Exemplos de read_distance_matrix_file em Python

Exemplo n.º 1

Arquivo: test_utils.py Projeto: nlesc-sherlock/cluster-analysis

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

Exemplo n.º 2

Arquivo: test_utils.py Projeto: sherlock-clustering/cluster-analysis

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

Exemplo n.º 3

Arquivo: clustit.py Projeto: nlesc-sherlock/cluster-analysis

    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)

Exemplo n.º 4

Arquivo: test_utils.py Projeto: sherlock-clustering/cluster-analysis

def test_read_distance_matrix_file3():
    output = utils.read_distance_matrix_file(test_files + 'bogus_file.file')

Exemplo n.º 5

Arquivo: clustit.py Projeto: sherlock-clustering/cluster-analysis

                        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,

Exemplo n.º 6

Arquivo: test_utils.py Projeto: nlesc-sherlock/cluster-analysis

def test_read_distance_matrix_file3():
    output = utils.read_distance_matrix_file(test_files + 'bogus_file.file')