func Print(xyvec map[float64]float64, xlabel string, ylabel string) []byte {
x := vlib.NewVectorF(len(xyvec))
y := vlib.NewVectorF(len(xyvec))
cnt := 0
for vx, _ := range xyvec {
x[cnt] = vx
cnt++
}
sort.Float64s([]float64(x))
for indx, vx := range x {
y[indx] = xyvec[vx]
}
type temp struct {
SNR vlib.VectorF
BER vlib.VectorF
}
data := temp{x, y}
//keys := []float64(x)
fmt.Printf("\n%s=%1.2e\n %s=%1.2e", xlabel, x, ylabel, y)
result, err := json.Marshal(data)
if err != nil {
return nil
} else {
return result
}
//fmt.Printf("\n%s=%f", xlabel, x)
//fmt.Printf("\n%s=%f", ylabel, y)
}
func CDF(xInput vlib.VectorF) (xOut, yOut vlib.VectorF) {
n := xInput.Size()
str := fmt.Sprintf("%f:%f:1", 1/float64(n), 1/float64(n))
yOut = vlib.ToVectorF(str)
if yOut[yOut.Size()-1] < 1 {
yOut = yOut.Insert(yOut.Size()-1, 1)
}
sort.Float64s([]float64(xInput))
xOut = xInput
// xx := vlib.NewVectorI(n)
for i := 0; i < n-1; i++ {
if xOut[i] == xOut[i+1] {
xOut.Delete(i)
yOut.Delete(i)
i--
n--
}
}
yOut = yOut.Insert(0, 0)
xOut1 := vlib.NewVectorF(2 * n)
yOut1 := vlib.NewVectorF(2 * n)
// fmt.Printf("\n %v \n", yOut)
for i := 0; i < n; i++ {
xOut1[2*i] = xOut[i]
xOut1[2*i+1] = xOut[i]
yOut1[2*i] = yOut[i+1]
yOut1[2*i+1] = yOut[i+1]
}
return xOut1, yOut1
}
func (l *LTECodec) lambda_calc(alpha *matrix.DenseMatrix, beta *matrix.DenseMatrix, gamma *matrix.DenseMatrix, lambda *matrix.DenseMatrix) {
La_0 := [][]int{{0, 0, 0}, {1, 4, 0}, {2, 5, 1}, {3, 1, 1}, {4, 2, 1}, {5, 6, 1}, {6, 7, 0}, {7, 3, 0}}
La_1 := [][]int{{0, 4, 3}, {1, 0, 3}, {2, 1, 2}, {3, 5, 2}, {4, 6, 2}, {5, 2, 2}, {6, 3, 3}, {7, 7, 3}}
temp_0, temp_1 := vlib.NewVectorF(8), vlib.NewVectorF(8)
LEN := alpha.Rows() - 1
for k := 1; k <= LEN; k++ {
for i := 0; i < 8; i++ {
temp_0[i] = alpha.Get(k-1, La_0[i][0]) + beta.Get(k, La_0[i][1]) + gamma.Get(k, La_0[i][2])
temp_1[i] = alpha.Get(k-1, La_1[i][0]) + beta.Get(k, La_1[i][1]) + gamma.Get(k, La_1[i][2])
}
lambda.Set(k, 0, vlib.Min(temp_0))
lambda.Set(k, 1, vlib.Min(temp_1))
}
}
func (l *LTECodec) extrinsic_info(alpha *matrix.DenseMatrix, beta *matrix.DenseMatrix, parllhd []float64, le *matrix.DenseMatrix) {
LE_0 := [][]int{{0, 0, 0}, {1, 4, 0}, {2, 5, 1}, {3, 1, 1}, {4, 2, 1}, {5, 6, 1}, {6, 7, 0}, {7, 3, 0}}
LE_1 := [][]int{{0, 4, 1}, {1, 0, 1}, {2, 1, 0}, {3, 5, 0}, {4, 6, 0}, {5, 2, 0}, {6, 3, 1}, {7, 7, 1}}
LEN := alpha.Rows() - 4
temp_0, temp_1 := vlib.NewVectorF(8), vlib.NewVectorF(8)
for k := 1; k <= LEN; k++ {
for i := 0; i < 8; i++ {
temp_0[i] = alpha.Get(k-1, LE_0[i][0]) + beta.Get(k, LE_0[i][1]) + float64((LE_0[i][2]+1)%2)*parllhd[k]
temp_1[i] = alpha.Get(k-1, LE_1[i][0]) + beta.Get(k, LE_1[i][1]) + float64((LE_1[i][2]+1)%2)*parllhd[k]
