tft.covariance | TFX | TensorFlow (original) (raw)
tft.covariance
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Computes the covariance matrix over the whole dataset.
tft.covariance(
x: tf.Tensor, dtype: tf.DType, name: Optional[str] = None
) -> tf.Tensor
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].
| Args | |
|---|---|
| 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. |
| Raises | |
|---|---|
| ValueError | if input is not a rank-2 Tensor. |
| Returns |
|---|
| A rank-2 (matrix) covariance Tensor |
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Last updated 2024-11-01 UTC.