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2024/11/21/synreflection.org.rs

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+The self awareness of the compiler while it compiles itself or another version of itself can
+    be seen as self harmonising strange loop.
+    Is compiling rust with rust not a form of self reflection? the syn of syn as well.
+    If we compare these two self reflectiions we can show two instances of the class of self reflection.
+    we can show that certain properties occur and also the terms converge in meaning in the vector embeddings.
+
+To achieve the goal of analyzing and comparing the profiles of Rust and Syn code, we can follow
+these steps:
+
+*** Step 1: Define Profiles
+First, we need to define what constitutes a "profile" for both Rust and Syn code. A profile could
+include various aspects such as:
+
+- Tokens generated by the parser.
+- Abstract Syntax Trees (ASTs).
+- Lexical analysis results.
+- Semantic analysis data.
+
+*** Step 2: Collect Data
+Collect data for different versions of Rust and Syn, parsing both Rust source files and Syn source
+files. For each version, collect the following profiles:
+- *Rust Profiles*:
+  - AST generated by parsing Rust code.
+  - Tokens and lexemes.
+- *Syn Profiles*:
+  - AST generated by parsing Syn code.
+  - Tokens and lexemes.
+
+*** Step 3: Vectorize the Data
+Use a technique like Word2Vec or BERT to vectorize the ASTs, tokens, and lexemes. This will convert
+textual data into numerical vectors that can be compared.
+
+*** Step 4: Train Model A
+Train model A to find the relationship between the profiles of Rust of Rust and Rust of Syn.
+- *Inputs*: Vectorized representations of Rust of Rust and Rust of Syn.
+- *Output*: Relationship score or classification indicating whether Syn is a subset of Rust code.
+
+*** Step 5: Train Model B
+Train model B to find the relationship between the profiles of Syn of Rust and Syn of Syn.
+- *Inputs*: Vectorized representations of Syn of Rust and Syn of Syn.
+- *Output*: Relationship score or classification indicating whether Syn of Rust is more complex than
+   Syn of Syn.
+
+*** Step 6: Train a Meta-Model
+Train a meta-model to find the relationship between models A and B.
+- *Inputs*: Outputs from models A and B.
+- *Output*: Combined relationship score or classification indicating the overall complexity and
+   nature of the code.
+
+*** Step 7: Analyze and Group Results
+Group results by:
+- Test cases (e.g., different modules, functions).
+- Versions of Rust and Syn.
+- Specific aspects of the code (e.g., syntax, semantic analysis).
+
+*** Step 8: Visualize Relations
+Visualize the relationship between the profile of Rust and Syn of Rust. This can be done using:
+- Heatmaps to show similarity scores.
+- Scatter plots to compare vector embeddings.
+- Word clouds to highlight common tokens or AST nodes.
+
+*** Example Workflow
+
+1. *Collect Data*:
+   - Parse Rust source files (e.g., ~example.rs~) and generate profiles.
+   - Parse Syn source files (e.g., ~syn_example.rs~) and generate profiles.
+
+2. *Vectorize Data*:
+   - Convert ASTs, tokens, and lexemes into vector representations using a pre-trained model like
+     BERT.
+
+3. *Train Model A*:
+   - Input: Vectorized Rust of Rust, Vectorized Rust of Syn.
+   - Output: Relationship score (e.g., 0-1 scale).
+
+4. *Train Model B*:
+   - Input: Vectorized Syn of Rust, Vectorized Syn of Syn.
+   - Output: Relationship score (e.g., 0-1 scale).
+
+5. *Train Meta-Model*:
+   - Input: Outputs from models A and B.
+   - Output: Combined relationship score.
+
+6. *Analyze and Group Results*:
+   - Group results by module, version, aspect type.
+
+7. *Visualize Results*:
+   - Create heatmaps, scatter plots, and word clouds to visualize relationships.
+
+*** Code Snippet Example
+#+BEGIN_SRC rust
+// Define the structure for a profile
+struct Profile {
+    ast: Vec<Vec<f32>>,
+    tokens: Vec<Vec<f32>>,
+    lexemes: Vec<Vec<f32>>,
+}
+
+// Function to parse Rust code and generate profiles
+fn parse_rust_code(code: &str) -> Profile {
+    // Implementation using Syn or any other parser
+    unimplemented!()
+}
+
+// Function to vectorize a profile
+fn vectorize_profile(profile: Profile) -> Vec<Vec<f32>> {
+    // Implementation using BERT or another NLP model
+    unimplemented!()
+}
+
+// Train Model A
+fn train_model_a(rust_rust_vecs: &[Vec<Vec<f32>>], rust_syn_vecs: &[Vec<Vec<f32>>]) -> f32 {
+    // Implementation using machine learning algorithms (e.g., SVM, Neural Network)
+    unimplemented!()
+}
+
+// Main function to demonstrate the workflow
+fn main() {
+    let rust_code = r#"
+        fn main() {
+            println!("Hello, world!");
+        }
+    "#;
+
+    let syn_code = r#"
+        use quote::quote;
+        let code = quote! {
+            fn main() {
+                println!("Hello, Syn!");
+            }
+        };
+    "#;
+
+    let rust_profile = parse_rust_code(rust_code);
+    let syn_profile = parse_rust_code(syn_code);
+
+    let rust_vecs = vectorize_profile(rust_profile);
+    let syn_vecs = vectorize_profile(syn_profile);
+
+    let relationship_score = train_model_a(&rust_vecs, &syn_vecs);
+
+    println!("Relationship Score: {}", relationship_score);
+}
+#+END_SRC
+
+This is a high-level overview of the process. Each step involves specific details and
+implementations that need to be tailored based on the tools and libraries available for Rust and NLP
+tasks.