# This file is a part of Julia. License is MIT: http://julialang.org/license
## types ##
typealias Dims{N} NTuple{N,Int}
typealias DimsInteger{N} NTuple{N,Integer}
typealias Indices{N} NTuple{N,AbstractUnitRange}
"""
<:(T1, T2)
Subtype operator, equivalent to `issubtype(T1,T2)`.
"""
const (<:) = issubtype
"""
supertype(T::DataType)
Return the supertype of DataType `T`.
```jldoctest
julia> supertype(Int32)
Signed
```
"""
supertype(T::DataType) = T.super
## generic comparison ##
==(x, y) = x === y
"""
isequal(x, y)
Similar to `==`, except treats all floating-point `NaN` values as equal to each other, and
treats `-0.0` as unequal to `0.0`. The default implementation of `isequal` calls `==`, so if
you have a type that doesn't have these floating-point subtleties then you probably only
need to define `==`.
`isequal` is the comparison function used by hash tables (`Dict`). `isequal(x,y)` must imply
that `hash(x) == hash(y)`.
This typically means that if you define your own `==` function then you must define a
corresponding `hash` (and vice versa). Collections typically implement `isequal` by calling
`isequal` recursively on all contents.
Scalar types generally do not need to implement `isequal` separate from `==`, unless they
represent floating-point numbers amenable to a more efficient implementation than that
provided as a generic fallback (based on `isnan`, `signbit`, and `==`).
"""
isequal(x, y) = x == y
# TODO: these can be deleted once the deprecations of ==(x::Char, y::Integer) and
# ==(x::Integer, y::Char) are gone and the above returns false anyway
isequal(x::Char, y::Integer) = false
isequal(x::Integer, y::Char) = false
## minimally-invasive changes to test == causing NotComparableError
# export NotComparableError
# =={T}(x::T, y::T) = x === y
# immutable NotComparableError <: Exception end
# const NotComparable = NotComparableError()
# ==(x::ANY, y::ANY) = NotComparable
# !(e::NotComparableError) = throw(e)
# isequal(x, y) = (x == y) === true
## alternative NotComparableError which captures context
# immutable NotComparableError; a; b; end
# ==(x::ANY, y::ANY) = NotComparableError(x, y)
isequal(x::AbstractFloat, y::AbstractFloat) = (isnan(x) & isnan(y)) | (signbit(x) == signbit(y)) & (x == y)
isequal(x::Real, y::AbstractFloat) = (isnan(x) & isnan(y)) | (signbit(x) == signbit(y)) & (x == y)
isequal(x::AbstractFloat, y::Real ) = (isnan(x) & isnan(y)) | (signbit(x) == signbit(y)) & (x == y)
isless(x::AbstractFloat, y::AbstractFloat) = (!isnan(x) & isnan(y)) | (signbit(x) & !signbit(y)) | (x < y)
isless(x::Real, y::AbstractFloat) = (!isnan(x) & isnan(y)) | (signbit(x) & !signbit(y)) | (x < y)
isless(x::AbstractFloat, y::Real ) = (!isnan(x) & isnan(y)) | (signbit(x) & !signbit(y)) | (x < y)
function ==(T::Type, S::Type)
@_pure_meta
typeseq(T, S)
end
function !=(T::Type, S::Type)
@_pure_meta
!(T == S)
end
==(T::TypeVar, S::Type) = false
==(T::Type, S::TypeVar) = false
## comparison fallbacks ##
"""
!=(x, y)
≠(x,y)
Not-equals comparison operator. Always gives the opposite answer as `==`. New types should
generally not implement this, and rely on the fallback definition `!=(x,y) = !(x==y)` instead.
"""
!=(x,y) = !(x==y)
const ≠ = !=
"""
is(x, y) -> Bool
===(x,y) -> Bool
≡(x,y) -> Bool
Determine whether `x` and `y` are identical, in the sense that no program could distinguish
them. Compares mutable objects by address in memory, and compares immutable objects (such as
numbers) by contents at the bit level. This function is sometimes called `egal`.
