--- a/scripts/learn-motion-patterns.py	Fri Jun 23 23:50:02 2017 -0400
+++ b/scripts/learn-motion-patterns.py	Mon Jun 26 00:10:35 2017 -0400
@@ -17,6 +17,7 @@
 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
 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('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
 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)
 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
@@ -60,7 +61,7 @@
 
 similarities = -np.ones((nTrajectories, nTrajectories))
 
-prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization, True, None) # 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.optimizeCentroid, args.randomInitialization, True, None) # this line can be called again without reinitializing similarities
 
 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
 print(clusterSizes)