knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1"), purl = FALSE )

```
library(torch)
```

In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.

Torch's indexing semantics are closer to numpy's semantics than R's. You will find a lot of similarities between this article and the `numpy`

indexing article available here.

Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)

x <- torch_tensor(1:10) x[1] x[-1]

You can also subset matrices and higher dimensions arrays using the same syntax:

x <- x$reshape(shape = c(2,5)) x x[1,3] x[1,-1]

Note that if one indexes a multidimensional tensor with fewer indices than dimensions, torch's behaviour differs from R,
which flattens the array. In torch, the missing indices are considered complete slices `:`

.

x[1]

It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:

x <- torch_tensor(1:10) x x[2:5] x[1:(-7)]

You can also use the `1:10:2`

syntax which means: In the range from 1 to 10, take every second item. For example:

```
x[1:5:2]
```

Another special syntax is the `N`

, meaning the size of the specified dimension.

```
x[5:N]
```

Note: the slicing behavior relies on Non Standard Evaluation. It requires that the expression is passed to the

`[`

not exactly the resulting R vector.

To allow dynamic dynamic indices, you can create a new slice using the `slc`

function.
For example:

```
x[1:5:2]
```

is equivalent to:

x[slc(start = 1, end = 5, step = 2)]

Like in R, you can take all elements in a dimension by leaving an index empty.

Consider a matrix:

x <- torch_randn(2, 3) x

The following syntax will give you the first row:

x[1,]

And this would give you the first 2 columns:

```
x[,1:2]
```

By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:

x <- torch_randn(2, 3) x[1,]$shape

You can optionally use the `drop = FALSE`

argument to avoid dropping the dimension.

x[1,,drop = FALSE]$shape

It's possible to add a new dimension to a tensor using index-like syntax:

x <- torch_tensor(c(10)) x$shape x[, newaxis]$shape x[, newaxis, newaxis]$shape

You can also use `NULL`

instead of `newaxis`

:

x[,NULL]$shape

Sometimes we don't know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use `..`

:

z <- torch_tensor(1:125)$reshape(c(5,5,5)) z[1,..] z[..,1]

Vector indexing is also supported but care must be taken regarding performance as, in general its much less performant than slice based indexing.

Note: Starting from version 0.5.0, vector indexing in torch follows R semantics, prior to that the behavior was similar to numpy's advanced indexing. To use the old behavior, consider using

`?torch_index`

,`?torch_index_put`

or`torch_index_put_`

.

x <- torch_randn(4,4) x[c(1,3), c(1,3)]

You can also use boolean vectors, for example:

x[c(TRUE, FALSE, TRUE, FALSE), c(TRUE, FALSE, TRUE, FALSE)]

The above examples also work if the index were long or boolean tensors, instead of R vectors. It's also possible to index with multi-dimensional boolean tensors:

x <- torch_tensor(rbind( c(1,2,3), c(4,5,6) )) x[x>3]

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