生成训练和测试数据
train_X, train_Y = generate_data(train_data[['red1', 'red2', 'red3', 'red4', 'red5', 'red6', 'blue']].values, lookback)
test_X, test_Y = generate_data(test_data[['red1', 'red2', 'red3', 'red4', 'red5', 'red6', 'blue']].values, lookback)
创建LSTM模型
model = Sequential()
model.add(LSTM(64, input_shape=(lookback, 7)))
model.add(Dense(32, activation='relu'))
model.add(Dense(7, activation='sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam')
训练模型
model.fit(train_X, train_Y, batch_size=batch_size, epochs=epochs, validation_data=(test_X, test_Y))
预测下一期双色球
last_data = data[['red1', 'red2', 'red3', 'red4', 'red5', 'red6', 'blue']].tail(lookback)
last_data = (last_data - 1) / np.array([33, 33, 33, 33, 33, 33, 16])
last_data = np.array(last_data)
last_data = np.reshape(last_data, (1, lookback, 7))
prediction = model.predict(last_data)
prediction = np.round(prediction * np.array([33, 33, 33, 33, 33, 33, 16]) + np.array([1, 1, 1, 1, 1, 1, 1]))
print("下一期双色球预测结果为：", prediction)
现在你是一个python专家，请优化上面的代码