def run_example(): """ Q-10 of the Application """ singleton_list = [] for line in DATA_TABLE: singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) k_means_distortion = [] for num_clusters in range(6, 21): cluster_list = proj3_solution.kmeans_clustering(singleton_list, num_clusters, 5) distortion = compute_distortion(cluster_list) k_means_distortion.append(distortion) print k_means_distortion hierarchical_distortion = [] cluster_list = singleton_list for num_clusters in range(20, 5, -1): cluster_list = proj3_solution.hierarchical_clustering(cluster_list, num_clusters) distortion = compute_distortion(cluster_list) hierarchical_distortion.append(distortion) hierarchical_distortion.reverse() print hierarchical_distortion compute_plot(range(6, 21), hierarchical_distortion, k_means_distortion)
def run_example(): """ Load a data table, compute a list of clusters and plot a list of clusters Set DESKTOP = True/False to use either matplotlib or simplegui """ data_table = load_data_table(DATA_3108_URL) singleton_list = [] for line in data_table: singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) #cluster_list = sequential_clustering(singleton_list, 15) #print "Displaying", len(cluster_list), "sequential clusters" #cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9) #print "Displaying", len(cluster_list), "hierarchical clusters" cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, 5) print "Displaying", len(cluster_list), "k-means clusters" # draw the clusters using matplotlib or simplegui if DESKTOP: alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False) alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers else: alg_clusters_simplegui.PlotClusters(data_table, cluster_list) # use toggle in GUI to add cluster centers
def run_example(): """ Load a data table, compute a list of clusters and plot a list of clusters Set DESKTOP = True/False to use either matplotlib or simplegui """ data_table = load_data_table(DATA_111_URL) singleton_list = [] for line in data_table: singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) #cluster_list = sequential_clustering(singleton_list, 15) #print "Displaying", len(cluster_list), "sequential clusters" #cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, 9) #print "Displaying", len(cluster_list), "hierarchical clusters" for idx in range(0, 6): cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, 9, idx) print "Displaying", len(cluster_list), "k-means clusters" # draw the clusters using matplotlib or simplegui if DESKTOP: #alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False) alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers else: alg_clusters_simplegui.PlotClusters(data_table, cluster_list) # use toggle in GUI to add cluster centers
def run_example(): """ Q-10 of the Application """ singleton_list = [] for line in DATA_TABLE: singleton_list.append( alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) k_means_distortion = [] for num_clusters in range(6, 21): cluster_list = proj3_solution.kmeans_clustering( singleton_list, num_clusters, 5) distortion = compute_distortion(cluster_list) k_means_distortion.append(distortion) print k_means_distortion hierarchical_distortion = [] cluster_list = singleton_list for num_clusters in range(20, 5, -1): cluster_list = proj3_solution.hierarchical_clustering( cluster_list, num_clusters) distortion = compute_distortion(cluster_list) hierarchical_distortion.append(distortion) hierarchical_distortion.reverse() print hierarchical_distortion compute_plot(range(6, 21), hierarchical_distortion, k_means_distortion)
def test_kmeans(): """ Test for k-means clustering kmeans_clustering should not mutate cluster_list, but make a new copy of each test anyways """ # load small data table print print "Testing kmeans_clustering on 24 county set" data_24_table = load_data_table(DATA_24_URL) kmeansdata_24 = [[15, 1, set([('34017', '36061'), ('06037',), ('06059',), ('36047',), ('36081',), ('06071', '08031'), ('36059',), ('36005',), ('55079',), ('34013', '34039'), ('06075',), ('01073',), ('06029',), ('41051', '41067'), ('11001', '24510', '51013', '51760', '51840', '54009')])], [15, 3, set([('34017', '36061'), ('06037', '06059'), ('06071',), ('36047',), ('36081',), ('08031',), ('36059',), ('36005',), ('55079',), ('34013', '34039'), ('06075',), ('01073',), ('06029',), ('41051', '41067'), ('11001', '24510', '51013', '51760', '51840', '54009')])], [15, 5, set([('34017', '36061'), ('06037', '06059'), ('06071',), ('36047',), ('36081',), ('08031',), ('36059',), ('36005',), ('55079',), ('34013', '34039'), ('06075',), ('01073',), ('06029',), ('41051', '41067'), ('11001', '24510', '51013', '51760', '51840', '54009')])], [10, 1, set([('34017', '36061'), ('06029', '06037', '06075'), ('11001', '24510', '34013', '34039', '51013', '51760', '51840', '54009'), ('06059',), ('36047',), ('36081',), ('06071', '08031', '41051', '41067'), ('36059',), ('36005',), ('01073', '55079')])], [10, 3, set([('34013', '34017', '36061'), ('06029', '06037', '06075'), ('08031', '41051', '41067'), ('06059', '06071'), ('34039', '36047'), ('36081',), ('36059',), ('36005',), ('01073', '55079'), ('11001', '24510', '51013', '51760', '51840', '54009')])], [10, 