Mercurial Hosting > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 1033:8ffb3ae9f3d2

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 20 Jun 2018 00:07:03 -0400
parents d0e339359d8a
children 933588568bec
comparison
equal deleted inserted replaced
1032:d0e339359d8a 1033:8ffb3ae9f3d2
23 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') 23 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
24 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int) 24 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
25 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') 25 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
26 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') 26 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
27 parser.add_argument('--save-matches', dest = 'saveMatches', help = 'saves the assignments of the objects (not for features) to the prototypes', action = 'store_true') 27 parser.add_argument('--save-matches', dest = 'saveMatches', help = 'saves the assignments of the objects (not for features) to the prototypes', action = 'store_true')
28 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves them (do not use min cluster size as it will discard prototypes at the beginning if the initial cluster is too small)', action = 'store_true') 28 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')
29 29
30 args = parser.parse_args() 30 args = parser.parse_args()
31 31
32 # use cases 32 # use cases
33 # 1. learn proto from one file, save in same or another 33 # 1. learn proto from one file, save in same or another
76 if args.learn: 76 if args.learn:
77 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices) 77 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
78 else: 78 else:
79 prototypeIndices = initialPrototypeIndices 79 prototypeIndices = initialPrototypeIndices
80 80
81 if args.assign: 81 if args.assign: # TODO don't touch initial prototypes if not from same db as trajectories
82 if not args.learn and args.minClusterSize >= 1: 82 #if not args.learn and args.minClusterSize >= 1: # allow only
83 print('Warning: you did not learn the prototypes and you are using minimum cluster size of {}, which may lead to removing prototypes and assigning them to others'.format(args.minClusterSize)) 83 # print('Warning: you did not learn the prototypes and you are using minimum cluster size of {}, which may lead to removing prototypes and assigning them to others'.format(args.minClusterSize))
84 prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize) 84 # if args.minClusterSize >= 1:
85 # if initialPrototypeIndices is None:
86 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize)
87 # else:
88 # print('Not assigning with non-zero minimum cluster size and initial prototypes (would remove initial prototypes based on other trajectories')
89 # else:
90 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
91 prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
85 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) 92 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
86 print(clusterSizes) 93 print(clusterSizes)
87 94
88 if args.learn or args.assign: 95 if args.learn and not args.assign:
89 prototypes = [] 96 prototypes = []
90 for i in prototypeIndices: 97 for i in prototypeIndices:
91 if args.assign:
92 nMatchings = clusterSizes[i]-1
93 #else:
94 # nMatchings = 0
95 if i<len(initialPrototypes): 98 if i<len(initialPrototypes):
96 if args.assign:
97 initialPrototypes[i].nMatchings += nMatchings
98 prototypes.append(initialPrototypes[i]) 99 prototypes.append(initialPrototypes[i])
99 else: 100 else:
100 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType, nMatchings)) 101 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType))
101 102
102 if args.outputPrototypeDatabaseFilename is None: 103 if args.outputPrototypeDatabaseFilename is None:
103 outputPrototypeDatabaseFilename = args.databaseFilename 104 outputPrototypeDatabaseFilename = args.databaseFilename
104 else: 105 else:
105 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename 106 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
106 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename: 107 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
107 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype') 108 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
108 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes) 109 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
109 110
110 if args.saveSimilarities: 111 if not args.learn and args.assign: # no new prototypes # not save assignments of past prototypes if removes with minClusterSize
111 # todo save trajectories and prototypes 112 prototypes = []
112 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') 113 for i in prototypeIndices:
114 nMatchings = clusterSizes[i]-1
115 if initialPrototypes[i].nMatchings is None:
116 initialPrototypes[i].nMatchings = nMatchings
117 else:
118 initialPrototypes[i].nMatchings += nMatchings
119 prototypes.append(initialPrototypes[i])
120 if args.outputPrototypeDatabaseFilename is None:
121 outputPrototypeDatabaseFilename = args.databaseFilename
122 else:
123 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
124 storage.setPrototypeMatchingsInSqlite(outputPrototypeDatabaseFilename, prototypes)
113 125
114 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)} 126 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)}
115 if args.assign and args.saveMatches: 127 if args.saveMatches:
116 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes) 128 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes)
117 129
118 if args.display and args.assign: 130 if (args.learn or args.assign) and args.saveSimilarities:
119 from matplotlib.pyplot import figure, show, axis 131 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
120 figure() 132
121 for i,o in enumerate(objects): 133 if args.display and args.assign:
122 if i not in prototypeIndices: 134 from matplotlib.pyplot import figure, show, axis
123 if labels[i] < 0: 135 figure()
124 o.plot('kx') 136 for i,o in enumerate(objects):
125 else: 137 if i not in prototypeIndices:
126 o.plot(utils.colors[labels[i]]) 138 if labels[i] < 0:
127 for i in prototypeIndices: 139 o.plot('kx')
128 objects[i].plot(utils.colors[i]+'o') 140 else:
129 axis('equal') 141 o.plot(utils.colors[labels[i]])
130 show() 142 for i in prototypeIndices:
131 else: 143 objects[i].plot(utils.colors[i]+'o')
132 print('Not learning nor assigning: doing nothing') 144 axis('equal')
145 show()