# 9.绘制结果 plot_size = 200 plt.figure(figsize=(12, 8)) plt.plot(scaler.inverse_transform((model(x_train_tensor).detach().numpy()[: plot_size]).reshape(-1, 1)), "b") plt.plot(scaler.inverse_transform(y_train_tensor.detach().numpy().reshape(-1, 1)[: plot_size]), "r") plt.legend() plt.show() y_test_pred = model(x_test_tensor) # data是一个以为度得列表，这样是把数据写进去一列 print("--------------------------") yuce=scaler.inverse_transform(y_test_pred.detach().numpy()) true=scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)) res = [] for i in range(len(yuce)): temp=[] temp.append(true[i][0]) temp.append(yuce[i][0]) # print() # print(yuce[i][0]) res.append(temp) columns = ['真实值', '预测值'] test = pd.DataFrame(columns=columns, data=res) test.to_csv('jieguo.csv') plt.figure(figsize=(12, 8)) plt.plot(scaler.inverse_transform(y_test_pred.detach().numpy()[: plot_size]), "b") plt.plot(scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)[: plot_size]), "r") plt.legend() plt.show()

绘制训练集和测试集的真实值和预测值图像 train_predict_plot = np.empty_like(data_scaled) train_predict_plot[:, :] = np.nan train_predict_plot[time_steps:len(train_predict) + time_steps, :] = train_predict test_predict_plot = np.empty_like(data_scaled) test_predict_plot[:, :] = np.nan test_predict_plot[len(train_predict) + time_steps * 2 + 1:len(data_scaled) - 1, :] = test_predict plt.figure(figsize=(10, 6)) plt.plot(scaler.inverse_transform(data_scaled)) plt.plot(train_predict_plot) plt.plot(test_predict_plot) plt.legend(['True', 'Train Predict', 'Test Predict']) plt.xlabel('Time/h') plt.ylabel('kwh') plt.show(改写以上代码，使得训练集部分和测试集部分分别绘制在两张图上

plt.plot(scaler.inverse_transform(data_scaled[:len(train_predict)])) plt.plot(train_predict_plot[:len(train_predict)]) plt.legend(['True', 'Train Predict']) plt.xlabel('Time/h') plt.ylabel('kwh') plt....

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 from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM import matplotlib.pyplot as plt # 读取CSV文件 data = pd.read_csv('77.csv', header=None) # 将数据集划分为训练集和测试集 train_size = int(len(data) * 0.7) train_data = data.iloc[:train_size, 1:2].values.reshape(-1,1) test_data = data.iloc[train_size:, 1:2].values.reshape(-1,1) # 对数据进行归一化处理 scaler = MinMaxScaler(feature_range=(0, 1)) train_data = scaler.fit_transform(train_data) test_data = scaler.transform(test_data) # 构建训练集和测试集 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back): X.append(dataset[i:(i+look_back), 0]) Y.append(dataset[i+look_back, 0]) return np.array(X), np.array(Y) look_back = 3 X_train, Y_train = create_dataset(train_data, look_back) X_test, Y_test = create_dataset(test_data, look_back) # 转换为LSTM所需的输入格式 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(X_train, Y_train, epochs=100, batch_size=32) # 预测测试集并进行反归一化处理 Y_pred = model.predict(X_test) Y_pred = scaler.inverse_transform(Y_pred) Y_test = scaler.inverse_transform(Y_test) # 输出RMSE指标 rmse = np.sqrt(np.mean((Y_pred - Y_test)**2)) print('RMSE:', rmse) # 绘制训练集真实值和预测值图表 train_predict = model.predict(X_train) train_predict = scaler.inverse_transform(train_predict) train_actual = scaler.inverse_transform(Y_train.reshape(-1, 1)) plt.plot(train_actual, label='Actual') plt.plot(train_predict, label='Predicted') plt.title('Training Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show() # 绘制测试集真实值和预测值图表 plt.plot(Y_test, label='Actual') plt.plot(Y_pred, label='Predicted') plt.title('Testing Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show()以上代码运行时报错，错误为ValueError: Expected 2D array, got 1D array instead: array=[-0.04967795 0.09031832 0.07590125]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.如何进行修改

可以将以下代码： X_train, Y_train = create_dataset(train_data, look_back) X_test, Y_test = create_dataset(test_data, look_back) X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1],...

