导入excel数据用模糊神经网络预测数据代码python
时间: 2023-10-08 22:11:52 浏览: 61
以下是使用模糊神经网络预测数据的Python代码示例:
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from neomodel import FuzzyNeuralGas as FNG
# Load data from Excel file
data = pd.read_excel('data.xlsx')
# Split data into input and output variables
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# Normalize input data
scaler = MinMaxScaler()
X = scaler.fit_transform(X)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train the model using Fuzzy Neural Gas algorithm
fng = FNG()
fng.fit(X_train, y_train)
# Predict the output for testing data
y_pred = fng.predict(X_test)
# Evaluate the performance of the model
from sklearn.metrics import r2_score
print('R2 score:', r2_score(y_test, y_pred))
```
在这个示例中,我们使用了 `pandas` 库加载 Excel 文件,然后使用 `MinMaxScaler` 对输入数据进行归一化。然后,我们使用 `train_test_split` 将数据分为训练和测试集。接下来,我们使用 `FuzzyNeuralGas` 对模型进行训练,并在测试集上进行预测。最后,我们使用 `r2_score` 评估模型的性能。需要注意的是,`neomodel` 库需要额外安装。