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🐚 🧠 TuskLang Bash @learn Function Guide

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🧠 TuskLang Bash @learn Function Guide

"We don't bow to any king" – Learning is your configuration's intelligence.

The @learn function in TuskLang is your machine learning and adaptive intelligence powerhouse, enabling dynamic learning, pattern recognition, and intelligent decision-making directly within your configuration files. Whether you're optimizing performance, predicting trends, or adapting to user behavior, @learn provides the cognitive capabilities to make your configurations truly intelligent.

🎯 What is @learn?

The @learn function provides machine learning capabilities in TuskLang. It offers: - Pattern recognition - Learn from data patterns and trends - Predictive analytics - Make predictions based on historical data - Adaptive optimization - Optimize settings based on performance data - Anomaly detection - Identify unusual patterns or outliers - Recommendation systems - Provide intelligent recommendations

📝 Basic @learn Syntax

Simple Learning

[simple_learning]

Learn from performance data

performance_model: @learn("response_time", @query("SELECT response_time FROM api_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 HOUR)"))
optimal_timeout: @learn.predict("response_time", {"load": @env("CURRENT_LOAD")})
Learn from user behavior

user_preferences: @learn("user_behavior", @query("SELECT action, timestamp FROM user_actions WHERE user_id = " + @env("USER_ID")))
recommended_action: @learn.recommend("user_behavior", {"context": "dashboard"})

Pattern Recognition

[pattern_recognition]

Learn traffic patterns

traffic_pattern: @learn("traffic_pattern", @query("SELECT hour, request_count FROM traffic_logs WHERE date >= DATE_SUB(NOW(), INTERVAL 7 DAY)"))
peak_hours: @learn.peak_hours("traffic_pattern")
Learn error patterns

error_pattern: @learn("error_pattern", @query("SELECT error_type, count FROM error_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 24 HOUR)"))
common_errors: @learn.common_patterns("error_pattern")

Predictive Analytics

[predictive_analytics]

Predict system load

load_prediction: @learn.predict("system_load", {
    "time": @date("H"),
    "day_of_week": @date("N"),
    "current_load": @shell("uptime | awk '{print $10}' | cut -d',' -f1")
})
Predict resource usage

memory_prediction: @learn.predict("memory_usage", {
    "current_memory": @shell("free -m | awk 'NR==2{print $3}'"),
    "active_users": @query("SELECT COUNT(*) FROM active_sessions")
})

🚀 Quick Start Example

#!/bin/bash
source tusk-bash.sh
cat > learn-quickstart.tsk << 'EOF'
[learning_demo]

Learn from system performance

$performance_data: @query("SELECT response_time, cpu_usage, memory_usage FROM performance_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 HOUR)")
performance_model: @learn("system_performance", $performance_data)
Predict optimal settings

current_cpu: @shell("top -bn1 | grep 'Cpu(s)' | awk '{print $2}' | cut -d'%' -f1")
current_memory: @shell("free | grep Mem | awk '{printf \"%.2f\", $3/$2 * 100.0}'")
optimal_settings: @learn.predict("system_performance", {
    "cpu_usage": $current_cpu,
    "memory_usage": $current_memory
})
[user_learning]

Learn user preferences

$user_actions: @query("SELECT action, page, duration FROM user_actions WHERE user_id = " + @env("USER_ID", "1"))
user_model: @learn("user_preferences", $user_actions)
Get recommendations

recommended_pages: @learn.recommend("user_preferences", {"context": "navigation"})
recommended_features: @learn.recommend("user_preferences", {"context": "features"})
[anomaly_detection]

Learn normal patterns

$normal_data: @query("SELECT value, timestamp FROM metrics WHERE created_at >= DATE_SUB(NOW(), INTERVAL 7 DAY)")
normal_pattern: @learn("normal_behavior", $normal_data)
Detect anomalies

current_value: @query("SELECT value FROM metrics ORDER BY created_at DESC LIMIT 1")
is_anomaly: @learn.detect_anomaly("normal_behavior", $current_value)
EOF
config=$(tusk_parse learn-quickstart.tsk)
echo "=== Learning Demo ==="
echo "Performance Model: $(tusk_get "$config" learning_demo.performance_model)"
echo "Optimal Settings: $(tusk_get "$config" learning_demo.optimal_settings)"
echo ""
echo "=== User Learning ==="
echo "User Model: $(tusk_get "$config" user_learning.user_model)"
echo "Recommended Pages: $(tusk_get "$config" user_learning.recommended_pages)"
echo "Recommended Features: $(tusk_get "$config" user_learning.recommended_features)"
echo ""
echo "=== Anomaly Detection ==="
echo "Normal Pattern: $(tusk_get "$config" anomaly_detection.normal_pattern)"
echo "Is Anomaly: $(tusk_get "$config" anomaly_detection.is_anomaly)"

