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

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

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