--- a/python/ml.py	Tue Aug 11 12:06:10 2015 -0400
+++ b/python/ml.py	Tue Aug 11 12:55:09 2015 -0400
@@ -161,9 +161,3 @@
             indices.append(smallestClusterIndex)
 
     return prototypeIndices, labels
-
-def motionPatternLearning(objects, maxDistance):
-    ''' 
-    Option to use only the (n?) longest features per object instead of all for speed up
-    TODO'''
-    pass

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/learn-motion-patterns.py	Tue Aug 11 12:55:09 2015 -0400
@@ -0,0 +1,60 @@
+#! /usr/bin/env python
+
+import sys, argparse
+
+#import matplotlib.pyplot as plt
+import numpy as np
+
+import ml, utils, storage
+
+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('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
+parser.add_argument('-n', dest = 'nTrajectories', 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)
+parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None)
+parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') # default is manhattan distance
+
+args = parser.parse_args()
+
+# TODO parameters (random init?) and what to learn from: objects, features, longest features from objects
+# TODO add possibility to cluter with velocities
+
+trajectoryType = args.trajectoryType
+if args.trajectoryType == 'objectfeatures':
+    trajectoryType = 'object'
+
+#features = storage.loadTrajectoriesFromSqlite(databaseFilename, args.trajectoryType)
+objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories)
+
+if args.trajectoryType == 'objectfeatures':
+    features = []
+    for o in objects:
+        tmp = utils.sortByLength(o.getFeatures(), reverse = True)
+        features += tmp[:min(len(tmp), 3)]
+    objects = features
+
+trajectories = [o.getPositions().asArray().T for o in objects]
+
+lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
+nTrajectories = len(trajectories)
+
+similarities = np.zeros((nTrajectories, nTrajectories))
+for i in xrange(nTrajectories):
+    for j in xrange(i):
+        similarities[i,j] = lcss.computeNormalized(trajectories[i], trajectories[j])
+        similarities[j,i] = similarities[i,j]
+
+prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, args.minClusterSize)
+
+if args.display:
+    for i,o in enumerate(objects):
+        if i not in prototypeIndices:
+            o.plot(utils.colors[labels[i]])
+    for i in prototypeIndices:
+            objects[i].plot(utils.colors[i]+'o')
+
+# TODO store the prototypes (if features, easy, if objects, info must be stored about the type)