"""
is
const ≡ = is
"""
!==(x, y)
≢(x,y)
Equivalent to `!is(x, y)`.
"""
!==(x,y) = !is(x,y)
const ≢ = !==
"""
<(x, y)
Less-than comparison operator. New numeric types should implement this function for two
arguments of the new type. Because of the behavior of floating-point NaN values, `<`
implements a partial order. Types with a canonical partial order should implement `<`, and
types with a canonical total order should implement `isless`.
"""
<(x,y) = isless(x,y)
"""
>(x, y)
Greater-than comparison operator. Generally, new types should implement `<` instead of this
function, and rely on the fallback definition `>(x,y) = y<x`.
"""
>(x,y) = y < x
"""
<=(x, y)
≤(x,y)
Less-than-or-equals comparison operator.
"""
<=(x,y) = !(y < x)
const ≤ = <=
"""
>=(x, y)
≥(x,y)
Greater-than-or-equals comparison operator.
"""
>=(x,y) = (y <= x)
const ≥ = >=
"""
.>(x, y)
Element-wise greater-than comparison operator.
"""
.>(x,y) = y .< x
"""
.>=(x, y)
.≥(x,y)
Element-wise greater-than-or-equals comparison operator.
"""
.>=(x,y) = y .<= x
const .≥ = .>=
# this definition allows Number types to implement < instead of isless,
# which is more idiomatic:
isless(x::Real, y::Real) = x<y
lexcmp(x::Real, y::Real) = isless(x,y) ? -1 : ifelse(isless(y,x), 1, 0)
"""
ifelse(condition::Bool, x, y)
Return `x` if `condition` is `true`, otherwise return `y`. This differs from `?` or `if` in
that it is an ordinary function, so all the arguments are evaluated first. In some cases,
using `ifelse` instead of an `if` statement can eliminate the branch in generated code and
provide higher performance in tight loops.
"""
ifelse(c::Bool, x, y) = select_value(c, x, y)
"""
cmp(x,y)
Return -1, 0, or 1 depending on whether `x` is less than, equal to, or greater than `y`,
respectively. Uses the total order implemented by `isless`. For floating-point numbers, uses `<`
but throws an error for unordered arguments.
"""
cmp(x,y) = isless(x,y) ? -1 : ifelse(isless(y,x), 1, 0)
"""
lexcmp(x, y)
Compare `x` and `y` lexicographically and return -1, 0, or 1 depending on whether `x` is
less than, equal to, or greater than `y`, respectively. This function should be defined for
lexicographically comparable types, and `lexless` will call `lexcmp` by default.
"""
lexcmp(x,y) = cmp(x,y)
"""
lexless(x, y)
Determine whether `x` is lexicographically less than `y`.
"""
lexless(x,y) = lexcmp(x,y)<0
# cmp returns -1, 0, +1 indicating ordering
cmp(x::Integer, y::Integer) = ifelse(isless(x,y), -1, ifelse(isless(y,x), 1, 0))
"""
max(x, y, ...)
Return the maximum of the arguments. See also the [`maximum`](:func:`maximum`) function
to take the maximum element from a collection.
"""
max(x,y) = ifelse(y < x, x, y)
"""
min(x, y, ...)
Return the minimum of the arguments. See also the [`minimum`](:func:`minimum`) function
to take the minimum element from a collection.
"""
min(x,y) = ifelse(y < x, y, x)
"""
minmax(x, y)
Return `(min(x,y), max(x,y))`. See also: [`extrema`](:func:`extrema`) that returns `(minimum(x), maximum(x))`.
