testX1 = feature1[le:] testY1 = target1[le:,0:1] #testX = testX.reshape(168,1,6) #a,b=testY.shape Predict_FG1 = model1.predict(testX1) #Predict_FG = np.array(Predict_FG) #Predict_FG = Predict_FG.reshape(a) Predict_FG1.shape from math import sqrt什么意思

这段代码中，首先定义了测试集的特征和标签，其中 `feature1[le:]` 和 `target1[le:,0:1]` 分别表示从原始数据中第 `le` 个数据开始的特征和标签数据。接着，代码调用模型 `model1` 对测试集的特征进行预测，预测结果保存在 `Predict_FG1` 中。

接下来，代码导入了 Python 标准库中的 `sqrt` 函数，该函数用于计算平方根。这里的 `sqrt` 函数可能是后续计算均方根误差时使用的。

最后，代码使用 `Predict_FG1.shape` 输出了预测结果的形状，即预测结果的样本数和特征数。

相关问题

trainX1 = feature1[:le] #trainX1 = feature[:le,:,:27] #trainX = trainX.reshape(le,1,6) trainY1 = target1[:le,0:1] #aa = trainY[:,0] #bb = trainY[:,1] #cc = trainY[:,2] testX1 = feature1[le:] #testX1 = feature[le:,:,:27] #testX = testX.reshape(168,1,6) testY1 = target1[le:,0:1] #aa1 = testY[:,0] #bb1 = testY[:,1] #cc1 = testY[:,2] model_input1 = Input(shape=trainX1[0].shape)什么意思

这段代码首先定义了训练集和测试集的特征和标签，其中 `feature1` 和 `target1` 分别表示特征和标签的原始数据。代码中使用 `feature1[:le]` 和 `target1[:le,0:1]` 分别表示取原始数据中前 `le` 个数据作为训练集的特征和标签，`feature1[le:]` 和 `target1[le:,0:1]` 分别表示取原始数据中从第 `le` 个数据开始的数据作为测试集的特征和标签。这里的 `le` 可能是一个预先定义好的整数值。

接着，代码定义了一个输入层 `model_input1`，其形状与训练集和测试集的特征形状相同。这里的 `Input(shape=trainX1[0].shape)` 表示创建一个输入层，其中 `shape=trainX1[0].shape` 表示输入层的形状与 `trainX1` 中的第一个样本的形状相同。

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])

以下是将代码改写为nn.module类的示例：

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super(LSTMModel, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

# 读取数据
data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030')
df = data1.fillna(method='ffill')
data = df.values.reshape(-1, 1)

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# 划分数据集
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]

# 创建数据集
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], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# 模型训练
input_dim = 1
hidden_dim = 50
output_dim = 1
num_layers = 2

model = LSTMModel(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

num_epochs = 6
for epoch in range(num_epochs):
    outputs = model(trainX)
    optimizer.zero_grad()
    loss = criterion(outputs, trainY)
    loss.backward()
    optimizer.step()
    if epoch % 1 == 0:
        print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))

# 预测结果
trainPredict = model(trainX)
testPredict = model(testX)

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