Leading axis agreement: Difference between revisions
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A scalar dyadic function works when the two arrays have the same shape: | A scalar dyadic function works when the two arrays have the same shape: | ||
< | <syntaxhighlight lang=j> | ||
]x =: 2 3 $ 10 | ]x =: 2 3 $ 10 | ||
10 10 10 | 10 10 10 | ||
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as well as when one is a [[scalar]]: | as well as when one is a [[scalar]]: | ||
< | <syntaxhighlight lang=j> | ||
]x =: 10 | ]x =: 10 | ||
10 | 10 | ||
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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: | 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: | ||
< | <syntaxhighlight lang=j> | ||
]x =: 10 20 | ]x =: 10 20 | ||
10 20 | 10 20 | ||
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{{Works in|[[J]]}} | {{Works in|[[J]]}} | ||
In this case, < | In this case, <syntaxhighlight lang=j inline>x</syntaxhighlight> has shape <syntaxhighlight lang=j inline>2</syntaxhighlight> and <syntaxhighlight lang=j inline>y</syntaxhighlight> has shape <syntaxhighlight lang=j inline>2 3</syntaxhighlight>. Since the leading axes agree and the rank difference is 1, each atom (or 0-[[cell]]) of <syntaxhighlight lang=j inline>x</syntaxhighlight> is matched with each row (or 1-cell) of <syntaxhighlight lang=j inline>y</syntaxhighlight>, and the two rows in the result are the results of <syntaxhighlight lang=j inline>10 + 0 1 2</syntaxhighlight> and <syntaxhighlight lang=j inline>20 + 3 4 5</syntaxhighlight>, respectively. | ||
== Model == | == Model == | ||
In dialects that do not feature leading axis agreement, it can nevertheless be utilised by the introduction of an explicit operator: | In dialects that do not feature leading axis agreement, it can nevertheless be utilised by the introduction of an explicit operator: | ||
< | <syntaxhighlight lang=apl> | ||
_LA←{⍺ ⍺⍺⍤(-⍺⌊⍥(≢⍴)⍵)⊢⍵} | _LA←{⍺ ⍺⍺⍤(-⍺⌊⍥(≢⍴)⍵)⊢⍵} | ||
⊢x ← 10 20 | ⊢x ← 10 20 | ||
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== Aligning axes using the Rank operator == | == Aligning axes using the Rank operator == | ||
When using the [[Rank (operator)|Rank operator]] for dyadic functions as in < | When using the [[Rank (operator)|Rank operator]] for dyadic functions as in <syntaxhighlight lang=apl inline>X (f⍤m n) Y</syntaxhighlight>, the [[Frame|frames]] of <syntaxhighlight lang=apl inline>X</syntaxhighlight> and <syntaxhighlight lang=apl inline>Y</syntaxhighlight> are checked for conformability. Combined with leading axis agreement, the Rank operator can be used to align the [[axis|axes]] to be matched. | ||
< | <syntaxhighlight lang=j> | ||
NB. $x : 2|3 | NB. $x : 2|3 | ||
NB. $y : |3 2 | NB. $y : |3 2 | ||
Revision as of 22:17, 10 September 2022
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
Works in: J
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
Works in: J
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
Works in: J
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
Works in: Dyalog APL
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
Works in: J
| 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 |