autopilot/agent: add weightedChoice and chooseN algorithm

The algorithms will be used to select nodes at random from the weighted
distribution set by the node's scores given by the heuristic.

autopilot/agent.go

@@ -2,9 +2,11 @@ package autopilot
 
 import (
 	"fmt"
+	"math/rand"
 	"net"
 	"sync"
 	"sync/atomic"
+	"time"
 
 	"github.com/btcsuite/btcd/btcec"
 	"github.com/btcsuite/btcutil"
@@ -197,6 +199,7 @@ func (a *Agent) Start() error {
 		return nil
 	}
 
+	rand.Seed(time.Now().Unix())
 	log.Infof("Autopilot Agent starting")
 
 	a.wg.Add(1)
@@ -362,6 +365,73 @@ func mergeChanState(pendingChans map[NodeID]Channel,
 	return totalChans
 }
 
+// weightedChoice draws a random index from the map of channel candidates, with
+// a probability propotional to their score.
+func weightedChoice(s map[NodeID]*AttachmentDirective) (NodeID, error) {
+	// Calculate the sum of scores found in the map.
+	var sum float64
+	for _, v := range s {
+		sum += v.Score
+	}
+
+	if sum <= 0 {
+		return NodeID{}, fmt.Errorf("non-positive sum")
+	}
+
+	// Create a map of normalized scores such, that they sum to 1.0.
+	norm := make(map[NodeID]float64)
+	for k, v := range s {
+		norm[k] = v.Score / sum
+	}
+
+	// Pick a random number in the range [0.0, 1.0), and iterate the map
+	// 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()
+	for k, v := range norm {
+		r -= v
+		if r <= 0 {
+			return k, nil
+		}
+	}
+	return NodeID{}, fmt.Errorf("no choice made")
+}
+
+// chooseN picks at random min[n, len(s)] nodes if from the
+// AttachmentDirectives map, with a probability weighted by their score.
+func chooseN(n int, s map[NodeID]*AttachmentDirective) (
+	map[NodeID]*AttachmentDirective, error) {
+
+	// Keep a map of nodes not yet choosen.
+	rem := make(map[NodeID]*AttachmentDirective)
+	for k, v := range s {
+		rem[k] = v
+	}
+
+	// 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]*AttachmentDirective)
+	for len(chosen) < n && len(rem) > 0 {
+		choice, err := weightedChoice(rem)
+		if err != nil {
+			return nil, err
+		}
+
+		chosen[choice] = rem[choice]
+		delete(rem, choice)
+	}
+
+	return chosen, nil
+}
+
 // controller implements the closed-loop control system of the Agent. The
 // controller will make a decision w.r.t channel placement within the graph
 // based on: its current internal state of the set of active channels open,