ProGENI.py 文件源码

def gen_network_matrix(num_nodes, net_df, node1, node2, weight, node2index):
    """Generates network adjacency matrix and normalizes it"""
    # Transform the first two columns of the DataFrame -- the nodes -- to their indexes
    net_df[node1] = net_df[node1].apply(lambda x: node2index[x])
    net_df[node2] = net_df[node2].apply(lambda x: node2index[x])
    # Create the sparse matrix
    network_matrix = sparse.csr_matrix((net_df[weight].values, (net_df[node1].values, net_df[node2].values)),
                                       shape=(num_nodes, num_nodes), dtype=float)
    # Make the ajdacency matrix symmetric
    network_matrix = (network_matrix + network_matrix.T)
    network_matrix.setdiag(0)
    # Normalize the rows of network_matrix because we are multiplying vector by matrix (from left)
    network_matrix = normalize(network_matrix, norm='l1', axis=1)
    return(net_df, network_matrix)



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