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annotate src/LazyTensors/lazy_tensor_operations.jl @ 487:6a6b7eaf9edf feature/compose_identity_mappings

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Move exception handling to end of file and update error message.
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Thu, 05 Nov 2020 11:30:53 +0100
parents 4b49f03bdb98
children df566372bb4f
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1 """
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2 LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
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3
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4 Struct for lazy application of a TensorMapping. Created using `*`.
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5
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6 Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
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7 With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
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8 The actual result will be calcualted when indexing into `m*v`.
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9 """
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10 struct LazyTensorMappingApplication{T,R,D, TM<:TensorMapping{T,R,D}, AA<:AbstractArray{T,D}} <: LazyArray{T,R}
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11 t::TM
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12 o::AA
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13 end
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14 # TODO: Do boundschecking on creation!
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15 export LazyTensorMappingApplication
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16
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17 # TODO: Go through and remove unneccerary type parameters on functions
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18
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19 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Index,R}) where {T,R,D} = apply(ta.t, ta.o, I...)
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20 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Int,R}) where {T,R,D} = apply(ta.t, ta.o, Index{Unknown}.(I)...)
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21 Base.size(ta::LazyTensorMappingApplication) = range_size(ta.t)
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22 # TODO: What else is needed to implement the AbstractArray interface?
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23
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24 Base.:*(a::TensorMapping, v::AbstractArray) = LazyTensorMappingApplication(a,v)
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25 Base.:*(a::TensorMapping, b::TensorMapping) = throw(MethodError(Base.:*,(a,b)))
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26 Base.:*(a::TensorMapping, args::Union{TensorMapping, AbstractArray}...) = foldr(*,(a,args...))
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28 # # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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29 # # Should we overload some other infix binary opesrator?
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30 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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31 # TODO: We need to be really careful about good error messages.
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32 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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33
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34 """
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35 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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36
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37 Struct for lazy transpose of a TensorMapping.
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38
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39 If a mapping implements the the `apply_transpose` method this allows working with
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40 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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41 the appropriate methods of `m`.
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42 """
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43 struct LazyTensorMappingTranspose{T,R,D, TM<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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44 tm::TM
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45 end
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46 export LazyTensorMappingTranspose
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47
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48 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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49 # Jonatan 2020-09-25: Is the problem that you can take the transpose of any TensorMapping even if it doesn't implement `apply_transpose`?
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50 Base.adjoint(tm::TensorMapping) = LazyTensorMappingTranspose(tm)
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51 Base.adjoint(tmt::LazyTensorMappingTranspose) = tmt.tm
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52
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53 apply(tmt::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Index,D}) where {T,R,D} = apply_transpose(tmt.tm, v, I...)
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54 apply_transpose(tmt::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D} = apply(tmt.tm, v, I...)
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55
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56 range_size(tmt::LazyTensorMappingTranspose) = domain_size(tmt.tm)
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57 domain_size(tmt::LazyTensorMappingTranspose) = range_size(tmt.tm)
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59
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60 struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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61 tm1::T1
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62 tm2::T2
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64 @inline function LazyTensorMappingBinaryOperation{Op,T,R,D}(tm1::T1,tm2::T2) where {Op,T,R,D, T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}}
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65 return new{Op,T,R,D,T1,T2}(tm1,tm2)
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66 end
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67 end
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68 # TODO: Boundschecking in constructor.
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70 apply(tmBinOp::LazyTensorMappingBinaryOperation{:+,T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) + apply(tmBinOp.tm2, v, I...)
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71 apply(tmBinOp::LazyTensorMappingBinaryOperation{:-,T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) - apply(tmBinOp.tm2, v, I...)
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72
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73 range_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = range_size(tmBinOp.tm1)
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74 domain_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = domain_size(tmBinOp.tm1)
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75
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76 Base.:+(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(tm1,tm2)
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77 Base.:-(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(tm1,tm2)
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79 """
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80 TensorMappingComposition{T,R,K,D}
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81
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82 Lazily compose two TensorMappings, so that they can be handled as a single TensorMapping.
