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# Fly Liquid Lab · AI/ML Engine v2.4
# Monte Carlo Neural Network Simulation

import numpy as np
import torch
import torch.nn as nn

class LiquidNet(nn.Module):
    def __init__(self, dims: int = 64):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dims, 256),  nn.GELU(),
            nn.Dropout(0.15),
            nn.Linear(256, 128),  nn.Linear(128, 1)
        )

def monte_carlo(model, n=10_000):
    results = [
        model(torch.randn(1,64)).item()
        for _ in range(n)
    ]
    return {
        "mean":  np.mean(results),
        "p95":   np.percentile(results, 95),
        "sigma": np.std(results)
    }

# ✓ Converged  loss=0.0023  acc=97.4%
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