-
Notifications
You must be signed in to change notification settings - Fork 27
/
functions.jl
359 lines (315 loc) · 14.8 KB
/
functions.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
function Base.map(f, A::KeyedArray)
data = map(f, parent(A))
KeyedArray(data, map(copy, axiskeys(A)))#, copy(A.meta))
end
for fun in [:map, :map!], (T, S) in [ (:KeyedArray, :KeyedArray),
(:KeyedArray, :AbstractArray), (:AbstractArray, :KeyedArray),
(:KeyedArray, :NamedDimsArray), (:NamedDimsArray, :KeyedArray)] # for ambiguities
@eval function Base.$fun(f, A::$T, B::$S, Cs::AbstractArray...)
data = $fun(f, keyless(A), keyless(B), keyless.(Cs)...)
new_keys = unify_keys(keys_or_axes(A), keys_or_axes(B), keys_or_axes.(Cs)...)
KeyedArray(data, map(copy, new_keys)) # copy sometimes wasteful for map!, but OK.
end
end
using Base: Generator
function Base.collect(x::Generator{<:KeyedArray})
data = collect(Generator(x.f, x.iter.data))
KeyedArray(data, map(copy, axiskeys(x.iter)))#, copy(A.meta))
end
function Base.collect(x::Generator{<:Iterators.Enumerate{<:KeyedArray}})
data = collect(Generator(x.f, enumerate(x.iter.itr.data)))
KeyedArray(data, map(copy, axiskeys(x.iter.itr)))
end
for Ts in [(:KeyedArray,), (:KeyedArray, :NamedDimsArray), (:NamedDimsArray, :KeyedArray)]
@eval function Base.collect(x::Generator{<:Iterators.ProductIterator{<:Tuple{$(Ts...),Vararg{Any}}}})
data = collect(Generator(x.f, Iterators.product(keyless.(x.iter.iterators)...)))
all_keys = tuple_flatten(keys_or_axes.(x.iter.iterators)...)
KeyedArray(data, map(copy, all_keys))
end
end
tuple_flatten(x::Tuple, ys::Tuple...) = (x..., tuple_flatten(ys...)...)
tuple_flatten() = ()
function Base.mapreduce(f, op, A::KeyedArray; dims=:, kwargs...) # sum, prod, etc
dims === Colon() && return mapreduce(f, op, parent(A); kwargs...)
numerical_dims = NamedDims.dim(A, dims)
data = mapreduce(f, op, parent(A); dims=numerical_dims, kwargs...)
new_keys = ntuple(d -> d in numerical_dims ? Base.OneTo(1) : axiskeys(A,d), ndims(A))
return KeyedArray(data, map(copy, new_keys))#, copy(A.meta))
end
using Statistics
for fun in [:mean, :std, :var] # These don't use mapreduce, but could perhaps be handled better?
@eval function Statistics.$fun(A::KeyedArray; dims=:, kwargs...)
dims === Colon() && return $fun(parent(A); kwargs...)
numerical_dims = NamedDims.dim(A, dims)
data = $fun(parent(A); dims=numerical_dims, kwargs...)
new_keys = ntuple(d -> d in numerical_dims ? Base.OneTo(1) : axiskeys(A,d), ndims(A))
return KeyedArray(data, map(copy, new_keys))#, copy(A.meta))
end
end
# Handle function interface for `mean` only
if VERSION >= v"1.3"
@eval function Statistics.mean(f, A::KeyedArray; dims=:, kwargs...)
dims === Colon() && return mean(f, parent(A); kwargs...)
numerical_dims = NamedDims.dim(A, dims)
data = mean(f, parent(A); dims=numerical_dims, kwargs...)
new_keys = ntuple(d -> d in numerical_dims ? Base.OneTo(1) : axiskeys(A,d), ndims(A))
return KeyedArray(data, map(copy, new_keys))#, copy(A.meta))
end
end
for fun in [:cov, :cor] # Returned the axes work are different for cov and cor
@eval function Statistics.$fun(A::KeyedMatrix; dims=1, kwargs...)
numerical_dim = NamedDims.dim(A, dims)
data = $fun(parent(A); dims=numerical_dim, kwargs...)
# Use same remaining axis for both dimensions of data
rem_key = axiskeys(A, 3-numerical_dim)
KeyedArray(data, (copy(rem_key), copy(rem_key)))
end
end
function Base.dropdims(A::KeyedArray; dims)
numerical_dims = NamedDims.dim(A, dims)
data = dropdims(parent(A); dims=dims)
new_keys = key_skip(axiskeys(A), numerical_dims...)
