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 (this *TrainingSet) sumParameters(i0, i1 int, Res **Matrix.Matrix, done chan<- bool) {
di := i1 - i0
if di >= THRESHOLD {
done2 := make(chan bool, THRESHOLD)
mi := i0 + di/2
res1 := Matrix.NullMatrixP(1, this.Xs.GetNColumns())
res2 := Matrix.NullMatrixP(1, this.Xs.GetNColumns())
go this.sumParameters(i0, mi, &res1, done2)
go this.sumParameters(mi, i1, &res2, done2)
<-done2
<-done2
SP, _ := Matrix.Sum(res1, res2)
*Res = SP
} else {
for i := i0; i <= i1; i++ {
xsi := this.Xs.GetRow(i)
SP, _ := Matrix.Sum(*Res, xsi)
*Res = SP
}
}
done <- true
}
func FFT_ct(this *Matrix.Matrix, N, skip int, tf *[]complex128) *Matrix.Matrix {
Xr := Matrix.NullMatrixP(N, this.GetNColumns())
RowTemp := Matrix.NullMatrixP(1, this.GetNColumns())
FFT_aux(this, Xr, RowTemp, N, skip, tf)
return Xr
}
func (this *Hypothesis) Parallel_DiffH1Ys(Ts *TrainingSet) (*Matrix.Matrix, *Matrix.Matrix) {
m := Ts.Xs.GetMRows()
hx := Matrix.NullMatrixP(m, 1)
hxt := Matrix.NullMatrixP(1, m)
if this.ThetaParameters.GetNColumns() == Ts.Xs.GetNColumns() {
done := make(chan bool)
go this.part_DiffH1Ys(1, m, Ts, hx, hxt, done)
<-done
}
return hx, hxt
}
func (this *TrainingSet) Variance() (V, Sustract, Mean *Matrix.Matrix) {
mean := this.Mean()
sum := Matrix.NullMatrixP(1, this.Xs.GetNColumns())
sustract := Matrix.NullMatrixP(this.Xs.GetMRows(), this.Xs.GetNColumns())
done := make(chan bool)
this.Variance_sum(1, this.Xs.GetMRows(), mean, &sum, sustract, done)
<-done
return sum.Scalar(1 / (complex(float64(this.Xs.GetMRows()), 0) - 1.0)), sustract, mean
}
func FFT_ct2(this *Matrix.Matrix, N, skip int, tf *[]complex128) *Matrix.Matrix {
Xr := Matrix.NullMatrixP(N, this.GetNColumns())
Scratch := Matrix.NullMatrixP(N, this.GetNColumns())
var E, D, Xp, Xstart *Matrix.Matrix
var evenIteration bool
if N%2 == 0 {
evenIteration = true
} else {
evenIteration = false
}
if N == 1 {
Xr.SetRow(1, this.GetReferenceRow(1))
}
E = this
for n := 1; n < N; n *= 2 {
if evenIteration {
Xstart = Scratch
} else {
Xstart = Xr
}
skip := N / (2 * n)
Xp = Xstart
for k := 0; k != n; k++ {
for m := 0; m != skip; m++ {
D = E.MatrixWithoutFirstRows(skip)
D.ScalarRow(1, (*tf)[skip*k])
sr, rr, _ := Matrix.Sum_Sustract(E.GetReferenceRow(1), D.GetReferenceRow(1))
Xp.SetRow(1, sr)
Xp.SetRow(N/2+1, rr)
Xp = Xp.MatrixWithoutFirstRows(1)
E = E.MatrixWithoutFirstRows(1)
}
E = E.MatrixWithoutFirstRows(skip)
}
E = Xstart
evenIteration = !evenIteration
}
return Scratch
}
func (this *TrainingSet) Mean() *Matrix.Matrix {
sum := Matrix.NullMatrixP(1, this.Xs.GetNColumns())
done := make(chan bool)
go this.sumParameters(1, this.Xs.GetMRows(), &sum, done)
<-done
return sum.Scalar(1.0 / (complex(float64(this.Xs.GetMRows()), 0.0)))
}
func (this *Hypothesis) Evaluate(x *Matrix.Matrix) (complex128, error) {
