DL之GRU(Tensorflow框架)：基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)

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DL之GRU(Tensorflow框架)：基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)

基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)

#1、定义数据集

# 2、数据集预处理

# 2.1、数据集切分

# 2.2、数据维度转换

# 2.3、训练集、测试集进行MinMax归一化

# 2.4、依次构建train、test的时序性数据集矩阵

# (1)、for循环构建train时序性数据集矩阵

# (2)、for循环构建test时序性数据集矩阵

# 3、模构建GRU模型

# 3.1、模型构建

# 3.2、模型编译并定义优化器、损失函数

# 3.3、模型训练并保存checkpoint文件

# 使入模数据维度标准化

# 创建并保存weights.tx权重文件

# 模型训练过程可视化：绘制loss

epoch=5

# 3.4、模型评估

# 对真实、预测数据进行MinMax反归一化还原

# 画出真实数据和预测数据的对比曲线

# 输出模型评估指标

# 保存预测结果

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DL之GRU(Tensorflow框架)：基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)

基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)

#1、定义数据集

# 数据集下载：.html

日期	股票代码	名称	收盘价	最高价	最低价	开盘价	前收盘	涨跌额	涨跌幅	换手率	成交量	成交金额	总市值	流通市值
2022/6/27	'600519	贵州茅台	2010.55	2049.94	2000.3	2019.94	2009.01	1.54	0.0767	0.3193	4011517	8124448900	2.53E+12	2.53E+12
2022/6/24	'600519	贵州茅台	2009.01	2020	1965	1970	1957.1	51.91	2.6524	0.3155	3963465	7921199792	2.52E+12	2.52E+12
2022/6/23	'600519	贵州茅台	1957.1	1965.04	1940	1942.7	1936	21.1	1.0899	0.2137	2684352	5239860443	2.46E+12	2.46E+12
2022/6/22	'600519	贵州茅台	1936	1958	1932	1955	1945.74	-9.74	-0.5006	0.1564	1964665	3813775294	2.43E+12	2.43E+12
2022/6/21	'600519	贵州茅台	1945.74	1966.99	1928	1949	1942.02	3.72	0.1916	0.1888	2371702	4617805127	2.44E+12	2.44E+12
2022/6/20	'600519	贵州茅台	1942.02	1970	1930	1950	1951	-8.98	-0.4603	0.2784	3497478	6802792459	2.44E+12	2.44E+12
2022/6/17	'600519	贵州茅台	1951	1952	1878.09	1878.09	1877	74	3.9425	0.4023	5054161	9749530916	2.45E+12	2.45E+12
2022/6/16	'600519	贵州茅台	1877	1907.63	1875.33	1894.59	1875.1	1.9	0.1013	0.214	2688670	5087605391	2.36E+12	2.36E+12
2022/6/15	'600519	贵州茅台	1875.1	1905	1862.99	1870	1871	4.1	0.2191	0.268	3366362	6354869100	2.36E+12	2.36E+12
2022/6/14	'600519	贵州茅台	1871	1875.42	1832	1834	1856	15	0.8082	0.2342	2941623	5467949348	2.35E+12	2.35E+12
2022/6/13	'600519	贵州茅台	1856	1892	1848.08	1890	1900.6	-44.6	-2.3466	0.2926	3675518	6847248995	2.33E+12	2.33E+12
2022/6/10	'600519	贵州茅台	1900.6	1907	1835	1845.01	1853	47.6	2.5688	0.3769	4734462	8882462598	2.39E+12	2.39E+12
2022/6/9	'600519	贵州茅台	1853	1888.35	1849	1872	1865.6	-12.6	-0.6754	0.2096	2632902	4897066622	2.33E+12	2.33E+12
2022/6/8	'600519	贵州茅台	1865.6	1882	1825	1825	1817.9	47.7	2.6239	0.3531	4435381	8236953846	2.34E+12	2.34E+12
2022/6/7	'600519	贵州茅台	1817.9	1825	1770.31	1784.14	1788	29.9	1.6723	0.279	3504859	6356031009	2.28E+12	2.28E+12
2022/6/6	'600519	贵州茅台	1788	1795	1758	1790	1786	2	0.112	0.2925	3674126	6535329352	2.25E+12	2.25E+12
2022/6/2	'600519	贵州茅台	1786	1795.8	1780	1787.97	1788.25	-2.25	-0.1258	0.1347	1691473	3019718032	2.24E+12	2.24E+12
2022/6/1	'600519	贵州茅台	1788.25	1814.78	1779	1802	1804.03	-15.78	-0.8747	0.1732	2176001	3897858999	2.25E+12	2.25E+12
2022/5/31	'600519	贵州茅台	1804.03	1814.9	1766.98	1774.77	1778.41	25.62	1.4406	0.3244	4075082	7329201058	2.27E+12	2.27E+12
2022/5/30	'600519	贵州茅台	1778.41	1790.55	1766	1766	1755.16	23.25	1.3247	0.2744	3446569	6135631304	2.23E+12	2.23E+12

