47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
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# Copyright 2025, MetaQuotes Ltd.
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# https://www.mql5.com/en/users/johnhlomohang/
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import torch.nn as nn
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import torch
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class EntropyModel(nn.Module):
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"""Enhanced model with 8 input features"""
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def __init__(self, dropout_rate=0.2):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(8, 32),
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nn.BatchNorm1d(32),
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nn.ReLU(),
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nn.Dropout(dropout_rate),
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nn.Linear(32, 16),
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nn.BatchNorm1d(16),
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nn.ReLU(),
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nn.Dropout(dropout_rate),
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nn.Linear(16, 8),
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nn.BatchNorm1d(8),
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nn.ReLU(),
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nn.Linear(8, 1),
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nn.Sigmoid()
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)
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def forward(self, x):
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return self.net(x)
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def predict_with_uncertainty(self, x, n_samples=10):
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"""Monte Carlo dropout for prediction uncertainty"""
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self.train() # Enable dropout
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predictions = []
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with torch.no_grad():
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for _ in range(n_samples):
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pred = self.net(x)
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predictions.append(pred.cpu().numpy())
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predictions = np.array(predictions)
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mean_pred = np.mean(predictions, axis=0)
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std_pred = np.std(predictions, axis=0)
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self.eval() # Back to eval mode
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return mean_pred, std_pred
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