5, set([('34013', '34017', '36061'), ('06029', '06037', '06075'), ('08031', '41051', '41067'), ('06059', '06071'), ('34039', '36047'), ('36081',), ('36059',), ('36005',), ('01073', '55079'), ('11001', '24510', '51013', '51760', '51840', '54009')])], [5, 1, set([('06029', '06037', '06075'), ('01073', '11001', '24510', '34013', '34017', '34039', '36047', '51013', '51760', '51840', '54009', '55079'), ('06059',), ('36005', '36059', '36061', '36081'), ('06071', '08031', '41051', '41067')])], [5, 3, set([('06029', '06037', '06075'), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013'), ('08031', '41051', '41067'), ('06059', '06071'), ('01073', '51760', '51840', '54009', '55079')])], [5, 5, set([('06029', '06037', '06075'), ('08031', '41051', '41067'), ('06059', '06071'), ('01073', '55079'), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009')])]] suite = poc_simpletest.TestSuite() for num_clusters, num_iterations, expected_county_tuple in kmeansdata_24: # build initial list of clusters for each test since mutation is allowed cluster_list = [] for idx in range(len(data_24_table)): line = data_24_table[idx] cluster_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) # compute student answer student_clustering = student.kmeans_clustering(cluster_list, num_clusters, num_iterations) student_county_tuple = set_of_county_tuples(student_clustering) # Prepare test error_message = "Testing kmeans_custering on 24 county table, num_clusters = " + str(num_clusters) error_message += " num_iterations = " + str(num_iterations) error_message += "\nStudent county tuples: " + str(student_county_tuple) error_message += "\nExpected county tuples: " + str(expected_county_tuple) suite.run_test(student_county_tuple == expected_county_tuple, True, error_message) suite.report_results()
def test_kmeans(): """ Test for k-means clustering kmeans_clustering should not mutate cluster_list, but make a new copy of each test anyways """ # load small data table print print "Testing kmeans_clustering on 24 county set" data_24_table = load_data_table(DATA_24_URL) kmeansdata_24 = [[ 15, 1, set([('34017', '36061'), ('06037', ), ('06059', ), ('36047', ), ('36081', ), ('06071', '08031'), ('36059', ), ('36005', ), ('55079', ), ('34013', '34039'), ('06075', ), ('01073', ), ('06029', ), ('41051', '41067'), ('11001', '24510', '51013', '51760', '51840', '54009')]) ], [ 15, 3, set([('34017', '36061'), ('06037', '06059'), ('06071', ), ('36047', ), ('36081', ), ('08031', ), ('36059', ), ('36005', ), ('55079', ), ('34013', '34039'), ('06075', ), ('01073', ), ('06029', ), ('41051', '41067'), ('11001', '24510', '51013', '51760', '51840', '54009')]) ], [ 15, 5, set([('34017', '36061'), ('06037', '06059'), ('06071', ), ('36047', ), ('36081', ), ('08031', ), ('36059', ), ('36005', ), ('55079', ), ('34013', '34039'), ('06075', ), ('01073', ), ('06029', ), ('41051', '41067'), ('11001', '24510', '51013', '51760', '51840', '54009')]) ], [ 10, 1, set([('34017', '36061'), ('06029', '06037', '06075'), ('11001', '24510', '34013', '34039', '51013', '51760', '51840', '54009'), ('06059', ), ('36047', ), ('36081', ), ('06071', '08031', '41051', '41067'), ('36059', ), ('36005', ), ('01073', '55079')]) ], [ 10, 3, set([('34013', '34017', '36061'), ('06029', '06037', '06075'), ('08031', '41051', '41067'), ('06059', '06071'), ('34039', '36047'), ('36081', ), ('36059', ), ('36005', ), ('01073', '55079'), ('11001', '24510', '51013', '51760', '51840', '54009')]) ], [ 10, 5, set([('34013', '34017', '36061'), ('06029', '06037', '06075'), ('08031', '41051', '41067'), ('06059', '06071'), ('34039', '36047'), ('36081', ), ('36059', ), ('36005', ), ('01073', '55079'), ('11001', '24510', '51013', '51760', '51840', '54009')]) ], [ 5, 1, set([('06029', '06037', '06075'), ('01073', '11001', '24510', '34013', '34017', '34039', '36047', '51013', '51760', '51840', '54009', '55079'), ('06059', ), ('36005', '36059', '36061', '36081'), ('06071', '08031', '41051', '41067')]) ], [ 5, 3, set([('06029', '06037', '06075'), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013'), ('08031', '41051', '41067'), ('06059', '06071'), ('01073', '51760', '51840', '54009', '55079')]) ], [ 5, 5, set([('06029', '06037', '06075'), ('08031', '41051', '41067'), ('06059', '06071'), ('01073', '55079'), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009')]) ]] suite = poc_simpletest.TestSuite() for num_clusters, num_iterations, expected_county_tuple in kmeansdata_24: # build initial list of clusters for each test since mutation is allowed cluster_list = [] for idx in range(len(data_24_table)): line = data_24_table[idx] cluster_list.append( alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) # compute student answer student_clustering = student.kmeans_clustering(cluster_list, num_clusters, num_iterations) student_county_tuple = set_of_county_tuples(student_clustering) # Prepare test error_message = "Testing kmeans_custering on 24 county table, num_clusters = " + str( num_clusters) error_message += " num_iterations = " + str(num_iterations) error_message += "\nStudent county tuples: " + str( student_county_tuple) error_message += "\nExpected county tuples: " + str( expected_county_tuple) suite.run_test(student_county_tuple == expected_county_tuple, True, error_message) suite.report_results()