# 2、数据集预处理

# 2.1、数据集切分

training_set [2019.94 1970.   1942.7  ...   26.07   25.92   26.5 ]
test_set [26.5   0.   25.69 25.6  26.3  25.92 26.   26.24 26.48 26.   25.8  25.825.98 25.78 26.05 26.13 27.2  26.75 26.95 26.7  26.22 26.08 26.03 26.2526.5  26.6  27.11 27.1  27.45 26.97 26.79 27.5  27.91 27.78 27.6  27.927.68 27.7  28.   28.15 28.12 28.36 27.98 28.4  28.68 28.97 28.8  28.9928.75 29.11 29.01 29.   29.46 30.   30.3  30.35 30.52 30.63 30.4  30.4530.56 30.55 30.89 30.73 31.15 31.15 31.   31.   30.59 30.79 30.5  30.9830.98 30.7  30.8  31.21 31.42 31.43 31.32 31.44 31.3  31.28 31.52 31.6832.2  32.5  32.61 36.3  36.45 36.68 36.37 36.05 35.95 35.68 36.01 35.9935.63 36.12 36.18 36.18 36.06 36.68 36.75 36.8  37.08 36.7  36.9  37.2839.04 35.   34.98 34.9  34.7  34.55 34.9  35.1  34.8  34.75 35.   34.834.38 34.5  34.9  34.9  35.   34.88 35.21 35.2  35.   35.01 35.88 35.135.54 34.99 34.89 35.25 35.68 35.4  35.57 36.05 36.   36.31 36.48 36.235.5  35.1  35.5  36.19 36.   36.39 37.   38.5  37.88 38.46 37.62 37.4937.43 37.   37.3  37.78 36.97 37.02 37.61 37.16 38.   38.01 38.15 38.738.49 38.92 39.3  38.8  38.1  38.12 38.02 38.11 38.31 39.45 39.69 38.5538.2  38.8  38.06 37.35 37.95 38.   37.85 37.99 37.6  37.18 37.86 37.9337.18 37.5  36.   35.6  35.2  37.   37.24 37.36 36.65 35.8  36.3  34.836.2  36.48 35.98 35.7  37.01 36.98 36.5  37.   37.15 38.72 37.67 37.337.22 36.54 36.45 35.99 34.7  35.9  35.9  35.48 35.11 35.02 35.61 35.636.   36.   36.1  35.9  37.   36.25 35.35 34.83 35.01 35.05 34.58 35.35.01 35.22 35.48 35.2  34.15 36.2  33.65 33.64 33.28 34.4  33.7  33.3535.   34.8  35.   35.28 35.05 35.   35.25 34.88 34.7  35.7  36.78 36.33.3  34.   34.2  34.79 35.13 35.9  35.9  36.01 37.3  36.6  37.   36.936.08 36.11 36.28 36.06 36.28 36.9  36.3  35.88 36.08 36.01 36.01 35.3336.8  35.4  36.5  37.35 37.61 37.01 37.2  37.15 36.28 36.98 34.99 34.51]