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83 """
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84 struct TensorMappingComposition{T,R,K,D, TM1<:TensorMapping{T,R,K}, TM2<:TensorMapping{T,K,D}} <: TensorMapping{T,R,D}
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85 t1::TM1
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86 t2::TM2
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87
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88 @inline function TensorMappingComposition(t1::TensorMapping{T,R,K}, t2::TensorMapping{T,K,D}) where {T,R,K,D}
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89 @boundscheck check_domain_size(t1, range_size(t2))
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90 return new{T,R,K,D, typeof(t1), typeof(t2)}(t1,t2)
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91 end
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92 end
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93 export TensorMappingComposition
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94
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95 range_size(tm::TensorMappingComposition) = range_size(tm.t1)
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96 domain_size(tm::TensorMappingComposition) = domain_size(tm.t2)
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97
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98 function apply(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg{S,R} where S) where {T,R,K,D}
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99 apply(c.t1, c.t2*v, I...)
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100 end
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101
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102 function apply_transpose(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,R}, I::Vararg{S,D} where S) where {T,R,K,D}
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103 apply_transpose(c.t2, c.t1'*v, I...)
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104 end
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105
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106 Base.@propagate_inbounds Base.:∘(s::TensorMapping, t::TensorMapping) = TensorMappingComposition(s,t)
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107
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108 """
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109 LazyLinearMap{T,R,D,...}(A, range_indicies, domain_indicies)
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110
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111 TensorMapping defined by the AbstractArray A. `range_indicies` and `domain_indicies` define which indicies of A should
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112 be considerd the range and domain of the TensorMapping. Each set of indices must be ordered in ascending order.
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113
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114 For instance, if A is a m x n matrix, and range_size = (1,), domain_size = (2,), then the LazyLinearMap performs the
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115 standard matrix-vector product on vectors of size n.
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116 """
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117 struct LazyLinearMap{T,R,D, RD, AA<:AbstractArray{T,RD}} <: TensorMapping{T,R,D}
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118 A::AA
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119 range_indicies::NTuple{R,Int}
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120 domain_indicies::NTuple{D,Int}
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121
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122 function LazyLinearMap(A::AA, range_indicies::NTuple{R,Int}, domain_indicies::NTuple{D,Int}) where {T,R,D, RD, AA<:AbstractArray{T,RD}}
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123 if !issorted(range_indicies) || !issorted(domain_indicies)
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124 throw(DomainError("range_indicies and domain_indicies must be sorted in ascending order"))
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125 end
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126
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127 return new{T,R,D,RD,AA}(A,range_indicies,domain_indicies)
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128 end
377
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129 end
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130 export LazyLinearMap
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131
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132 range_size(llm::LazyLinearMap) = size(llm.A)[[llm.range_indicies...]]
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133 domain_size(llm::LazyLinearMap) = size(llm.A)[[llm.domain_indicies...]]
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134
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135 function apply(llm::LazyLinearMap{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D}
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136 view_index = ntuple(i->:,ndims(llm.A))
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137 for i ∈ 1:R
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138 view_index = Base.setindex(view_index, Int(I[i]), llm.range_indicies[i])
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139 end
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140 A_view = @view llm.A[view_index...]
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141 return sum(A_view.*v)
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142 end
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143
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144 function apply_transpose(llm::LazyLinearMap{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Index,D}) where {T,R,D}
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145 apply(LazyLinearMap(llm.A, llm.domain_indicies, llm.range_indicies), v, I...)
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146 end
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147
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148
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149 """
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150 IdentityMapping{T,D} <: TensorMapping{T,D,D}
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151
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152 The lazy identity TensorMapping for a given size. Usefull for building up higher dimensional tensor mappings from lower
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153 dimensional ones through outer products. Also used in the Implementation for InflatedTensorMapping.
