Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-poi.py @ 913:1cd878812529

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 28 Jun 2017 17:57:06 -0400
parents 6db83beb5350
children f228fd649644
comparison
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912:fd057a6b04db 913:1cd878812529
15 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int) 15 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int)
16 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full") 16 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
17 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image') 17 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
18 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.) 18 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.)
19 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance 19 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
20 parser.add_argument('--assign', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
20 21
21 args = parser.parse_args() 22 args = parser.parse_args()
22 23
23 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType) 24 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType)
24 25
35 if args.nDestinationClusters is None: 36 if args.nDestinationClusters is None:
36 nDestinationClusters = args.nOriginClusters 37 nDestinationClusters = args.nOriginClusters
37 38
38 gmmId=0 39 gmmId=0
39 for nClusters, points, gmmType in zip([args.nOriginClusters, nDestinationClusters], 40 for nClusters, points, gmmType in zip([args.nOriginClusters, nDestinationClusters],
40 [beginnings, ends], 41 [beginnings, ends],
41 ['beginning', 'end']): 42 ['beginning', 'end']):
42 # estimation 43 # estimation
43 gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType) 44 gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType)
44 model=gmm.fit(beginnings) 45 model=gmm.fit(points)
45 if not model.converged_: 46 if not model.converged_:
46 print('Warning: model for '+gmmType+' points did not converge') 47 print('Warning: model for '+gmmType+' points did not converge')
47 # plot 48 # plot
48 if args.display: 49 if args.display:
49 fig = plt.figure() 50 fig = plt.figure()
50 if args.worldImageFilename is not None and args.unitsPerPixel is not None: 51 if args.worldImageFilename is not None and args.unitsPerPixel is not None:
51 img = plt.imread(args.worldImageFilename) 52 img = plt.imread(args.worldImageFilename)
52 plt.imshow(img) 53 plt.imshow(img)
53 labels = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel) 54 labels = model.predict(points)
55 labels = ml.plotGMMClusters(model, labels, points, fig, nUnitsPerPixel = args.unitsPerPixel)
54 plt.axis('image') 56 plt.axis('image')
55 plt.title(gmmType) 57 plt.title(gmmType)
56 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components)))) 58 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
57 # save 59 # save
58 storage.savePOIs(args.databaseFilename, model, gmmType, gmmId) 60 storage.savePOIs(args.databaseFilename, model, gmmType, gmmId)
61 # save assignments
62 if args.assign:
63 pass
59 gmmId += 1 64 gmmId += 1
60 65
61 if args.display: 66 if args.display:
62 plt.axis('equal') 67 plt.axis('equal')
63 plt.show() 68 plt.show()