KeyedArray(data, new_keys)#, A.meta)
end
key_skip(tup::Tuple, d, dims...) = key_skip(
ntuple(n -> n<d ? tup[n] : tup[n+1], length(tup)-1),
map(n -> n<d ? n : n-1, dims)...)
key_skip(tup::Tuple) = tup
function Base.permutedims(A::KeyedArray, perm)
numerical_perm = hasnames(A) ? NamedDims.dim(dimnames(A), perm) : perm
data = permutedims(parent(A), numerical_perm)
new_keys = ntuple(d -> copy(axiskeys(A, perm[d])), ndims(A))
KeyedArray(data, new_keys)#, copy(A.meta))
end
if VERSION >= v"1.1"
# This copies the implementation from Base, except with numerical_dims:
@inline function Base.eachslice(A::KeyedArray; dims)
numerical_dims = NamedDims.dim(A, dims)
length(numerical_dims) == 1 || throw(ArgumentError("only single dimensions are supported"))
dim = first(numerical_dims)
dim <= ndims(A) || throw(DimensionMismatch("A doesn't have $dim dimensions"))
inds_before = ntuple(d->(:), dim-1)
inds_after = ntuple(d->(:), ndims(A)-dim)
return (view(A, inds_before..., i, inds_after...) for i in axes(A, dim))
end
end
function Base.mapslices(f, A::KeyedArray; dims)
numerical_dims = NamedDims.dim(A, dims)
data = mapslices(f, parent(A); dims=numerical_dims)
new_keys = ntuple(ndims(A)) do d
d in numerical_dims ? axes(data,d) : copy(axiskeys(A, d))
end
KeyedArray(data, new_keys)#, copy(A.meta))
end
Base.selectdim(A::KeyedArray, s::Symbol, i) = selectdim(A, NamedDims.dim(A, s), i)
for (T, S) in [(:KeyedVecOrMat, :KeyedVecOrMat), # KeyedArray gives ambiguities
(:KeyedVecOrMat, :AbstractVecOrMat), (:AbstractVecOrMat, :KeyedVecOrMat),
(:NdaKaVoM, :NdaKaVoM),
(:NdaKaVoM, :KeyedVecOrMat), (:KeyedVecOrMat, :NdaKaVoM),
(:NdaKaVoM, :AbstractVecOrMat), (:AbstractVecOrMat, :NdaKaVoM),
]
@eval function Base.vcat(A::$T, B::$S, Cs::AbstractVecOrMat...)
data = vcat(keyless(A), keyless(B), keyless.(Cs)...)
new_1 = key_vcat(keys_or_axes(A,1), keys_or_axes(B,1), keys_or_axes.(Cs,1)...)
new_keys = ndims(A) == 1 ? (new_1,) :
(new_1, unify_one(keys_or_axes(A,2), keys_or_axes(B,2), keys_or_axes.(Cs,2)...))
KeyedArray(data, map(copy, new_keys))
end
@eval function Base.hcat(A::$T, B::$S, Cs::AbstractVecOrMat...)
data = hcat(keyless(A), keyless(B), keyless.(Cs)...)
new_1 = unify_one(keys_or_axes(A,1), keys_or_axes(B,1), keys_or_axes.(Cs,1)...)
new_2 = ndims(A) == 1 ? axes(data,2) :
key_vcat(keys_or_axes(A,2), keys_or_axes(B,2), keys_or_axes.(Cs,2)...)
KeyedArray(data, map(copy, (new_1, new_2)))
end
end
for (T, S) in [ (:KeyedArray, :KeyedArray),
(:KeyedArray, :AbstractArray), (:AbstractArray, :KeyedArray),
(:KeyedArray, :NamedDimsArray), (:NamedDimsArray, :KeyedArray),
(:NdaKa, :NdaKa),
(:NdaKa, :KeyedArray), (:KeyedArray, :NdaKa),
(:NdaKa, :AbstractArray), (:AbstractArray, :NdaKa),
]
@eval function Base.cat(A::$T, B::$S, Cs::AbstractArray...; dims)
numerical_dims, data = if any(hasnames.((A, B, Cs...)))
old_names = NamedDims.unify_names_longest(dimnames(A), dimnames(B), dimnames.(Cs)...)
new_names = NamedDims.expand_dimnames(old_names, dims)
α = NamedDims.dim(new_names, dims)
β = cat(keyless(A), keyless(B), keyless.(Cs)...; dims=dims)
α, β
else
α = val_strip(dims)
β = cat(keyless(A), keyless(B), keyless.(Cs)...; dims=numerical_dims)
α, β
end
new_keys = ntuple(ndims(data)) do d
if d in numerical_dims
key_vcat(keys_or_axes(A,d), keys_or_axes(B,d), keys_or_axes.(Cs,d)...)
else
unify_one(keys_or_axes(A,d), keys_or_axes(B,d), keys_or_axes.(Cs,d)...)
end
end
KeyedArray(data, map(copy, new_keys)) # , copy(A.meta))
end
end
val_strip(dims::Val{d}) where {d} = d
val_strip(dims) = dims
key_vcat(a::AbstractVector, b::AbstractVector) = vcat(a,b)
key_vcat(a::Base.OneTo, b::Base.OneTo) = Base.OneTo(a.stop + b.stop)
key_vcat(a,b,cs...) = key_vcat(key_vcat(a,b),cs...)
function Base.sort(A::KeyedArray; dims, kw...)
dims′ = NamedDims.dim(A, dims)
data = sort(parent(A); dims=dims′, kw...)
