Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 908:b297525b2cbf

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added options to the prototype cluster algorithm, work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 26 Jun 2017 00:10:35 -0400
parents 9fd7b18f75b4
children b58a1061a717
comparison
equal deleted inserted replaced
907:9fd7b18f75b4 908:b297525b2cbf
15 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None) 15 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
16 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True) 16 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
17 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance 17 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
18 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True) 18 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
19 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) 19 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None)
20 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
20 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') 21 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
21 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int) 22 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
22 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') 23 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
23 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') 24 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
24 #parser.add_argument('--save-matches', dest = 'saveMatches', help = 'save the matched prototype information', action = 'store_true') 25 #parser.add_argument('--save-matches', dest = 'saveMatches', help = 'save the matched prototype information', action = 'store_true')
58 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) 59 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
59 nTrajectories = len(trajectories) 60 nTrajectories = len(trajectories)
60 61
61 similarities = -np.ones((nTrajectories, nTrajectories)) 62 similarities = -np.ones((nTrajectories, nTrajectories))
62 63
63 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 64 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
64 65
65 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) 66 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
66 print(clusterSizes) 67 print(clusterSizes)
67 68
68 storage.savePrototypesToSqlite(args.databaseFilename, [objects[i].getNum() for i in prototypeIndices], prototypeType, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices] 69 storage.savePrototypesToSqlite(args.databaseFilename, [objects[i].getNum() for i in prototypeIndices], prototypeType, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices]