gestures

commit a0dd6fde57ce762301d09dc43dae8aee5d2efe8b
parent 9b9182d168f954a5c26d64012680b16534e28265
Author: Stefan Koch <taikano@lavabit.com>
Date:   Sat, 15 Jun 2013 17:30:43 +0200

save training data and train model in one step

Diffstat:
M
models.py
 | 
17
++++++++++-------
M
track.py
 | 
6
++----

2 files changed, 12 insertions(+), 11 deletions(-)
diff --git a/models.py b/models.py
@@ -20,6 +20,8 @@ gestures = [
     ([UP], ['amixer', 'set', 'Master', '10%+']),
 ]
 
+training_data = [[] for gesture in gestures]
+
 # TODO: Zuordnung von Indizes stimmt noch nicht
 def transition_matrix(gesture):
     gestes = list(set(gesture))
@@ -66,15 +68,16 @@ models = []
 sigma = ghmm.IntegerRange(0, 8)
 i = 0
 for gesture in gestures:
-    if not os.path.isfile('/'.join(('models', str(i)))):
-        A = transition_matrix(gesture[0])
-        B = emission_matrix(gesture[0])
-        pi = initial_vector(gesture[0])
-    else:
-        with open('/'.join(('models', str(i)))) as f:
-            (A, B, pi) = simplejson.load(f)
+    A = transition_matrix(gesture[0])
+    B = emission_matrix(gesture[0])
+    pi = initial_vector(gesture[0])
     
     m = ghmm.HMMFromMatrices(sigma, ghmm.DiscreteDistribution(sigma), A, B, pi)
+    
+    if os.path.isfile('/'.join(('models', str(i)))):
+        with open('/'.join(('models', str(i)))) as f:
+            training_data[i] = simplejson.load(f)
+            m.baumWelch(ghmm.SequenceSet(sigma, training_data[i]))
     print(m)
     models.append((m, gesture[1]))
 
diff --git a/track.py b/track.py
@@ -125,15 +125,13 @@ while True:
             if train_mode == False:
                 execute(ghmm.EmissionSequence(models.sigma, path), models.models)
             else:
+                models.training_data[train_target].append(path)
                 print("Training model %d" % (train_target,))
-                train(ghmm.EmissionSequence(models.sigma, path), models.models[train_target][0])
                 train_mode = False
                 print("Leaving training mode")
-                print(models.models[train_target][0])
 
                 with open("/".join(('models', str(train_target))), 'w') as f:
-                    (A, B, pi) = tuple(models.models[train_target][0].asMatrices())
-                    simplejson.dump((A, B, pi), f)
+                    simplejson.dump(models.training_data[train_target], f)
 
         path = []
         not_changed = 0