Numpy交叉协方差
令x为（d_x，n）包含n观察d_x -Dimerensional变量的矩阵。 x，让w是维度n的权重（概率）的向量。加权协方差在numpy中给出，
CX = numpy.cov(X, ddof=0, aweights=w)
现在y为（d_y，n）包含n观察的矩阵观察d_y - 维矢量。是否有一种巧妙的方法来计算伪代码中加权交叉协方差
CXY = sum(W[i] * numpy.outer((X[i, :] - X_mean),(Y[i, :] - Y_mean)))
？
Let X be a (d_x,n) matrix containing n observations of a d_x-dimensional variable x, and let w be a vector of weights (probabilities) of dimension n. The weighted covariance is given in numpy by
CX = numpy.cov(X, ddof=0, aweights=w)
Let now Y be a (d_y,n) matrix containing n observations of a d_y-dimensional vector. Is there a clever way to compute the weighted cross covariance, in pseudocode
CXY = sum(W[i] * numpy.outer((X[i, :] - X_mean),(Y[i, :] - Y_mean)))
?
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