# 2.2、数据维度转换

进行MinMaxScaler之前，需要将数据从(4754,)→(4754, 1)

before reshape <class 'numpy.ndarray'> (4752,) (300,)
after reshape <class 'numpy.ndarray'> (4752, 1) (300, 1)

# 2.3、训练集、测试集进行MinMax归一化

# 2.4、依次构建train、test的时序性数据集矩阵

# (1)、for循环构建train时序性数据集矩阵

# 提取训练集中连续X_num=60天的开盘价，作为输入特征x_train；以第61天的数据作为label，for循环共构建4752-300-60=4392组数据

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59
0	0.78050835	0.761211447	0.750662679	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843
1	0.761211447	0.750662679	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795
2	0.750662679	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241
3	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983
4	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248
5	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616
6	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206
7	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009
8	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459
9	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887
10	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027
11	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549
12	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363
13	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327
14	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327	0.680059351
15	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327	0.680059351	0.68014436
16	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327	0.680059351	0.68014436	0.6847039
17	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327	0.680059351	0.68014436	0.6847039	0.713220349
18	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327	0.680059351	0.68014436	0.6847039	0.713220349	0.710979219
19	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983	0.666110248	0.666156616	0.663838206	0.664611009	0.631380459	0.637562887	0.668475027	0.68238549	0.698614363	0.681651327	0.680059351	0.68014436	0.6847039	0.713220349	0.710979219	0.696295953