```jldoctest
julia> minmax('c','b')
('b','c')
```
"""
minmax(x,y) = y < x ? (y, x) : (x, y)
scalarmax(x,y) = max(x,y)
scalarmax(x::AbstractArray, y::AbstractArray) = throw(ArgumentError("ordering is not well-defined for arrays"))
scalarmax(x , y::AbstractArray) = throw(ArgumentError("ordering is not well-defined for arrays"))
scalarmax(x::AbstractArray, y ) = throw(ArgumentError("ordering is not well-defined for arrays"))
scalarmin(x,y) = min(x,y)
scalarmin(x::AbstractArray, y::AbstractArray) = throw(ArgumentError("ordering is not well-defined for arrays"))
scalarmin(x , y::AbstractArray) = throw(ArgumentError("ordering is not well-defined for arrays"))
scalarmin(x::AbstractArray, y ) = throw(ArgumentError("ordering is not well-defined for arrays"))
## definitions providing basic traits of arithmetic operators ##
"""
identity(x)
The identity function. Returns its argument.
"""
identity(x) = x
+(x::Number) = x
*(x::Number) = x
(&)(x::Integer) = x
(|)(x::Integer) = x
($)(x::Integer) = x
# foldl for argument lists. expand recursively up to a point, then
# switch to a loop. this allows small cases like `a+b+c+d` to be inlined
# efficiently, without a major slowdown for `+(x...)` when `x` is big.
afoldl(op,a) = a
afoldl(op,a,b) = op(a,b)
afoldl(op,a,b,c...) = afoldl(op, op(a,b), c...)
function afoldl(op,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,qs...)
y = op(op(op(op(op(op(op(op(op(op(op(op(op(op(op(a,b),c),d),e),f),g),h),i),j),k),l),m),n),o),p)
for x in qs; y = op(y,x); end
y
end
for op in (:+, :*, :&, :|, :$, :min, :max, :kron)
@eval begin
# note: these definitions must not cause a dispatch loop when +(a,b) is
# not defined, and must only try to call 2-argument definitions, so
# that defining +(a,b) is sufficient for full functionality.
($op)(a, b, c, xs...) = afoldl($op, ($op)(($op)(a,b),c), xs...)
# a further concern is that it's easy for a type like (Int,Int...)
# to match many definitions, so we need to keep the number of
# definitions down to avoid losing type information.
end
end
"""
\\(x, y)
Left division operator: multiplication of `y` by the inverse of `x` on the left. Gives
floating-point results for integer arguments.
"""
\(x,y) = (y'/x')'
# .<op> defaults to <op>
./(x::Number,y::Number) = x/y
.\(x::Number,y::Number) = y./x
.*(x::Number,y::Number) = x*y
.^(x::Number,y::Number) = x^y
.+(x::Number,y::Number) = x+y
.-(x::Number,y::Number) = x-y
.<<(x::Integer,y::Integer) = x<<y
.>>(x::Integer,y::Integer) = x>>y
.==(x::Number,y::Number) = x == y
"""
.!=(x, y)
.≠(x,y)
Element-wise not-equals comparison operator.
"""
.!=(x::Number,y::Number) = x != y
.<( x::Real,y::Real) = x < y
"""
.<=(x, y)
.≤(x,y)
Element-wise less-than-or-equals comparison operator.
"""
.<=(x::Real,y::Real) = x <= y
const .≤ = .<=
const .≠ = .!=
# Core <<, >>, and >>> take either Int or UInt as second arg. Signed shift
# counts can shift in either direction, and are translated here to unsigned
# counts. Integer datatypes only need to implement the unsigned version.
"""
<<(x, n)
Left bit shift operator, `x << n`. For `n >= 0`, the result is `x` shifted left
by `n` bits, filling with `0`s. This is equivalent to `x * 2^n`. For `n < 0`,
this is equivalent to `x >> -n`.
```jldoctest
julia> Int8(3) << 2
12
julia> bits(Int8(3))
"00000011"
julia> bits(Int8(12))
"00001100"
```
See also [`>>`](:func:`>>`), [`>>>`](:func:`>>>`).
