// Calculate covariance of rates.
func (cmd *cmdCr) calcCr(pis []Pi, profile []profiling.Pos, posType byte, maxl int) (covs []Covariance) {
corrs := make([]Covariance, maxl)
for i := 0; i < maxl; i++ {
corrs[i] = correlation.NewBivariateCovariance(false)
}
for i := 0; i < len(pis); i++ {
p1 := pis[i]
pos1 := profile[p1.Pos]
if checkPosType(posType, pos1.Type) {
for j := i; j < len(pis); j++ {
p2 := pis[j]
pos2 := profile[p2.Pos]
distance := p2.Pos - p1.Pos
if distance < 0 {
distance = -distance
}
if distance >= maxl {
break
}
if checkPosType(posType, pos2.Type) {
x, y := p1.Pi(), p2.Pi()
corrs[distance].Increment(x, y)
}
}
}
}
covs = corrs
return
}
// NewCovariances create a new Covariances
func NewCovariances(maxl int) *Covariances {
cc := Covariances{}
for i := 0; i < maxl; i++ {
bc := correlation.NewBivariateCovariance(false)
cc.corrs = append(cc.corrs, bc)
}
return &cc
}
// Increment add x and y arrays, which separate at distance l.
func (c *Calculator) Increment(xArr, yArr []float64, l int) {
if l < c.MaxL {
// calculate covariance of x and y.
cov := correlation.NewBivariateCovariance(false)
for i := range xArr {
x, y := xArr[i], yArr[i]
cov.Increment(x, y)
}
c.Cs.Increment(l, cov.GetResult())
c.Ct.AppendAt(l, cov)
c.Cr.Increment(l, cov.MeanX(), cov.MeanY())
}
}