def clr(M, **kwargs):
    """Implementation of the Context Likelihood or Relatedness Network algorithm.

    Args:
     mat (numpy.ndarray): matrix, if it is a square matrix, the program assumes
         it is a relevance matrix where mat(i,j) represents the similarity content
         between nodes i and j. Elements of matrix should be
         non-negative.

    Returns:
    mat_nd (numpy.ndarray): Output deconvolved matrix (direct dependency matrix). Its components
        represent direct edge weights of observed interactions.

    .. note::
       Ref:Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey
       Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy
       S. Gardner. Large-scale mapping and validation of escherichia coli
       transcriptional regulation from a compendium of expression profiles.
       PLoS Biology, 2007
    """
    R = np.zeros(M.shape)
    Id = [[0, 0] for i in range(M.shape[0])]
    for i in range(M.shape[0]):
        mu_i = np.mean(M[i, :])
        sigma_i = np.std(M[i, :])
        Id[i] = [mu_i, sigma_i]

    for i in range(M.shape[0]):
        for j in range(i + 1, M.shape[0]):
            z_i = np.max([0, (M[i, j] - Id[i][0]) / Id[i][0]])
            z_j = np.max([0, (M[i, j] - Id[j][0]) / Id[j][0]])
            R[i, j] = np.sqrt(z_i**2 + z_j**2)
            R[j, i] = R[i, j]  # Symmetric

    return R
def clr(M, **kwargs):
    """Implementation of the Context Likelihood or Relatedness Network algorithm.

    Args:
     mat (numpy.ndarray): matrix, if it is a square matrix, the program assumes
         it is a relevance matrix where mat(i,j) represents the similarity content
         between nodes i and j. Elements of matrix should be
         non-negative.

    Returns:
    mat_nd (numpy.ndarray): Output deconvolved matrix (direct dependency matrix). Its components
        represent direct edge weights of observed interactions.

    .. note::
       Ref:Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey
       Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy
       S. Gardner. Large-scale mapping and validation of escherichia coli
       transcriptional regulation from a compendium of expression profiles.
       PLoS Biology, 2007
    """
    R = np.zeros(M.shape)
    Id = [[0, 0] for i in range(M.shape[0])]
    for i in range(M.shape[0]):
        mu_i = np.mean(M[i, :])
        sigma_i = np.std(M[i, :])
        Id[i] = [mu_i, sigma_i]

    for i in range(M.shape[0]):
        for j in range(i + 1, M.shape[0]):
            z_i = np.max([0, (M[i, j] - Id[i][0]) / Id[i][0]])
            z_j = np.max([0, (M[i, j] - Id[j][0]) / Id[j][0]])
            R[i, j] = np.sqrt(z_i**2 + z_j**2)
            R[j, i] = R[i, j]  # Symmetric

    return R
def clr ( M , * * kwargs ) : R = np . zeros ( M . shape ) Id = [ [ 0 , 0 ] for i in range ( M . shape [ 0 ] ) ] for i in range ( M . shape [ 0 ] ) : mu_i = np . mean ( M [ i , : ] ) sigma_i = np . std ( M [ i , : ] ) Id [ i ] = [ mu_i , sigma_i ] for i in range ( M . shape [ 0 ] ) : for j in range ( i + 1 , M . shape [ 0 ] ) : z_i = np . max ( [ 0 , ( M [ i , j ] - Id [ i ] [ 0 ] ) / Id [ i ] [ 0 ] ] ) z_j = np . max ( [ 0 , ( M [ i , j ] - Id [ j ] [ 0 ] ) / Id [ j ] [ 0 ] ] ) R [ i , j ] = np . sqrt ( z_i ** 2 + z_j ** 2 ) R [ j , i ] = R [ i , j ] # Symmetric return R
Implementation of the Context Likelihood or Relatedness Network algorithm.

    Args:
     mat (numpy.ndarray): matrix, if it is a square matrix, the program assumes
         it is a relevance matrix where mat(i,j) represents the similarity content
         between nodes i and j. Elements of matrix should be
         non-negative.

    Returns:
    mat_nd (numpy.ndarray): Output deconvolved matrix (direct dependency matrix). Its components
        represent direct edge weights of observed interactions.

    .. note::
       Ref:Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey
       Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy
       S. Gardner. Large-scale mapping and validation of escherichia coli
       transcriptional regulation from a compendium of expression profiles.
       PLoS Biology, 2007
Implementation of the Context Likelihood or Relatedness Network algorithm .