mirror of
https://github.com/lightningnetwork/lnd.git
synced 2024-11-19 18:10:34 +01:00
25de66d27b
We create a new type NodeScore which is a tuple (NodeID, score). The weightedChoice and chooseN algorithms are altered to expect this type. This is done in order to simplify the types we are using, since we were only using a subset of the fields in AttachmentDirective.
88 lines
2.2 KiB
Go
88 lines
2.2 KiB
Go
package autopilot
|
|
|
|
import (
|
|
"errors"
|
|
"fmt"
|
|
"math/rand"
|
|
)
|
|
|
|
// ErrNoPositive is returned from weightedChoice when there are no positive
|
|
// weights left to choose from.
|
|
var ErrNoPositive = errors.New("no positive weights left")
|
|
|
|
// weightedChoice draws a random index from the slice of weights, with a
|
|
// probability propotional to the weight at the given index.
|
|
func weightedChoice(w []float64) (int, error) {
|
|
// Calculate the sum of weights.
|
|
var sum float64
|
|
for _, v := range w {
|
|
sum += v
|
|
}
|
|
|
|
if sum <= 0 {
|
|
return 0, ErrNoPositive
|
|
}
|
|
|
|
// Pick a random number in the range [0.0, 1.0) and multiply it with
|
|
// the sum of weights. Then we'll iterate the weights until the number
|
|
// goes below 0. This means that each index is picked with a probablity
|
|
// equal to their normalized score.
|
|
//
|
|
// Example:
|
|
// Items with scores [1, 5, 2, 2]
|
|
// Normalized scores [0.1, 0.5, 0.2, 0.2]
|
|
// Imagine they each occupy a "range" equal to their normalized score
|
|
// in [0, 1.0]:
|
|
// [|-0.1-||-----0.5-----||--0.2--||--0.2--|]
|
|
// The following loop is now equivalent to "hitting" the intervals.
|
|
r := rand.Float64() * sum
|
|
for i := range w {
|
|
r -= w[i]
|
|
if r <= 0 {
|
|
return i, nil
|
|
}
|
|
}
|
|
|
|
return 0, fmt.Errorf("unable to make choice")
|
|
}
|
|
|
|
// chooseN picks at random min[n, len(s)] nodes if from the NodeScore map, with
|
|
// a probability weighted by their score.
|
|
func chooseN(n uint32, s map[NodeID]*NodeScore) (
|
|
map[NodeID]*NodeScore, error) {
|
|
|
|
// Keep track of the number of nodes not yet chosen, in addition to
|
|
// their scores and NodeIDs.
|
|
rem := len(s)
|
|
scores := make([]float64, len(s))
|
|
nodeIDs := make([]NodeID, len(s))
|
|
i := 0
|
|
for k, v := range s {
|
|
scores[i] = v.Score
|
|
nodeIDs[i] = k
|
|
i++
|
|
}
|
|
|
|
// Pick a weighted choice from the remaining nodes as long as there are
|
|
// nodes left, and we haven't already picked n.
|
|
chosen := make(map[NodeID]*NodeScore)
|
|
for len(chosen) < int(n) && rem > 0 {
|
|
choice, err := weightedChoice(scores)
|
|
if err == ErrNoPositive {
|
|
return chosen, nil
|
|
} else if err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
nID := nodeIDs[choice]
|
|
|
|
chosen[nID] = s[nID]
|
|
|
|
// We set the score of the chosen node to 0, so it won't be
|
|
// picked the next iteration.
|
|
scores[choice] = 0
|
|
}
|
|
|
|
return chosen, nil
|
|
}
|