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comparison src/LazyTensors/lazy_tensor_operations.jl @ 433:7327a3e41df0 feature/lazy_identity

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Merge in default
author Jonatan Werpers <jonatan@werpers.com>
date Mon, 19 Oct 2020 09:50:16 +0200
parents 541c16a8c791 2958b4ebd565
children 648a36ebac99
comparison
equal deleted inserted replaced
432:f24daf573bc6 433:7327a3e41df0
72 domain_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = domain_size(tmBinOp.tm1) 72 domain_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = domain_size(tmBinOp.tm1)
73 73
74 Base.:+(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(tm1,tm2) 74 Base.:+(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(tm1,tm2)
75 Base.:-(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(tm1,tm2) 75 Base.:-(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(tm1,tm2)
76 76
77 """
78 TensorMappingComposition{T,R,K,D}
77 79
78 # TODO: Write tests and documentation for LazyTensorMappingComposition 80 Lazily compose two TensorMappings, so that they can be handled as a single TensorMapping.
79 # struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D} 81 """
80 # t1::TensorMapping{T,R,K} 82 struct TensorMappingComposition{T,R,K,D, TM1<:TensorMapping{T,R,K}, TM2<:TensorMapping{T,K,D}} <: TensorMapping{T,R,D}
81 # t2::TensorMapping{T,K,D} 83 t1::TM1
82 # end 84 t2::TM2
83 85
84 # Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t) 86 @inline function TensorMappingComposition(t1::TensorMapping{T,R,K}, t2::TensorMapping{T,K,D}) where {T,R,K,D}
87 @boundscheck if domain_size(t1) != range_size(t2)
88 throw(DimensionMismatch("the first argument has domain size $(domain_size(t1)) while the second has range size $(range_size(t2)) "))
89 end
90 return new{T,R,K,D, typeof(t1), typeof(t2)}(t1,t2)
91 end
92 # Add check for matching sizes as a boundscheck
93 end
94 export TensorMappingComposition
85 95
86 # function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D} 96 range_size(tm::TensorMappingComposition) = range_size(tm.t1)
87 # range_size(tm.t1, domain_size(tm.t2, domain_size)) 97 domain_size(tm::TensorMappingComposition) = domain_size(tm.t2)
88 # end
89 98
90 # function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D} 99 function apply(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg{S,R} where S) where {T,R,K,D}
91 # domain_size(tm.t1, domain_size(tm.t2, range_size)) 100 apply(c.t1, c.t2*v, I...)
92 # end 101 end
93 102
94 # function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,K,D} 103 function apply_transpose(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,R}, I::Vararg{S,D} where S) where {T,R,K,D}
95 # apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...) 104 apply_transpose(c.t2, c.t1'*v, I...)
96 # end 105 end
97 106
98 # function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{D,Int}) where {T,R,K,D} 107 Base.@propagate_inbounds Base.:∘(s::TensorMapping, t::TensorMapping) = TensorMappingComposition(s,t)
99 # apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
100 # end
101 108
102 # # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
103
104 # export →
105 """ 109 """
106 LazyLinearMap{T,R,D,...}(A, range_indicies, domain_indicies) 110 LazyLinearMap{T,R,D,...}(A, range_indicies, domain_indicies)
107 111
108 TensorMapping defined by the AbstractArray A. `range_indicies` and `domain_indicies` define which indicies of A should 112 TensorMapping defined by the AbstractArray A. `range_indicies` and `domain_indicies` define which indicies of A should
109 be considerd the range and domain of the TensorMapping. Each set of indices must be ordered in ascending order. 113 be considerd the range and domain of the TensorMapping. Each set of indices must be ordered in ascending order.