# 依次对x_train、y_train打乱数据并转为array格式

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59
0	0.076739387	0.078284994	0.077284214	0.07651141	0.074355289	0.074142768	0.073563165	0.073470429	0.072821274	0.074420977	0.073458837	0.074119584	0.073161307	0.075348341	0.073416332	0.068300373	0.064706837	0.069938717	0.074003663	0.077601063	0.077183749	0.079977434	0.080379292	0.081337568	0.08172397	0.081140503	0.082033091	0.079989026	0.080174499	0.079687633	0.082276525	0.08085843	0.079212359	0.079869242	0.080692277	0.080286556	0.077226254	0.08384918	0.085201586	0.084231717	0.085008385	0.085684588	0.087771157	0.088486001	0.097087304	0.095634433	0.098532446	0.099691651	0.09930525	0.091523118	0.088486001	0.096600437	0.103536349	0.098532446	0.098648367	0.098532446	0.095302128	0.095970603	0.096608165	0.101044058
1	0.449385235	0.449771637	0.453998872	0.455954065	0.446294021	0.447066824	0.444748414	0.443589209	0.428326339	0.422723514	0.418473095	0.411904265	0.432468566	0.438295505	0.442430003	0.442236802	0.436247575	0.440807116	0.440497995	0.440494131	0.434701968	0.426973933	0.42851954	0.431610754	0.430065147	0.426973933	0.414605213	0.413488512	0.407653846	0.409972256	0.405660013	0.397220999	0.39800153	0.392646002	0.390385552	0.378094112	0.368434068	0.366888461	0.359740029	0.365149653	0.364763252	0.377325173	0.376741706	0.377321309	0.371730075	0.371706891	0.365524463	0.370292661	0.371834404	0.37094568	0.369013671	0.371525282	0.374036894	0.376915587	0.373959613	0.379176037	0.382522276	0.379033068	0.378403233	0.384489061
2	0.019961514	0.020201083	0.019629209	0.019895826	0.018933686	0.018740485	0.018431363	0.018203386	0.018160882	0.018218842	0.018593652	0.019049606	0.017310798	0.017368759	0.016163185	0.015649271	0.017194878	0.017233518	0.017252838	0.017001677	0.01758128	0.018508644	0.018570468	0.018697981	0.01893755	0.01893755	0.018547284	0.019277583	0.018933686	0.018732757	0.018744349	0.018740485	0.019590569	0.019706489	0.020286092	0.020208812	0.02040974	0.020046523	0.020089027	0.018972326	0.018674797	0.018006322	0.017959953	0.018354083	0.018810037	0.019126887	0.018895046	0.018663205	0.018632292	0.019242807	0.019351	0.01978377	0.019385776	0.019397368	0.018922094	0.018578196	0.01816861	0.018547284	0.018968462	0.018276803
3	0	0.034389756	0.034582957	0.032368875	0.034768429	0.032028841	0.029115372	0.02646852	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.022685647	0.022546542	0.021978532	0.022326293	0.022666327	0.022276061	0.022990904	0	0.022326293	0.018701845	0.019223487	0.019192575	0.018315443	0.018373403	0.017627648	0.017391943
4	0.101044058	0.096600437	0.09265914	0.095004598	0.097767371	0.098130588	0.100078053	0.103161539	0.101283627	0.100464455	0.098779743	0.102396464	0.097183904	0.099734156	0.101615932	0.102466016	0.104521673	0.101986878	0.100468319	0.106357082	0.110325428	0.110742741	0.104328472	0.100340806	0.098918848	0.097373241	0.093142142	0.094668429	0.097145264	0.099015448	0.098068764	0.09938253	0.09718004	0.095101199	0.094668429	0.094274299	0.097373241	0.097628266	0.097044799	0.095093471	0.098146044	0.099529363	0.097326873	0.102010062	0.10386479	0.09273642	0.088872402	0.092311378	0.080584085	0.080796606	0.078825957	0.077879273	0.077276486	0.07901143	0.078246354	0.076063184	0.074961939	0.075719287	0.075746335	0.075997496