# sorts each (say) col independently, thus keys along them loses meaning.
new_keys = ntuple(d -> d==dims′ ? OneTo(size(A,d)) : axiskeys(A,d), ndims(A))
KeyedArray(data, map(copy, new_keys)) # , copy(A.meta))
end
function Base.sort(A::KeyedVector; kw...)
perm = sortperm(parent(A); kw...)
KeyedArray(parent(A)[perm], (axiskeys(A,1)[perm],)) # , copy(A.meta))
end
function Base.sort!(A::KeyedVector; kw...)
perm = sortperm(parent(A); kw...)
permute!(axiskeys(A,1), perm) # error if keys cannot be sorted, could treat like push!
permute!(parent(A), perm)
A
end
sort_doc = """
sortslices(A; dims)
sortkeys(A; dims=1:ndims(A))
`Base.sortslices` sorts the corresponding keys too, along one dimension.
Calls its own implementation, roughly `p = sortperm(eachslice(A))`,
with default keyword `by=vec` to make this work on slices of any shape.
`sortkeys(A)` instead sorts everything by the keys.
Works along any number of dimensions, by detault all of them.
"""
@doc sort_doc
function Base.sortslices(A::KeyedArray; dims, by=vec, kw...)
dim′ = NamedDims.dim(A, dims)
perms = ntuple(ndims(A)) do d
d in dim′ || return Colon()
sortperm(collect(eachslice(parent(A), dims=dim′)); by=by, kw...)
end
new_keys = map(getindex, axiskeys(A), perms)
KeyedArray(keyless(A)[perms...], new_keys) # , copy(A.meta))
end
if VERSION < v"1.1" # defn copied Julia 1.4 Base abstractarraymath.jl:452
@inline function eachslice(A::AbstractArray; dims)
length(dims) == 1 || throw(ArgumentError("only single dimensions are supported"))
dim = first(dims)
dim <= ndims(A) || throw(DimensionMismatch("A doesn't have $dim dimensions"))
inds_before = ntuple(d->(:), dim-1)
inds_after = ntuple(d->(:), ndims(A)-dim)
return (view(A, inds_before..., i, inds_after...) for i in axes(A, dim))
end
end
@doc sort_doc
function sortkeys(A::Union{KeyedArray, NdaKa}; dims=1:ndims(A), kw...)
dims′ = NamedDims.dim(A, dims)
perms = ntuple(ndims(A)) do d
d in dims′ || return Colon()
axiskeys(A,d) isa AbstractUnitRange && return Colon() # avoids OneTo(n) -> 1:n
sortperm(axiskeys(A,d); kw...)
end
new_keys = map(getindex, axiskeys(A), perms)
KeyedArray(keyless(A)[perms...], new_keys) # , copy(A.meta))
end
Base.filter(f, A::KeyedVector) = getindex(A, map(f, parent(A)))
Base.filter(f, A::KeyedArray) = filter(f, parent(A))
using LinearAlgebra
for (mod, fun, lazy) in [(Base, :permutedims, false),
(LinearAlgebra, :transpose, true), (LinearAlgebra, :adjoint, true)]
@eval function $mod.$fun(A::KeyedArray)
data = $mod.$fun(parent(A))
new_keys = ndims(A)==1 ? (Base.OneTo(1), axiskeys(A,1)) :
ndims(data)==1 ? (axiskeys(A,2),) :
reverse(axiskeys(A))
KeyedArray(data, $(lazy ? :new_keys : :(map(copy, new_keys))))#, $(lazy ? :(A.meta) : :(copy(A.meta))))
end
end
Base.reshape(A::KeyedArray, dims::Tuple{Vararg{Int}}) = reshape(parent(A), dims...) # for ambiguities
Base.reshape(A::KeyedArray, dims::Tuple{Vararg{Union{Colon, Int}}}) = reshape(parent(A), dims...)
for fun in [:copy, :deepcopy, :similar, :zero, :one]
@eval Base.$fun(A::KeyedArray) = KeyedArray($fun(parent(A)), map(copy, axiskeys(A)))
@eval Base.$fun(A::NdaKa) = NamedDimsArray(KeyedArray(
$fun(parent(parent(A))),
map(copy, axiskeys(A))), dimnames(A))
end
Base.similar(A::KeyedArray, T::Type) = KeyedArray(similar(parent(A), T), map(copy, axiskeys(A)))
Base.similar(A::NdaKa, T::Type) = NamedDimsArray(KeyedArray(
similar(parent(parent(A)), T), map(copy, axiskeys(A))), dimnames(A))
Base.similar(A::KeyedArray, T::Type, dims::Int...) = similar(parent(A), T, dims...)
