Merge pull request #140 from paulz/godep

add Go dependencies by godep

1 files changed, 325 insertions, 0 deletions

diff --git a/vendor/github.com/soniakeys/graph/random.go b/vendor/github.com/soniakeys/graph/random.go
new file mode 100644
index 0000000..99f0445
--- /dev/null
+++ b/vendor/github.com/soniakeys/graph/random.go
@@ -0,0 +1,325 @@
+// Copyright 2016 Sonia Keys
+// License MIT: https://opensource.org/licenses/MIT
+
+package graph
+
+import (
+	"errors"
+	"math"
+	"math/rand"
+	"time"
+)
+
+// Euclidean generates a random simple graph on the Euclidean plane.
+//
+// Nodes are associated with coordinates uniformly distributed on a unit
+// square.  Arcs are added between random nodes with a bias toward connecting
+// nearer nodes.
+//
+// Unfortunately the function has a few "knobs".
+// The returned graph will have order nNodes and arc size nArcs.  The affinity
+// argument controls the bias toward connecting nearer nodes.  The function
+// selects random pairs of nodes as a candidate arc then rejects the candidate
+// if the nodes fail an affinity test.  Also parallel arcs are rejected.
+// As more affine or denser graphs are requested, rejections increase,
+// increasing run time.  The patience argument controls the number of arc
+// rejections allowed before the function gives up and returns an error.
+// Note that higher affinity will require more patience and that some
+// combinations of nNodes and nArcs cannot be achieved with any amount of
+// patience given that the returned graph must be simple.
+//
+// If Rand r is nil, the method creates a new source and generator for
+// one-time use.
+//
+// Returned is a directed simple graph and associated positions indexed by
+// node number.
+//
+// See also LabeledEuclidean.
+func Euclidean(nNodes, nArcs int, affinity float64, patience int, r *rand.Rand) (g Directed, pos []struct{ X, Y float64 }, err error) {
+	a := make(AdjacencyList, nNodes) // graph
+	// generate random positions
+	if r == nil {
+		r = rand.New(rand.NewSource(time.Now().UnixNano()))
+	}
+	pos = make([]struct{ X, Y float64 }, nNodes)
+	for i := range pos {
+		pos[i].X = r.Float64()
+		pos[i].Y = r.Float64()
+	}
+	// arcs
+	var tooFar, dup int
+arc:
+	for i := 0; i < nArcs; {
+		if tooFar == nArcs*patience {
+			err = errors.New("affinity not found")
+			return
+		}
+		if dup == nArcs*patience {
+			err = errors.New("overcrowding")
+			return
+		}
+		n1 := NI(r.Intn(nNodes))
+		var n2 NI
+		for {
+			n2 = NI(r.Intn(nNodes))
+			if n2 != n1 { // no graph loops
+				break
+			}
+		}
+		c1 := &pos[n1]
+		c2 := &pos[n2]
+		dist := math.Hypot(c2.X-c1.X, c2.Y-c1.Y)
+		if dist*affinity > r.ExpFloat64() { // favor near nodes
+			tooFar++
+			continue
+		}
+		for _, nb := range a[n1] {
+			if nb == n2 { // no parallel arcs
+				dup++
+				continue arc
+			}
+		}
+		a[n1] = append(a[n1], n2)
+		i++
+	}
+	g = Directed{a}
+	return
+}
+
+// LabeledEuclidean generates a random simple graph on the Euclidean plane.
+//
+// Arc label values in the returned graph g are indexes into the return value
+// wt.  Wt is the Euclidean distance between the from and to nodes of the arc.
+//
+// Otherwise the function arguments and return values are the same as for
+// function Euclidean.  See Euclidean.
