fuzzy_c_means.py 文件源码

def get_dissimilarity_matrix(U,V,X,n,error_list,beta,alpha_w,alpha_e_avg_t,alpha_n0,maxconn):
    row_size = X.shape[0]
    col_size = X.shape[1]
    channel_count = X.shape[2]
    alpha = get_alpha(n,error_list,alpha_w,alpha_e_avg_t,alpha_n0)
    cluster_number = V.shape[0]
    D = np.zeros((row_size,col_size,cluster_number)) 
    index_arr = np.array([[k,l] for k in xrange(row_size) for l in xrange(col_size)],dtype='int32')
    U_new = U.reshape(row_size*col_size,cluster_number, order='F')
    data_inputs = [0 for i in xrange(0,row_size*col_size)]
    for i in xrange(0, row_size*col_size):
        x = index_arr[i][0]
        y = index_arr[i][1]
        data_inputs[i] = [U_new,V,X[x][y],x,y,alpha,beta[x*row_size+y,:]]
    pool = Pool(maxconn) 
    outputs = pool.map(compute_cluster_distances_pool, data_inputs)
    pool.close()
    pool.join()
    for i in xrange(0,row_size*col_size):
        x = index_arr[i][0]
        y = index_arr[i][1]
        D[x][y] = outputs[i]
    return D