def test_read_distance_matrix_file2(): output = utils.read_distance_matrix_file(test_files + 'matrix_little_endian.dat') assert output.size == 9 assert output.shape == (3,3) assert output[0][0] == 0.0 assert output[0][1] == 1.0 assert output[0][2] == 2.0
def test_read_distance_matrix_file2(): output = utils.read_distance_matrix_file(test_files + 'matrix_little_endian.dat') assert output.size == 9 assert output.shape == (3, 3) assert output[0][0] == 0.0 assert output[0][1] == 1.0 assert output[0][2] == 2.0
parser.add_argument("-c", "--convert", help="convert similarity to distance with specified a cut-off value", metavar='convert') parser.add_argument("clustering_algorithm", help="name of the clustering algorithm to use", choices=["hierarchical", "dbscan", "spectral", "agglomerative"], metavar='clustering_algorithm') return parser.parse_args() if __name__ == "__main__": args = parse_arguments() edgelist = None if args.edgelist: print("edgelist filename=" + args.edgelist) edgelist = utils.read_edgelist_file(args.edgelist) matrix = None if args.matrix: print("matrix filename=" + args.matrix) matrix = utils.read_distance_matrix_file(args.matrix) if args.convert: print("convert=" + args.convert) if args.edgelist: edgelist['d'] = utils.similarity_to_distance(edgelist['d'], float(args.convert)) if args.matrix: matrix = utils.similarity_to_distance(matrix, float(args.convert)) if args.names: print("names filenname=" + args.names) print("clustering_algorithm=" + args.clustering_algorithm) if args.clustering_algorithm == 'hierarchical': clustering = hierarchical_clustering(edgelist=edgelist, distance_matrix=matrix)
def test_read_distance_matrix_file3(): output = utils.read_distance_matrix_file(test_files + 'bogus_file.file')
metavar='clustering_algorithm') return parser.parse_args() if __name__ == "__main__": args = parse_arguments() edgelist = None if args.edgelist: print("edgelist filename=" + args.edgelist) edgelist = utils.read_edgelist_file(args.edgelist) matrix = None if args.matrix: print("matrix filename=" + args.matrix) matrix = utils.read_distance_matrix_file(args.matrix) if args.convert: print("convert=" + args.convert) if args.edgelist: edgelist['d'] = utils.similarity_to_distance( edgelist['d'], float(args.convert)) if args.matrix: matrix = utils.similarity_to_distance(matrix, float(args.convert)) if args.names: print("names filenname=" + args.names) print("clustering_algorithm=" + args.clustering_algorithm) if args.clustering_algorithm == 'hierarchical': clustering = hierarchical_clustering(edgelist=edgelist,
def test_read_distance_matrix_file3(): output = utils.read_distance_matrix_file(test_files + 'bogus_file.file')