train_x, train_y = [], [] for i in range(look_back, len(train_data)): x = [] for j in range(len(svr_models)): x.append(svr_models[j].predict(np.reshape(low_freq_imfs.iloc[i-look_back:i, j].values, (1, -1)))[0]) x.append(lstm_model.predict(np.reshape(high_freq_imfs.iloc[i-look_back:i, :].values, (1, look_back, -1)))[0]) train_x.append(x) train_y.append(train_data.iloc[i, 1]) train_x, train_y = np.array(train_x), np.array(train_y) stack_model = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1) stack_model.fit(train_x, train_y) 将以上代码按规范排列,突出显示缩进情况
时间: 2024-01-21 18:02:20 浏览: 30
```
train_x, train_y = [], []
for i in range(look_back, len(train_data)):
x = []
for j in range(lenvr_models)):
x.append(svr_models[j].predict(np.reshape(low_freq_imfs.iloc[i-look_back:i, j].values, (1, -1)))[0])
x.append(lstm_model.predict(np.reshape(high_freq_imfs.iloc[i-look_back:i, :].values, (1, look_back, -1)))[0])
train_x.append(x)
train_y.append(train_data.iloc[i, 1])
train_x, train_y = np.array(train_x), np.array(train_y)
stack_model = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
stack_model.fit(train_x, train_y)
```
相关问题
import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码,并写出预测值和真实值的代码
以下是适当修改后的代码以及预测值和真实值的代码:
```
import numpy as np
import matplotlib.pyplot as plt
import math
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
dataset = []
for data in np.arange(0, 3, .01):
data = math.sin(data * math.pi)
dataset.append(data)
dataset = np.array(dataset)
dataset = dataset.astype('float32')
max_value = np.max(dataset)
min_value = np.min(dataset)
scalar = max_value - min_value
print(scalar)
dataset = list(map(lambda x: x / scalar, dataset))
def create_dataset(dataset, look_back=3):
dataX, dataY = [], []
for i in range(len(dataset) - look_back):
a = dataset[i:(i + look_back)]
dataX.append(a)
dataY.append(dataset[i + look_back])
return np.array(dataX), np.array(dataY)
data_X, data_Y = create_dataset(dataset)
train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))]
test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):]
train_X = train_X.reshape(-1, 1, 3).astype('float32')
train_Y = train_Y.reshape(-1, 1, 1).astype('float32')
test_X = test_X.reshape(-1, 1, 3).astype('float32')
train_X = torch.from_numpy(train_X)
train_Y = torch.from_numpy(train_Y)
test_X = torch.from_numpy(test_X)
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size=1, num_layer=2):
super(RNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layer = num_layer
self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, h = self.rnn(x)
out = self.linear(out[:, -1, :])
return out
net = RNN(3, 20)
criterion = nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
train_loss = []
test_loss = []
for e in range(1000):
pred = net(train_X)
loss = criterion(pred, train_Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (e + 1) % 100 == 0:
print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item()))
train_loss.append(loss.item())
plt.plot(train_loss, label='train_loss')
plt.legend()
plt.show()
# 预测值和真实值的代码
net.eval()
pred_test = net(test_X)
pred_test = pred_test.detach().numpy() * scalar
test_Y = test_Y.reshape(-1, 1) * scalar
plt.plot(pred_test, label='predict')
plt.plot(test_Y, label='true')
plt.legend()
plt.show()
```
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:
```python
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])
```