Fixes for Chebyshev #23
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@kalmarek I found the issue with the tests, |
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julia> v = sparsevec(UInt16[1, 2], Int[2, 3])
2-element SparseVector{Int64, UInt16} with 2 stored entries:
[1] = 2
[2] = 3
julia> SparseArrays.indtype(v)
UInt16
julia> SparseArrays.indtype(typeof(v))
ERROR: MethodError: no method matching indtype(::Type{SparseVector{Int64, UInt16}})
Closest candidates are:
indtype(::SubArray{Tv, 1, <:AbstractSparseVector{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}}, false} where {Tv, Ti})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/sparsevector.jl:151
indtype(::SubArray{Tv, 1, <:SparseArrays.AbstractSparseMatrixCSC{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, false} where {Tv, Ti})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/sparsevector.jl:150
indtype(::Union{LowerTriangular{var"#s2", var"#s1"}, UnitLowerTriangular{var"#s2", var"#s1"}, UnitUpperTriangular{var"#s2", var"#s1"}, UpperTriangular{var"#s2", var"#s1"}} where {var"#s2", var"#s1"<:AbstractSparseArray})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/abstractsparse.jl:79
...
Stacktrace:
[1] top-level scope
@ REPL[34]:1
julia> keytype(v)
Int64
julia> keytype(typeof(v))
Int64
julia> SparseArrays.indtype([1, 3])
ERROR: MethodError: no method matching indtype(::Vector{Int64})
Closest candidates are:
indtype(::SubArray{Tv, 1, <:AbstractSparseVector{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}}, false} where {Tv, Ti})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/sparsevector.jl:151
indtype(::SubArray{Tv, 1, <:SparseArrays.AbstractSparseMatrixCSC{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, false} where {Tv, Ti})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/sparsevector.jl:150
indtype(::Union{LowerTriangular{var"#s2", var"#s1"}, UnitLowerTriangular{var"#s2", var"#s1"}, UnitUpperTriangular{var"#s2", var"#s1"}, UpperTriangular{var"#s2", var"#s1"}} where {var"#s2", var"#s1"<:AbstractSparseArray})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/abstractsparse.jl:79
...
Stacktrace:
[1] top-level scope
@ REPL[37]:1
julia> SparseArrays.indtype(Vector{Int})
ERROR: MethodError: no method matching indtype(::Type{Vector{Int64}})
Closest candidates are:
indtype(::SubArray{Tv, 1, <:AbstractSparseVector{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}}, false} where {Tv, Ti})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/sparsevector.jl:151
indtype(::SubArray{Tv, 1, <:SparseArrays.AbstractSparseMatrixCSC{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, false} where {Tv, Ti})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/sparsevector.jl:150
indtype(::Union{LowerTriangular{var"#s2", var"#s1"}, UnitLowerTriangular{var"#s2", var"#s1"}, UnitUpperTriangular{var"#s2", var"#s1"}, UpperTriangular{var"#s2", var"#s1"}} where {var"#s2", var"#s1"<:AbstractSparseArray})
@ SparseArrays ~/.julia/juliaup/julia-1.10.1+0.x64.linux.gnu/share/julia/stdlib/v1.10/SparseArrays/src/abstractsparse.jl:79
...
Stacktrace:
[1] top-level scope
@ REPL[38]:1so we'll have to create our own. |
Codecov ReportAttention: Patch coverage is
❗ Your organization needs to install the Codecov GitHub app to enable full functionality. Additional details and impacted files@@ Coverage Diff @@
## mk/non_monomial_basis #23 +/- ##
=========================================================
- Coverage 51.83% 51.82% -0.01%
=========================================================
Files 15 15
Lines 463 465 +2
=========================================================
+ Hits 240 241 +1
- Misses 223 224 +1
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
blegat
added a commit
that referenced
this pull request
May 27, 2024
* Fixes for Chebyshev * Add key_type
blegat
added a commit
that referenced
this pull request
May 28, 2024
* remove CachedMTable
* introduce ImplicitBasis and ExplicitBasis
* rename TrivialMStructure → LazyMStructure
* remove baseless StarAlgebras
* AlgebraElement coefficients don't need to be vector
* first try on coefficients
* Fixes
* rename Basis → Fixed Basis
* introduce DiracBasis <: ImplicitBasis
* move mul! to mstructures
* fix the order {K,V} of params in AbstractCoefficients
* fix printing of LazyMStructure
* add keytype, valtype for AbstractCoefficients
* add getindex for LazyMStructure over DiracBasis
* add == for AbstractCoefficients
* make DiracBasis threadsafe
* give it a try with different parametrization of MStructures
* Fixes
* move star_of to FixedBasis
* return DiracDeltas on indexing DiracBasis
* dispatch star through star(basis, coeffs)
* move fmac! so that StarAlgebras actually load
* implement basic canonicalize
* more star dispatch
* simplify AlgebraElement
* fix: define MA.operate!(zero, ::SparseVector)
* fix show methods
* fix getindex for LazyMStructure
* move coefficients to their separate files
* move Dirac/FixedBases to their separate files
* add (Dirac|Augmented)MStructure
* reimplement MTables
* fix the definitions of of StarAlgebra/AlgebraElements
* add a new implementation of star
* reimplement augmentation
* various small fixes
* add fmac! for AbstractVector
* remove show for DiracMStructure now that it is basis free
* quickly fix one
* commit tmp.jl as an example of what works
* move tmp.jl to test/perm_grp_algebra.jl
* turn demo perm_grp_algebra.jl into testset
* disable the old test for now
* move compat section for Groups etc to test/Project.toml
* Let AlgebraElement be an AbstractMutable
* zero!(.) -> MA.operate!(zero, .)