🔗 Real-World Use Cases

1. Performance Optimization

[performance_optimization]

Learn optimal database connection pool size

$db_performance_data: @query("""
    SELECT 
        connection_pool_size,
        avg_response_time,
        max_concurrent_connections,
        error_rate
    FROM database_performance 
    WHERE created_at >= DATE_SUB(NOW(), INTERVAL 7 DAY)
""")
db_optimization_model: @learn("database_performance", $db_performance_data)
Predict optimal pool size

current_load: @query("SELECT COUNT(*) FROM active_connections")
optimal_pool_size: @learn.predict("database_performance", {
    "current_load": $current_load,
    "expected_concurrent_users": @env("EXPECTED_USERS", "100")
})
Apply learned optimization

@if($optimal_pool_size != @env("CURRENT_POOL_SIZE"), {
    "action": "update_pool_size",
    "new_size": $optimal_pool_size,
    "reason": "learned_optimization"
}, "no_change_needed")

2. User Behavior Analysis

[user_behavior]

Learn user interaction patterns

$user_interaction_data: @query("""
    SELECT 
        user_id,
        page_visited,
        time_spent,
        actions_performed,
        session_duration
    FROM user_interactions 
    WHERE created_at >= DATE_SUB(NOW(), INTERVAL 30 DAY)
""")
user_behavior_model: @learn("user_behavior", $user_interaction_data)
Predict user engagement

current_user: @env("CURRENT_USER_ID")
user_engagement_score: @learn.predict("user_behavior", {
    "user_id": $current_user,
    "recent_activity": @query("SELECT COUNT(*) FROM user_actions WHERE user_id = " + $current_user + " AND created_at >= DATE_SUB(NOW(), INTERVAL 7 DAY)")
})
Personalized recommendations

personalized_content: @learn.recommend("user_behavior", {
    "user_id": $current_user,
    "context": "homepage",
    "engagement_level": $user_engagement_score
})

3. Resource Usage Prediction

[resource_prediction]

Learn resource usage patterns

$resource_data: @query("""
    SELECT 
        hour,
        cpu_usage,
        memory_usage,
        disk_io,
        network_traffic,
        active_users
    FROM resource_metrics 
    WHERE created_at >= DATE_SUB(NOW(), INTERVAL 14 DAY)
""")
resource_model: @learn("resource_usage", $resource_data)
Predict resource needs

current_hour: @date("H")
current_day: @date("N")
current_users: @query("SELECT COUNT(*) FROM active_sessions")
predicted_usage: @learn.predict("resource_usage", {
    "hour": $current_hour,
    "day_of_week": $current_day,
    "active_users": $current_users
})
Proactive scaling

scaling_needed: @validate.greater_than($predicted_usage.cpu_usage, 80)
scaling_action: @if($scaling_needed, {
    "action": "scale_up",
    "reason": "predicted_high_load",
    "target_cpu": $predicted_usage.cpu_usage
}, "no_scaling_needed")

4. Anomaly Detection and Alerting

[anomaly_detection]

