CSMSSMTools.py 文件源码

def getW(D, K, Mu = 0.5):
    """
    Return affinity matrix
    [1] Wang, Bo, et al. "Similarity network fusion for aggregating data types on a genomic scale." 
        Nature methods 11.3 (2014): 333-337.
    :param D: Self-similarity matrix
    :param K: Number of nearest neighbors
    """
    #W(i, j) = exp(-Dij^2/(mu*epsij))
    DSym = 0.5*(D + D.T)
    np.fill_diagonal(DSym, 0)

    Neighbs = np.partition(DSym, K+1, 1)[:, 0:K+1]
    MeanDist = np.mean(Neighbs, 1)*float(K+1)/float(K) #Need this scaling
    #to exclude diagonal element in mean
    #Equation 1 in SNF paper [1] for estimating local neighborhood radii
    #by looking at k nearest neighbors, not including point itself
    Eps = MeanDist[:, None] + MeanDist[None, :] + DSym
    Eps = Eps/3
    W = np.exp(-DSym**2/(2*(Mu*Eps)**2))
    return W