Mercurial Hosting > traffic-intelligence
comparison scripts/learn-motion-patterns.py @ 843:5dc7a507353e
updated to learn prototypes
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 13 Jul 2016 23:45:47 -0400 |
parents | f3ae72d86762 |
children | 5a68779d7777 |
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842:75530d8c0090 | 843:5dc7a507353e |
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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('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') | |
20 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int, default = None) | 21 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int, default = None) |
21 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') # default is manhattan distance | 22 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') | |
22 | 24 |
23 args = parser.parse_args() | 25 args = parser.parse_args() |
24 | 26 |
25 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects | 27 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects |
26 # TODO add possibility to cluter with velocities | 28 # TODO add possibility to cluter with velocities |
43 | 45 |
44 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) | 46 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) |
45 nTrajectories = len(trajectories) | 47 nTrajectories = len(trajectories) |
46 | 48 |
47 similarities = -np.ones((nTrajectories, nTrajectories)) | 49 similarities = -np.ones((nTrajectories, nTrajectories)) |
48 # for i in xrange(nTrajectories): | |
49 # for j in xrange(i): | |
50 # similarities[i,j] = lcss.computeNormalized(trajectories[i], trajectories[j]) | |
51 # similarities[j,i] = similarities[i,j] | |
52 | 50 |
53 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize) # this line can be called again without reinitializing similarities | 51 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization) # this line can be called again without reinitializing similarities |
54 | 52 |
55 print(ml.computeClusterSizes(labels, prototypeIndices, -1)) | 53 print(ml.computeClusterSizes(labels, prototypeIndices, -1)) |
54 | |
55 if args.saveSimilarities: | |
56 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4') | |
56 | 57 |
57 if args.display: | 58 if args.display: |
58 from matplotlib.pyplot import figure, show | 59 from matplotlib.pyplot import figure, show |
59 figure() | 60 figure() |
60 for i,o in enumerate(objects): | 61 for i,o in enumerate(objects): |
65 o.plot(utils.colors[labels[i]]) | 66 o.plot(utils.colors[labels[i]]) |
66 for i in prototypeIndices: | 67 for i in prototypeIndices: |
67 objects[i].plot(utils.colors[i]+'o') | 68 objects[i].plot(utils.colors[i]+'o') |
68 show() | 69 show() |
69 | 70 |
70 # TODO store the prototypes (if features, easy, if objects, info must be stored about the type) | 71 # TODO store the prototypes trajectories, add option so store similarities (the most expensive stuff) with limited accuracy |