From 7e5d0fcc39fd8a683fc7010af064849b454b432b Mon Sep 17 00:00:00 2001 From: Marshall Lochbaum Date: Sat, 4 Jun 2022 17:40:31 -0400 Subject: Further editing --- docs/doc/transpose.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'docs/doc/transpose.html') diff --git a/docs/doc/transpose.html b/docs/doc/transpose.html index 952abc2c..807173ba 100644 --- a/docs/doc/transpose.html +++ b/docs/doc/transpose.html @@ -27,7 +27,7 @@ 3

With two axes the only interesting operation of this sort is to swap them (and with one or zero axes there's nothing interesting to do, and ⍉ just returns the argument array). But a BQN programmer may well want to work with higher-rank arrays—although such a programmer might call them "tensors"—and this means there are many more ways to rearrange the axes. Transpose extends to high-rank arrays to allow some useful special cases as well as completely general axis rearrangement, as described below.

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Monadic Transpose

+

Transposing tensors

APL extends matrix transposition to any rank by reversing all axes for its monadic ⍉, but this generalization isn't very natural and is almost never used. The main reason for it is to maintain the equivalence a MP b ←→ b MP⌾⍉ a, where MP ← +˝∘×⎉1‿∞ is the generalized matrix product. But even here APL's Transpose is suspect. It does much more work than it needs to, as we'll see.

BQN's transpose takes the first axis of 𝕩 and moves it to the end.

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