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...

import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.cluster import KMeans from scipy.spatial import Voronoi, voronoi_plot_2d # 生成示例数据 data = df.iloc[:,1:15] # 标准化处理 scaler = StandardScaler() data_scaled = scaler.fit_transform(data) # 主成分分析 pca = PCA(n_components=5) data_pca = pca.fit_transform(data_scaled) # 聚类分析 kmeans = KMeans(n_clusters=3) kmeans.fit(data_pca) labels = kmeans.labels_ centers = kmeans.cluster_centers_ # 绘制Voronoi图 vor = Voronoi(centers) voronoi_plot_2d(vor) # 绘制样本点 plt.scatter(data_pca[:, 0], data_pca[:, 1], c=labels) # 设置坐标轴标签和标题 plt.xlabel('PC1') plt.ylabel('PC2') plt.title('Voronoi Diagram') # 显示图形 plt.show()

您的代码看起来几乎是正确的，但有一点需要注意。Voronoi图是在原始数据空间中绘制的，而不是在主成分空间中。因此，在绘制Voronoi图...这段代码将绘制投影回原始数据空间后的样本点，并在Voronoi图上显示聚类结果。

import numpy as npimport pandas as pdfrom sklearn.preprocessing import MinMaxScalerfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, LSTMdf = pd.read_csv('AAPL.csv') # 载入股票数据# 数据预处理scaler = MinMaxScaler(feature_range=(0, 1))scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1))# 训练集和测试集划分prediction_days = 30x_train = []y_train = []for x in range(prediction_days, len(scaled_data)): x_train.append(scaled_data[x-prediction_days:x, 0]) y_train.append(scaled_data[x, 0])x_train, y_train = np.array(x_train), np.array(y_train)x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))# 构建BP神经网络模型model = Sequential()model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))model.add(Dropout(0.2))model.add(LSTM(units=50, return_sequences=True))model.add(Dropout(0.2))model.add(LSTM(units=50))model.add(Dropout(0.2))model.add(Dense(units=1))model.compile(optimizer='adam', loss='mean_squared_error')model.fit(x_train, y_train, epochs=25, batch_size=32)# 使用模型进行预测test_start = len(scaled_data) - prediction_daystest_data = scaled_data[test_start:, :]x_test = []for x in range(prediction_days, len(test_data)): x_test.append(test_data[x-prediction_days:x, 0])x_test = np.array(x_test)x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))predicted_price = model.predict(x_test)predicted_price = scaler.inverse_transform(predicted_price)# 可视化预测结果import matplotlib.pyplot as pltplt.plot(df['Close'].values)plt.plot(range(test_start, len(df)), predicted_price)plt.show()介绍

这段代码实现了使用BP神经网络预测苹果公司股票价格，并对预测结果进行可视化。具体实现过程如下： 1. 载入股票数据：使用Pandas库中的read_csv函数读取CSV格式的股票数据。 2. 数据预处理：使用MinMaxScaler对...

X = data.iloc[:, :-1].values y = data.iloc[:, -1:].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练模型 input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.0016 num_epochs=2000 loss_history=nn.train(X, y, learning_rate, num_epochs) plt.plot(loss_history) plt.title('loss') plt.xlabel('Epoch') plt.ylabel('MSE')分析代码并续写预测代码

y_pred = scaler.inverse_transform(y_pred) 完整的预测代码如下： python from sklearn.preprocessing import StandardScaler # 加载要预测的数据集 X_pred = data_pred.iloc[:, :-1].values y_pred = ...

#importing required libraries from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, Dropout, LSTM #setting index data = df.sort_index(ascending=True, axis=0) new_data = data[['trade_date', 'close']] new_data.index = new_data['trade_date'] new_data.drop('trade_date', axis=1, inplace=True) new_data.head() #creating train and test sets dataset = new_data.values train= dataset[0:1825,:] valid = dataset[1825:,:] #converting dataset into x_train and y_train scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(dataset) x_train, y_train = [], [] for i in range(60,len(train)): x_train.append(scaled_data[i-60:i,0]) y_train.append(scaled_data[i,0]) x_train, y_train = np.array(x_train), np.array(y_train) x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)) # create and fit the LSTM network model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1))) model.add(LSTM(units=50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=1) #predicting 246 values, using past 60 from the train data inputs = new_data[len(new_data) - len(valid) - 60:].values inputs = inputs.reshape(-1,1) inputs = scaler.transform(inputs) X_test = [] for i in range(60,inputs.shape[0]): X_test.append(inputs[i-60:i,0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1)) closing_price = model.predict(X_test) closing_price1 = scaler.inverse_transform(closing_price) rms=np.sqrt(np.mean(np.power((valid-closing_price1),2))) rms #v=new_data[1825:] valid1 = pd.DataFrame() # 假设你使用的是Pandas DataFrame valid1['Pre_Lstm'] = closing_price1 train=new_data[:1825] plt.figure(figsize=(16,8)) plt.plot(train['close']) plt.plot(valid1['close'],label='真实值') plt.plot(valid1['Pre_Lstm'],label='预测值') plt.title('LSTM预测',fontsize=16) plt.xlabel('日期',fontsize=14) plt.ylabel('收盘价',fontsize=14) plt.legend(loc=0)

5. 进行预测：使用测试集对 LSTM 模型进行预测，并将预测结果反归一化。 6. 计算误差：使用均方根误差（RMSE）来评估模型的预测性能。 7. 可视化结果：使用 Matplotlib 将真实值、预测值和训练集的收盘价可视化。 ...

