func main() {
// create file storing ks and vard
f, err := os.Create(fname + ".csv")
if err != nil {
panic(err)
}
defer f.Close()
ksarray := []float64{} // store ks
vdarray := []float64{} // store VarD
ngarray := []float64{} // store generation number
// do evolution
for i := 0; i < ngen; i++ {
pop.Evolve()
// we make 10 samples and average
ksmean := desc.NewMean()
vdmean := desc.NewMean()
for j := 0; j < sampleTime; j++ {
sample := fwd.Sample(pop.Genomes, sampleSize)
dmatrix := fwd.GenerateDistanceMatrix(sample)
cmatrix := covs.NewCMatrix(sampleSize, pop.Length, dmatrix)
ks, vard := cmatrix.D()
ksmean.Increment(ks)
vdmean.Increment(vard)
}
// write to csv
f.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ksmean.GetResult(), vdmean.GetResult()))
if (i+1)%1000 == 0 {
fmt.Println("Generation: ", pop.NumOfGen, ksmean.GetResult(), vdmean.GetResult())
}
// store array for draw
ksarray = append(ksarray, ksmean.GetResult())
vdarray = append(vdarray, vdmean.GetResult())
ngarray = append(ngarray, float64(pop.NumOfGen))
}
// draw
svger := render.NewSVG(fname, 1, 2, 800, 200)
pl := chart.ScatterChart{Title: "KS"}
pl.AddDataPair("KS", ngarray, ksarray, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
pl = chart.ScatterChart{Title: "VarD"}
pl.AddDataPair("VarD", ngarray, vdarray, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
svger.Close()
// save population
jf, err := os.Create(fname + ".json")
if err != nil {
panic(err)
}
defer jf.Close()
b, err := json.Marshal(pop)
if err != nil {
panic(err)
}
jf.Write(b)
}
// the converge of KS equalibrium.
func main() {
ksarray := []float64{} // store ks
vdarray := []float64{} // store VarD
ngarray := []float64{} // store generation number
// do evolution
for i := 0; i < ngen; i++ {
pop.Evolve()
// select 10 samples and averge
mean := desc.NewMean()
vmean := desc.NewMean()
ch := make(chan dResult)
num := 10
for j := 0; j < num; j++ {
sample := fwd1.Sample(pop.Genomes, sampleSize)
go calculateD(sample, pop.Length, ch)
}
for j := 0; j < num; j++ {
dr := <-ch
mean.Increment(dr.ks)
vmean.Increment(dr.vard)
}
ksarray = append(ksarray, mean.GetResult())
vdarray = append(vdarray, vmean.GetResult())
ngarray = append(ngarray, float64(pop.NumOfGen))
}
// draw
svger := render.NewSVG(fname, 1, 2, 800, 200)
pl := chart.ScatterChart{Title: "KS"}
pl.AddDataPair("KS", ngarray, ksarray, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
pl = chart.ScatterChart{Title: "VarD"}
pl.AddDataPair("VarD", ngarray, vdarray, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
svger.Close()
}
func main() {
// ks file
ksfilestr := dir + "/" + prex + "_ks.txt"
ksfile, err := os.Create(ksfilestr)
if err != nil {
log.Fatalf("Can not create file: %s, %v", ksfilestr, err)
}
defer ksfile.Close()
ksMean := desc.NewMean()
ksVar := desc.NewVarianceWithBiasCorrection()
vardMean := desc.NewMean()
vardVar := desc.NewVarianceWithBiasCorrection()
scovsMeanArray := make([]*desc.Mean, maxL)
scovsVarArray := make([]*desc.Variance, maxL)
rcovsMeanArray := make([]*desc.Mean, maxL)
rcovsVarArray := make([]*desc.Variance, maxL)
xyPLMeanArray := make([]*desc.Mean, maxL)
xyPLVarArray := make([]*desc.Variance, maxL)
xsysPLMeanArray := make([]*desc.Mean, maxL)
xsysPLVarArray := make([]*desc.Variance, maxL)
smXYPLMeanArray := make([]*desc.Mean, maxL)
smXYPLVarArray := make([]*desc.Variance, maxL)
for i := 0; i < maxL; i++ {
scovsMeanArray[i] = desc.NewMean()
scovsVarArray[i] = desc.NewVarianceWithBiasCorrection()
