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annotate scripts/learn-poi.py @ 786:1f2b2d1f4fbf dev

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added script and code to learn POIs
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 11 Mar 2016 17:38:48 -0500
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children 0a428b449b80
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786
1f2b2d1f4fbf added script and code to learn POIs
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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1 #! /usr/bin/env python
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2
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3 import argparse
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4
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5 import numpy as np
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6 from sklearn import mixture
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7 import matplotlib.pyplot as plt
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8
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9 import storage, ml
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10
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11 parser = argparse.ArgumentParser(description='The program learns and displays Gaussians fit to beginnings and ends of object trajectories (based on Mohamed Gomaa Mohamed 2015 PhD). TODO: save the data')
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12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
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13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object')
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14 parser.add_argument('-n', dest = 'nClusters', help = 'number of point clusters', required = True, type = int)
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15 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
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16
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17 args = parser.parse_args()
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18
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19 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType)
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20
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21 beginnings = []
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22 ends = []
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23 for o in objects:
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24 beginnings.append(o.getPositionAt(0).aslist())
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25 ends.append(o.getPositionAt(int(o.length())-1).aslist())
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26
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27 beginnings = np.array(beginnings)
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28 ends = np.array(ends)
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29
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30 gmm = mixture.GMM(n_components=args.nClusters, covariance_type = args.covarianceType)
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31 beginningModel=gmm.fit(beginnings)
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32 gmm = mixture.GMM(n_components=args.nClusters, covariance_type = args.covarianceType)
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33 endModel=gmm.fit(ends)
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34
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35 ml.plotGMMClusters(beginningModel, beginnings)
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36 plt.axis('equal')
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37 plt.title('Origins')
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38 print('Origin Clusters:\n{}'.format(ml.computeClusterSizes(beginningModel.predict(beginnings), range(args.nClusters))))
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39
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40 ml.plotGMMClusters(endModel, ends)
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41 plt.axis('equal')
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42 plt.title('Destinations')
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43 print('Destination Clusters:\n{}'.format(ml.computeClusterSizes(endModel.predict(ends), range(args.nClusters))))