"""
function <<(x::Integer, c::Integer)
typemin(Int) <= c <= typemax(Int) && return x << (c % Int)
(x >= 0 || c >= 0) && return zero(x)
oftype(x, -1)
end
<<(x::Integer, c::Unsigned) = c <= typemax(UInt) ? x << (c % UInt) : zero(x)
<<(x::Integer, c::Int) = c >= 0 ? x << unsigned(c) : x >> unsigned(-c)
"""
>>(x, n)
Right bit shift operator, `x >> n`. For `n >= 0`, the result is `x` shifted
right by `n` bits, where `n >= 0`, filling with `0`s if `x >= 0`, `1`s if `x <
0`, preserving the sign of `x`. This is equivalent to `fld(x, 2^n)`. For `n <
0`, this is equivalent to `x << -n`.
```jldoctest
julia> Int8(13) >> 2
3
julia> bits(Int8(13))
"00001101"
julia> bits(Int8(3))
"00000011"
julia> Int8(-14) >> 2
-4
julia> bits(Int8(-14))
"11110010"
julia> bits(Int8(-4))
"11111100"
```
See also [`>>>`](:func:`>>>`), [`<<`](:func:`<<`).
"""
function >>(x::Integer, c::Integer)
typemin(Int) <= c <= typemax(Int) && return x >> (c % Int)
(x >= 0 || c < 0) && return zero(x)
oftype(x, -1)
end
>>(x::Integer, c::Unsigned) = c <= typemax(UInt) ? x >> (c % UInt) : zero(x)
>>(x::Integer, c::Int) = c >= 0 ? x >> unsigned(c) : x << unsigned(-c)
"""
>>>(x, n)
Unsigned right bit shift operator, `x >>> n`. For `n >= 0`, the result is `x`
shifted right by `n` bits, where `n >= 0`, filling with `0`s. For `n < 0`, this
is equivalent to `x << -n`.
For `Unsigned` integer types, this is equivalent to [`>>`](:func:`>>`). For
`Signed` integer types, this is equivalent to `signed(unsigned(x) >> n)`.
```jldoctest
julia> Int8(-14) >>> 2
60
julia> bits(Int8(-14))
"11110010"
julia> bits(Int8(60))
"00111100"
```
`BigInt`s are treated as if having infinite size, so no filling is required and this
is equivalent to [`>>`](:func:`>>`).
See also [`>>`](:func:`>>`), [`<<`](:func:`<<`).
"""
>>>(x::Integer, c::Integer) =
typemin(Int) <= c <= typemax(Int) ? x >>> (c % Int) : zero(x)
>>>(x::Integer, c::Unsigned) = c <= typemax(UInt) ? x >>> (c % UInt) : zero(x)
>>>(x::Integer, c::Int) = c >= 0 ? x >>> unsigned(c) : x << unsigned(-c)
# fallback div, fld, and cld implementations
# NOTE: C89 fmod() and x87 FPREM implicitly provide truncating float division,
# so it is used here as the basis of float div().
div{T<:Real}(x::T, y::T) = convert(T,round((x-rem(x,y))/y))
"""
fld(x, y)
Largest integer less than or equal to `x/y`.
```jldoctest
julia> fld(7.3,5.5)
1.0
```
"""
fld{T<:Real}(x::T, y::T) = convert(T,round((x-mod(x,y))/y))
"""
cld(x, y)
Smallest integer larger than or equal to `x/y`.
```jldoctest
julia> cld(5.5,2.2)
3.0
```
"""
cld{T<:Real}(x::T, y::T) = convert(T,round((x-modCeil(x,y))/y))
#rem{T<:Real}(x::T, y::T) = convert(T,x-y*trunc(x/y))
#mod{T<:Real}(x::T, y::T) = convert(T,x-y*floor(x/y))
modCeil{T<:Real}(x::T, y::T) = convert(T,x-y*ceil(x/y))
# operator alias
"""
rem(x, y)
%(x, y)
Remainder from Euclidean division, returning a value of the same sign as `x`, and smaller in
magnitude than `y`. This value is always exact.
