--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/learn-poi.py	Fri Mar 11 17:38:48 2016 -0500
@@ -0,0 +1,43 @@
+#! /usr/bin/env python
+
+import argparse
+
+import numpy as np
+from sklearn import mixture
+import matplotlib.pyplot as plt
+
+import storage, ml
+
+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')
+parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
+parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object')
+parser.add_argument('-n', dest = 'nClusters', help = 'number of point clusters', required = True, type = int)
+parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
+
+args = parser.parse_args()
+
+objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType)
+
+beginnings = []
+ends = []
+for o in objects:
+    beginnings.append(o.getPositionAt(0).aslist())
+    ends.append(o.getPositionAt(int(o.length())-1).aslist())
+
+beginnings = np.array(beginnings)
+ends = np.array(ends)
+
+gmm = mixture.GMM(n_components=args.nClusters, covariance_type = args.covarianceType)
+beginningModel=gmm.fit(beginnings)
+gmm = mixture.GMM(n_components=args.nClusters, covariance_type = args.covarianceType)
+endModel=gmm.fit(ends)
+
+ml.plotGMMClusters(beginningModel, beginnings)
+plt.axis('equal')
+plt.title('Origins')
+print('Origin Clusters:\n{}'.format(ml.computeClusterSizes(beginningModel.predict(beginnings), range(args.nClusters))))
+
+ml.plotGMMClusters(endModel, ends)
+plt.axis('equal')
+plt.title('Destinations')
+print('Destination Clusters:\n{}'.format(ml.computeClusterSizes(endModel.predict(ends), range(args.nClusters))))