clustering.py 文件源码

def csoms(X, D, params=()):
    pnames = ['neighbour', 'learning_rate', 'input_length_ratio']
    dflts  = [        0.1,             0.2,                   -1]
    if isinstance(params, np.ndarray):
        paramsloc = params.tolist()
    else:
        paramsloc = params
    (neighbour, learning_rate, input_length_ratio) = ds.resolveargumentpairs(pnames, dflts, paramsloc)

    Xloc = np.array(X)

    K = D[0] * D[1] # Number of clusters
    N = Xloc.shape[0] # Number of genes
    Ndim = Xloc.shape[1] # Number of dimensions in X

    som = sompy.SOM(D, Xloc)
    som.set_parameter(neighbor=neighbour, learning_rate=learning_rate, input_length_ratio=input_length_ratio)

    centres = som.train(N).reshape(K, Ndim)
    dists = [[spdist.euclidean(c, x) for c in centres] for x in Xloc]
    C = [np.argmin(d) for d in dists]
    return clustVec2partMat(C, K)