--- a/scripts/learn-motion-patterns.py	Fri Jun 23 00:03:17 2017 -0400
+++ b/scripts/learn-motion-patterns.py	Fri Jun 23 23:50:02 2017 -0400
@@ -18,14 +18,25 @@
 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None)
 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
-parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int, default = None)
+parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
+#parser.add_argument('--save-matches', dest = 'saveMatches', help = 'save the matched prototype information', action = 'store_true')
 
 args = parser.parse_args()
 
-# TODO parameters (random init?) and what to learn from: objects, features, longest features from objects
+# use cases
+# 1. learn proto from one file, save in same or another (with traj)
+# 2. load proto, load objects, update proto, save proto
+# 3. assign objects from one db to proto
+# 4. load objects from several files, save in another
+
 # TODO add possibility to cluter with velocities
+# TODO add possibility to start with saved prototypes so that one can incrementally learn from several databases
+# save prototypes with database name, add option to keep trajectory along: if saved in same db, no need
+# load proto must load the movingobject
+# save the objects that match the prototypes
+# write an assignment function for objects
 
 trajectoryType = args.trajectoryType
 prototypeType = args.trajectoryType
@@ -49,7 +60,7 @@
 
 similarities = -np.ones((nTrajectories, nTrajectories))
 
-prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization) # this line can be called again without reinitializing similarities
+prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization, True, None) # this line can be called again without reinitializing similarities
 
 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
 print(clusterSizes)
@@ -59,6 +70,11 @@
 if args.saveSimilarities:
     np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
 
+# if args.saveMatches:
+#     out = storage.openCheck(utils.removeExtension(args.databaseFilename)+'prototypes-matches.csv', 'w')
+#     for o in ojbects:
+#         out.write('')
+
 if args.display:
     from matplotlib.pyplot import figure, show, axis
     figure()
@@ -72,5 +88,3 @@
             objects[i].plot(utils.colors[i]+'o')
     axis('equal')
     show()
-
-# TODO store the prototypes trajectories, add option so store similarities (the most expensive stuff) with limited accuracy