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


def read_data(path):
    lines = []
    with open(path) as f:
        for line in f.readlines()[:]:
            line = eval(line.strip())
            lines.append(line)

    size = len(lines[0])
    train_x=[s[:size - 2] for s in lines]
    train_y=[s[size-1] for s in lines]
    return np.array(train_x, dtype=np.float32),np.array(train_y, dtype=np.float32),lines


def _score(fact,):
    up_right = 0
    up_error = 0

    if fact[0] == 1:
        up_right = up_right + 1.1
    elif fact[1] == 1:
        up_error = up_error + 0.4
        up_right = up_right + 1.03
    elif fact[2] == 1:
        up_error = up_error + 0.7
        up_right = up_right + 0.96
    else:
        up_error = up_error + 1
        up_right = up_right + 0.9
    return up_right,up_error


def random_predict(file_path=''):
    test_x,test_y,lines=read_data(file_path)
    a=0
    b=0
    c=0
    d=0
    R=0

    up_num = 0
    up_error = 0
    up_right = 0
    down_num = 0
    down_error = 0
    down_right = 0

    for x in test_y:
        xx = random.uniform(0, 4)
        if xx > 3:
            if x[0] == 1:
                R = R + 1
            tmp_right,tmp_error = _score(x,)
            up_num = up_num + 1
            up_right = up_right + tmp_right
            up_error = tmp_error + up_error
        elif xx > 2:
            if x[1] == 1:
                R = R + 1
        elif xx > 1:
            if x[2] == 1:
                R = R + 1
        elif xx > 0:
            if x[3] == 1:
                R = R + 1

        if x[0] == 1:
            a = a + 1
        elif x[1] == 1:
            b = b + 1
        elif x[2] == 1:
            c = c + 1
        else:
            d = d + 1

    if up_num == 0:
        up_num = 1
    if down_num == 0:
        down_num = 1

    print(R/(a + b + c +d), up_right/up_num, up_error/up_num)


def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20):
    test_x,test_y,lines=read_data(file_path)

    test_x_a = test_x[:,:row*col]
    test_x_a = test_x_a.reshape(test_x.shape[0], row, col, 1)
    # test_x_b = test_x[:, 18*col:row*col]
    # test_x_b = test_x_b.reshape(test_x.shape[0], 10, col, 1)
    test_x_c = test_x[:,row*col:]

    model=load_model(model_path)
    score = model.evaluate([test_x_c, test_x_a, ], test_y)
    print('MIX', score)

    up_num = 0
    up_error = 0
    up_right = 0
    down_num = 0
    down_error = 0
    down_right = 0
    i = 0
    result = model.predict([test_x_c, test_x_a, ])


    win_dnn = []
    for r in result:
        fact = test_y[i]
        xx = random.uniform(0, 4)

        if idx in [-2]:
            if r[0] > 0.5 or r[1] > 0.5:
                pass
        else:
            if r[1] > 0.6 :
                tmp_right,tmp_error = _score(fact,)
                up_right = tmp_right + up_right
                up_error = tmp_error + up_error
                up_num = up_num + 1
            elif r[2] > 0.5 or r[3] > 0.5:
                if fact[0] == 1:
                    down_error = down_error + 1
                    down_right = down_right + 1.1
                elif fact[1] == 1:
                    down_error = down_error + 0.7
                    down_right = down_right + 1.04
                elif fact[2] == 1:
                    down_error = down_error + 0.3
                    down_right = down_right + 0.94
                else:
                    down_right = down_right + 0.9
                down_num = down_num + 1

        i = i + 1
    if up_num == 0:
        up_num = 1
    if down_num == 0:
        down_num = 1
    print('MIX', up_right, up_num, up_right/up_num, up_error/up_num, down_right/down_num, down_error/down_num)
    return win_dnn,up_right/up_num,down_right/down_num


if __name__ == '__main__':
    xx = random.uniform(0,4)
    # predict(file_path='D:\\data\\quantization\\stock513_28d_train1.log', model_path='513_28d_mix_3D_ma5_s_seq.h5', row=28, col=16)
    predict(file_path='D:\\data\\quantization\\stock538_28d_train1.log', model_path='539_28d_mix_5D_ma5_s_seq.h5', row=28, col=17)
    # random_predict(file_path='D:\\data\\quantization\\week101_18d_test.log')
    # random_predict(file_path='D:\\data\\quantization\\stock537_28d_train1.log')