func PseudoMetric(m markov.MarkovChain, lambda float64, TPSolver func(markov.MarkovChain, *coupling.Node, [][]float64, float64, int, int)) [][]float64 {
// initialize all the sets and the coupling
n := len(m.Transitions)
tocompute := sets.InitToCompute(len(m.Transitions))
visited := sets.MakeMatrix(n)
exact := sets.MakeMatrix(n)
c := coupling.New()
d := InitD(n)
for !sets.EmptySet(tocompute) {
var node *coupling.Node
s, t := extractrandomfromset(tocompute)
s, t = utils.GetMinMax(s, t)
tocompute[s][t], tocompute[t][s] = false, false
log.Printf("Run with node: (%v,%v)", s, t)
if m.Labels[s] != m.Labels[t] {
// s and t have the same label, so we set its distance to 0, and its exact and visited to true
log.Printf("State %v and %v had different labels", s, t)
d[s][t] = 1
exact[s][t] = true
visited[s][t] = true
continue
} else if s == t {
// s and t are the same state, so we set its distance to 1, and its exact and visited to true
log.Printf("State %v %v) was the same state", s, t)
d[s][t] = 0
exact[s][t] = true
visited[s][t] = true
continue
}
// since the pair of states is not the same and share a label, we have to further process it
// try to find the correpsonding node in the coupling
node = coupling.FindNode(s, t, &c)
if node == nil {
// if FindNode returned a nil pointer, the node does not exist, and we have to make it
node = matching.FindFeasibleMatching(m, s, t, &c)
setpair.Setpair(m, node, exact, visited, d, &c)
} else if node.Adj == nil {
// if FindNode did return a node reference, but its adjacency matrix(matching) is nil, we have to create it
node = matching.FindFeasibleMatching(m, s, t, &c)
setpair.Setpair(m, node, exact, visited, d, &c)
}
disc.Disc(lambda, node, exact, d, &c)
updateUntilOptimalSolutionsFound(lambda, m, node, exact, visited, d, c, TPSolver, []*coupling.Node{})
removeExactEdges(node, exact)
// remove everything that has been computed to exact, such that we will not try to solve it again
tocompute = *sets.DifferensReal(&tocompute, &exact)
}
return d
}
func SetUpTest() (Coupling, markov.MarkovChain, [][]bool, [][]bool, [][]float64) {
c := New()
m := SetUpMarkov()
n := len(m.Transitions)
visited := sets.MakeMatrix(n)
exact := sets.MakeMatrix(n)
d := make([][]float64, n, n)
for i := 0; i < n; i++ {
d[i] = make([]float64, n, n)
}
return c, m, visited, exact, d
}
