feat: add learning program

main.py

@@ -1 +1,53 @@
-import network
+from network import *
+
+def data(size:int, max_val: int):
+    def int_to_bits(n: int):
+        return [(n >> i) & 1 
+            for i in reversed(range(size))
+        ]
+   
+    return [(int_to_bits(i),[i / max_val]) 
+        for i in range(max_val + 1)
+    ]
+
+def train_network(network: NeuralNetwork, epochs=10000, learning_rate=0.1, 
+                  verbose: bool = False, size_data: int = 8, max_val: int = 255):
+    
+    train_data = data(size_data, max_val)
+    
+    for epoch in range(epochs):
+        for bits, target in train_data:
+            network.backward(bits, target, learning_rate)
+
+        if verbose and epoch % 100 == 0:
+            output = network.forward(bits)[0]
+            loss = (output - target[0]) ** 2
+
+            print(f"Epoch: {epoch}, Loss: {loss:.6f}")
+
+def main():
+    size = 8
+    max_val = (1 << size) - 1
+
+    network = NeuralNetwork([8, 16, 1])
+
+    print("Start training...")
+    train_network(network, verbose=True, size_data=size, epochs=45_000)
+    print("End training...")
+
+    while True:
+        string = input("Enter 8 bit number (ex: 01101001) or 'quit' to close: ") \
+            .strip().lower()
+        
+        if (string == 'quit'): break
+        if (len(string) != 8 or any (char not in '01' for char in string)):
+            print("Error: please enter exactly 8 bits (only 0 or 1).") 
+            continue
+        
+        bits_input = [int(char) for char in string]
+        output = network.forward(bits_input)[0] * max_val
+
+        print(f"Estimated value: {output} (approx: {round(output)})\n")
+
+if __name__ == "__main__":
+    main()