parameters

[imago.git] / src / ransac.py

diff --git a/src/ransac.py b/src/ransac.py
index 5d5d218..6557ea4 100644 (file)
--- a/src/ransac.py
+++ b/src/ransac.py
@@ -48,23 +48,27 @@ class Linear_model:
             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()
     estimate = model.get(consensual)
-    new_score, consensual = model.score(estimate, distance)
-    while (new_score < score):
-        score = new_score
-        try:
-            estimate = model.get(consensual)
-        except NP.linalg.LinAlgError:
-            pass
-        estimate = model.get(consensual)
-        new_score, consensual = model.score(estimate, distance)
+    new_score, new_consensual = model.score(estimate, distance)
+    if new_consensual != []:
+        while (new_score < score):
+            score, consensual = new_score, new_consensual
+            try:
+                estimate = model.get(consensual)
+                new_score, new_consensual = model.score(estimate, distance)
+            except (NP.linalg.LinAlgError):
+                pass
     return score, estimate, consensual
         
-def estimate(data, dist, k, modelClass=Linear_model):
-    model = modelClass(data)
+def estimate(data, dist, k, modelClass=Linear_model, model=None):
+    if not model:
+        model = modelClass(data)
     best = float("inf")
     estimate = None
     consensual = None
@@ -77,9 +81,13 @@ def estimate(data, dist, k, modelClass=Linear_model):
 
     return estimate, consensual
 
-def ransac_duo(data, dist, k, mk, modelClass=Linear_model):
+def ransac_multi(m, data, dist, k, modelClass=Linear_model, model=None):
+    if not model:
+        model = modelClass(data)
+    ests = []
     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)
-
+    for i in xrange(m):
+        est, cons_new = estimate(None, dist, k, model=model)
+        model.remove(cons_new)
+        ests.append((est, cons_new))
+    return ests