+func LabeledEuclidean(nNodes, nArcs int, affinity float64, patience int, r *rand.Rand) (g LabeledDirected, pos []struct{ X, Y float64 }, wt []float64, err error) {
+	a := make(LabeledAdjacencyList, nNodes) // graph
+	wt = make([]float64, nArcs)             // arc weights
+	// generate random positions
+	if r == nil {
+		r = rand.New(rand.NewSource(time.Now().UnixNano()))
+	}
+	pos = make([]struct{ X, Y float64 }, nNodes)
+	for i := range pos {
+		pos[i].X = r.Float64()
+		pos[i].Y = r.Float64()
+	}
+	// arcs
+	var tooFar, dup int
+arc:
+	for i := 0; i < nArcs; {
+		if tooFar == nArcs*patience {
+			err = errors.New("affinity not found")
+			return
+		}
+		if dup == nArcs*patience {
+			err = errors.New("overcrowding")
+			return
+		}
+		n1 := NI(r.Intn(nNodes))
+		var n2 NI
+		for {
+			n2 = NI(r.Intn(nNodes))
+			if n2 != n1 { // no graph loops
+				break
+			}
+		}
+		c1 := &pos[n1]
+		c2 := &pos[n2]
+		dist := math.Hypot(c2.X-c1.X, c2.Y-c1.Y)
+		if dist*affinity > r.ExpFloat64() { // favor near nodes
+			tooFar++
+			continue
+		}
+		for _, nb := range a[n1] {
+			if nb.To == n2 { // no parallel arcs
+				dup++
+				continue arc
+			}
+		}
+		wt[i] = dist
+		a[n1] = append(a[n1], Half{n2, LI(i)})
+		i++
+	}
+	g = LabeledDirected{a}
+	return
+}
+
+// Geometric generates a random geometric graph (RGG) on the Euclidean plane.
+//
+// An RGG is an undirected simple graph.  Nodes are associated with coordinates
+// uniformly distributed on a unit square.  Edges are added between all nodes
+// falling within a specified distance or radius of each other.
+//
+// The resulting number of edges is somewhat random but asymptotically
+// approaches m = πr²n²/2.   The method accumulates and returns the actual
+// number of edges constructed.
+//
+// If Rand r is nil, the method creates a new source and generator for
+// one-time use.
+//
+// See also LabeledGeometric.
+func Geometric(nNodes int, radius float64, r *rand.Rand) (g Undirected, pos []struct{ X, Y float64 }, m int) {
+	// Expected degree is approximately nπr².
+	a := make(AdjacencyList, nNodes)
+	if r == nil {
+		r = rand.New(rand.NewSource(time.Now().UnixNano()))
+	}
+	pos = make([]struct{ X, Y float64 }, nNodes)
+	for i := range pos {
+		pos[i].X = r.Float64()
+		pos[i].Y = r.Float64()
+	}
+	for u, up := range pos {
+		for v := u + 1; v < len(pos); v++ {
+			vp := pos[v]
+			if math.Hypot(up.X-vp.X, up.Y-vp.Y) < radius {
+				a[u] = append(a[u], NI(v))
+				a[v] = append(a[v], NI(u))
+				m++
+			}
+		}
+	}
+	g = Undirected{a}
+	return
+}
+
+// LabeledGeometric generates a random geometric graph (RGG) on the Euclidean
+// plane.
+//
+// Edge label values in the returned graph g are indexes into the return value
+// wt.  Wt is the Euclidean distance between nodes of the edge.  The graph
+// size m is len(wt).
+//
+// See Geometric for additional description.
+func LabeledGeometric(nNodes int, radius float64, r *rand.Rand) (g LabeledUndirected, pos []struct{ X, Y float64 }, wt []float64) {
+	a := make(LabeledAdjacencyList, nNodes)
+	if r == nil {
+		r = rand.New(rand.NewSource(time.Now().UnixNano()))
+	}
+	pos = make([]struct{ X, Y float64 }, nNodes)
+	for i := range pos {
+		pos[i].X = r.Float64()
+		pos[i].Y = r.Float64()
+	}
+	for u, up := range pos {
+		for v := u + 1; v < len(pos); v++ {
+			vp := pos[v]
+			if w := math.Hypot(up.X-vp.X, up.Y-vp.Y); w < radius {
+				a[u] = append(a[u], Half{NI(v), LI(len(wt))})
+				a[v] = append(a[v], Half{NI(u), LI(len(wt))})
+				wt = append(wt, w)
+			}
+		}
+	}
+	g = LabeledUndirected{a}
+	return
+}
+
+// KroneckerDirected generates a Kronecker-like random directed graph.
