示例#1
0
	def testTrimDataToEvents(self):
		test_data = [
			[ 'company1', {
				20091101: 1, 20091102: 2, 20091103: 3,
				20091104: 4, 20091105: 5, 20091108: 6
			} ],
			[ 'company2', {
				20091101: 1, 20091102: 1, 20091103: 1,
				20091104: 1, 20091105: 1, 20091108: 1
			} ]		
		]

		test_events = { 
			'event_type_1': [ (20091101, 20091101, "desc1") ], 
			'event_type_2': [ (20091107, 20091108, "desc2") ]
		}

		range = 2

		expected_result = [
			[ 'company1', { 
				20091101: 1, 20091102: 2,
				20091104: 4, 20091105: 5, 20091108: 6
			} ],
			[ 'company2', {
				20091101: 1, 20091102: 1,
				20091104: 1, 20091105: 1, 20091108: 1
			}]
		]

		actual_result = eventutils.trim_data_to_events(
			test_data, test_events, range)

		self.assertEquals(actual_result, expected_result)
示例#2
0
		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, 
				method = dist_method)
	elif algorithm_type == ClusterAlg.HIERARCHICAL:
		tree = Pycluster.treecluster(input_vecs, method = dist_method,