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 本帖最后由 125404629 于 2023-10-10 17:40 编辑  
 
求助大神! 
深度学习MiniRocket 报错! QQ8065559062,如能解决必有重谢 
 
发生异常: ZeroDivisionError 
division by zero 
  File "D:\0000可见光2\程序\MiniRocket\from sktime.transformations.panel.py", line 20, in <module> 
    X_train_trans = minirocket.fit_transform(X_train) 
ZeroDivisionError: division by zero 
 
数据请联系QQ 
 
- import numpy as np
 
 - import pandas as pd
 
 - from keras.utils import to_categorical
 
 - from sklearn.model_selection import train_test_split
 
 - from sklearn.linear_model import LogisticRegression
 
 - from sklearn.metrics import accuracy_score
 
 - from sktime.transformations.panel.rocket import MiniRocketMultivariate
 
  
 
- df1 = pd.read_csv("train1.csv")
 
 - df1 = np.array(df1)
 
 - X = df1[:, 1:891]  # 对数据进行增维并转化为32为
 
 - Y = df1[:, 0]
 
 - print(X.shape, Y.shape)
 
 - X_train, X_val, y_train, y_val = train_test_split(X, Y, test_size=0.8, random_state=42)
 
  
- # Apply MINIROCKET transformation (feature extraction) to train and test data
 
 - minirocket = MiniRocketMultivariate(num_kernels=10, random_state=42)
 
 - #minirocket = MiniRocket()
 
 - X_train_trans = minirocket.fit_transform(X_train)
 
 - X_val_trans= minirocket.fit_transform(X_val)
 
 - print(X_train_trans.shape, X_val_trans.shape)
 
 - print(2)
 
 - # Train a linear classifier (Logistic Regression) on the transformed training data
 
 - classifier = LogisticRegression(max_iter=1000, random_state=42)
 
 - classifier.fit(X_train_trans, y_train)
 
 - print(3)
 
 - y_train_pre = classifier.predict_proba(X_train_trans)
 
 - accuracy_cal = accuracy_score(y_train, y_train_pre)
 
 - print(f'Calibration Accuracy: {accuracy_cal * 100:.2f}%')
 
 - print(4)
 
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