Modify interfaces to compile
diff --git a/graph/graphs/gen/gen.go b/graph/graphs/gen/gen.go
index 0969b41..cb45d25 100644
--- a/graph/graphs/gen/gen.go
+++ b/graph/graphs/gen/gen.go
@@ -21,6 +21,9 @@
DirectedBuilder
graph.EdgeRemover
Edge(from, to graph.Node) graph.Edge
+ From(graph.Node) []graph.Node
+ Nodes() []graph.Node
+ To(graph.Node) []graph.Node
}
func abs(a int) int {
diff --git a/graphs/gen/uniformgraph.go b/graphs/gen/uniformgraph.go
index d75f4ce..a7b1059 100644
--- a/graphs/gen/uniformgraph.go
+++ b/graphs/gen/uniformgraph.go
@@ -14,8 +14,73 @@
)
type UniformGenerator interface {
+ // Modify adjusts the graph based on the proposed edge. See the documentation
+ // for UniformEdges to see the required properties of Modify.
Modify(g GraphBuilder, from, to graph.Node)
- Start() graph.Directed
+ // DefaultStart provides a default starting graph for the class if no graph
+ // has been provided.
+ DefaultStart() graph.Directed
+}
+
+// UniformEdges generates the edges of a directed graph uniformly at random across
+// the class of graphs specified by class.
+//
+// UniformEdges runs a Markov chain starting with the graph specified by start
+// and running a number of steps equal to steps. Each step in the markov chain
+// proposes an edge from from to to. The class function modifies the graph
+// according to the generation procedure. If start is nil, the initial graph
+// will be that specified in class.DefaultStart().
+//
+// class cannot be an arbitrary function, it must obey specific properties in
+// order for the generation probability to be uniform over the class of possible
+// graphs. class must ensure that the implicit Markov chain is irreducible,
+// aperiodic, and, importantly, doubly stochastic. Furthermore, every graph
+// in the desired class must be reachable from the starting graph through
+// individual edge modifications.
+//
+// step specifies the number of steps in the Markov chain. step should be
+// sufficiently high to allow for convergence of the chain.
+//
+// src specifies the random number generator. If src is nil, math/rand is used.
+//
+// More information can be found in
+// Ide, Jaime S., and Fabio G. Cozman. "Random generation of Bayesian networks."
+// Brazilian symposium on artificial intelligence. Springer Berlin Heidelberg, 2002.
+func UniformEdges(dst DirectedMutator, start graph.Directed, class UniformGenerator, steps int, src *rand.Rand) {
+ if steps <= 0 {
+ panic("gen: number of steps must be positive")
+ }
+ if start == nil {
+ start = class.DefaultStart()
+ }
+ var rnd func(int) int
+ if src == nil {
+ rnd = rand.Intn
+ } else {
+ rnd = src.Intn
+ }
+
+ // Copy the starting graph into the graph builder.
+ nodes := start.Nodes()
+ for _, node := range nodes {
+ dst.AddNode(node)
+ tos := start.From(node)
+ for _, to := range tos {
+ edge := start.Edge(node, to)
+ dst.SetEdge(edge)
+ }
+ }
+ nNodes := len(nodes)
+
+ // Run the MarkovChain, choosing an edge at random in each step.
+ for step := 0; step < steps; step++ {
+ i := rnd(nNodes)
+ j := rnd(nNodes - 1)
+ if j >= i {
+ j++
+ }
+ class.Modify(dst, nodes[i], nodes[j])
+ }
}
// UniformDAG generates a graph uniformly at random over the set of directed
@@ -27,7 +92,7 @@
EdgeGen func(from, to graph.Node) graph.Edge
}
-func (u UniformDAG) Start() graph.Directed {
+func (u UniformDAG) DefaultStart() graph.Directed {
if u.N == 0 {
panic("uniformdag: number of edges must be positive")
}
@@ -70,63 +135,3 @@
}
}
}
-
-// UniformEdges generates the edges of a graph uniformly at random across the
-// class of graphs specified by class.
-//
-// UniformEdges runs a Markov chain starting with the graph specified by start
-// and running a number of steps equal to steps. Each step in the markov chain
-// proposes an edge from from to to. The class function modifies the graph
-// according to the generation procedure. If start is nil, the initial graph
-// will be that specified in class.DefaultStart().
-//
-// class cannot be an arbitrary function, it must obey specific properties in
-// order for the generation probability to be uniform over the class of possible
-// graphs. class must ensure that the implicit Markov chain is irreducible,
-// aperiodic, and, importantly, doubly stochastic. Furthermore, it must be that
-// the starting graph
-//
-// step specifies the number of steps in the Markov chain. step should be
-// sufficiently high to allow for convergence of the Markov chain.
-//
-// src specifies the random number generator. If src is nil, math/rand is used.
-//
-// More information can be found in
-// Ide, Jaime S., and Fabio G. Cozman. "Random generation of Bayesian networks."
-// Brazilian symposium on artificial intelligence. Springer Berlin Heidelberg, 2002.
-func UniformEdges(dst DirectedMutator, start graph.Directed, class UniformGenerator, steps int, src *rand.Rand) {
- if steps <= 0 {
- panic("gen: number of steps must be positive")
- }
- if start == nil {
- start = class.Start()
- }
- var rnd func(int) int
- if src == nil {
- rnd = rand.Intn
- } else {
- rnd = src.Intn
- }
-
- // Copy the starting graph into the graph builder.
- nodes := start.Nodes()
- for _, node := range nodes {
- dst.AddNode(node)
- tos := start.From(node)
- for _, to := range tos {
- edge := start.Edge(node, to)
- dst.SetEdge(edge)
- }
- }
- nNodes := len(nodes)
-
- // Run the MarkovChain, choosing an edge at random in each step.
- for step := 0; step < steps; step++ {
- i := rand.Intn(nNodes)
- j := rand.Intn(nNodes - 1)
- if j >= i {
- j++
- }
- class.Modify(dst, nodes[i], nodes[j])
- }
-}