5	0.073690678	0.073744774	0.074988988	0.076256385	0.079591032	0.080371564	0.081913307	0.076507546	0.076240929	0.076314346	0.074710778	0.075730879	0.076314346	0.074146632	0.07722239	0.076507546	0.078903237	0.078439555	0.083605747	0.079308959	0.078427963	0.077485143	0.074189136	0.068926344	0.067573938	0.066113339	0.064355211	0.067968068	0.071866861	0.070321254	0.067832827	0.070321254	0.064374531	0.063215326	0.063366023	0.063872209	0.063350567	0.062983485	0.063570816	0.063060766	0.063655824	0.064876854	0.065205295	0.066770222	0.065301896	0.064714565	0.064760933	0.06186292	0.06237297	0.06182428	0.061252405	0.06221841	0.06414269	0.064842078	0.067233904	0.065301896	0.064359075	0.065842858	0.065143471	0.064181331
6	0.019049606	0.017310798	0.017368759	0.016163185	0.015649271	0.017194878	0.017233518	0.017252838	0.017001677	0.01758128	0.018508644	0.018570468	0.018697981	0.01893755	0.01893755	0.018547284	0.019277583	0.018933686	0.018732757	0.018744349	0.018740485	0.019590569	0.019706489	0.020286092	0.020208812	0.02040974	0.020046523	0.020089027	0.018972326	0.018674797	0.018006322	0.017959953	0.018354083	0.018810037	0.019126887	0.018895046	0.018663205	0.018632292	0.019242807	0.019351	0.01978377	0.019385776	0.019397368	0.018922094	0.018578196	0.01816861	0.018547284	0.018968462	0.018276803	0.018160882	0.018933686	0.018895046	0.018160882	0.018160882	0.018350219	0.018044962	0.017967681	0.018276803	0.0176972	0.01761992
7	0.087535452	0.088482137	0.087713197	0.090147528	0.092006121	0.087666829	0.086940394	0.084621983	0.083447322	0.084618119	0.082342213	0.084544703	0.081994451	0.080282692	0.07959876	0.077906321	0.078516836	0.078725492	0.077496735	0.07728035	0.079208495	0.079135078	0.078277266	0.078053153	0.078091794	0.076306618	0.077183749	0.077272622	0.077662888	0.078439555	0.078482059	0.077767216	0.079985162	0.077666752	0.076507546	0.079115758	0.076163649	0.076932588	0.077705392	0.078501379	0.078891645	0.080754102	0.082218564	0.081144367	0.084235581	0.08432059	0.085077937	0.084475151	0.087125867	0.086940394	0.086546264	0.087114274	0.087635917	0.084235581	0.083269577	0.082840671	0.082848399	0.082612694	0.081217784	0.082110372
8	0.078825957	0.07952148	0.078748677	0.080023802	0.083115016	0.076893948	0.07728035	0.077821312	0.076893948	0	0.071441819	0.071472732	0.068238549	0.067427105	0.066665894	0.068702231	0.067628034	0.069629595	0.069127273	0.068586311	0.068779511	0.070325118	0.071870725	0.070518319	0.069552315	0.070904721	0.072284175	0.068694503	0.066461101	0.066310404	0.066468829	0.069057721	0.070518319	0.069583227	0.071097922	0.07071152	0.074382337	0.072570113	0.074196864	0.074579402	0.07370227	0.073029931	0.072662849	0.070904721	0.068779511	0.068199909	0.069042265	0.071097922	0.073026067	0.068045348	0.067658946	0.063694464	0.062098625	0.056607856	0.058551457	0.056406927	0.056287143	0.053713707	0.054660392	0.055641852
9	0.096310636	0.096527021	0.094710933	0.094579556	0.093748792	0.093644464	0.093895625	0.093115094	0.094784349	0.092357746	0.090804411	0.091963616	0.089826815	0.088486001	0.088219383	0.089220164	0.092558675	0.093041677	0.094668429	0.093122822	0.092910301	0.093316023	0.09285234	0.093397167	0.090108888	0.09051461	0.089838407	0.091577215	0	0.084235581	0.083845316	0.086940394	0.088486001	0.089065603	0.087794342	0.089606566	0.091546303	0.089904095	0.088721706	0.092125905	0.094382491	0.095433504	0.095827634	0.096013107	0.096986839	0.097295961	0.097550986	0.096144483	0.095302128	0.093702424	0.093938129	0.095124383	0.096407237	0.096310636	0.096940471	0.098493806	0.096600437	0.092972125	0.092690052	0.094857766
