gatos.jl

gatos.jl

using Catlab

# Define the category of sets
C = Category()

# Define the presheaf to represent the data
data = Presheaf(C)

# Define the presheaf to represent the latent variables
latent_variables = Presheaf(C)

# Define the presheaf to represent the neural network
neural_network = Presheaf(C)

# Define the presheaf to represent the dependency between the neural network and the data
dependency = Presheaf(C)

# Define the presheaf to represent the composition of morphisms
composition = Presheaf(C)

# Define the presheaf to represent the active variables
active_variables = Presheaf(C)

# Define the presheaf to represent the stopped variables
stopped_variables = Presheaf(C)

# Define the morphisms to represent the encoding and decoding processes
encoding = Morphism(data, latent_variables)
decoding = Morphism(latent_variables, data)

# Define the natural transformation to represent the specific neural network we are using
specific_neural_network = NaturalTransformation(neural_network, neural_network)

# Define the operad to represent the composition of morphisms
operad = Operad(C)

# Define the composition operation for the operad
composition_operation = Operation(operad)

# Define the subcategory to represent the active variables
active_variables_subcategory = Subcategory(C)

# Define the quotient category to represent the stopped variables
stopped_variables_quotient_category = QuotientCategory(C)

# Ensure that certain matrices are lower triangular
for m in matrices
    m = lower_triangular(m)
end

# Use identity morphisms where appropriate
for i in 1:length(morphisms)
    if morphisms[i] == identity_morphism
        morphisms[i] = IdentityMorphism(C)
    end
end