#全连接神经网络 模型预测
# data_x是输入，输入的尺寸是（n, 6)，n是数据的个数
model.stride = 25
data_x = data[-25:, :]
# data_y是输出的预测的结果，输出的矩阵的形状是(n, 4), n是数据的个数
data_y = model.predict_LSTM(data_x)
print(data_y)
# LSTM 模型预测
# data_x是输入，输入的矩阵形状是（n,stride*6) stride是步长，n是数据的个数
#data_x = [[1,2,3,4,5,6],[1,2,3,4,5,6],[1,2,3,4,5,6],[1,2,3,4,5,6],[1,2,3,4,5,6]]
# data_y是输出的预测的结果，输出的矩阵的形状是(4,n), n是数据的个数
#model.stride = 5
data_y = model.predict_Net(data_x)
print(type(data_y[0]))
############################################
T_len, H2_len, CH4_len, CO_len = 5,10,5,20
# 训练LSTM & Net
for stride in [T_len, H2_len, CH4_len, CO_len]:
model.online_train_Net(train_path='./data/data_1.csv',model_path='./newmodels',stride=stride, EPOCH=1000)
model.online_train_LSTM(train_path='./data/data_1.csv',model_path='./newmodels',stride=stride, EPOCH=1000)
# 测试LSTM & Net，返回的res_Net和res_LSTM的格式一样：[气化温度误差率，H2误差率，CH4误差率，CO误差率，平均耗时]
res_Net = model.online_test_Net(test_path='./data/data_1.csv',model_path='./newmodels',stride=5)
res_LSTM = model.online_test_LSTM(test_path='./data/data_1.csv',model_path='./newmodels',stride=5)
#############################################
from Network import Model
from PSO import optimize
from InputParam import InputParam
import numpy as np
import time
data = np.loadtxt('./data/data.csv', delimiter=',', skiprows=2)[:, 1:7]
model = Model(stride=30)
param = InputParam()
best_x = [[28.12421607, 14.41792793], [74.20071455, 9.92461491],
[58.43944117, 11.0825983], [69.17058085, 10.75863371],
[36.35079316, 11.28547075], [57.59862793, 10.92375781],
[27.67301199, 14.30238826], [75.87876121, 13.22701089],
[100.0, 16.10638031], [100.0, 13.78599625]]
best_y = 26.45409999049893
# 调用optimize
#best_x, best_y = optimize(20, data, 2, model, param)
print('best_x = ', best_x)
print('best_y = ', best_y)
model.stride = 10
predict_y = model.predict_Net(best_x)
print(predict_y)