chrislit / abydos

    return mismatch_cost

def needleman_wunsch(src, tar, gap_cost=1, sim_func=sim_ident):

    """Return the Needleman-Wunsch score of two strings.

    Needleman-Wunsch score

    This is the standard edit distance measure.

    Cf. https://en.wikipedia.org/wiki/Needleman–Wunsch_algorithm

    Cf.

    http://csb.stanford.edu/class/public/readings/Bioinformatics_I_Lecture6/Needleman_Wunsch_JMB_70_Global_alignment.pdf

    :param str src, tar: two strings to be compared

    :param float gap_cost: the cost of an alignment gap (1 by default)

    :param function sim_func: a function that returns the similarity of two

        characters (identity similarity by default)

    :returns: Needleman-Wunsch score

    :rtype: int (in fact dependent on the gap_cost & return value of sim_func)

    >>> needleman_wunsch('cat', 'hat')

    2.0

    >>> needleman_wunsch('Niall', 'Neil')

    1.0

    >>> needleman_wunsch('aluminum', 'Catalan')

    -1.0

    >>> needleman_wunsch('ATCG', 'TAGC')

    0.0

    """

    # pylint: disable=no-member

    d_mat = np.zeros((len(src)+1, len(tar)+1), dtype=np.float)

    # pylint: enable=no-member

    for i in range(len(src)+1):

        d_mat[i, 0] = -(i * gap_cost)

    for j in range(len(tar)+1):

        d_mat[0, j] = -(j * gap_cost)

    for i in range(1, len(src)+1):

        for j in range(1, len(tar)+1):

            match = d_mat[i-1, j-1] + sim_func(src[i-1], tar[j-1])

            delete = d_mat[i-1, j] - gap_cost

            insert = d_mat[i, j-1] - gap_cost

            d_mat[i, j] = max(match, delete, insert)

    return d_mat[d_mat.shape[0]-1, d_mat.shape[1]-1]

def smith_waterman(src, tar, gap_cost=1, sim_func=sim_ident):

    return d_mat[d_mat.shape[0]-1, d_mat.shape[1]-1]

def smith_waterman(src, tar, gap_cost=1, sim_func=sim_ident):

    """Return the Smith-Waterman score of two strings.

    Smith-Waterman score

    This is the standard edit distance measure.

    Cf. https://en.wikipedia.org/wiki/Smith–Waterman_algorithm

    :param str src, tar: two strings to be compared

    :param float gap_cost: the cost of an alignment gap (1 by default)

    :param function sim_func: a function that returns the similarity of two

        characters (identity similarity by default)

    :returns: Smith-Waterman score

    :rtype: int (in fact dependent on the gap_cost & return value of sim_func)

    >>> smith_waterman('cat', 'hat')

    2.0

    >>> smith_waterman('Niall', 'Neil')

    1.0

    >>> smith_waterman('aluminum', 'Catalan')

    0.0

    >>> smith_waterman('ATCG', 'TAGC')

    1.0

    """

    # pylint: disable=no-member

    d_mat = np.zeros((len(src)+1, len(tar)+1), dtype=np.float)

    # pylint: enable=no-member

    for i in range(len(src)+1):

        d_mat[i, 0] = 0

    for j in range(len(tar)+1):

        d_mat[0, j] = 0

    for i in range(1, len(src)+1):

        for j in range(1, len(tar)+1):

            match = d_mat[i-1, j-1] + sim_func(src[i-1], tar[j-1])

            delete = d_mat[i-1, j] - gap_cost

            insert = d_mat[i, j-1] - gap_cost

            d_mat[i, j] = max(0, match, delete, insert)

    return d_mat[d_mat.shape[0]-1, d_mat.shape[1]-1]

def gotoh(src, tar, gap_open=1, gap_ext=0.4, sim_func=sim_ident):