+//
+// The returned graph g is simple and has no isolated nodes but is not
+// necessarily fully connected.  The number of of nodes will be <= 2^scale,
+// and will be near 2^scale for typical values of arcFactor, >= 2.
+// ArcFactor * 2^scale arcs are generated, although loops and duplicate arcs
+// are rejected.
+//
+// If Rand r is nil, the method creates a new source and generator for
+// one-time use.
+//
+// Return value ma is the number of arcs retained in the result graph.
+func KroneckerDirected(scale uint, arcFactor float64, r *rand.Rand) (g Directed, ma int) {
+	a, m := kronecker(scale, arcFactor, true, r)
+	return Directed{a}, m
+}
+
+// KroneckerUndirected generates a Kronecker-like random undirected graph.
+//
+// The returned graph g is simple and has no isolated nodes but is not
+// necessarily fully connected.  The number of of nodes will be <= 2^scale,
+// and will be near 2^scale for typical values of edgeFactor, >= 2.
+// EdgeFactor * 2^scale edges are generated, although loops and duplicate edges
+// are rejected.
+//
+// If Rand r is nil, the method creates a new source and generator for
+// one-time use.
+//
+// Return value m is the true number of edges--not arcs--retained in the result
+// graph.
+func KroneckerUndirected(scale uint, edgeFactor float64, r *rand.Rand) (g Undirected, m int) {
+	al, s := kronecker(scale, edgeFactor, false, r)
+	return Undirected{al}, s
+}
+
+// Styled after the Graph500 example code.  Not well tested currently.
+// Graph500 example generates undirected only.  No idea if the directed variant
+// here is meaningful or not.
+//
+// note mma returns arc size ma for dir=true, but returns size m for dir=false
+func kronecker(scale uint, edgeFactor float64, dir bool, r *rand.Rand) (g AdjacencyList, mma int) {
+	if r == nil {
+		r = rand.New(rand.NewSource(time.Now().UnixNano()))
+	}
+	N := NI(1 << scale)                  // node extent
+	M := int(edgeFactor*float64(N) + .5) // number of arcs/edges to generate
+	a, b, c := 0.57, 0.19, 0.19          // initiator probabilities
+	ab := a + b
+	cNorm := c / (1 - ab)
+	aNorm := a / ab
+	ij := make([][2]NI, M)
+	var bm Bits
+	var nNodes int
+	for k := range ij {
+		var i, j NI
+		for b := NI(1); b < N; b <<= 1 {
+			if r.Float64() > ab {
+				i |= b
+				if r.Float64() > cNorm {
+					j |= b
+				}
+			} else if r.Float64() > aNorm {
+				j |= b
+			}
+		}
+		if bm.Bit(i) == 0 {
+			bm.SetBit(i, 1)
+			nNodes++
+		}
+		if bm.Bit(j) == 0 {
+			bm.SetBit(j, 1)
+			nNodes++
+		}
+		r := r.Intn(k + 1) // shuffle edges as they are generated
+		ij[k] = ij[r]
+		ij[r] = [2]NI{i, j}
+	}
+	p := r.Perm(nNodes) // mapping to shuffle IDs of non-isolated nodes
+	px := 0
+	rn := make([]NI, N)
+	for i := range rn {
+		if bm.Bit(NI(i)) == 1 {
+			rn[i] = NI(p[px]) // fill lookup table
+			px++
+		}
+	}
+	g = make(AdjacencyList, nNodes)
+ij:
+	for _, e := range ij {
+		if e[0] == e[1] {
+			continue // skip loops
+		}
+		ri, rj := rn[e[0]], rn[e[1]]
+		for _, nb := range g[ri] {
+			if nb == rj {
+				continue ij // skip parallel edges
+			}
+		}
+		g[ri] = append(g[ri], rj)
+		mma++
+		if !dir {
+			g[rj] = append(g[rj], ri)
+		}
+	}
+	return
+}