x0 := Matrix.NullMatrixP(1, 1)
x0.SetValue(1, 1, 1)
x0 = x0.AddColumn(x)
if x0.GetNColumns() == this.ThetaParameters.GetNColumns() {
xt := x0.Transpose()
res := Matrix.Product(this.ThetaParameters, xt)
return this.H(res.GetValue(1, 1)), nil
}
return 0, errors.New(" The number of parameters is not equal to the parameters of the hypotesis")
}
func (this *Hypothesis) DiffH1Ys(Ts TrainingSet) *Matrix.Matrix {
m := Ts.Xs.GetMRows()
hx := Matrix.NullMatrixP(m, 1)
if this.ThetaParameters.GetNColumns() == Ts.Xs.GetNColumns() {
for i := 1; i <= Ts.Xs.GetMRows(); i++ {
xi := Ts.Xs.GetRow(i)
Thi := Matrix.Product(xi, this.ThetaParameters.Transpose())
hx.SetValue(i, 1, Thi.GetValue(1, 1)-Ts.Y.GetValue(1, i))
}
return hx
}
return nil
}
func CreateANN(Inputs int, NeuronsByLayer []int, Act func(*Matrix.Matrix) *Matrix.Matrix, Derivate func(*Matrix.Matrix) *Matrix.Matrix, Cost func(*Matrix.Matrix, *Matrix.Matrix) *Matrix.Matrix, DCost func(*Matrix.Matrix, *Matrix.Matrix) *Matrix.Matrix, path string) ANN {
var out ANN
out.Weights = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.BestWeightsFound = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.LearningRates = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.Δ = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.Δ1 = make([]*Matrix.Matrix, len(NeuronsByLayer), len(NeuronsByLayer))
out.ð = make([]*Matrix.Matrix, len(NeuronsByLayer)+1, len(NeuronsByLayer)+1)
out.Inputs = Inputs
out.Outputs = NeuronsByLayer[len(NeuronsByLayer)-1]
out.ActivationLayer = Act
out.DarivateActivationLayer = Derivate
out.CostFunction = Cost
out.DerviateCostFunction = DCost
out.PathWeightsInCSV = path
m := Inputs
for i := 0; i < (len(NeuronsByLayer)); i++ {
n := NeuronsByLayer[i]
// one row extra for Bias weights, we need to change to random values for this matrixes
//temp := Matrix.RandomRealMatrix(m+1, n)
out.Weights[i] = Matrix.RandomRealMatrix(m+1, n, 1.2)
out.BestWeightsFound[i] = Matrix.NullMatrixP(m+1, n)
out.LearningRates[i] = Matrix.FixValueMatrix(m+1, n, 0.0001)
//tempdelta := Matrix.NullMatrix(m+1, n)
out.ð[i] = Matrix.NullMatrix(m+1, n)
out.Δ[i] = Matrix.NullMatrixP(m+1, n)
out.Δ1[i] = Matrix.NullMatrixP(m+1, n)
m = n
}
out.AcumatedError = Matrix.NullMatrixP(m, 1)
out.MinimumErrorFound = Matrix.NullMatrixP(m, 1)
out.AcumatedError1 = Matrix.NullMatrixP(m, 1)
return out
}
func GradientDescent(alpha complex128, Tolerance complex128, ts *TrainingSet, f func(x complex128) complex128) *Hypothesis {
n := ts.Xs.GetNColumns()
m := ts.Xs.GetMRows()
//Xsc:=ts.Xs.Copy()
ts.AddX0() // add the parametrer x0, with value 1, to all elements of the training set
t := Matrix.NullMatrixP(1, n+1) // put 0 to the parameters theta
thetaP := t
//thetaP:=Matrix.RandomMatrix(1,n+1) // Generates a random values of parameters theta