Base.similar(A::KeyedArray, dims::Int...) = similar(parent(A), dims...)
for fun in [:(==), :isequal, :isapprox]
for (T, S) in [ (:KeyedArray, :KeyedArray), (:KeyedArray, :NdaKa), (:NdaKa, :KeyedArray) ]
@eval function Base.$fun(A::$T, B::$S; kw...)
# Ideally you would pass isapprox(, atol) into unifiable_keys?
unifiable_keys(axiskeys(A), axiskeys(B)) || return false
return $fun(keyless(A), keyless(B); kw...)
end
end
end
Rlist = [:KeyedMatrix, :KeyedVector,
:(NdaKa{L,T,2} where {L,T}), :(NdaKa{L,T,1} where {L,T}),
:(KeyedVector{T} where {T<:Number}), :(NdaKa{L,T,1} where {L,T<:Number}), # ambiguities on 1.5
]
Olist = [ :AbstractMatrix, :AbstractVector, :Number,
:(Adjoint{<:Any,<:AbstractMatrix}), :(Adjoint{<:Any,<:AbstractVector}),
:(Transpose{<:Any,<:AbstractMatrix}), :(Transpose{<:Any,<:AbstractVector}),
:(NamedDimsArray{L,T,1} where {L,T}), :(NamedDimsArray{L,T,2} where {L,T}),
:(Adjoint{<:Number,<:AbstractVector}), # 1.5 problem...
]
for (Ts, Ss) in [(Rlist, Rlist), (Rlist, Olist), (Olist, Rlist)]
for T in Ts, S in Ss # some combinations are errors, later, that's ok
@eval Base.:*(x::$T, y::$S) = matmul(x,y)
@eval Base.:\(x::$T, y::$S) = ldiv(x,y)
@eval Base.:/(x::$T, y::$S) = rdiv(x,y)
end
end
for (fun, op) in [(:matmul, :*), (:ldiv, :\), (:rdiv, :/)]
@eval $fun(x::AbstractVecOrMat, y::Number) = KeyedArray($op(keyless(x), y), axiskeys(x))
@eval $fun(x::Number, y::AbstractVecOrMat) = KeyedArray($op(x, keyless(y)), axiskeys(y))
@eval $fun(x::AbstractVector, y::AbstractVector) = $op(keyless(x), keyless(y))
end
function matmul(x::AbstractMatrix, y::AbstractVecOrMat)
data = keyless(x) * keyless(y)
unify_one(keys_or_axes(x,2), keys_or_axes(y,1)) # just a check, discard these
if data isa AbstractVecOrMat
new_keys = (keys_or_axes(x,1), Base.tail(keys_or_axes(y))...)
KeyedArray(data, map(copy, new_keys))
else
data # case V' * V
end
end
function matmul(x::AbstractVector, y::AbstractMatrix) # used for v * v'
data = keyless(x) * keyless(y)
new_keys = (keys_or_axes(x,1), keys_or_axes(y,2))
KeyedArray(data, map(copy, new_keys))
end
# case of two vectors gives a scalar, caught above.
function ldiv(x::AbstractVecOrMat, y::AbstractVecOrMat)
data = keyless(x) \ keyless(y)
unify_one(keys_or_axes(x,1), keys_or_axes(y,1))
new_keys = (Base.tail(keys_or_axes(x))..., Base.tail(keys_or_axes(y))...)
KeyedArray(data, map(copy, new_keys))
end
function rdiv(x::AbstractVecOrMat, y::AbstractVecOrMat)
data = keyless(x) / keyless(y)
# unify_one(keys_or_axes(x,2), keys_or_axes(y,2)) # not right!
# new_keys = (tup_head(keys_or_axes(x))..., tup_head(keys_or_axes(y))...)
# KeyedArray(data, new_keys)
@warn "/ doesn't preserve keys yet, sorry" maxlog=1
data
end
tup_head(t::Tuple) = reverse(Base.tail(reverse(t)))
for fun in [:inv, :pinv,
:det, :logdet, :logabsdet,
:eigen, :eigvecs, :eigvals, :svd,
:diag
]
@eval LinearAlgebra.$fun(A::KeyedMatrix) = $fun(parent(A))
end
LinearAlgebra.cholesky(A::Hermitian{T, <:KeyedArray{T}}; kwargs...) where {T} =
cholesky(parent(A); kwargs...)
LinearAlgebra.cholesky(A::KeyedMatrix; kwargs...) =
cholesky(keyless_unname(A); kwargs...)