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"""
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Threshold Network for Half Adder
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Adds two 1-bit inputs, producing sum (XOR) and carry (AND) outputs.
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Sum uses 2-layer XOR, Carry uses single AND neuron.
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"""
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import torch
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from safetensors.torch import load_file
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def heaviside(x):
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return (x >= 0).float()
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class ThresholdHalfAdder:
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"""
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Half adder: sum = a XOR b, carry = a AND b
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"""
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def __init__(self, weights_dict):
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self.weights = weights_dict
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def __call__(self, a, b):
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inputs = torch.tensor([float(a), float(b)])
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or_out = heaviside((inputs * self.weights['sum.layer1.or.weight']).sum() +
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self.weights['sum.layer1.or.bias'])
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nand_out = heaviside((inputs * self.weights['sum.layer1.nand.weight']).sum() +
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self.weights['sum.layer1.nand.bias'])
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layer1 = torch.tensor([or_out, nand_out])
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sum_out = heaviside((layer1 * self.weights['sum.layer2.weight']).sum() +
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self.weights['sum.layer2.bias'])
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carry_out = heaviside((inputs * self.weights['carry.weight']).sum() +
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self.weights['carry.bias'])
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return int(sum_out.item()), int(carry_out.item())
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@classmethod
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def from_safetensors(cls, path="model.safetensors"):
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return cls(load_file(path))
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if __name__ == "__main__":
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model = ThresholdHalfAdder.from_safetensors("model.safetensors")
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print("Half Adder Truth Table:")
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print("-" * 30)
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print("a | b | sum | carry")
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print("-" * 30)
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for a in [0, 1]:
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for b in [0, 1]:
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s, c = model(a, b)
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expected_s = a ^ b
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expected_c = a & b
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status = "OK" if (s == expected_s and c == expected_c) else "FAIL"
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print(f"{a} | {b} | {s} | {c} [{status}]")
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