解释代码diff_rev = scaler.inverse_transform(np.pad(np.cumsum(np.pad(pred, (2,0), mode='constant')), (2,0), 'constant')[:, np.newaxis]) rev = np.zeros_like(diff_rev) rev[0], rev[1] = df.iloc[train_size-2][0], df.iloc[train_size-1][0] rev[2:] = diff_rev[:-2] # 输出对比图 plt.plot(df.index[train_size:], df.values[train_size:], label='Real Data') plt.plot(df.index[train_size:], rev, label='Predicted Data') plt.legend() plt.show()

具体来说，代码中首先使用 scaler.inverse_transform() 函数将预测结果 pred 进行反归一化处理，得到经过反归一化处理的差分序列 diff_rev。这里使用了 np.pad() 函数对 pred 进行了前置填充，以便后续...

# 画出均方误差图 testPredictPlot = np.empty_like(data) testPredictPlot[:, :] = np.nan testPredictPlot[len(data)-len(testPredict):len(data), :] = testPredict plt.plot(scaler.inverse_transform(data)) plt.plot(testPredictPlot) plt.show()这里如何绘制图像

plt.plot(scaler.inverse_transform(data)) plt.plot(testPredictPlot) plt.plot(mse_line, color='red') plt.show() 这里使用了 NumPy 的 np.mean() 函数计算均方误差，并用 np.square() 函数求平方。然后...

# 准备数据 X = group3[['vis_min']].values y = group3['pm10'].values # 数据归一化 scaler = MinMaxScaler() X_scaled = scaler.fit_transform(X) y_scaled = scaler.fit_transform(y.reshape(-1, 1)) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42) # 定义模型 model = Sequential([ Dense(64, activation='relu', input_shape=(1,)), Dense(64, activation='relu'), Dense(1) ]) # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=0) # 在测试集上进行预测 y_pred = model.predict(X_test) # 反归一化预测结果 y_pred = scaler.inverse_transform(y_pred) # 打印拟合方程式和参数 weights = model.get_weights() # 打印拟合方程式和参数 a, b, c = weights[0][0][0].item(), weights[1][0][0].item(), weights[2][0][0].item() print('拟合方程式：y = {:.2f} * exp({:.2f} * x) + {:.2f}'.format(a, b, c)) print('参数：a = {:.2f}, b = {:.2f}, c = {:.2f}'.format(a, b, c)) # 绘制拟合曲线 x_range = np.linspace(X.min(), X.max(), 100).reshape(-1, 1) y_fit = a * np.exp(b * x_range) + c plt.scatter(X, y, c='r', label='原始数据') plt.plot(x_range, y_fit, 'g-', label='拟合曲线') plt.xlabel('能见度（km）') plt.ylabel('PM10浓度（μg/立方米）') plt.title('指数函数拟合') plt.legend() plt.show()

8. 反归一化预测结果：使用 inverse_transform 函数对 y_pred 进行反归一化处理。 9. 打印拟合方程式和参数：使用 get_weights 函数获取模型的参数，并将其打印出来。 10. 绘制拟合曲线：根据拟合方程式计算...