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154 """
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155 struct IdentityMapping{T,D} <: TensorMapping{T,D,D}
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156 size::NTuple{D,Int}
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157 end
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158 export IdentityMapping
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159
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160 IdentityMapping{T}(size::NTuple{D,Int}) where {T,D} = IdentityMapping{T,D}(size)
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161 IdentityMapping{T}(size::Vararg{Int,D}) where {T,D} = IdentityMapping{T,D}(size)
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162 IdentityMapping(size::Vararg{Int,D}) where D = IdentityMapping{Float64,D}(size)
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163
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164 range_size(tmi::IdentityMapping) = tmi.size
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165 domain_size(tmi::IdentityMapping) = tmi.size
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166
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167 apply(tmi::IdentityMapping{T,D}, v::AbstractArray{T,D}, I::Vararg{Any,D}) where {T,D} = v[I...]
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168 apply_transpose(tmi::IdentityMapping{T,D}, v::AbstractArray{T,D}, I::Vararg{Any,D}) where {T,D} = v[I...]
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169
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170 """
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171 Base.:∘(tm, tmi)
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172 Base.:∘(tmi, tm)
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173
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174 Composes a `Tensormapping` `tm` with an `IdentityMapping` `tmi`, by returning `tm`
479
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175 """
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176 @inline function Base.:∘(tm::TensorMapping{T,R,D}, tmi::IdentityMapping{T,D}) where {T,R,D}
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177 @boundscheck check_domain_size(tm, range_size(tmi))
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178 return tm
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179 end
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180
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181 @inline function Base.:∘(tmi::IdentityMapping{T,R}, tm::TensorMapping{T,R,D}) where {T,R,D}
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182 @boundscheck check_domain_size(tmi, range_size(tm))
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183 return tm
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184 end
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185 # Specialization for the case where tm is an IdentityMapping. Required to resolve ambiguity.
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186 @inline function Base.:∘(tm::IdentityMapping{T,D}, tmi::IdentityMapping{T,D}) where {T,D}
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187 @boundscheck check_domain_size(tm, range_size(tmi))
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188 return tmi
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189 end
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190
460
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191
459
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192 """
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193 InflatedTensorMapping{T,R,D} <: TensorMapping{T,R,D}
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194
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195 An inflated `TensorMapping` with dimensions added before and afer its actual dimensions.
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196 """
449
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197 struct InflatedTensorMapping{T,R,D,D_before,R_middle,D_middle,D_after, TM<:TensorMapping{T,R_middle,D_middle}} <: TensorMapping{T,R,D}
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198 before::IdentityMapping{T,D_before}
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199 tm::TM
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200 after::IdentityMapping{T,D_after}
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201
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202 function InflatedTensorMapping(before, tm::TensorMapping{T}, after) where T
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203 R_before = range_dim(before)
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204 R_middle = range_dim(tm)
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205 R_after = range_dim(after)
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206 R = R_before+R_middle+R_after
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207
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208 D_before = domain_dim(before)
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209 D_middle = domain_dim(tm)
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210 D_after = domain_dim(after)
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211 D = D_before+D_middle+D_after
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212 return new{T,R,D,D_before,R_middle,D_middle,D_after, typeof(tm)}(before, tm, after)
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213 end
446
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214 end
451
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215 export InflatedTensorMapping
461
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216 """
464
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217 InflatedTensorMapping(before, tm, after)
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218 InflatedTensorMapping(before,tm)
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219 InflatedTensorMapping(tm,after)
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220
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221 The outer product of `before`, `tm` and `after`, where `before` and `after` are `IdentityMapping`s.
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222
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223 If one of `before` or `after` is left out, a 0-dimensional `IdentityMapping` is used as the default value.