* neg! -> operate_to!(-
* add! -> operate_to!(+
* mul! -> MA.operate_to!
* mul! with mstruct to MA
* sub! -> MA
* fmac -> MA
* Check for alias
* unsafe_add! -> UnsafeAddMul
* Document UnsafeAddMul
* Fixes for Chebyshev (#23)
* Fixes for Chebyshev
* Add key_type
* replace keytype → key_type
* rename _nzpairs → nonzero_pairs
* implement canonicalization via MA.operate!!(canonical, ...)
* move the mutable API to coefficients
* remove Dirac and use GroupElements directly
This is akin to using monomials
* rename AugmentedDirac to Augmented
* fix: == for SparseCoefficients
* reorganize defs for star, aug and norm/dot
* implement MA API for SparseCoefficients
* add zero_coeffs(::Type, ::AbstractBasis)
currently defaults to
* SparseCoefficients (ImplicitBasis)
* SparseVectir (ExplicitBasis)
* fix: use nonzero_pairs instead of pairs
* fix: logic for canonical(::SparseCoefficients)
* add getindex/setindex! for SparseCoefficients
* reshuffle definitions in types.jl
(this file needs to be broken down anyway)
* one/zero & isone/iszero are surprisingly hard...
* various small fixes/convenience additions
* fix: use getindex instead of broadcasting
* [tests] implement iteration over free words
* [test] rewrite testset Arithmetic in group algebra
* [tests] rewrite tests with free monoid algebra
* [tests] rewrite tests for chaching multiplication
* [tests] update constructors and sohs in group algebra
* [tests] reenable tests
* fix: test LinearAlgebra.dot
* fix includes
* bump version to 0.3
* [tests] fix: @Allocations exist in julia ≥ 1.9
* tidy up aug, star and adjoint
* allow no cache in FixedBasis
* document minimal AbstractCoefficients API
* de-randomize !isone test
* add default cache parameter (UInt32(0)) for FixedBasis
* fix: compute hash on canonicalized coeffs
* add Comparable to pass < for sorting in sparse coeffs
* caching for MTables does not work for isbits elements
This is because isbits elements may not track isdefined / isassigned status.
As a consequence Matrix{T}(undef, dims) is initialized to garbage.
* fix: use proper (double) indexing when evaluating AlgebraElement
* enable canonical for Vectors
* add += with MTable for AbstractVectors
* fix: MA.operate!(zero, ...) failed for SparseVector{Rational...}
Most probably due to Rationals initialized from random memory
* remove old/unused functions
* add Base.keys(b::ExplicitBasis)
* expand docstring for AbstractBasis
* inlining nonzero_pairs saves lots of allocations
* test aug for Augmented basis
* reduce allocations by hinting size
* unbreak tests with MutableArithmetics 1.4.3
* test allocations only on 1.10
the number of allocations on nightly increased
due to the introduction of Memory{T} in base julia
* rearrange tests
* lock complete! of MTable behind a SpinLock
* Unbreak broken tests
* Exclude on Julia latest
* Missing newline
* remove Comparable
* update README with working examples
* fix (?) for multithreading access to MTable cache
* make augmented stuff a self-contained file
* don't export supp
* fix nonzero_pairs for a vector
* add tests for arithmetic on coeffs
* add tests for using dense vector as coeffs
* add test for abstract coeffs
* add a bit of docs what AbstractCoefficients is
* formatting
* increase coverage by removing old methods
* fix norm/dot and provide coverage
* fix iteration on AugmentedBasis and add tests
* move the generic MA.operate! from ACoeffs to the lib
* one more broken test
* Base._return_type -> MA.promote_operation
* Fix broken all tests but one
* Turn last broken test into a passable test
* Add eachindex back
* Assume mutability for canonical (#31)
* Assume mutability for canonical
* Fix allocation test
* Debug for Julia v1.6
* Use 0.7
---------
Co-authored-by: Benoît Legat <benoit.legat@gmail.com>
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This is to help
MultivariatePolynomials.AbstractPolynomialbe used as well for coefficients