Leading axis agreement: Difference between revisions
(List languages that use it in the intro) |
|||
| Line 1: | Line 1: | ||
'''Leading axis agreement''', sometimes called '''prefix agreement''', is a [[conformability]] rule designed for [[leading axis theory]]. It states that a [[dyadic]] [[scalar function]] can be applied between two [[array]]s only if one of their [[shape]]s is a [[prefix]] of the other. The shape of the result is that of the [[argument]] with higher [[rank]]. | '''Leading axis agreement''', sometimes called '''prefix agreement''', is a [[conformability]] rule designed for [[leading axis theory]] and used by [[J]] and [[BQN]]. It states that a [[dyadic]] [[scalar function]] can be applied between two [[array]]s only if one of their [[shape]]s is a [[prefix]] of the other. The shape of the result is that of the [[argument]] with higher [[rank]]. | ||
== Examples == | == Examples == | ||
Revision as of 19:49, 19 February 2021
Leading axis agreement, sometimes called prefix agreement, is a conformability rule designed for leading axis theory and used by J and BQN. It states that a dyadic scalar function can be applied between two arrays only if one of their shapes is a prefix of the other. The shape of the result is that of the argument with higher rank.
Examples
The following examples use J for demonstration purposes.
A scalar dyadic function works when the two arrays have the same shape:
]x =: 2 3 $ 10 10 10 10 10 10 10 ]y =: 2 3 $ i.6 0 1 2 3 4 5 x + y 10 11 12 13 14 15
as well as when one is a scalar:
]x =: 10 10 ]y =: 2 3 $ i.6 0 1 2 3 4 5 x + y 10 11 12 13 14 15
The two cases above are already supported in other APLs in the form of scalar extension. J goes one step further, allowing the lower-rank array argument to have nonzero rank, as long as the leading dimensions match:
]x =: 10 20 10 20 ]y =: 2 3 $ i.6 0 1 2 3 4 5 x + y 10 11 12 23 24 25
In this case, x has shape 2 and y has shape 2 3. Since the leading axes agree and the rank difference is 1, each atom (or 0-cell) of x is matched with each row (or 1-cell) of y, and the two rows in the result are the results of 10 + 0 1 2 and 20 + 3 4 5, respectively.
Model
In dialects that do not feature leading axis agreement, it can nevertheless be utilised by the introduction of an explicit operator:
_LA←{⍺ ⍺⍺⍤(-⍺⌊⍥(≢⍴)⍵)⊢⍵}
⊢x ← 10 20
10 20
⊢y ← 2 3 ⍴ ⍳ 6
0 1 2
3 4 5
x +_LA y
10 11 12
23 24 25
Aligning axes using the Rank operator
When using the Rank operator for dyadic functions as in X (f⍤m n) Y, the frames of X and Y are checked for conformability. Combined with leading axis agreement, the Rank operator can be used to align the axes to be matched.
NB. $x : 2|3 NB. $y : |3 2 NB. ------------------ NB. $x +"1 2 y : 2 3 2 ]x =: 2 3 $ 10 20 30 40 50 60 10 20 30 40 50 60 ]y =: 3 2 $ 1 2 3 4 5 6 1 2 3 4 5 6 x +"1 2 y 11 12 23 24 35 36 41 42 53 54 65 66
| APL features [edit] | |
|---|---|
| Built-ins | Primitives (functions, operators) ∙ Quad name |
| Array model | Shape ∙ Rank ∙ Depth ∙ Bound ∙ Index (Indexing) ∙ Axis ∙ Ravel ∙ Ravel order ∙ Element ∙ Scalar ∙ Vector ∙ Matrix ∙ Simple scalar ∙ Simple array ∙ Nested array ∙ Cell ∙ Major cell ∙ Subarray ∙ Empty array ∙ Prototype |
| Data types | Number (Boolean, Complex number) ∙ Character (String) ∙ Box ∙ Namespace ∙ Function array |
| Concepts and paradigms | Conformability (Scalar extension, Leading axis agreement) ∙ Scalar function (Pervasion) ∙ Identity element ∙ Complex floor ∙ Array ordering (Total) ∙ Tacit programming (Function composition, Close composition) ∙ Glyph ∙ Leading axis theory ∙ Major cell search ∙ First-class function |
| Errors | LIMIT ERROR ∙ RANK ERROR ∙ SYNTAX ERROR ∙ DOMAIN ERROR ∙ LENGTH ERROR ∙ INDEX ERROR ∙ VALUE ERROR ∙ EVOLUTION ERROR |