func (d *ESHyperNEAT) pruneAndExtract(cppn neat.Network, t int, a []float64, p *espoint, outgoing bool) (conns esconns, err error) {
for _, c := range p.children {
if variance(c) > d.VarianceThreshold() {
var con2 esconns
con2, err = d.pruneAndExtract(cppn, t, a, c, outgoing)
if err != nil {
return
}
conns = append(conns, con2...)
} else {
max := 0.0
// Determine if point is in a band by checking neighbor CPPN values
var outputs []float64
for i := 0; i < d.dims; i++ {
min := math.Inf(1)
for j := 0; j < 2; j++ {
x := c.position[i]
if j == 0 {
c.position[i] -= p.width
} else {
c.position[i] += p.width
}
if outgoing {
outputs, err = cppn.Activate(append(a, append(c.position, 1.0)...))
} else {
outputs, err = cppn.Activate(append(c.position, append(a, 0.0)...))
}
if err != nil {
return
}
if min > outputs[t] {
min = outputs[t]
}
c.position[i] = x
}
if max < min {
max = min
}
}
if max > d.BandThreshold() {
// Create new connection specified by source, target, weight
// and scale weight based on weight range
var conn *esconn
if outgoing {
conn = &esconn{source: a, target: c.position}
} else {
conn = &esconn{source: c.position, target: a}
}
if !conns.contains(conn) {
conn.weight = c.weight * d.WeightRange()
conns = append(conns, conn)
}
}
}
}
return
}
func (d *ESHyperNEAT) divAndInit(cppn neat.Network, t int, a []float64, outgoing bool) (root *espoint, err error) {
root = &espoint{
position: make([]float64, d.dims),
width: 1,
level: 1,
children: make([]*espoint, len(d.divs)),
}
q := make([]*espoint, 0, 16)
q = append(q, root)
for len(q) > 0 {
// Dequeue the next item
p := q[0]
q[0] = nil
q = q[1:]
// Divide into subregions
for i := 0; i < len(p.children); i++ {
c := &espoint{
position: make([]float64, len(p.position)),
width: p.width / 2.0,
level: p.level + 1,
}
for j := 0; j < d.dims; j++ {
c.position[j] = p.position[j] + c.width*d.divs[i][j]
}
p.children[i+0] = c
}
// Process the children
for _, c := range p.children {
var outputs []float64
if outgoing {
outputs, err = cppn.Activate(append(a, append(c.position, 1.0)...))
} else {
outputs, err = cppn.Activate(append(c.position, append(a, 0.0)...))
}
if err != nil {
return
}
c.weight = outputs[t]
}
// Divide until intial resolution or if variance is still high
if p.level < d.InitialDepth() || (p.level < d.MaxDepth() && variance(p) > d.DivisionThreshold()) {
for i, _ := range p.children {
q = append(q, p.children[i])
}
}
}
return
}
