# 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, dist = dist_method) labels = tree.cut(number_of_clusters) elif algorithm_type == ClusterAlg.SELFORGMAPS: labels, celldata = Pycluster.somcluster(input_vecs, nxgrid = xgrid, nygrid = ygrid, niter = number_of_iters)
def testGetPricesVecs(self): """Obtaining list of prices vectors.""" vecs = utils.make_prices_vecs(self.data1) self.assertEqual(vecs, [[9.5, 9, 10.5, 20.5], [9.5, 16.5, 17.5, 18.5]])