'Numpy cross covariance
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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