Learn normal system behavior

$normal_metrics: @query("""
    SELECT 
        response_time,
        error_rate,
        cpu_usage,
        memory_usage,
        disk_usage
    FROM system_metrics 
    WHERE created_at >= DATE_SUB(NOW(), INTERVAL 30 DAY)
    AND error_rate < 0.01
""")
normal_behavior_model: @learn("normal_behavior", $normal_metrics)
Detect anomalies in real-time

current_metrics: {
    "response_time": @query("SELECT AVG(response_time) FROM api_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 5 MINUTE)"),
    "error_rate": @query("SELECT COUNT()  1.0 / (SELECT COUNT(*) FROM api_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 5 MINUTE)) FROM api_logs WHERE status_code >= 400 AND created_at >= DATE_SUB(NOW(), INTERVAL 5 MINUTE)"),
    "cpu_usage": @shell("top -bn1 | grep 'Cpu(s)' | awk '{print $2}' | cut -d'%' -f1"),
    "memory_usage": @shell("free | grep Mem | awk '{printf \"%.2f\", $3/$2 * 100.0}'"),
    "disk_usage": @shell("df -h / | awk 'NR==2{print $5}' | cut -d'%' -f1")
}
anomaly_detected: @learn.detect_anomaly("normal_behavior", $current_metrics)
Alert on anomalies

@if($anomaly_detected, {
    "alert": "anomaly_detected",
    "severity": "high",
    "metrics": $current_metrics,
    "timestamp": @date("Y-m-d H:i:s")
}, "system_normal")

🧠 Advanced @learn Patterns

Multi-Model Learning

[multi_model_learning]

Learn multiple aspects of system behavior

$models: {
    "performance": @learn("performance", @query("SELECT * FROM performance_logs")),
    "security": @learn("security", @query("SELECT * FROM security_logs")),
    "user_behavior": @learn("user_behavior", @query("SELECT * FROM user_actions")),
    "resource_usage": @learn("resource_usage", @query("SELECT * FROM resource_metrics"))
}
Combined intelligence

system_intelligence: @learn.combine($models, {
    "weights": {
        "performance": 0.3,
        "security": 0.2,
        "user_behavior": 0.3,
        "resource_usage": 0.2
    }
})
Make intelligent decisions

intelligent_action: @learn.decide("system_intelligence", {
    "current_state": $current_metrics,
    "available_actions": ["scale_up", "scale_down", "alert", "optimize"]
})

Continuous Learning

[continuous_learning]

Implement continuous learning

$learning_config: {
    "update_frequency": "1h",
    "retention_period": "90d",
    "min_data_points": 1000
}
Update models periodically

model_update_needed: @learn.should_update("performance_model", $learning_config)
@if($model_update_needed, {
    "action": "update_model",
    "model": "performance_model",
    "new_data": @query("SELECT * FROM performance_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 HOUR)")
}, "model_current")
Adaptive learning rate

learning_rate: @learn.adaptive_rate("performance_model", {
    "accuracy": @learn.accuracy("performance_model"),
    "data_freshness": @learn.data_age("performance_model")
})

Ensemble Learning

[ensemble_learning]

Create ensemble of models

$ensemble_models: [
    @learn("linear_model", $performance_data),
    @learn("neural_network", $performance_data),
    @learn("random_forest", $performance_data)
]
Ensemble prediction

ensemble_prediction: @learn.ensemble_predict($ensemble_models, {
    "method": "weighted_average",
    "weights": [0.4, 0.3, 0.3]
})
Model performance comparison

model_performance: @learn.compare_models($ensemble_models, {
    "test_data": @query("SELECT * FROM performance_logs WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 DAY)")
})

🛡️ Security & Performance Notes

- Data privacy: Ensure sensitive data is anonymized before learning - Model security: Protect learned models from unauthorized access - Performance impact: Monitor computational overhead of learning operations - Data quality: Validate input data quality for reliable learning - Model validation: Regularly validate model accuracy and performance - Bias detection: Monitor for bias in learned patterns and predictions

🐞 Troubleshooting

- Insufficient data: Ensure adequate data points for reliable learning - Model accuracy: Monitor and improve model accuracy over time - Performance issues: Optimize learning algorithms for better performance - Data drift: Detect and handle changes in data patterns - Memory usage: Monitor memory consumption of learning models

💡 Best Practices

- Start simple: Begin with simple learning models and gradually increase complexity - Validate models: Regularly test and validate learned models - Monitor performance: Track learning model performance and accuracy - Handle edge cases: Account for unusual data patterns and outliers - Document models: Document learning models and their purposes - Regular updates: Update models with fresh data regularly

🔗 Cross-References

- @ Operator Introduction - @cache Function - @query Function - Performance Optimization - Machine Learning Integration

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Master @learn in TuskLang and bring artificial intelligence to your configurations. 🧠

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