from  keras.models import Sequential
from  keras.layers import Dense, Dropout, Embedding
from  keras.layers import InputLayer
from  keras.layers import LSTM
from  keras import backend 
from keras.layers.recurrent import SimpleRNN

import pymysql
import pickle
import os
import numpy

from data import load_pay_data


if __name__ == "__main__":

    region = "Region_Arab"
    x_train, y_train, tx_train, ty_train, input_shape = load_pay_data(80, region)

    
    model = Sequential()
    units = 500
    model.add(LSTM(units, activation='relu', dropout=0.1, input_shape=input_shape))
    
    # model.add(SimpleRNN(units, activation='relu'))
    # model.add(Dropout(0.1))
    model.add(Dense(1))
    model.summary()
    model.compile(loss = 'mse', optimizer = 'adam')

    model.fit(x_train, y_train, batch_size=96, epochs=1200)
    model.save("./predict_pay_" + region)

    p_data = model.predict(tx_train)
    for i in range(len(p_data)):
        print((p_data[i][0] - ty_train[i]) / ty_train[i], p_data[i][0], ty_train[i])
    # print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])