score += min(d, dist)
return score, cons
+ def remove(self, data):
+ self.data = list(set(self.data) - set(data))
+
def iterate(model, distance):
score = float("inf")
consensual = model.initial()
return estimate, consensual
-def ransac_duo(data, dist, k, mk, modelClass=Linear_model):
- cons = []
- for i in xrange(mk):
- model, cons = estimate(set(data) - set(cons), dist, k, modelClass)
- return (model, cons), estimate(set(data) - set(cons), dist, k, modelClass)
-
def ransac_multi(m, data, dist, k, modelClass=Linear_model, model=None):
+ if not model:
+ model = modelClass(data)
ests = []
cons = []
for i in xrange(m):
est, cons_new = estimate(None, dist, k, model=model)
model.remove(cons_new)
- ests.append(est)
+ ests.append((est, cons_new))
return ests
-
-
-