rcovsMeanArray[i] = desc.NewMean()
rcovsVarArray[i] = desc.NewVarianceWithBiasCorrection()
xyPLMeanArray[i] = desc.NewMean()
xyPLVarArray[i] = desc.NewVarianceWithBiasCorrection()
xsysPLMeanArray[i] = desc.NewMean()
xsysPLVarArray[i] = desc.NewVarianceWithBiasCorrection()
smXYPLMeanArray[i] = desc.NewMean()
smXYPLVarArray[i] = desc.NewVarianceWithBiasCorrection()
}
stepNum := etime / stepSize
for i := 0; i < stepNum; i++ {
// simulate
pop.Evolve(stepSize)
// create distance matrix
dmatrix := pop.DistanceMatrix(sampleSize)
c := covs.NewCMatrix(dmatrix, sampleSize, pop.Length)
// calculate ks
ks := c.CalculateKS()
vard := c.CalculateVarD()
ksfile.WriteString(fmt.Sprintf("%d\t%g\t%g\n", pop.Time, ks, vard))
ksMean.Increment(ks)
ksVar.Increment(ks)
vardMean.Increment(vard)
vardVar.Increment(vard)
log.Printf("KsMean: %g, KsVar: %g, VardMean: %g, VardVar: %g\n", ksMean.GetResult(), ksVar.GetResult(), vardMean.GetResult(), vardVar.GetResult())
// calculate covs
scovs, rcovs, xyPL, xsysPL, smXYPL := c.CalculateCovs(maxL)
for j := 0; j < maxL; j++ {
scovsMeanArray[j].Increment(scovs[j])
scovsVarArray[j].Increment(scovs[j])
rcovsMeanArray[j].Increment(rcovs[j])
rcovsVarArray[j].Increment(rcovs[j])
xyPLMeanArray[j].Increment(xyPL[j])
xyPLVarArray[j].Increment(xyPL[j])
xsysPLMeanArray[j].Increment(xsysPL[j])
xsysPLVarArray[j].Increment(xsysPL[j])
smXYPLMeanArray[j].Increment(smXYPL[j])
smXYPLVarArray[j].Increment(smXYPL[j])
}
svger := render.NewSVG(dir+"/"+prex+"_covs", 1, 5, 800, 200)
scovMeans := make([]float64, maxL-1)
rcovMeans := make([]float64, maxL-1)
xyPLMeans := make([]float64, maxL-1)
xsysPLMeans := make([]float64, maxL-1)
smXYPLMeans := make([]float64, maxL-1)
xs := make([]float64, maxL-1)
for j := 1; j < maxL; j++ {
xs[j-1] = float64(j)
scovMeans[j-1] = scovsMeanArray[j].GetResult()
rcovMeans[j-1] = rcovsMeanArray[j].GetResult()
xyPLMeans[j-1] = xyPLMeanArray[j].GetResult()
xsysPLMeans[j-1] = xsysPLMeanArray[j].GetResult()
smXYPLMeans[j-1] = smXYPLMeanArray[j].GetResult()
}
pl := chart.ScatterChart{Title: "scovs"}
pl.AddDataPair("struc covs", xs, scovMeans, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
pl = chart.ScatterChart{Title: "rcovs"}
pl.AddDataPair("rate covs", xs, rcovMeans, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
pl = chart.ScatterChart{Title: "xyPL"}
pl.AddDataPair("xyPL", xs, xyPLMeans, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
pl = chart.ScatterChart{Title: "xsysPL"}
pl.AddDataPair("xsysPL", xs, xsysPLMeans, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
pl = chart.ScatterChart{Title: "smXYPL"}
pl.AddDataPair("smXYPL", xs, smXYPLMeans, chart.PlotStyleLines, chart.Style{Symbol: '+', SymbolColor: "#0000ff", LineStyle: chart.SolidLine})
svger.Plot(&pl)
svger.Close()
// write to file
covsfilestr := dir + "/" + prex + "_covs.txt"
covsfile, err := os.Create(covsfilestr)
if err != nil {
log.Fatalf("Can not create file: %s, %v", covsfilestr, err)
}
for j := 0; j < maxL; j++ {
covsfile.WriteString(
fmt.Sprintf("%d\t%g\t%g\t%g\t%g\t%g\t%g\t%g\t%g\t%g\t%g\n",
j,
scovsMeanArray[j].GetResult(), scovsVarArray[j].GetResult(),
rcovsMeanArray[j].GetResult(), rcovsVarArray[j].GetResult(),
xyPLMeanArray[j].GetResult(), xyPLVarArray[j].GetResult(),
xsysPLMeanArray[j].GetResult(), xsysPLVarArray[j].GetResult(),
smXYPLMeanArray[j].GetResult(), smXYPLVarArray[j].GetResult(),
),
)
}
covsfile.Close()
}
tnow = time.Now()
log.Printf("Done at %v\n", tnow)
}