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"""
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Threshold Network for 4-input OR Gate
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"""
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import torch
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from safetensors.torch import load_file
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class ThresholdOR4:
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"""
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4-input OR gate implemented as a threshold neuron.
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Circuit: output = (w1*x1 + w2*x2 + w3*x3 + w4*x4 + bias >= 0)
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With weights=[1,1,1,1], bias=-1: any single input reaches threshold.
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"""
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def __init__(self, weights_dict):
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self.weight = weights_dict['weight']
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self.bias = weights_dict['bias']
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def __call__(self, x1, x2, x3, x4):
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inputs = torch.tensor([float(x1), float(x2), float(x3), float(x4)])
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weighted_sum = (inputs * self.weight).sum() + self.bias
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return (weighted_sum >= 0).float()
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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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def forward(x, weights):
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"""
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Forward pass with Heaviside activation.
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Args:
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x: Input tensor of shape [..., 4]
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weights: Dict with 'weight' and 'bias' tensors
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Returns:
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OR(x[0], x[1], x[2], x[3])
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"""
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x = torch.as_tensor(x, dtype=torch.float32)
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weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
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return (weighted_sum >= 0).float()
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if __name__ == "__main__":
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weights = load_file("model.safetensors")
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model = ThresholdOR4(weights)
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print("4-input OR Gate Truth Table:")
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print("-" * 35)
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correct = 0
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for x1 in [0, 1]:
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for x2 in [0, 1]:
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for x3 in [0, 1]:
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for x4 in [0, 1]:
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out = int(model(x1, x2, x3, x4).item())
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expected = x1 | x2 | x3 | x4
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status = "OK" if out == expected else "FAIL"
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if out == expected:
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correct += 1
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print(f"OR4({x1}, {x2}, {x3}, {x4}) = {out} [{status}]")
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print(f"\nTotal: {correct}/16 correct")
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