From e7b0fffa12102a822e9d153b5831c2e02b68949b Mon Sep 17 00:00:00 2001
From: Essam <essamwisam@outlook.com>
Date: Mon, 5 Aug 2024 20:56:30 -0500
Subject: [PATCH] =?UTF-8?q?=F0=9F=94=A5=20Delete=20old=20file?=
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---
 src/entity_embedding_old.jl | 50 -------------------------------------
 1 file changed, 50 deletions(-)
 delete mode 100644 src/entity_embedding_old.jl

diff --git a/src/entity_embedding_old.jl b/src/entity_embedding_old.jl
deleted file mode 100644
index b87a79ba..00000000
--- a/src/entity_embedding_old.jl
+++ /dev/null
@@ -1,50 +0,0 @@
-# This is just some experimental code
-# to implement EntityEmbeddings for purely
-# categorical features
-
-
-using Flux
-
-mutable struct EmbeddingMatrix
-    e
-    levels
-
-    function EmbeddingMatrix(levels; dim=4)
-        if dim <= 0
-            dimension = div(length(levels), 2)
-        else
-            dimension = min(length(levels), dim)      # Dummy function for now
-        end
-        return new(Dense(length(levels), dimension), levels), dimension
-    end
-
-end
-
-Flux.@treelike EmbeddingMatrix
-
-function (embed::EmbeddingMatrix)(ip)
-    return embed.e(Flux.onehot(ip, embed.levels))
-end
-
-mutable struct EntityEmbedding
-    embeddingmatrix
-
-    function EntityEmbedding(a...)
-        return new(a)
-    end
-end
-
-Flux.@treelike EntityEmbedding
-
-
-# ip is an array of tuples
-function (embed::EntityEmbedding)(ip)
-    return hcat((vcat((embed.embeddingmatrix[i](ip[idx][i]) for i=1:length(ip[idx]))...) for idx =1:length(ip))...)
-end
-
-
-#   Q1. How should this be called in the API?
-#   nn = NeuralNetworkClassifier(builder=builder, optimiser = .., embeddingdimension = 5)
-#
-#
-#