我有一个$100$X$100$看起来像这样的协方差矩阵。

一些行/列全为零，因为那些相应的元素不存在于计算协方差的样本中。我是这样做的：

    ...
    adjmats = [get_adjmat(graph) for graph in samples]                   # array of adjacency matrices
    reduced = functools.reduce(lambda x, y: np.add(x, y), adjmats)       # add all elem-wise
    adjacency = np.divide(reduced, len(adjmats))                         # divide by number: "mean"


    fig, ax = plt.subplots()

    covariance= np.cov(adjacency)                                        # getting covariance 


    def correlation_from_covariance(covariance):
        v = np.sqrt(np.diag(covariance))
        outer_v = np.outer(v, v)
        correlation = covariance / outer_v                   <<<<<<      # complains here!
        correlation[covariance == 0] = 0
        return correlation

    correlation = correlation_from_covariance(covariance)                # attempting to convert

    im = ax.imshow(correlation)

当我尝试获取相关矩阵时，我隐约知道它是协方差矩阵的标准“归一化”版本，numpy 抱怨：subunit_graph.py:218: RuntimeWarning: invalid value encountered in true_divide correlation = covariance / outer_v，但我仍然得到一个合理的相关矩阵。有人可以向我解释那里到底发生了什么true_divide吗？
非常感谢！