--- a/scripts/learn-motion-patterns.py	Thu Jul 05 17:45:18 2018 -0400
+++ b/scripts/learn-motion-patterns.py	Thu Jul 05 23:12:11 2018 -0400
@@ -14,7 +14,7 @@
 parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to process', choices = ['feature', 'object'], default = 'feature')
 parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int)
-parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
+parser.add_argument('-n', dest = 'nObjects', help = 'number of the object or feature trajectories to load', type = int, default = None)
 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
 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)
@@ -44,7 +44,7 @@
     print('Cannot learn and assign simultaneously')
     sys.exit(0)
 
-objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nTrajectories, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject)
+objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject)
 if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
     objectsWithFeatures = objects
     objects = [f for o in objectsWithFeatures for f in o.getFeatures()]