用python 写一个使用lstm预测车辆轨迹

好的，下面是一个使用LSTM预测车辆轨迹的Python代码示例：

首先，需要导入必要的库和模块：

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

接下来，加载数据集并进行处理：

# 加载数据集
df = pd.read_csv('trajectory_data.csv', header=None)

# 将数据集转换为NumPy数组
dataset = df.values

# 将数据集转换为浮点型
dataset = dataset.astype('float32')

# 对数据集进行归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

然后，将数据集划分为训练集和测试集：

# 将数据集划分为训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

接下来，定义一个函数来创建数据集，该函数接受两个参数：数据集和时间步长：

# 创建数据集
def create_dataset(dataset, time_step=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-time_step-1):
        a = dataset[i:(i+time_step), 0]
        dataX.append(a)
        dataY.append(dataset[i + time_step, 0])
    return np.array(dataX), np.array(dataY)

然后，使用该函数来创建训练集和测试集的数据集：

# 创建训练集和测试集的数据集
time_step = 100
X_train, y_train = create_dataset(train, time_step)
X_test, y_test = create_dataset(test, time_step)

接下来，将数据集的维度进行调整：

# 将数据集的维度进行调整
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))

然后，定义一个LSTM模型：

# 定义LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))

接下来，编译模型并训练：

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, verbose=1)

最后，使用模型进行预测：

# 使用模型进行预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 对预测结果进行反归一化
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])

完整代码如下：

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

# 加载数据集
df = pd.read_csv('trajectory_data.csv', header=None)

# 将数据集转换为NumPy数组
dataset = df.values

# 将数据集转换为浮点型
dataset = dataset.astype('float32')

# 对数据集进行归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 将数据集划分为训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# 创建数据集
def create_dataset(dataset, time_step=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-time_step-1):
        a = dataset[i:(i+time_step), 0]
        dataX.append(a)
        dataY.append(dataset[i + time_step, 0])
    return np.array(dataX), np.array(dataY)

# 创建训练集和测试集的数据集
time_step = 100
X_train, y_train = create_dataset(train, time_step)
X_test, y_test = create_dataset(test, time_step)

# 将数据集的维度进行调整
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))

# 定义LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, verbose=1)

# 使用模型进行预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 对预测结果进行反归一化
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])