```julia
x == div(x,y)*y + rem(x,y)
```
"""
rem
const % = rem
.%(x::Real, y::Real) = x%y
"""
div(x, y)
÷(x, y)
The quotient from Euclidean division. Computes `x/y`, truncated to an integer.
"""
div
const ÷ = div
.÷(x::Real, y::Real) = x÷y
"""
mod1(x, y)
Modulus after flooring division, returning a value `r` such that `mod(r, y) == mod(x, y)`
in the range ``(0, y]`` for positive `y` and in the range ``[y,0)`` for negative `y`.
"""
mod1{T<:Real}(x::T, y::T) = (m=mod(x,y); ifelse(m==0, y, m))
# efficient version for integers
mod1{T<:Integer}(x::T, y::T) = mod(x+y-T(1),y)+T(1)
"""
fld1(x, y)
Flooring division, returning a value consistent with `mod1(x,y)`
```julia
x == fld(x,y)*y + mod(x,y)
x == (fld1(x,y)-1)*y + mod1(x,y)
```
"""
fld1{T<:Real}(x::T, y::T) = (m=mod(x,y); fld(x-m,y))
# efficient version for integers
fld1{T<:Integer}(x::T, y::T) = fld(x+y-T(1),y)
"""
fldmod1(x, y)
Return `(fld1(x,y), mod1(x,y))`.
"""
fldmod1{T<:Real}(x::T, y::T) = (fld1(x,y), mod1(x,y))
# efficient version for integers
fldmod1{T<:Integer}(x::T, y::T) = (fld1(x,y), mod1(x,y))
# transpose
"""
ctranspose(A)
The conjugate transposition operator (`'`).
"""
ctranspose(x) = conj(transpose(x))
conj(x) = x
# transposed multiply
"""
Ac_mul_B(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ⋅B``.
"""
Ac_mul_B(a,b) = ctranspose(a)*b
"""
A_mul_Bc(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``A⋅Bᴴ``.
"""
A_mul_Bc(a,b) = a*ctranspose(b)
"""
Ac_mul_Bc(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ Bᴴ``.
"""
Ac_mul_Bc(a,b) = ctranspose(a)*ctranspose(b)
"""
At_mul_B(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᵀ⋅B``.
"""
At_mul_B(a,b) = transpose(a)*b
"""
A_mul_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``A⋅Bᵀ``.
"""
A_mul_Bt(a,b) = a*transpose(b)
"""
At_mul_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᵀ⋅Bᵀ``.
"""
At_mul_Bt(a,b) = transpose(a)*transpose(b)
# transposed divide
"""
Ac_rdiv_B(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ / B``.
"""
Ac_rdiv_B(a,b) = ctranspose(a)/b
"""
A_rdiv_Bc(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``A / Bᴴ``.
"""
A_rdiv_Bc(a,b) = a/ctranspose(b)
"""
Ac_rdiv_Bc(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ / Bᴴ``.
"""
Ac_rdiv_Bc(a,b) = ctranspose(a)/ctranspose(b)
"""
At_rdiv_B(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᵀ / B``.
"""
At_rdiv_B(a,b) = transpose(a)/b
"""
A_rdiv_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``A / Bᵀ``.
"""
A_rdiv_Bt(a,b) = a/transpose(b)
"""
At_rdiv_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᵀ / Bᵀ``.
"""
At_rdiv_Bt(a,b) = transpose(a)/transpose(b)
"""
Ac_ldiv_B(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ`` \\ ``B``.
"""
Ac_ldiv_B(a,b) = ctranspose(a)\b
"""
A_ldiv_Bc(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``A`` \\ ``Bᴴ``.
"""
A_ldiv_Bc(a,b) = a\ctranspose(b)
"""
Ac_ldiv_Bc(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ`` \\ ``Bᴴ``.
"""
Ac_ldiv_Bc(a,b) = ctranspose(a)\ctranspose(b)
"""
At_ldiv_B(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᵀ`` \\ ``B``.
