X = StandardScaler().fit_transform(X) Traceback (most recent call last): File "C:\Users\lenovo\AppData\Local\Temp\ipykernel_4900\2092310805.py", line 1, in <module> X = StandardScaler().fit_transform(X) NameError: name 'X' is not defined

train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) y_train = scaler.inverse_transform([y_train]) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test]) plt.plot(y_train, label='train') plt.plot(train_predict, label='train predict') plt.plot(y_test, label='test') plt.plot(test_predict, label='test predict') plt.legend() plt.show()报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 48, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)

这个错误通常是由于数组形状不兼容而导致的。在您的代码中，似乎您正在尝试将形状为（110，1）的数组与形状为（110，4）的数组进行广播操作，从而导致此错误。您可以尝试使用 reshape() 方法来重新调整数组的...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 48, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)

这个错误提示是说在进行逆归一化操作时，输出的数据形状与原始数据形状不匹配。具体来说，逆归一化输出的数据形状...y_train = scaler.inverse_transform(y_train.T) #进行逆归一化操作，并将结果reshape为(110,4)

import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense data = pd.read_csv('车辆：274序：4结果数据.csv') x = data[['车头间距', '原车道前车速度']].values y = data['本车速度'].values train_size = int(len(x) * 0.7) test_size = len(x) - train_size x_train, x_test = x[0:train_size,:], x[train_size:len(x),:] y_train, y_test = y[0:train_size], y[train_size:len(y)] from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0, 1)) x_train = scaler.fit_transform(x_train) x_test = scaler.transform(x_test) model = Sequential() model.add(LSTM(50, input_shape=(2, 1))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(x_train.reshape(-1, 2, 1), y_train, epochs=100, batch_size=32, validation_data=(x_test.reshape(-1, 2, 1), y_test)) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper right') plt.show() train_predict = model.predict(x_train.reshape(-1, 2, 1)) test_predict = model.predict(x_test.reshape(-1, 2, 1)) train_predict = scaler.inverse_transform(train_predict) train_predict = train_predict.reshape(-1) # 将结果变为一维数组 y_train = scaler.inverse_transform(y_train.reshape(-1, 1)).reshape(-1) # 将结果变为一维数组 test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test]) plt.plot(y_train[0], label='train') plt.plot(train_predict[:,0], label='train predict') plt.plot(y_test[0], label='test') plt.plot(test_predict[:,0], label='test predict') plt.legend() plt.show()报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py", line 42, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (611,1) doesn't match the broadcast shape (611,2)

x_train = scaler.fit_transform(x_train) x_test = scaler.transform(x_test) model = Sequential() model.add(LSTM(50, input_shape=(2, 1))) model.add(Dense(1)) model.compile(loss='mean_squared_error'...

像你上面那样修改之后还是报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 49, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)

具体来说，scaler.inverse_transform(train_predict)的输出shape是(110,1)，而原始数据的shape是(110,4)，因此无法进行逆归一化操作。解决方法是，将逆归一化时的数据转换成与原始数据shape相同的形式。可以使用...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense data = pd.read_csv('车辆：274序：4结果数据.csv') x = data[['车头间距', '原车道前车速度']].values y = data['本车速度'].values train_size = int(len(x) * 0.7) test_size = len(x) - train_size x_train, x_test = x[0:train_size,:], x[train_size:len(x),:] y_train, y_test = y[0:train_size], y[train_size:len(y)] from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0, 1)) x_train = scaler.fit_transform(x_train) x_test = scaler.transform(x_test) model = Sequential() model.add(LSTM(50, input_shape=(2, 1))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(x_train.reshape(-1, 2, 1), y_train, epochs=100, batch_size=32, validation_data=(x_test.reshape(-1, 2, 1), y_test)) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper right') plt.show() train_predict = model.predict(x_train.reshape(-1, 2, 1)) test_predict = model.predict(x_test.reshape(-1, 2, 1)) train_predict = scaler.inverse_transform(train_predict) train_predict = train_predict.reshape(-1, 1) y_train = scaler.inverse_transform([y_train]) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test]) plt.plot(y_train[0], label='train') plt.plot(train_predict[:,0], label='train predict') plt.plot(y_test[0], label='test') plt.plot(test_predict[:,0], label='test predict') plt.legend() plt.show()报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py", line 42, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (611,1) doesn't match the broadcast shape (611,2)

这个错误提示是因为在使用 scaler.inverse_transform() 函数时，输入的 train_predict 和 y_train 的维度不匹配，导致无法进行运算。具体来说，train_predict 是一个形状为 (611, 1) 的二维数组，而 y_...

Traceback (most recent call last): File "D:\Users\admin\anaconda3\lib\site-packages\sklearn\utils\init.py", line 94, in getattr return self[key] KeyError: 'iloc' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "F:\py\matlab\untitled2.py", line 8, in <module> data.iloc[:, -1] = le.fit_transform(data.iloc[:, -1]) File "D:\Users\admin\anaconda3\lib\site-packages\sklearn\utils\init.py", line 96, in getattr raise AttributeError(key) AttributeError: iloc

data.iloc[:, -1] = le.fit_transform(data.iloc[:, -1]) 如果您不是在使用pandas DataFrame，则无法使用iloc属性。您可以使用numpy库中的切片（slice）语法来访问数组中的数据： import numpy as np #...

