def testWeeklyCompression(self):
"""Testing whether weekly compression works"""
test_data = [
["Company1", { 20091101: 1, 20091102: 2, 20091103: 3, 20091104: 4, 20091105: 5}],
["Company2", { 20091101: 1, 20091102: 1, 20091103: 1, 20091104: 1, 20091105: 1}]
]
expected_result = [
["Company1", { 20091101: 3}],
["Company2", { 20091101: 1}]
]
actual_result = utils.compress_data_weekly(test_data)
self.assertEqual(actual_result, expected_result)
other_events = eventutils.import_events( "../data/wydarzenia-inne-polska.txt") events[other_events[0]] = other_events[1] if import_political_events: political_events = eventutils.import_events( "../data/wydarzenia-polityczne-polska.txt") events[political_events[0]] = political_events[1] except IOError, err: sys.exit(err) # TODO(patryk): plot events. # Preprocessing phase. if compress_to_weekly_data: data = utils.compress_data_weekly(data) if trimming_range > 0: data = eventutils.trim_data_to_events(data, events, trimming_range) input_vecs = [] if treat_data_differentially: input_vecs = utils.make_prices_diffs_vecs(data) else: input_vecs = utils.make_prices_vecs(data) # Run clustering algorithm. if algorithm_type == ClusterAlg.KMEANS: labels, wcss, n = Pycluster.kcluster(input_vecs, number_of_clusters, dist = dist_measure, npass = number_of_iters,