10	0.772370729	0.772842139	0.78632756	0.78632756	0.772803499	0.763116407	0.792123587	0.799851622	0.763143456	0.763916259	0.755415421	0.801768174	0.772803499	0.809511665	0.788259569	0.842355814	0.841969412	0.811443674	0.853561465	0.891811374	0.875254059	0.948616295	0.947132513	1	0.960208348	0.915308465	0.903020889	0.898384068	0.846606233	0.830763762	0.816165504	0.823035727	0.811903492	0.803715639	0.827630044	0.844287823	0.804874844	0.79946522	0.791350783	0.775894713	0.801053331	0.79639719	0.8172397	0.833082172	0.836173386	0.806806853	0.807579657	0.827672548	0.810284469	0.797842333	0.768939482	0.772795771	0.750005796	0.722571272	0.723730477	0.705801436	0.696678491	0.702478381	0.733386657	0.714831645
11	0.3091214	0.300620561	0.299867078	0.301393365	0.305249654	0.304484579	0.303904976	0.290709356	0.290237946	0.282169878	0.28099908	0.285473613	0.277436456	0.275118046	0.27820926	0.285937295	0.284090294	0.287482902	0.279751003	0.284051654	0.285937295	0.288255705	0.279754867	0.277703073	0.271833631	0.273989753	0.269720013	0.255005835	0.259267846	0.257343565	0.255025155	0.264781799	0.267390011	0.266230805	0.261207583	0.257347429	0.263139591	0.254967194	0.259275574	0.264685198	0.262977303	0.269901622	0.272026832	0.274345242	0.272335953	0.26936066	0.263525993	0.261980386	0.26275319	0.2650716	0.263603274	0.270469633	0.279368465	0.273572439	0.270867626	0.287482902	0.289843816	0.289801312	0.28747131	0.288873948
12	0.061051476	0.061360598	0.061256269	0.058609417	0.057960262	0.057921622	0.058153463	0.058083911	0.057284059	0.058420081	0.059073099	0.059884543	0.059042187	0.058118687	0.057527492	0.057110179	0.056414655	0.058582369	0.057284059	0.057110179	0.055672764	0.054869048	0.05525545	0.053566875	0.053818036	0.055228402	0.055100889	0.054134885	0.05437059	0.053392994	0.053632563	0.052747703	0.05254291	0.051410753	0.050579989	0.050425428	0.050000386	0.050518165	0.050541349	0.049683537	0.049115526	0.049304863	0.04864798	0.048879821	0.048899141	0.049401464	0.04868662	0.049656489	0.050309508	0.050154947	0.050970255	0.051005031	0.051491897	0.051159592	0.052554502	0.053787124	0.054702896	0.054637207	0.054181253	0.054505831
13	0.118857178	0.120905107	0.119842503	0.119251308	0.121909752	0.122682556	0.122535723	0.122137729	0.124819357	0.122010216	0.119409733	0.121148541	0.122025673	0.115843245	0.114000108	0.110897302	0.110974582	0.112064235	0.111109823	0.11090503	0.106260481	0.106368674	0.108180898	0.105785207	0.107048741	0.108103617	0.105719519	0.106206385	0.104602818	0.103443612	0.105622918	0.106801444	0.106840084	0.108965293	0.103169267	0.101627524	0.102551024	0.098918848	0.09718004	0.09765145	0.097369377	0.098331517	0.098304469	0.096492245	0.095379408	0.094730253	0.096244948	0.096990703	0.096932743	0.09891112	0.098153772	0.097508482	0.096407237	0.09665067	0.099425034	0.099112049	0.100464455	0.096812958	0.095591929	0.094989142
14	0.088099599	0.089258804	0.089305172	0.09059189	0.090727131	0.087519996	0.087319067	0.087326795	0.083729395	0.082786575	0.082091052	0.081990587	0.081808978	0.080487484	0.082604966	0.081302792	0.082241748	0.082110372	0.082110372	0.080700005	0.079212359	0.080866158	0.080197683	0.079451928	0.075773383	0.077728576	0.077589471	0.076893948	0.081229376	0.081577137	0.080178363	0.078949605	0.080294284	0.080197683	0.080294284	0.077585607	0.076909404	0.077894729	0.07731899	0.077326718	0.075228557	0.074656682	0.075240149	0.076893948	0.077608791	0.078296587	0.078640484	0.077666752	0.077060101	0.07680894	0.075031492	0.074390065	0.074482801	0.074107992	0.074560082	0.074915571	0.074776467	0.074471209	0.074455753	0.073223132