Traceback (most recent call last): File "5.2.py", line 2, in <module> X1=step2.return_values() File "/data/workspace/myshixun/step2.py", line 9, in return_values scaler = StandardScaler() NameError: name 'StandardScaler' is not defined

X_scaled = scaler.fit_transform(X[:, 0:6]) # 合并标准化后的x1~x6和未标准化的x7~x15 X1 = np.concatenate((X_scaled, X[:, 6:]), axis=1) return X1 #********** End **********# 请注意，在运行代码...

报错：Traceback (most recent call last): File "C:\Users\admin\PycharmProjects\pythonProject\PPP检验.py", line 54, in <module> y_pred = scaler.inverse_transform(y_pred) File "C:\Users\admin\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (67,1) doesn't match the broadcast shape (67,4)

这个报错是因为 y_pred 的形状为 (67, 1)，而 scaler.inverse_transform() 函数期望的形状是 (67, 4)。这意味着你的 scaler 对应的训练数据集中有 4 个特征，但是你的测试数据只有一个特征。解决这个...

def create_dataset(X, y, time_steps=1): Xs, ys = [], [] for i in range(len(X) - time_steps): Xs.append(X.iloc[i:(i + time_steps)].values) ys.append(y.iloc[i + time_steps]) return np.array(Xs), np.array(ys) TIME_STEPS = 10 X_train, y_train = create_dataset(train[['本车速度', '车头间距', '原车道前车速度']], train['本车加速度'], time_steps=TIME_STEPS) X_test, y_test = create_dataset(test[['本车速度', '车头间距', '原车道前车速度']], test['本车加速度'], time_steps=TIME_STEPS) train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) y_train = scaler.inverse_transform([y_train]) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test])Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 48, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)

这个错误是由于 train_predict 的形状为 (110, 1)...同样，如果您在对 test_predict 和 y_test 进行 inverse_transform() 操作时也遇到了类似的问题，您也可以使用相同的方法来调整 test_predict 的形状。

改进代码ValueError Traceback (most recent call last) Input In [15], in <cell line: 26>() 24 y.append(row[-1]) 25 le = LabelEncoder() ---> 26 X = le.fit_transform(X) 27 y = le.fit_transform(y)# 生成决策树 28 clf = DecisionTreeClassifier(criterion='entropy') File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py:115, in LabelEncoder.fit_transform(self, y) 102 def fit_transform(self, y): 103 """Fit label encoder and return encoded labels. 104 105 Parameters (...) 113 Encoded labels. 114 """ --> 115 y = column_or_1d(y, warn=True) 116 self.classes_, y = _unique(y, return_inverse=True) 117 return y File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:1038, in column_or_1d(y, warn) 1029 warnings.warn( 1030 "A column-vector y was passed when a 1d array was" 1031 " expected. Please change the shape of y to " (...) 1034 stacklevel=2, 1035 ) 1036 return np.ravel(y) -> 1038 raise ValueError( 1039 "y should be a 1d array, got an array of shape {} instead.".format(shape) 1040 ) ValueError: y should be a 1d array, got an array of shape (15, 4) instead.

这段代码出现了一个 ValueError，原因是 y 的形状应当是一维数组，但是在这里 y 的形状是 ...X = le.fit_transform(X) y = le.fit_transform(y) clf = DecisionTreeClassifier(criterion='entropy') clf.fit(X, y)

Traceback (most recent call last):clf.fit(X_train, y_train)

这是一个 Python 代码中的错误信息，提示在执行 clf.fit(X_train, y_train) 这一行代码时出现了错误。具体的错误类型和错误信息需要查看完整的错误信息才能确定。常见的错误包括语法错误、变量未定义、数据类型不...

如何解决Traceback (most recent call last): File "C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py", line 42, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (611,1) doesn't match the broadcast shape (611,2)

这个错误的原因是因为你的训练预测的结果...3. 另外一个解决方法是在初始化缩放器(scaler)时，将参数fit_inverse_transform设置为True，这将允许你使用inverse_transform()方法来逆转缩放操作。希望能对你有所帮助。

Applying contenttypes.0001_initial...Traceback (most recent call last):

This error message suggests that there was an issue when running a Django migration for the contenttypes app. Here are some steps you can try to resolve the issue: 1. Check if there is a more ...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 50, in <module> y_train = scaler.inverse_transform([y_train]) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: operands could not be broadcast together with shapes (1,110) (4,) (1,110)

这个错误通常是由于数组形状不兼容而导致的。在您的代码中，似乎您正在尝试将形状为（1，110）的数组与形状为（4，）的数组进行广播操作，从而导致此错误。...然后，您可以再次尝试执行 inverse_transform() 操作。

X = StandardScaler().fit_transform(X) Traceback (most recent call last): File "C:\Users\lenovo\AppData\Local\Temp\ipykernel_4900\2092310805.py", line 1, in <module> X = StandardScaler().fit_transform(X) NameError: name 'X' is not defined

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