# unfold-methods

##### Tensor Unfolding

Unfolds the tensor into a matrix, with the modes in `rs`

onto the rows and modes in `cs`

onto the columns. Note that `c(rs,cs)`

must have the same elements (order doesn't matter) as `x@modes`

. Within the rows and columns, the order of the unfolding is determined by the order of the modes. This convention is consistent with Kolda and Bader (2009).

##### Usage

`unfold(tnsr, row_idx, col_idx)`# S4 method for Tensor
unfold(tnsr, row_idx = NULL, col_idx = NULL)

##### Arguments

- tnsr
the Tensor instance

- row_idx
the indices of the modes to map onto the row space

- col_idx
the indices of the modes to map onto the column space

##### Details

For Row Space Unfolding or m-mode Unfolding, see `rs_unfold-methods`

. For Column Space Unfolding or matvec, see `cs_unfold-methods`

.

`vec-methods`

returns the vectorization of the tensor.

`unfold(tnsr,row_idx=NULL,col_idx=NULL)`

##### Value

matrix with `prod(row_idx)`

rows and `prod(col_idx)`

columns

##### References

T. Kolda, B. Bader, "Tensor decomposition and applications". SIAM Applied Mathematics and Applications 2009.

##### See Also

##### Examples

```
# NOT RUN {
tnsr <- rand_tensor()
matT3<-unfold(tnsr,row_idx=2,col_idx=c(3,1))
# }
```

*Documentation reproduced from package rTensor, version 1.4, License: GPL (>= 2)*