diff --git a/common/src/features/types.rs b/common/src/features/types.rs index fb79005e4..3b0b05911 100644 --- a/common/src/features/types.rs +++ b/common/src/features/types.rs @@ -3,10 +3,6 @@ /// Standard 51-dimensional feature vector (Proxy OFI removed in WAVE 10) pub type FeatureVector51 = [f64; 51]; -/// DEPRECATED: 54-dimensional feature vector (backward compatibility only) -#[deprecated(since = "WAVE 10", note = "Use FeatureVector51 instead")] -pub type FeatureVector54 = [f64; 54]; - /// OHLCV bar data for batch processing #[derive(Debug, Clone)] pub struct BarData { diff --git a/common/src/lib.rs b/common/src/lib.rs index ea20f2b6a..b3371afe9 100644 --- a/common/src/lib.rs +++ b/common/src/lib.rs @@ -82,9 +82,9 @@ pub use ml_strategy::{ // Re-export regime persistence manager pub use regime_persistence::RegimePersistenceManager; -// Re-export feature extraction types and functions (54-feature architecture) +// Re-export feature extraction types and functions pub use features::{ - FeatureVector54, BarData, + BarData, // Technical indicators (streaming + batch) RSI, EMA, MACD, BollingerBands, ATR, ADX, rsi_batch, ema_batch, macd_batch, bollinger_batch, atr_batch, adx_batch, diff --git a/ml/src/features/extraction.rs b/ml/src/features/extraction.rs index 2b680a554..1924cfd9f 100644 --- a/ml/src/features/extraction.rs +++ b/ml/src/features/extraction.rs @@ -60,10 +60,6 @@ pub struct OHLCVBar { /// Feature extraction result: 51-dimensional feature vector per bar pub type FeatureVector = [f64; 51]; -/// DEPRECATED: 54-dimensional feature vector (backward compatibility only) -#[deprecated(since = "WAVE 10", note = "Use FeatureVector (51 dimensions) instead")] -pub type FeatureVector54 = [f64; 54]; - /// Base feature vector without OFI: 43-dimensional feature vector pub type FeatureVector43 = [f64; 43]; diff --git a/ml/src/lib.rs b/ml/src/lib.rs index 291109e91..532411941 100644 --- a/ml/src/lib.rs +++ b/ml/src/lib.rs @@ -1805,7 +1805,6 @@ pub struct LatencyOptimizer { } #[derive(Debug, Clone)] -#[allow(dead_code)] struct PerformancePoint { timestamp: std::time::Instant, latency_us: u64, @@ -1815,7 +1814,6 @@ struct PerformancePoint { } #[derive(Debug, Clone)] -#[allow(dead_code)] struct OptimizationParams { max_batch_size: u32, adaptive_batching: bool, diff --git a/ml/src/mamba/mod.rs b/ml/src/mamba/mod.rs index 1608d9a59..e6e01b1db 100644 --- a/ml/src/mamba/mod.rs +++ b/ml/src/mamba/mod.rs @@ -1625,7 +1625,6 @@ impl Mamba2SSM { } /// Forward pass with gradient computation enabled - #[allow(dead_code)] // Used in tests pub fn forward_with_gradients(&mut self, input: &Tensor) -> Result { // Gradient flow enabled - do not detach let input = input; @@ -1882,7 +1881,6 @@ impl Mamba2SSM { } /// Compute training loss - #[allow(dead_code)] // Used in tests pub fn compute_loss(&self, output: &Tensor, target: &Tensor) -> Result { // Mean Squared Error for regression let diff = (output - target)?; @@ -1893,7 +1891,6 @@ impl Mamba2SSM { } /// Backward pass - compute gradients for model parameters - #[allow(dead_code)] // Used in tests pub fn backward_pass( &mut self, loss: &Tensor, @@ -1992,7 +1989,6 @@ impl Mamba2SSM { } /// Zero gradients - #[allow(dead_code)] // Used in tests pub fn zero_gradients(&mut self) -> Result<(), MLError> { // Clear all gradients for SSM parameters for _ssm_state in &mut self.state.ssm_states { diff --git a/ml/src/ppo/unified_ppo.rs b/ml/src/ppo/unified_ppo.rs index c925c8c79..66d78ef18 100644 --- a/ml/src/ppo/unified_ppo.rs +++ b/ml/src/ppo/unified_ppo.rs @@ -5,13 +5,10 @@ // Placeholder types to satisfy mod.rs exports #[derive(Debug)] -#[allow(dead_code)] pub struct UnifiedPPO; #[derive(Debug)] -#[allow(dead_code)] pub struct UnifiedPPOConfig; #[derive(Debug)] -#[allow(dead_code)] pub struct UnifiedTrajectoryBatch; diff --git a/ml/tests/dqn_call_sites_remaining_test.rs b/ml/tests/dqn_call_sites_remaining_test.rs index 4222697f3..addd3e062 100644 --- a/ml/tests/dqn_call_sites_remaining_test.rs +++ b/ml/tests/dqn_call_sites_remaining_test.rs @@ -6,8 +6,6 @@ use ml::dqn::portfolio_tracker::PortfolioTracker; use ml::dqn::trading_action::TradingAction; use ml::trainers::dqn::{DQNHyperparameters, DQNTrainer}; -use ml::types::FeatureVector54; - #[tokio::test] async fn test_process_training_sample_call_sites() { // Test that process_training_sample() correctly passes close_price to feature_vector_to_state()