/neural-train

neural-train

Train neural patterns with SONA (Self-Optimizing Neural Architecture) for adaptive learning and pattern recognition.

Usage

npx claude-flow neural train [options]

Options

• `-p, --pattern <type>` - Pattern type: coordination, optimization, prediction (default: coordination)
• `-e, --epochs <n>` - Number of training epochs (default: 50)
• `-d, --data <file>` - Training data file (JSON)
• `-m, --model <id>` - Model ID to train
• `-l, --learning-rate <rate>` - Learning rate (default: 0.001)
• `-b, --batch-size <n>` - Batch size (default: 32)

Pattern Persistence

Trained patterns are **automatically persisted** to disk:

• **Location**: `.claude-flow/neural/patterns.json`
• **Stats**: `.claude-flow/neural/stats.json`

Patterns survive process restarts and are loaded automatically on next session.

Examples

# Train coordination patterns (50 epochs)
npx claude-flow neural train -p coordination -e 50

# Train with custom learning rate
npx claude-flow neural train -p optimization -l 0.005

# Train from file
npx claude-flow neural train -d ./training-data.json

# Quick training (10 epochs)
npx claude-flow neural train -e 10

Output

Training produces:

• **Patterns Recorded**: Number of patterns stored in ReasoningBank
• **Trajectories**: Complete learning sequences recorded
• **SONA Adaptation**: Target is <0.05ms per operation
• **Persistence Path**: Where patterns are saved

List Trained Patterns

# List all persisted patterns
npx claude-flow neural patterns --action list

# Search patterns by query
npx claude-flow neural patterns --action list -q "error handling"

# Analyze patterns
npx claude-flow neural patterns --action analyze -q "coordination"

Performance Targets

| Metric | Target | |--------|--------| | SONA Adaptation | <0.05ms (achieved: ~2μs) | | Pattern Search | O(log n) with HNSW | | Memory Efficient | Circular buffers |

Related Commands

• `neural patterns` - List and search patterns
• `neural status` - Check neural system status
• `neural predict` - Make predictions using trained models