d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
74 lines
1.5 KiB
Markdown
74 lines
1.5 KiB
Markdown
# Neural Pattern Training
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## Purpose
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Continuously improve coordination through neural network learning.
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## How Training Works
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### 1. Automatic Learning
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Every successful operation trains the neural networks:
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- Edit patterns for different file types
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- Search strategies that find results faster
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- Task decomposition approaches
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- Agent coordination patterns
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### 2. Manual Training
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```
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Tool: mcp__claude-flow__neural_train
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Parameters: {
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"pattern_type": "coordination",
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"training_data": "successful task patterns",
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"epochs": 50
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}
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```
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### 3. Pattern Types
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**Cognitive Patterns:**
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- Convergent: Focused problem-solving
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- Divergent: Creative exploration
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- Lateral: Alternative approaches
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- Systems: Holistic thinking
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- Critical: Analytical evaluation
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- Abstract: High-level design
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### 4. Improvement Tracking
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```
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Tool: mcp__claude-flow__neural_status
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Result: {
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"patterns": {
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"convergent": 0.92,
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"divergent": 0.87,
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"lateral": 0.85
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},
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"improvement": "5.3% since last session",
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"confidence": 0.89
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}
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```
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## Pattern Analysis
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```
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Tool: mcp__claude-flow__neural_patterns
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Parameters: {
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"action": "analyze",
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"operation": "recent_edits"
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}
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```
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## Benefits
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- 🧠 Learns your coding style
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- 📈 Improves with each use
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- 🎯 Better task predictions
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- ⚡ Faster coordination
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## CLI Usage
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```bash
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# Train neural patterns via CLI
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npx claude-flow neural train --type coordination --epochs 50
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# Check neural status
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npx claude-flow neural status
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# Analyze patterns
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npx claude-flow neural patterns --analyze
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``` |