()
Y := X.Scalar(complex(Total, 0))
S, _ := Matrix.Sustract(Matrix.FixValueMatrix(X.GetNColumns(), X.GetNColumns(), 1.0), X)
YD := Matrix.DotMultiplication(Y, S)
return YD
}
func (this *ANN) BackPropagation(As, AsDerviate *[](*Matrix.Matrix), ForwardOutput *Matrix.Matrix, Y *Matrix.Matrix, flen float64) {
ð := this.DerviateCostFunction(ForwardOutput, Y)
this.ð[len(this.ð)-1] = ð
this.AcumatedError, _ = Matrix.Sum(this.CostFunction(ForwardOutput, Y), this.AcumatedError)
for i := len(this.Weights) - 1; i >= 0; i-- {
A := (*As)[i]
Aderviate := (*AsDerviate)[i]
var ðtemp *Matrix.Matrix
if i == len(this.Weights)-1 {
ðtemp = this.ð[i+1].Transpose()
} else {
ðtemp = this.ð[i+1].MatrixWithoutLastRow().Transpose()
}
//Calc ð
//fmt.Println("ð(i+1)", this.ð[i+1].ToString())
//fmt.Println("W(i)", this.Weights[i].ToString())
Product := Matrix.Product(this.Weights[i], ðtemp.Transpose())
//fmt.Println("Product", i, " ", Product.ToString())
this.ð[i] = Matrix.DotMultiplication(Product, Aderviate.AddRowsToDown(Matrix.I(1)))
//Calc of Derivate with respect to the Weights
//ðtemp:= i==len(this.Weights) - 1? this.ð[i+1].Transpose() : this.ð[i+1].MatrixWithoutLastRow().Transpose()
Dw := Matrix.Product(A, ðtemp)
this.Δ[i], _ = Matrix.Sum(this.Δ[i], Dw)
}
return
}
func CorssEntorpy(T, O *Matrix.Matrix) *Matrix.Matrix {
log := func(x complex128) complex128 { return cmplx.Log(x) }
return Matrix.DotMultiplication(T, O.Apply(log))
}