15	0.009273642	0.00937797	0.009273642	0.009111353	0.009115217	0.009088169	0.009092033	0.009177042	0.009188634	0.009149993	0.009235002	0.009041801	0.009103625	0.008983841	0.008960657	0.009003161	0.008922016	0.008922016	0.00888724	0.009003161	0.008918152	0.009034073	0.00882928	0.00879064	0.00888724	0.008918152	0.00880996	0.008883376	0.00888724	0.00890656	0.00888724	0.009003161	0.009146129	0.008979977	0.008794504	0.008763592	0.008694039	0.008539479	0.008241949	0.008191717	0.00829991	0.008346278	0.008427422	0.008288318	0.008265133	0.008261269	0.008292182	0.008369462	0.008404238	0.00833855	0.008346278	0.008164669	0.008191717	0.008160805	0.008160805	0.008153077	0.008180125	0.008191717	0.008176261	0.008075797
16	0.875254059	0.948616295	0.947132513	1	0.960208348	0.915308465	0.903020889	0.898384068	0.846606233	0.830763762	0.816165504	0.823035727	0.811903492	0.803715639	0.827630044	0.844287823	0.804874844	0.79946522	0.791350783	0.775894713	0.801053331	0.79639719	0.8172397	0.833082172	0.836173386	0.806806853	0.807579657	0.827672548	0.810284469	0.797842333	0.768939482	0.772795771	0.750005796	0.722571272	0.723730477	0.705801436	0.696678491	0.702478381	0.733386657	0.714831645	0.710979219	0.715542624	0.713104429	0.707308403	0.708467608	0.706253526	0.708962202	0.710979219	0.721006345	0.701319176	0.696566434	0.676975865	0.671570105	0.674657455	0.66692942	0.670407036	0.669502856	0.66692942	0.680646682	0.683927233
17	0.173540754	0.171937187	0.17312344	0.173880787	0.170982774	0.173880787	0.174197637	0.173382329	0.175001352	0.170886174	0.169645824	0.1659866	0.164240064	0.165766351	0.162134174	0.159630291	0.1598119	0.157806475	0.157806475	0.156276324	0.16081268	0.160820408	0.160213757	0.158811119	0.15904296	0.163057674	0.160561519	0.161844373	0.156415428	0.154800269	0.160356726	0.156913887	0.155719905	0.15247413	0.151083084	0.154340451	0.150998076	0.14887673	0.149943199	0.152242289	0.151852024	0.150314145	0.149019699	0.149228356	0.14799187	0.147222931	0.146813345	0.149162667	0.151593134	0.151481078	0.150901475	0.149363596	0.147025866	0.144166493	0.145503443	0.142775446	0.14356757	0.142987967	0.142937735	0.141809442
18	0.288622787	0.283108834	0.284391688	0.286516897	0.285550893	0.286876251	0.292506124	0.290690036	0.286826019	0.277436456	0.274345242	0.281686875	0.278220852	0.273186037	0.270481225	0.267390011	0.274252506	0.283819813	0.275233966	0.275890849	0.290574116	0.295717123	0.29598374	0.291346919	0.295682347	0.286323696	0.297142945	0.298765833	0.305257382	0.3091214	0.300620561	0.299867078	0.301393365	0.305249654	0.304484579	0.303904976	0.290709356	0.290237946	0.282169878	0.28099908	0.285473613	0.277436456	0.275118046	0.27820926	0.285937295	0.284090294	0.287482902	0.279751003	0.284051654	0.285937295	0.288255705	0.279754867	0.277703073	0.271833631	0.273989753	0.269720013	0.255005835	0.259267846	0.257343565	0.255025155
19	0.715731961	0.720264453	0.726435289	0.716775246	0.711404261	0.708274407	0.695523149	0.729526503	0.730337947	0.745341927	0.722571272	0.721025665	0.710789882	0.704186277	0.702547933	0.699000765	0.714070433	0.676203062	0.629062048	0.632926066	0.640974814	0.625970834	0.619402005	0.637562887	0.641040503	0.643358913	0.631411371	0.628289245	0.641426904	0.641824898	0.637176485	0.622114545	0.630990193	0.602400328	0.614301502	0.620174808	0.61399238	0.643355049	0.636650979	0.608582756	0.594239523	0.600920409	0.626828646	0.627771467	0.656882974	0.655337367	0.653018957	0.66924783	0.687798978	0.656882974	0.637949289	0.652632555	0.654131794	0.670043818	0.668475027	0.642972511	0.666543018	0.699391031	0.65804218	0.696682355

# (2)、for循环构建test时序性数据集矩阵

# 测试集：csv表格中后300天数据，for循环共构建300-60=240组数据。

# 将df格式数据转为array格式