CompositionalNetworks.jl

Documentation for CompositionalNetworks.jl.

Utilities

CompositionalNetworks.map_tr! Function

map_tr!(f, x, X, param)

Return an anonymous function that applies f to all elements of x and store the result in X, with a parameter param (which is set to nothing for function with no parameter).

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CompositionalNetworks.lazy Function

lazy(funcs::Function...)

Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V).

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CompositionalNetworks.lazy_param Function

lazy_param(funcs::Function...)

Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V; param).

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CompositionalNetworks.as_bitvector Function

as_bitvector(n::Int, max_n::Int = n)

Convert an Int to a BitVector of minimal size (relatively to max_n).

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CompositionalNetworks.as_int Function

as_int(v::AbstractVector)

Convert a BitVector into an Int.

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CompositionalNetworks.reduce_symbols Function

reduce_symbols(symbols, sep)

Produce a formatted string that separates the symbols by sep. Used internally for show_composition.

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CompositionalNetworks.tr_in Function

tr_in(tr, X, x, param)

Application of an operation from the transformation layer. Used to generate more efficient code for all compositions.

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Metrics

CompositionalNetworks.hamming Function

hamming(x, X)

Compute the hamming distance of x over a collection of solutions X, i.e. the minimal number of variables to switch in xto reach a solution.

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CompositionalNetworks.minkowski Function

minkowski(x, X, p)

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CompositionalNetworks.manhattan Function

manhattan(x, X)

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CompositionalNetworks.weights_bias Function

weights_bias(x)

A metric that bias x towards operations with a lower bit. Do not affect the main metric.

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