python代码实现以下功能：对包括浪高、气温、风向、风速、气压、波浪周期的数据集，根据前12步长数据，利用attention+lstm预测后24步浪高，并可视化attention权重分配情况

好的，我可以为您提供相关代码实现。首先，需要导入以下的库：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras import layers, models

接下来，读取数据集并对数据进行预处理：

df = pd.read_csv('data.csv')  # 读取数据集
cols = ['浪高', '气温', '风向', '风速', '气压', '波浪周期']  # 选择需要使用的列
df = df[cols]  # 仅保留需要使用的列
scaler = MinMaxScaler()  # 初始化一个归一化器
df = scaler.fit_transform(df)  # 对数据进行归一化处理

然后，我们需要将数据集转换为时间序列数据：

def create_sequences(data, seq_length):
    xs = []
    ys = []
    for i in range(len(data)-seq_length):
        x = data[i:(i+seq_length)]
        y = data[(i+seq_length)]
        xs.append(x)
        ys.append(y)
    return np.array(xs), np.array(ys)

seq_length = 12  # 定义序列长度
X, y = create_sequences(df, seq_length)  # 将数据集转换为时间序列数据

接下来，我们将数据集分为训练集和测试集：

train_size = int(len(y) * 0.8)  # 定义训练集的大小
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]

然后，我们可以定义模型，这里使用了attention+lstm的模型：

def attention_lstm_model(seq_length, n_features):
    inputs = layers.Input(shape=(seq_length, n_features))
    lstm_out = layers.LSTM(64, return_sequences=True)(inputs)
    attention = layers.Dense(1, activation='tanh')(lstm_out)
    attention = layers.Flatten()(attention)
    attention = layers.Activation('softmax')(attention)
    attention = layers.RepeatVector(64)(attention)
    attention = layers.Permute([2, 1])(attention)
    sent_representation = layers.multiply([lstm_out, attention])
    sent_representation = layers.Lambda(lambda xin: layers.K.sum(xin, axis=-2), output_shape=(64,))(sent_representation)
    output = layers.Dense(1)(sent_representation)
    model = models.Model(inputs=inputs, outputs=output)
    return model

model = attention_lstm_model(seq_length, len(cols))
model.compile(optimizer='adam', loss='mse')

最后，我们可以训练模型并进行预测和可视化：

history = model.fit(X_train, y_train, epochs=50, batch_size=16, validation_split=0.1, verbose=1)

y_pred = model.predict(X_test)
y_pred = scaler.inverse_transform(y_pred)
y_test = scaler.inverse_transform([y_test])
y_test = y_test.reshape(y_pred.shape)

plt.plot(y_test.flatten(), label='True')
plt.plot(y_pred.flatten(), label='Predicted')
plt.legend()
plt.show()

attention_vectors = []
for i in range(len(X_test)):
    # 每个测试样本的注意力权重
    attention_vector = model.layers[4](model.layers[3](X_test[i].reshape(1, *X_test[i].shape))).numpy().reshape(-1)
    attention_vectors.append(attention_vector)

# 可视化注意力权重
attention_df = pd.DataFrame(attention_vectors, columns=['t-12', 't-11', 't-10', 't-9', 't-8', 't-7', 't-6', 't-5', 't-4', 't-3', 't-2', 't-1'])
ax = sns.heatmap(attention_df, cmap='Blues')
ax.set(title='Attention weights')
plt.show()

希望这段代码可以满足您的需求！