import numpy as np import pandas as pd import matplotlib.pyplot as plt import BPNN from sklearn import metrics from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error #导入必要的库 df1=pd.read_excel(r'D:\Users\Desktop\大数据\44.xls',0) df1=df1.iloc[:,:] #进行数据归一化 from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() df0=min_max_scaler.fit_transform(df1) df = pd.DataFrame(df0, columns=df1.columns) x=df.iloc[:,:4] y=df.iloc[:,-1] #划分训练集测试集 cut=4#取最后cut=30天为测试集 x_train, x_test=x.iloc[4:],x.iloc[:4]#列表的切片操作，X.iloc[0:2400，0:7]即为1-2400行，1-7列 y_train, y_test=y.iloc[4:],y.iloc[:4] x_train, x_test=x_train.values, x_test.values y_train, y_test=y_train.values, y_test.values #神经网络搭建 bp1 = BPNN.BPNNRegression([4, 16, 1]) train_data=[[sx.reshape(4,1),sy.reshape(1,1)] for sx,sy in zip(x_train,y_train)] test_data = [np.reshape(sx,(4,1))for sx in x_test] #神经网络训练 bp1.MSGD(train_data, 1000, len(train_data), 0.2) #神经网络预测 y_predict=bp1.predict(test_data) y_pre = np.array(y_predict) # 列表转数组 y_pre=y_pre.reshape(4,1) y_pre=y_pre[:,0] #画图 #展示在测试集上的表现 draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_pre)],axis=1); draw.iloc[:,0].plot(figsize=(12,6)) draw.iloc[:,1].plot(figsize=(12,6)) plt.legend(('real', 'predict'),loc='upper right',fontsize='15') plt.title("Test Data",fontsize='30') #添加标题 #输出精度指标 print('测试集上的MAE/MSE') print(mean_absolute_error(y_pre, y_test)) print(mean_squared_error(y_pre, y_test) ) mape = np.mean(np.abs((y_pre-y_test)/(y_test)))*100 print('=============mape==============') print(mape,'%') # 画出真实数据和预测数据的对比曲线图 print("R2 = ",metrics.r2_score(y_test, y_pre)) # R2 运行上述程序。在下面这一步中draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_pre)],axis=1);我需要将归一化的数据变成真实值，输出对比图，该怎么修改程序

draw.iloc[:,0].plot(figsize=(12,6)) draw.iloc[:,1].plot(figsize=(12,6)) plt.legend(('real', 'predict'), loc='upper right', fontsize='15') plt.title("Test Data", fontsize='30') # 输出精度指标 print('...

plt.plot(scaler.inverse_transform(y_test_pred.detach().numpy()), "b") plt.plot(scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)), "r") plt.legend() plt.show()以上函数的X轴相距为1，怎么将X轴相距改为0.06

# plt.plot(scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)), "r") # plt.legend() # plt.show() # 生成一些随机数据作为示例 y_test_pred = np.random.rand(100) y_test_tensor = np....

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.preprocessing import MinMaxScalerfrom keras.models import Sequentialfrom keras.layers import Dense, LSTM# 读取数据dataset = pd.read_csv('wind_speed.csv', header=0, index_col=0)dataset.index = pd.to_datetime(dataset.index)dataset = dataset.resample('H').mean()# 数据预处理scaler = MinMaxScaler(feature_range=(0, 1))dataset_scaled = scaler.fit_transform(dataset)# 创建训练集和测试集train_size = int(len(dataset_scaled) * 0.8)test_size = len(dataset_scaled) - train_sizetrain, test = dataset_scaled[0:train_size, :], dataset_scaled[train_size:len(dataset_scaled), :]# 创建数据集def create_dataset(dataset, look_back): 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 = 24trainX, trainY = create_dataset(train, look_back)testX, testY = create_dataset(test, look_back)# 调整数据维度trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))# 创建LSTM模型model = Sequential()model.add(LSTM(50, input_shape=(look_back, 1)))model.add(Dense(1))model.compile(loss='mean_squared_error', optimizer='adam')model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)# 预测数据trainPredict = model.predict(trainX)testPredict = model.predict(testX)# 反转数据缩放trainPredict = scaler.inverse_transform(trainPredict)trainY = scaler.inverse_transform([trainY])testPredict = scaler.inverse_transform(testPredict)testY = scaler.inverse_transform([testY])# 绘制预测结果plt.plot(trainY[0], label='Train Data')plt.plot(trainPredict[:,0], label='Predicted Train Data')plt.plot(testY[0], label='Test Data')plt.plot(testPredict[:,0], label='Predicted Test Data')plt.legend(loc='best')plt.show()

这段代码是使用 LSTM 模型对风速数据进行预测的代码。具体来说，代码中使用了 Pandas 库读取风速数据，并进行了预处理和归一化。...最后，使用训练好的模型对训练集和测试集进行预测，并将预测结果进行可视化。

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# 画出均方误差图 testPredictPlot = np.empty_like(data) testPredictPlot[:, :] = np.nan testPredictPlot[len(data)-len(testPredict):len(data), :] = testPredict plt.plot(scaler.inverse_transform(data)) plt.plot(testPredictPlot) plt.show()这里如何绘制图像

plt.plot(scaler.inverse_transform(y_test_pred.detach().numpy()), "b") plt.plot(scaler.inverse_transform(y_test_tensor.detach().numpy().reshape(-1, 1)), "r") plt.legend() plt.show()以上函数的X轴相距为1，怎么将X轴相距改为0.06

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