Optimize ml neural models 2517950088406006131#5
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- Upgrade EvasionModel to use Discounted UCB1 (0.98 decay) for faster adaptation to interference. - Enhance NeuralNetwork architecture with increased capacity, Leaky ReLU activation, and Momentum-based optimization. - Completely remove raw socket implementation and related low-level networking helpers. - Clean up desync strategies to rely exclusively on standard socket writes (splitsni, segmentation, etc.). - Update README.md to reflect new algorithmic improvements and remove raw socket specific documentation. - Recalibrate scoring logic for improved sensitivity to network lag.
- Integrate high-resolution metric tracking: throughput (BPS) and average packet size (EMA). - Expand Neural Network architecture: - Increase input layer to 10 features including live traffic metrics. - Expand hidden layer to 24 units for complex interference detection. - Hyper-aggressive penalty (-1.5) for unreachability. - Implement "Brutal Persistence" logic: - Dynamic connection hedging up to 6 parallel attempts during bandwidth crisis. - Accelerated learning (lower decay factor) when throughput drops below 50KB/s. - Bandwidth-aware desync engine: - Automatically selects more aggressive fragmentation (32-byte segments) and timing jitter when network pressure is high. - Update README.md with the latest architecture and persistence capabilities.
- Upgrade NeuralNetwork to a deep 3-layer architecture (Input -> 48 -> 24 -> Output). - Implement Mini-Attention mechanism to learn feature relevance dynamically. - Add Semantic Tokenizer for proxy configurations (embedding-like feature extraction). - Implement Laser-Lock Policy using Softmax-Temperature selection for optimal config pinning. - Integrated high-resolution traffic metrics (BPS, Average Packet Size) into the model's decision loop. - Hyper-aggressive learning and penalty parameters for maximum persistence in extreme interference. - Use Swish activation and Gradient Clipping for deep training stability.
- Replace the static token list with a Feature Hashing (Hashing Trick) mechanism. - Implement Unigram and Bigram hashing to capture semantic relationships between arbitrary proxy flags. - Add L2 normalization to hashed feature vectors for stable neural network training. - Use an optimized DJB2-based hashing function for consistent mapping of configuration patterns. - Ensure the tokenizer handles complex delimiters to extract all meaningful sub-tokens. - Maintain compatibility with the 21-feature input vector of the Deep-Watchdog.
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