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139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
"""
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Hello AI World - Your First AI Program
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=======================================
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This is a simple pattern recognition example that demonstrates core AI concepts:
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- Learning from data
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- Making predictions
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- Understanding patterns
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What this program does:
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- Learns a simple mathematical pattern (y = 2x)
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- Uses that pattern to make predictions
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- No complex libraries needed - just pure Python!
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Perfect for understanding AI basics before diving into neural networks.
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"""
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import random
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class SimpleAILearner:
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"""
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A very simple AI that learns linear relationships.
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This demonstrates the fundamental concept of AI: learning from data.
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"""
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def __init__(self):
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# The "weight" is what our AI learns
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# It starts with a random guess
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self.weight = random.uniform(0, 5)
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self.learning_rate = 0.01 # How fast our AI learns
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def predict(self, x):
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"""
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Make a prediction based on what we've learned.
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Args:
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x: Input value
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Returns:
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Predicted output
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"""
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return self.weight * x
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def train(self, training_data, epochs=100):
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"""
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Train the AI to learn the pattern in the data.
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Args:
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training_data: List of (input, output) pairs
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epochs: Number of times to go through all the data
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"""
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print("🎓 Training started...")
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print(f"Initial guess for weight: {self.weight:.2f}")
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for epoch in range(epochs):
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total_error = 0
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# Learn from each example
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for x, y_actual in training_data:
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# Make a prediction
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y_predicted = self.predict(x)
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# Calculate error (how wrong we were)
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error = y_actual - y_predicted
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total_error += abs(error)
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# Update our weight to reduce error (this is learning!)
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self.weight += self.learning_rate * error * x
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# Print progress every 20 epochs
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if (epoch + 1) % 20 == 0:
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avg_error = total_error / len(training_data)
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print(f"Epoch {epoch + 1}/{epochs} - Average error: {avg_error:.4f} - Weight: {self.weight:.2f}")
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print(f"✅ Training complete! Final weight: {self.weight:.2f}")
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def main():
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"""
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Main function - Let's teach our AI!
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"""
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print("=" * 60)
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print("Welcome to Hello AI World!")
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print("=" * 60)
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print()
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print("Today, we'll teach an AI to learn a simple pattern:")
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print("Given x, predict y where y = 2x")
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print()
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# Step 1: Create training data
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# The pattern we want the AI to learn: y = 2 * x
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print("📊 Creating training data...")
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training_data = [
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(1, 2), # When x=1, y should be 2
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(2, 4), # When x=2, y should be 4
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(3, 6), # When x=3, y should be 6
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(4, 8), # When x=4, y should be 8
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(5, 10), # When x=5, y should be 10
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]
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print(f"Training examples: {training_data}")
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print()
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# Step 2: Create and train our AI
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ai = SimpleAILearner()
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ai.train(training_data, epochs=100)
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print()
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# Step 3: Test our AI with new data
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print("🧪 Testing our AI with new inputs...")
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print("-" * 60)
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test_inputs = [6, 7, 10, 15]
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for x in test_inputs:
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prediction = ai.predict(x)
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actual = 2 * x # The true answer
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print(f"Input: {x:2d} | Prediction: {prediction:6.2f} | Actual: {actual:6.2f} | Difference: {abs(prediction - actual):.2f}")
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print("-" * 60)
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print()
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# Explanation
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print("💡 What just happened?")
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print("1. We gave the AI examples of the pattern (y = 2x)")
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print("2. The AI learned by adjusting its 'weight' to minimize errors")
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print("3. After training, it can predict outputs for new inputs!")
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print()
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print("🎉 Congratulations! You just trained your first AI!")
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print()
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print("🚀 Next steps:")
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print(" - Try changing the training data to learn different patterns")
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print(" - Experiment with the learning_rate (line 29)")
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print(" - Modify epochs to see how training time affects accuracy")
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print()
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if __name__ == "__main__":
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# This runs when you execute the script
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main()
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