python如何引用scaler.inverse_transform

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]） train_predict = np.tile（train_predict，（1， 4）） #将train_predict复制4次，变为（110，4） train_predict = scaler.inverse_transform（train_predict） #进行逆归一化操作 y_train = np.tile（y_train，（1， 4）） #将y_train复制4次，变为（1， 440） y_train = scaler.inverse_transform（y_train.T） #进行逆归一化操作，并将结果reshape为（110，4） y_train = y_train.reshape（110， 4） test_predict = scaler.inverse_transform（test_predict） y_test = scaler.inverse_transform（[y_test]）报错Traceback（最近一次调用）：文件 “C：\Users\马斌\Desktop\cnn测试\cnn改.py”，第 48 行，在 <module> train_predict = scaler.inverse_transform（train_predict）文件 “D：\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing_data.py”，第 541 行，在 X inverse_transform -= self.min_ 值错误：形状为（110，1）的不可广播输出操作数与广播形状（110，4）不匹配

train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1)) y_train = scaler.inverse_transform([y_train.reshape(-1, 1)]) train_predict = np.tile(train_predict, (1, 4)) y_train = np.tile(y_...

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)

python train_predict = np.tile(train_predict, (1, 4)) 这将使用 tile() 函数将 train_predict 数组沿列方向复制 4 次，以便它的形状变为（110，4）。然后，您可以再次尝试执行广播操作。

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。

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_...

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)

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.reshape(-1...

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。

dataset = np.loadtxt(r'D:\python-learn\testdata.csv', delimiter=",",skiprows=1) X = dataset[:,0:17] scaler = MinMaxScaler(feature_range=(0, 1)) X = scaler.fit_transform(X) pred_Y = model.predict(X) pred_Y = scaler.inverse_transform(pred_Y) print("Predicted value:", pred_Y)这段代码的反归一化有些错误，请你更改一下

在这段代码中，scaler.inverse_transform() 方法的参数应该是 pred_Y 而不是 Y_pred。此外，由于 pred_Y 的形状是 (样本数量, 1)，我们需要使用 reshape() 方法将其转换为 (样本数量,) 的形状，以便与...

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)

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。

scaler.inverse_transform(np.array(future_sales).reshape(-1, 1))

scaler.inverse_transform() 是用于将经过标准化（如缩放）的数据转换回原始数值范围的方法，通常在使用预处理技术（如MinMaxScaler或StandardScaler）之后。future_sales 是一个未来销售预测的结果数组，可能...

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...

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...

在使用scaler.inverse_transform时出现Found array with dim 3. None expected <= 2.报错怎么办

这个报错提示你的输入数据的维度不正确，scaler.inverse_transform函数期望的是一个二维矩阵，但是你输入的数据维度可能是三维的。因此，你需要检查一下输入的数据，确保它是一个二维矩阵。通常情况下，当你使用...

minmaxscaler.inverse_transform

original_data = scaler.inverse_transform(scaled_data) print(original_data) Output: [[1. 2. 3.] [4. 5. 6.] [7. 8. 9.]] In this example, we first created a sample dataset data ...

将冒号后面的代码改写成一个nn.module类：data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

trainPredict = scaler.inverse_transform(trainPredict.detach().numpy()) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict.detach().numpy()) testY = scaler....

python如何引用scaler.inverse_transform

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