"""
At_ldiv_B(a,b) = transpose(a)\b
"""
A_ldiv_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``A`` \\ ``Bᵀ``.
"""
A_ldiv_Bt(a,b) = a\transpose(b)
"""
At_ldiv_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᵀ`` \\ ``Bᵀ``.
"""
At_ldiv_Bt(a,b) = At_ldiv_B(a,transpose(b))
"""
Ac_ldiv_Bt(A, B)
For matrices or vectors ``A`` and ``B``, calculates ``Aᴴ`` \\ ``Bᵀ``.
"""
Ac_ldiv_Bt(a,b) = Ac_ldiv_B(a,transpose(b))
widen{T<:Number}(x::T) = convert(widen(T), x)
"""
eltype(type)
Determine the type of the elements generated by iterating a collection of the given `type`.
For associative collection types, this will be a `Pair{KeyType,ValType}`. The definition
`eltype(x) = eltype(typeof(x))` is provided for convenience so that instances can be passed
instead of types. However the form that accepts a type argument should be defined for new
types.
"""
eltype(::Type) = Any
eltype(::Type{Any}) = Any
eltype(t::DataType) = eltype(supertype(t))
eltype(x) = eltype(typeof(x))
# function pipelining
"""
|>(x, f)
Applies a function to the preceding argument. This allows for easy function chaining.
```jldoctest
julia> [1:5;] |> x->x.^2 |> sum |> inv
0.01818181818181818
```
"""
|>(x, f) = f(x)
# array shape rules
promote_shape(::Tuple{}, ::Tuple{}) = ()
function promote_shape(a::Tuple{Int,}, b::Tuple{Int,})
if a[1] != b[1]
throw(DimensionMismatch("dimensions must match"))
end
return a
end
function promote_shape(a::Tuple{Int,Int}, b::Tuple{Int,})
if a[1] != b[1] || a[2] != 1
throw(DimensionMismatch("dimensions must match"))
end
return a
end
promote_shape(a::Tuple{Int,}, b::Tuple{Int,Int}) = promote_shape(b, a)
function promote_shape(a::Tuple{Int, Int}, b::Tuple{Int, Int})
if a[1] != b[1] || a[2] != b[2]
throw(DimensionMismatch("dimensions must match"))
end
return a
end
"""
promote_shape(s1, s2)
Check two array shapes for compatibility, allowing trailing singleton dimensions, and return
whichever shape has more dimensions.
```jldoctest
julia> a = ones(3,4,1,1,1);
julia> b = ones(3,4);
julia> promote_shape(a,b)
(Base.OneTo(3),Base.OneTo(4),Base.OneTo(1),Base.OneTo(1),Base.OneTo(1))
julia> promote_shape((2,3,1,4), (2,3,1,4,1))
(2,3,1,4,1)
```
"""
function promote_shape(a::Dims, b::Dims)
if length(a) < length(b)
return promote_shape(b, a)
end
for i=1:length(b)
if a[i] != b[i]
throw(DimensionMismatch("dimensions must match"))
end
end
for i=length(b)+1:length(a)
if a[i] != 1
throw(DimensionMismatch("dimensions must match"))
end
end
return a
end
function promote_shape(a::AbstractArray, b::AbstractArray)
promote_shape(indices(a), indices(b))
end
function promote_shape(a::Indices, b::Indices)
if length(a) < length(b)
return promote_shape(b, a)
end
for i=1:length(b)
if a[i] != b[i]
throw(DimensionMismatch("dimensions must match"))
end
end
for i=length(b)+1:length(a)
if a[i] != 1:1
throw(DimensionMismatch("dimensions must match"))
end
end
return a
end
function throw_setindex_mismatch(X, I)
if length(I) == 1
throw(DimensionMismatch("tried to assign $(length(X)) elements to $(I[1]) destinations"))
else
throw(DimensionMismatch("tried to assign $(dims2string(size(X))) array to $(dims2string(I)) destination"))
end
end
# check for valid sizes in A[I...] = X where X <: AbstractArray
# we want to allow dimensions that are equal up to permutation, but only
# for permutations that leave array elements in the same linear order.
# those are the permutations that preserve the order of the non-singleton
# dimensions.
function setindex_shape_check(X::AbstractArray, I::Integer...)
