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# tft.covariance

Computes the covariance matrix over the whole dataset.

The covariance matrix M is defined as follows: Let x[:j] be a tensor of the jth element of all input vectors in x, and let u_j = mean(x[:j]). The entry M[i,j] = E[(x[:i] - u_i)(x[:j] - u_j)]. Notice that the diagonal entries correspond to variances of individual elements in the vector, i.e. M[i,i] corresponds to the variance of x[:i].

`x` A rank-2 `Tensor`, 0th dim are rows, 1st dim are indices in each input vector.
`dtype` Tensorflow dtype of entries in the returned matrix.
`name` (Optional) A name for this operation.

`ValueError` if input is not a rank-2 Tensor.

A rank-2 (matrix) covariance `Tensor`

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