Exemple #1
0
func calculateD(sample []fwd1.Sequence, length int, ch chan dResult) {
	dmatrix := fwd1.GenerateDistanceMatrix(sample)
	cmatrix := covs.NewCMatrix(sampleSize, length, dmatrix)
	ks, vard := cmatrix.D()
	dr := dResult{ks: ks, vard: vard}
	ch <- dr
}
Exemple #2
0
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)
}
Exemple #3
0
func main() {
	// use all cpus
	runtime.GOMAXPROCS(runtime.NumCPU())

	// population paramters
	length := 1000      // genome length = 1000
	mutation := 0.00001 // mutation rate = 1e-5
	transfer := 0.0     // transfer rate = 0
	fragment := 0       // transferred length = 0

	// simulation parameters
	samplesize := 100
	numofgen := 100000

	// population size array
	sizearray := []int{100, 1000, 10000}
	for _, size := range sizearray {
		// population
		pop := fwd.NewSeqPop(size, length, mutation, transfer, fragment)

		// result files
		ksname := fmt.Sprintf("ks_size_%d.csv", size)
		ksfile, err := os.Create(ksname)
		if err != nil {
			panic(err)
		}
		vdname := fmt.Sprintf("vd_size_%d.csv", size)
		vdfile, err := os.Create(vdname)
		if err != nil {
			panic(err)
		}

		// info file
		infoname := fmt.Sprintf("ks_size_%d.txt", size)
		infofile, err := os.Create(infoname)
		if err != nil {
			panic(err)
		}
		infofile.WriteString(fmt.Sprintf("Size = %d\n", pop.Size))
		infofile.WriteString(fmt.Sprintf("Length = %d\n", pop.Length))
		infofile.WriteString(fmt.Sprintf("Mutation = %g\n", pop.Mutation))

		// population evolve
		for i := 0; i < numofgen; i++ {
			pop.Evolve()
			sample := fwd.Sample(pop.Genomes, samplesize)
			dmatrix := fwd.GenerateDistanceMatrix(sample)
			cmatrix := covs.NewCMatrix(samplesize, length, dmatrix)
			ks, vd := cmatrix.D()
			ksfile.WriteString(fmt.Sprintf("%g\n", ks))
			vdfile.WriteString(fmt.Sprintf("%g\n", vd))
		}

		// close files
		ksfile.Close()
		vdfile.Close()
		infofile.Close()
	}
}