li = ndims(X)
lj = length(I)
i = j = 1
while true
ii = length(indices(X,i))
jj = I[j]
if i == li || j == lj
while i < li
i += 1
ii *= length(indices(X,i))
end
while j < lj
j += 1
jj *= I[j]
end
if ii != jj
throw_setindex_mismatch(X, I)
end
return
end
if ii == jj
i += 1
j += 1
elseif ii == 1
i += 1
elseif jj == 1
j += 1
else
throw_setindex_mismatch(X, I)
end
end
end
setindex_shape_check(X::AbstractArray) =
(_length(X)==1 || throw_setindex_mismatch(X,()))
setindex_shape_check(X::AbstractArray, i::Integer) =
(_length(X)==i || throw_setindex_mismatch(X, (i,)))
setindex_shape_check{T}(X::AbstractArray{T,1}, i::Integer) =
(_length(X)==i || throw_setindex_mismatch(X, (i,)))
setindex_shape_check{T}(X::AbstractArray{T,1}, i::Integer, j::Integer) =
(_length(X)==i*j || throw_setindex_mismatch(X, (i,j)))
function setindex_shape_check{T}(X::AbstractArray{T,2}, i::Integer, j::Integer)
if length(X) != i*j
throw_setindex_mismatch(X, (i,j))
end
sx1 = length(indices(X,1))
if !(i == 1 || i == sx1 || sx1 == 1)
throw_setindex_mismatch(X, (i,j))
end
end
setindex_shape_check(X, I...) = nothing # Non-arrays broadcast to all idxs
# convert to a supported index type (Array, Colon, or Int)
to_index(i::Int) = i
to_index(i::Integer) = convert(Int,i)::Int
to_index(c::Colon) = c
to_index(I::AbstractArray{Bool}) = find(I)
to_index(A::AbstractArray) = A
to_index{T<:AbstractArray}(A::AbstractArray{T}) = throw(ArgumentError("invalid index: $A"))
to_index(A::AbstractArray{Colon}) = throw(ArgumentError("invalid index: $A"))
to_index(i) = throw(ArgumentError("invalid index: $i"))
to_indexes() = ()
to_indexes(i1) = (to_index(i1),)
to_indexes(i1, I...) = (to_index(i1), to_indexes(I...)...)
# Addition/subtraction of ranges
for f in (:+, :-)
@eval begin
function $f(r1::OrdinalRange, r2::OrdinalRange)
r1l = length(r1)
(r1l == length(r2) ||
throw(DimensionMismatch("argument dimensions must match")))
range($f(first(r1),first(r2)), $f(step(r1),step(r2)), r1l)
end
function $f{T<:AbstractFloat}(r1::FloatRange{T}, r2::FloatRange{T})
len = r1.len
(len == r2.len ||
throw(DimensionMismatch("argument dimensions must match")))
divisor1, divisor2 = r1.divisor, r2.divisor
if divisor1 == divisor2
FloatRange{T}($f(r1.start,r2.start), $f(r1.step,r2.step),
len, divisor1)
else
d1 = Int(divisor1)
d2 = Int(divisor2)
d = lcm(d1,d2)
s1 = div(d,d1)
s2 = div(d,d2)
FloatRange{T}($f(r1.start*s1, r2.start*s2),
$f(r1.step*s1, r2.step*s2), len, d)
end
end
function $f{T<:AbstractFloat}(r1::LinSpace{T}, r2::LinSpace{T})
len = r1.len
(len == r2.len ||
throw(DimensionMismatch("argument dimensions must match")))
divisor1, divisor2 = r1.divisor, r2.divisor
if divisor1 == divisor2
LinSpace{T}($f(r1.start, r2.start), $f(r1.stop, r2.stop),
len, divisor1)
else
linspace(convert(T, $f(first(r1), first(r2))),
convert(T, $f(last(r1), last(r2))), len)
end
end
$f(r1::Union{FloatRange, OrdinalRange, LinSpace},
r2::Union{FloatRange, OrdinalRange, LinSpace}) =
$f(promote(r1, r2)...)
