用lsmt算法写一个生猪价格预测的python程序

您好，我可以回答这个问题。以下是一个使用 LSTM 算法进行生猪价格预测的 Python 程序的示例代码：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_csv('pig_price.csv', usecols=[1])
dataset = data.values
dataset = dataset.astype('float32')

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

# 划分训练集和测试集
train_size = int(len(dataset) * .67)
test_size = len(dataset) - train_size
train, test = dataset[:train_size,:], dataset[train_size:len(dataset),:]

# 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), ]
        dataX.append(a)
        dataY.append(dataset[i + look_back, ])
    return np.array(dataX), np.array(dataY)

look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 转换为 LSTM 可以接受的三维格式 [样本数, 时间步, 特征数]
trainX = np.reshape(trainX, (trainX.shape[], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[], testX.shape[1], 1))

# 创建 LSTM 模型
model = Sequential()
model.add(LSTM(4, 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
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 反归一化
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

# 计算误差
trainScore = np.sqrt(mean_squared_error(trainY[], trainPredict[:,]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[], testPredict[:,]))
print('Test Score: %.2f RMSE' % (testScore))

请注意，这只是一个示例程序，实际上，您需要根据您的数据和需求进行修改和调整。