53 lines
1.1 KiB
Python
53 lines
1.1 KiB
Python
import numpy as np
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import torch
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from sklearn.preprocessing import MinMaxScaler
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def seed_everything(seed):
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"""
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Set a seed for all random number generators to ensure reproducibility.
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Parameters:
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seed (int): The seed to use.
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"""
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np.random.seed(seed)
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torch.manual_seed(seed)
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def scale_data(data):
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"""
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Scale data using MinMaxScaler.
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Parameters:
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data (np.array): The data to scale.
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Returns:
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np.array: The scaled data.
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"""
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scaler = MinMaxScaler()
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scaled_data = scaler.fit_transform(data)
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return scaled_data, scaler
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def save_model(model, path):
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"""
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Save a PyTorch model.
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Parameters:
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model (torch.nn.Module): The model to save.
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path (str): The path where to save the model.
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"""
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torch.save(model.state_dict(), path)
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def load_model(model, path):
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"""
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Load a PyTorch model.
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Parameters:
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model (torch.nn.Module): The model to load.
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path (str): The path from where to load the model.
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Returns:
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torch.nn.Module: The loaded model.
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"""
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model.load_state_dict(torch.load(path))
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model.eval()
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return model
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