end
end
# vectorized ifelse
function ifelse(c::AbstractArray{Bool}, x, y)
[ifelse(ci, x, y) for ci in c]
end
function ifelse(c::AbstractArray{Bool}, x::AbstractArray, y::AbstractArray)
[ifelse(c_elem, x_elem, y_elem) for (c_elem, x_elem, y_elem) in zip(c, x, y)]
end
function ifelse(c::AbstractArray{Bool}, x::AbstractArray, y)
[ifelse(c_elem, x_elem, y) for (c_elem, x_elem) in zip(c, x)]
end
function ifelse(c::AbstractArray{Bool}, x, y::AbstractArray)
[ifelse(c_elem, x, y_elem) for (c_elem, y_elem) in zip(c, y)]
end
# Pair
immutable Pair{A,B}
first::A
second::B
end
const => = Pair
start(p::Pair) = 1
done(p::Pair, i) = i>2
next(p::Pair, i) = (getfield(p,i), i+1)
indexed_next(p::Pair, i::Int, state) = (getfield(p,i), i+1)
hash(p::Pair, h::UInt) = hash(p.second, hash(p.first, h))
==(p::Pair, q::Pair) = (p.first==q.first) & (p.second==q.second)
isequal(p::Pair, q::Pair) = isequal(p.first,q.first) & isequal(p.second,q.second)
isless(p::Pair, q::Pair) = ifelse(!isequal(p.first,q.first), isless(p.first,q.first),
isless(p.second,q.second))
getindex(p::Pair,i::Int) = getfield(p,i)
getindex(p::Pair,i::Real) = getfield(p, convert(Int, i))
reverse{A,B}(p::Pair{A,B}) = Pair{B,A}(p.second, p.first)
endof(p::Pair) = 2
length(p::Pair) = 2
# some operators not defined yet
global //, >:, <|, hcat, hvcat, ⋅, ×, ∈, ∉, ∋, ∌, ⊆, ⊈, ⊊, ∩, ∪, √, ∛
this_module = current_module()
baremodule Operators
export
!,
!=,
!==,
===,
$,
%,
.%,
÷,
.÷,
&,
*,
+,
-,
.!=,
.+,
.-,
.*,
./,
.<,
.<=,
.==,
.>,
.>=,
.\,
.^,
/,
//,
<,
<:,
>:,
<<,
<=,
==,
>,
>=,
≥,
≤,
≠,
.≥,
.≤,
.≠,
>>,
.>>,
.<<,
>>>,
\,
^,
|,
|>,
<|,
~,
⋅,
×,
∈,
∉,
∋,
∌,
⊆,
⊈,
⊊,
∩,
∪,
√,
∛,
colon,
hcat,
vcat,
hvcat,
getindex,
setindex!,
transpose,
ctranspose
import ..this_module: !, !=, $, %, .%, ÷, .÷, &, *, +, -, .!=, .+, .-, .*, ./, .<, .<=, .==, .>,
.>=, .\, .^, /, //, <, <:, <<, <=, ==, >, >=, >>, .>>, .<<, >>>,
<|, |>, \, ^, |, ~, !==, ===, >:, colon, hcat, vcat, hvcat, getindex, setindex!,
transpose, ctranspose,
≥, ≤, ≠, .≥, .≤, .≠, ⋅, ×, ∈, ∉, ∋, ∌, ⊆, ⊈, ⊊, ∩, ∪, √, ∛
end