33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
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import pandas as pd
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import torch
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import numpy as np
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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# 数据预处理
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def process_data(file_path):
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# 读取Excel文件，跳过首行，使用科学计数法解析
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df = pd.read_excel(file_path, header=None,sheet_name="sample1")
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# 提取输入输出数据
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inputs = df.iloc[:, 1:9].values.astype(np.float32) # 第2-9列
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outputs = df.iloc[:, 10:19].values.astype(np.float32) # 第11-19列
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# 数据标准化
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input_scaler = StandardScaler()
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output_scaler = StandardScaler()
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inputs = input_scaler.fit_transform(inputs)
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outputs = output_scaler.fit_transform(outputs)
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#
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# input_scaler = MinMaxScaler(feature_range=(-1, 1))
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# output_scaler = MinMaxScaler(feature_range=(-1, 1))
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# inputs = input_scaler.fit_transform(inputs)
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# outputs = output_scaler.fit_transform(outputs)
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return inputs, outputs, input_scaler, output_scaler
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# 创建序列数据集 look_back：依据时间序列 pred_step：推测时间序列
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def create_sequences(inputs, outputs, look_back=8, pred_step=1):
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X, y = [], []
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for i in range(len(inputs) - look_back - pred_step):
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X.append(inputs[i:i + look_back])
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y.append(outputs[(i+look_back):(i+look_back+pred_step)])
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return torch.FloatTensor(np.array(X)), torch.FloatTensor(np.array(y))
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