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224 """
464
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225 InflatedTensorMapping(::IdentityMapping, ::TensorMapping, ::IdentityMapping)
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226 InflatedTensorMapping(before::IdentityMapping, tm::TensorMapping{T}) where T = InflatedTensorMapping(before,tm,IdentityMapping{T}())
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227 InflatedTensorMapping(tm::TensorMapping{T}, after::IdentityMapping) where T = InflatedTensorMapping(IdentityMapping{T}(),tm,after)
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228 # Resolve ambiguity between the two previous methods
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229 InflatedTensorMapping(I1::IdentityMapping{T}, I2::IdentityMapping{T}) where T = InflatedTensorMapping(I1,I2,IdentityMapping{T}())
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230
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231 # TODO: Implement syntax and constructors for products of different combinations of InflatedTensorMapping and IdentityMapping
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232
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233 # TODO: Implement some pretty printing in terms of ⊗. E.g InflatedTensorMapping(I(3),B,I(2)) -> I(3)⊗B⊗I(2)
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234
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235 function range_size(itm::InflatedTensorMapping)
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236 return flatten_tuple(
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237 range_size(itm.before),
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238 range_size(itm.tm),
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239 range_size(itm.after),
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240 )
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241 end
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242
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243 function domain_size(itm::InflatedTensorMapping)
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244 return flatten_tuple(
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245 domain_size(itm.before),
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246 domain_size(itm.tm),
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247 domain_size(itm.after),
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248 )
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249 end
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250
452
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251 function apply(itm::InflatedTensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,D}
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252 view_index, inner_index = split_index(itm, I...)
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253
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254 v_inner = view(v, view_index...)
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255 return apply(itm.tm, v_inner, inner_index...)
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256 end
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257
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258
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259 """
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260 split_index(...)
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261
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262 Splits the multi-index into two parts. One part for the view that the inner TensorMapping acts on, and one part for indexing the result
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263 Eg.
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264 ```
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265 (1,2,3,4) -> (1,:,:,4), (2,3)
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266 ```
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267 """
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268 function split_index(itm::InflatedTensorMapping{T,R,D}, I::Vararg{Any,R}) where {T,R,D}
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269 I_before = slice_tuple(I, Val(1), Val(range_dim(itm.before)))
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270 I_after = slice_tuple(I, Val(R-range_dim(itm.after)+1), Val(R))
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271
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272 view_index = (I_before..., ntuple((i)->:,domain_dim(itm.tm))..., I_after...)
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273 inner_index = slice_tuple(I, Val(range_dim(itm.before)+1), Val(R-range_dim(itm.after)))
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274
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275 return (view_index, inner_index)
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276 end
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277
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278 # TODO: Can this be replaced by something more elegant while still being type stable? 2020-10-21
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279 # See:
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280 # https://github.com/JuliaLang/julia/issues/34884
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281 # https://github.com/JuliaLang/julia/issues/30386
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282 """
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283 slice_tuple(t, Val(l), Val(u))
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284
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285 Get a slice of a tuple in a type stable way.
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286 Equivalent to t[l:u] but type stable.
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287 """
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288 function slice_tuple(t,::Val{L},::Val{U}) where {L,U}
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289 return ntuple(i->t[i+L-1], U-L+1)
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290 end
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291
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292 """
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293 flatten_tuple(t)
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294
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295 Takes a nested tuple and flattens the whole structure
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296 """
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297 flatten_tuple(t::NTuple{N, Number} where N) = t
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298 flatten_tuple(t::Tuple) = ((flatten_tuple.(t)...)...,) # simplify?
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299 flatten_tuple(ts::Vararg) = flatten_tuple(ts)
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300
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301 function check_domain_size(tm::TensorMapping, sz)
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302 if domain_size(tm) != sz
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303 throw(SizeMismatch(tm,sz))
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304 end
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305 end
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306
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307 struct SizeMismatch <: Exception
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308 tm::TensorMapping
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309 sz
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310 end
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311 export SizeMismatch
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312
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313 function Base.showerror(io::IO, err::SizeMismatch)
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314 print(io, "SizeMismatch: ")
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315 print(io, "domain size $(domain_size(err.tm)) of TensorMapping not matching size $(err.sz)")
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316 end