var h1 Hypothesis
h1.H = f
h1.ThetaParameters = thetaP
var Error complex128
Error = complex(1.0, 0)
var it = 1
diferencia, diferenciaT := h1.Parallel_DiffH1Ys(ts)
jt := Matrix.Product(diferenciaT, diferencia).Scalar(1/complex(2.0*float64(m), 0.0)).GetValue(1, 1)
alpha = 1 / jt
for cmplx.Abs(Error) >= cmplx.Abs(Tolerance) { // Until converges
ThetaPB := h1.ThetaParameters.Copy() //for Error Calc
//diff:=h1.DiffH1Ys(ts)
_, diffT := h1.Parallel_DiffH1Ys(ts) //h(x)-y
product := Matrix.Product(diffT, ts.Xs) //Sum( (hi(xi)-yi)*xij) in matrix form
h1.Sum = product
alpha_it := alpha / (cmplx.Sqrt(complex(float64(it), 0.0))) // re-calc alpha
scalar := product.Scalar(-alpha_it / complex(float64(m), 0.0))
//println("Delta", scalar.ToString())
ThetaTemp, _ := Matrix.Sum(h1.ThetaParameters, scalar) //Theas=Theas-alfa/m*Sum( (hi(xi)-yi)*xij) update the parameters
h1.ThetaParameters = ThetaTemp
diffError, _ := Matrix.Sustract(ThetaPB, h1.ThetaParameters) //diff between theta's Vector , calc the error
Error = complex(diffError.FrobeniusNorm(), 0) //Frobenius Norm
//Error=diffError.InfinityNorm() //Infinty Norm
//println("->", h1.ThetaParameters.ToString())
//println("Error", Error)
/*if it > 10 {
break
}*/
it++
}
h1.M = m
return &h1
}
func FFT_ct3(this *Matrix.Matrix, N, skip int, tf *[]complex128) *Matrix.Matrix {
Xr := Matrix.NullMatrixP(N, this.GetNColumns())
Scratch := Matrix.NullMatrixP(N, this.GetNColumns())
var E, D, Xp, Xstart *Matrix.Matrix
var evenIteration bool
if N%2 == 0 {
evenIteration = true
} else {
evenIteration = false
}
if N == 1 {
Xr.SetRow(1, this.GetReferenceRow(1))
}
E = this
for n := 1; n < N; n *= 2 {
if evenIteration {
Xstart = Scratch
} else {
Xstart = Xr
}
skip := N / (2 * n)
Xp = Xstart
for k := 0; k != n; k++ {
var Aux = func(m0, m1 int, Xp, E, D *Matrix.Matrix) {
println("-", m0)
Xp = Xp.MatrixWithoutFirstRows(m0)
E = E.MatrixWithoutFirstRows(m0)
//D = E.MatrixWithoutFirstRows(skip)
for m := m0; m < m1; m++ {
D = E.MatrixWithoutFirstRows(skip)
D.ScalarRow(1, (*tf)[skip*k])
sr, rr, _ := Matrix.Sum_Sustract(E.GetReferenceRow(1), D.GetReferenceRow(1))
Xp.SetRow(1, sr)
Xp.SetRow(N/2+1, rr)
Xp = Xp.MatrixWithoutFirstRows(1)
println("E", E.ToString())
E = E.MatrixWithoutFirstRows(1)
}
}
mm := skip / 2
m0 := 0
//m1 := skip
go Aux(m0, mm, Xp, E, D)
//println("->E", E.ToString(), ">XP", Xp.ToString())
//go Aux(mm, m1, Xp, E, D)
//for m := 0; m != skip; m++ {
// D = E.MatrixWithoutFirstRows(skip)
// D.ScalarRow(1, (*tf)[skip*k])
// sr, rr, _ := Matrix.Sum_Sustract(E.GetReferenceRow(1), D.GetReferenceRow(1))
// Xp.SetRow(1, sr)
// Xp.SetRow(N/2+1, rr)
// Xp = Xp.MatrixWithoutFirstRows(1)
// E = E.MatrixWithoutFirstRows(1)
//}
E = E.MatrixWithoutFirstRows(skip)
}
E = Xstart
evenIteration = !evenIteration
}
return Scratch
}