// calculate the KS and VarD.
func (cm *CMatrix) D() (m float64, v float64) {
mean := desc.NewMean()
variance := desc.NewVariance()
for i := 0; i < len(cm.Matrix); i++ {
d := float64(len(cm.Matrix[i])) / float64(cm.Length)
mean.Increment(d)
variance.Increment(d)
}
m = mean.GetResult()
v = variance.GetResult()
return
}
func New() *MeanVar {
var mv MeanVar
mv.Mean = desc.NewMean()
mv.Var = desc.NewVariance()
return &mv
}
func TestEvolve(t *testing.T) {
// population parameter
var (
numofgens, samplesize int
size, length, fragment int
mutation, transfer float64
pop *SeqPop
ksmean *desc.Mean
ksvar *desc.Variance
expectedKS, tolerance float64
)
samplesize = 100
size = 1000
length = 1000
mutation = 1e-4
fragment = 100
fmt.Println("--Population parameters:")
fmt.Printf("---Size: %d\n", size)
fmt.Printf("---Genome Length: %d\n", length)
fmt.Printf("---Mutation rate: %g\n", mutation)
fmt.Printf("---Transferred fragment: %d\n", fragment)
fmt.Println("--Test no horizontal transfer")
transfer = 0
pop = NewSeqPop(size, length, mutation, transfer, fragment)
ksmean = desc.NewMean()
ksvar = desc.NewVariance()
numofgens = 100
for {
for i := 0; i < numofgens; i++ {
pop.Evolve()
sample := Sample(pop.Genomes, samplesize)
dmatrix := GenerateDistanceMatrix(sample)
cmatrix := covs.NewCMatrix(samplesize, length, dmatrix)
ks, _ := cmatrix.D()
ksmean.Increment(ks)
ksvar.Increment(ks)
}
expectedKS = covs.CalculateKS(size, mutation, transfer, fragment, 4)
tolerance = math.Sqrt(ksvar.GetResult())
if math.Abs(ksmean.GetResult()-expectedKS) > tolerance {
if numofgens < 10000 {
numofgens *= 10
fmt.Printf("try %d generation again ...\n", numofgens)
} else {
t.Errorf("ks = %g, with tolerance = %g, expect %g\n", ksmean.GetResult(), tolerance, expectedKS)
}
} else {
break
}
}
fmt.Println("--Test horizontal transfer with same rate as mutation")
transfer = mutation
pop = NewSeqPop(size, length, mutation, transfer, fragment)
ksmean = desc.NewMean()
ksvar = desc.NewVariance()
numofgens = 100
for {
for i := 0; i < numofgens; i++ {
pop.Evolve()
sample := Sample(pop.Genomes, samplesize)
dmatrix := GenerateDistanceMatrix(sample)
cmatrix := covs.NewCMatrix(samplesize, length, dmatrix)
ks, _ := cmatrix.D()
ksmean.Increment(ks)
ksvar.Increment(ks)
}
expectedKS = covs.CalculateKS(size, mutation, transfer, fragment, 4)
tolerance = math.Sqrt(ksvar.GetResult())
if math.Abs(ksmean.GetResult()-expectedKS) > tolerance {
if numofgens < 10000 {
numofgens *= 10
fmt.Printf("try %d generation again ...\n", numofgens)
} else {
t.Errorf("ks = %g, with tolerance = %g, expect %g\n", ksmean.GetResult(), tolerance, expectedKS)
}
} else {
break
}
}
fmt.Println("--Test horizontal transfer with 100 times of mutation rate")
transfer = mutation * 100.0
pop = NewSeqPop(size, length, mutation, transfer, fragment)
ksmean = desc.NewMean()
ksvar = desc.NewVariance()
numofgens = 100
for {
for i := 0; i < numofgens; i++ {
pop.Evolve()
sample := Sample(pop.Genomes, samplesize)
dmatrix := GenerateDistanceMatrix(sample)
cmatrix := covs.NewCMatrix(samplesize, length, dmatrix)
ks, _ := cmatrix.D()
ksmean.Increment(ks)
ksvar.Increment(ks)
}
expectedKS = covs.CalculateKS(size, mutation, transfer, fragment, 4)
tolerance = math.Sqrt(ksvar.GetResult())
if math.Abs(ksmean.GetResult()-expectedKS) > tolerance {
if numofgens < 10000 {
numofgens *= 10
fmt.Printf("try %d generation again ...\n", numofgens)
} else {
t.Errorf("ks = %g, with tolerance = %g, expect %g\n", ksmean.GetResult(), tolerance, expectedKS)
}
} else {
break
}
}
}