# -*- encoding:utf-8 -*-
import numpy as np
from keras.models import load_model
import joblib


def read_data(path):
    day_lines = {}
    with open(path) as f:
        for line in f.readlines()[:]:
            line = eval(line.strip())
            date = str(line[-2][-1])
            if date in day_lines:
                day_lines[date].append(line)
            else:
                day_lines[date] = [line]
    # print(len(day_lines['20191230']))
    return day_lines


def predict(file_path='', model_path='15min_dnn_seq', rows=18, cols=18):
    day_lines = read_data(file_path)
    print('数据读取完毕')

    model=load_model(model_path + '.h5')
    print('模型加载完毕')

    items = sorted(day_lines.keys())
    for key in items:
        # print(day)
        lines = day_lines[key]

        up_num = 0
        down_num = 0
        size = len(lines[0])
        x0 = 0
        x1 = 0
        x2 = 0
        x3 = 0
        x4 = 0

        for line in lines:
            train_x = np.array([line[:size - 2]])

            result = model.predict([train_x])

            ratio = 1
            if train_x[0][-1] == 1:
                ratio = 2
            elif train_x[0][-2] == 1:
                ratio = 1.6
            elif train_x[0][-3] == 1:
                ratio = 1.3

            if result[0][0]> 0.5:
                up_num = up_num + ratio
            elif result[0][1] > 0.5:
                up_num = up_num + 0.4*ratio
            elif result[0][2] > 0.5:
                down_num = down_num + ratio

            maxx = max(result[0])
            if maxx - result[0][0] == 0:
                x0 = x0 + 1
            elif maxx - result[0][1] == 0:
                x1 = x1 + 1
            else:
                x2 = x2 + 1

        # print(key, int(up_num), int(down_num), (down_num*1.2 + 2)/(up_num*1.2 + 2), )
        print(key, x0, x1, x2, (down_num*1.5 + 2)/(up_num*1.2 + 2))


import datetime
if __name__ == '__main__':
    today = datetime.datetime.now()
    today = today
    today =  '' #today.strftime('%Y%m%d')
    # predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock9_18_20200220.log', model_path='18d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock9_18_2.log', model_path='18d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock16_18d_20200310.log', model_path='16_18d_mix_seq')
    # predict(file_path='D:\\data\\quantization\\stock196_18d_20200326.log', model_path='196_18d_mix_6D_ma5_s_seq')


    predict(file_path='D:\\data\\quantization\\stock578A_12d_train3.log', model_path='5d_578A_dnn_seq', rows=28, cols=20)

    # predict(file_path='D:\\data\\quantization\\stock578A_12d_' + today + '.log', model_path='5d_578A_dnn_seq', rows=28, cols=20)



    # predict(file_path='D:\\data\\quantization\\stock9_18_4.log', model_path='18d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock324_28d_3D_20200414_A.log', model_path='324_28d_mix_5D_ma5_s_seq', rows=28, cols=18)
    # predict(file_path='D:\\data\\quantization\\stock324_28d_3D_20200414_A.log', model_path='603_30d_mix_5D_ma5_s_seq', rows=30, cols=19)