--- a/scripts/learn-motion-patterns.py	Wed Jul 05 16:30:04 2017 -0400
+++ b/scripts/learn-motion-patterns.py	Wed Jul 05 18:01:43 2017 -0400
@@ -5,12 +5,13 @@
 #import matplotlib.pyplot as plt
 import numpy as np
 
-import ml, utils, storage
+import ml, utils, storage, moving
 
 parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = ''
 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
-parser.add_argument('-r', dest = 'initialPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
+parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
+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 learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
@@ -34,7 +35,7 @@
 # 4. load objects from several files, save in another -> see metadata: site with view and times
 # 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index)
 
-# TODO add possibility to cluter with velocities
+# TODO add possibility to clutesr with velocities
 # TODO add possibility to start with saved prototypes so that one can incrementally learn from several databases
 # save the objects that match the prototypes
 # write an assignment function for objects
@@ -54,6 +55,9 @@
 else:
     objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
 
+if args.inputPrototypeDatabaseFilename is not None:
+    prototypeIndices, dbFilenames, trajectoryTypes, nMatchings, prototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
+    
 trajectories = [o.getPositions().asArray().T for o in objects]
 
 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
@@ -66,8 +70,13 @@
 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
 print(clusterSizes)
 
-prototypes = [objects[i] for i in prototypeIndices]
-storage.savePrototypesToSqlite(args.databaseFilename, [p.getNum() for p in prototypes], prototypeType, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices]
+
+prototypes = [moving.Prototype(objects[i].getNum(), args.databaseFilename, prototypeType, clusterSizes[i]) for i in prototypeIndices]
+if args.outputPrototypeDatabaseFilename is None:
+    outputPrototypeDatabaseFilename = args.databaseFilename
+else:
+    outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
+storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
 
 if args.saveSimilarities:
     np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')