fold-methods | R Documentation |

General folding of a 2D DelayedArray into a higher-order DelayedArray(Tensor).
This is designed to be the inverse function to
`unfold`

, with the same ordering of the indices.
This amounts to following: if we were to unfold a Tensor using a set of
`row_idx`

and `col_idx`

, then we can fold the resulting matrix
back into the original Tensor using the same `row_idx`

and `col_idx`

.

fold(mat, row_idx = NULL, col_idx = NULL, modes = NULL) ## S4 method for signature 'DelayedArray' fold(mat, row_idx, col_idx, modes)

`mat` |
DelayedArray object (only 2D) |

`row_idx` |
the indices of the modes that are mapped onto the row space |

`col_idx` |
the indices of the modes that are mapped onto the column space |

`modes` |
the modes of the output DelayedArray |

This function is an extension of the `fold`

by DelayedArray.

DelayedArray object with modes given by `modes`

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

`unfold`

, `k_fold`

,
`unmatvec`

,
`rs_fold`

, `cs_fold`

library("DelayedRandomArray") darr <- RandomUnifArray(c(2,3,4)) matT3 <- DelayedTensor::unfold(darr, row_idx=2, col_idx=c(3,1)) identical( as.array(DelayedTensor::fold(matT3, row_idx=2,col_idx=c(3,1), modes=c(2,3,4))), as.array(darr))

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