}
le.Set(k, 0, vlib.Min(temp_0))
le.Set(k, 1, vlib.Min(temp_1))
}
}
func main() {
log.Println("Reading after init")
go func() {
s := wm.NewSession("HETNET")
for i := 0; i < 10; i++ {
s.Plot(vlib.RandUFVec(10), "holdon", "title=CDF Plot of received signal", "LineWidth=2")
time.Sleep(5 * time.Second)
}
}()
time.Sleep(4 * time.Second)
s := wm.NewSession("Single Cell")
nsamples := 50
x := vlib.NewVectorF(nsamples)
for i := 0; i < nsamples; i++ {
x[i] = float64(i) * 10
}
NPLOTS := 3
for i := 0; i < NPLOTS; i++ {
// s.Plot(vlib.RandUFVec(10), "handle=4", "holdon", "title=CDF Plot of received signal", "style=+", "LineWidth=2")
if i < 2 {
if i == 1 {
s.PlotXY(x, vlib.RandUFVec(nsamples), "handle=4", "holdon", "title=CDF Plot of received signal", "LineWidth=2")
} else {
y := x.Add(5.5)
s.PlotXY(y, vlib.RandUFVec(nsamples), "handle=4", "holdoff", "title=CDF Plot of received signal", "LineWidth=2")
}
} else {
s.Plot(vlib.RandUFVec(nsamples), "handle=3", "holdon", "title=CDF Plot of received signal", "style=+", "LineWidth=2")
}
time.Sleep(4 * time.Second)
}
// wait if someone closes
}
func (l *LTECodec) forward_metric(lgr *matrix.DenseMatrix, la *matrix.DenseMatrix) {
alpha_t := [][]int{{0, 1}, {3, 2}, {4, 5}, {7, 6}, {1, 0}, {2, 3}, {5, 4}, {6, 7}}
br_metric := [][]int{{0, 3}, {1, 2}, {1, 2}, {0, 3}, {0, 3}, {1, 2}, {1, 2}, {0, 3}}
xtemp := vlib.NewVectorF(2)
var min_m float64
LEN := lgr.Rows() - 1
la.Set(0, 0, 0)
for i := 1; i < 8; i++ {
la.Set(0, i, 1e8)
}
for k := 1; k <= LEN; k++ {
for i := 0; i < 8; i++ {
for j := 0; j < 2; j++ {
xtemp[j] = la.Get(k-1, alpha_t[i][j]) + lgr.Get(k, br_metric[i][j])
la.Set(k, i, vlib.Min(xtemp))
}
}
min_m = vlib.Min(la.GetRowVector(k).Array())
for i := 0; i < 8; i++ {
num := 2
la.Set(k, i, la.Get(k, i)-min_m)
if k > LEN-3 {
diff := LEN - k
if i >= (int)(num*diff) {
la.Set(k, i, 1e8)
}
}
}
}
}
func (l *LTECodec) backward_metric(lgr *matrix.DenseMatrix, lb *matrix.DenseMatrix) {
beta_t := [][]int{{0, 4}, {4, 0}, {5, 1}, {1, 5}, {2, 6}, {6, 2}, {7, 3}, {3, 7}}
br_metric := [][]int{{0, 3}, {0, 3}, {1, 2}, {1, 2}, {1, 2}, {1, 2}, {0, 3}, {0, 3}}
temp := vlib.NewVectorF(2)
var min_m float64
LEN := lgr.Rows() - 1
var num int
lb.Set(LEN, 0, 0)
for i := 1; i < 8; i++ {
lb.Set(LEN, i, 1e8)
}
for k := LEN - 1; k >= 0; k-- {
for i := 0; i < 8; i++ {
for j := 0; j < 2; j++ {
temp[j] = lb.Get(k+1, beta_t[i][j]) + lgr.Get(k+1, br_metric[i][j])
}
lb.Set(k, i, vlib.Min(temp))
}
min_m = vlib.Min(lb.GetRowVector(k).Array())
for i := 0; i < 8; i++ {
num = 2
lb.Set(k, i, lb.Get(k, i)-min_m)
if k < 3 {
diff := float64(2 - k)
num = num * int(math.Pow(2, diff))
if i%num != 0 {
lb.Set(k, i, 1e8)
}
}
}
}
}
