Merge main branch to update PR with latest changes

.github/copilot-instructions.md

@@ -2,7 +2,7 @@
 
 ## Project Overview
 
-This is a Python implementation of LODA - an assembly language designed for integer sequences. The project enables reading, writing, evaluating, and generating LODA programs using machine learning techniques to discover new integer sequence programs.
+This is a Python implementation of LODA - an assembly language designed for integer sequences. The project enables reading, writing, evaluating LODA programs and searching for matches in the OEIS database.
 
 ## Core Concepts
 
@@ -15,10 +15,6 @@ This is a Python implementation of LODA - an assembly language designed for inte
 - **Operations**: `mov`, `add`, `sub`, `mul`, `div`, `dif`, `mod`, `pow`, `gcd`, `bin`, `cmp`, `min`, `max`, `lpb`, `lpe`
 - **Loops**: `lpb $n` starts loop, `lpe` ends loop (counter-based termination)
 
-### Token Encoding for ML
-Each operation becomes 3 tokens: `[operation_type, target_operand, source_operand]`
-Example: `mov $1,5` → `["mov", "$1", "5"]`
-
 ## Source Code Structure
 
 ### Core Language (`loda/lang/`)
@@ -36,9 +32,8 @@ Example: `mov $1,5` → `["mov", "$1", "5"]`
 - **`program_cache.py`**: `ProgramCache` manages filesystem loading/caching
 - **`prefix_index.py`**: `PrefixIndex` enables sequence matching by prefix patterns
 
-### Machine Learning (`loda/ml/`)
+### Utilities (`loda/ml/`)
 - **`util.py`**: Token conversion utilities (program ↔ tokens, merging)
-- **`keras/program_generation_rnn.py`**: RNN model for program generation using TensorFlow
 
 ### Mining (`loda/mine/`)
 - **`miner.py`**: `Miner` searches for programs matching OEIS sequences
@@ -68,15 +63,6 @@ elif operand.type == OperandType.INDIRECT:
     value = memory[memory[operand.value]]
 ```
 
-### When working with ML tokens:
-```python
-# Convert programs to tokens for ML
-from loda.ml.util import program_to_tokens, tokens_to_program
-
-tokens = program_to_tokens(program)
-reconstructed = tokens_to_program(tokens)
-```
-
 ### When working with sequences:
 ```python
 # Always specify term count and handle evaluation errors
@@ -112,11 +98,11 @@ program = program_cache.get_program(sequence_id)
 
 ### Token Conversion Pattern:
 ```python
-# ML workflow
-tokens = program_to_tokens(program)
-# Process with ML model
-new_tokens = model.generate(tokens)
-new_program = tokens_to_program(new_tokens)
+# Token conversion utilities
+from loda.ml.util import program_to_tokens, tokens_to_program
+
+tokens, vocab = program_to_tokens(program)
+reconstructed = tokens_to_program(tokens)
 ```
 
 ## Testing Conventions
@@ -138,13 +124,11 @@ Always set appropriate limits:
 
 - Programs: `A######.asm` format (OEIS sequence numbers)
 - B-files: `b######.txt` format for sequence terms
-- Models: Use descriptive names with hyperparameters
 - Use relative paths from project root
 
 ## Integration Points
 
 - OEIS database integration via sequence IDs
-- TensorFlow/Keras for neural networks
 - File system caching for performance
 - CSV parsing for test data
 

README.md

@@ -4,18 +4,16 @@ This Python package contains an implementation of the [LODA Language](https://lo
 an assembly language and computational model for finding integer sequence programs.
 
 This Python package allows you to read and write LODA programs, to evaluate
-them to integer sequences, to search for matches in the
-[OEIS](https://www.oeis.org/) database,
-and to use machine learning tools from [Tensorflow](https://www.tensorflow.org/)
-to find new integer sequence programs.
+them to integer sequences, and to search for matches in the
+[OEIS](https://www.oeis.org/) database.
 
 ## Getting Started
 
 You need Python 3.7 or higher. To install the dependencies for LODA, run these commands:
 
 ```bash
-python3 -m venv env
-source env/bin/activate
+python3 -m venv ./venv
+source ./venv/bin/activate
 pip install -r requirements.txt
 ```
 

loda/documentation.md

@@ -1,8 +1,6 @@
 This Python package allows you to read and write LODA programs, to evaluate
-them to integer sequences, to search for matches in the
-[OEIS](https://www.oeis.org/) database,
-and to use machine learning from [Tensorflow](https://www.tensorflow.org/)
-to generate new integer sequence programs.
+them to integer sequences, and to search for matches in the
+[OEIS](https://www.oeis.org/) database.
 
 ## Installation
 

requirements.txt

@@ -2,12 +2,3 @@ argparse
 nose2
 parameterized
 requests
-tensorflow; sys_platform != 'darwin'
-tensorflow-macos; sys_platform == 'darwin'
-
-# LLM Dependencies
-torch>=1.9.0
-transformers>=4.20.0
-datasets>=2.0.0
-tqdm>=4.62.0
-scikit-learn>=1.0.0

sample.py

@@ -4,7 +4,6 @@
 from loda.oeis import ProgramCache, Sequence
 from loda.runtime import Evaluator, Interpreter
 from loda.mine import Miner
-from loda.ml.keras.program_generation_rnn import load_model, train_model, Generator
 
 
 class SampleLODA:

tests/test_ml.py

@@ -1,28 +1,12 @@
 # -*- coding: utf-8 -*-
 
 from unittest import TestCase
-from loda.ml.keras.program_generation_rnn import *
 
 from loda.oeis import ProgramCache
 from loda.ml import util
 from tests.helpers import PROGRAMS_TEST_DIR
 
 
-#class ProgramGenerationRNNTests(TestCase):
-#
-#    def setUp(self):
-#        self.program_cache = ProgramCache(PROGRAMS_TEST_DIR)
-#
-#    def test_model(self):
-#        model = train_model(self.program_cache)
-#        model.save("test_model")
-#        loaded = load_model("test_model")
-#        loaded.summary()
-#        generator = Generator(loaded, num_lanes=10)
-#        for _ in range(10):
-#            generator()
-
-
 class UtilTests(TestCase):
 
     def setUp(self):