new results
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3
anova.py
3
anova.py
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@ -56,6 +56,7 @@ def anova(lst_col_seg, n_level, oa_file, result_file):
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arr_f = df_s_non_error / ms_of_error
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arr_f = df_s_non_error / ms_of_error
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do_print(arr_f.values.tolist()[0], f'{str_ind_name}\tF ratio')
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do_print(arr_f.values.tolist()[0], f'{str_ind_name}\tF ratio')
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# alpha = 0.05
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alpha = 0.1
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alpha = 0.1
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arr_p_value = f.sf(arr_f, n_level - 1, n_degree_error)
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arr_p_value = f.sf(arr_f, n_level - 1, n_degree_error)
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do_print(arr_p_value.tolist()[0], f'{str_ind_name}\tP value')
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do_print(arr_p_value.tolist()[0], f'{str_ind_name}\tP value')
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@ -71,7 +72,7 @@ def anova(lst_col_seg, n_level, oa_file, result_file):
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if __name__ == '__main__':
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if __name__ == '__main__':
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# first test
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# first test
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the_lst_col_seg = [5, 1, 5] # 11 factors, 2 for error, and 6 indicators
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the_lst_col_seg = [5, 1, 6] # 11 factors, 2 for error, and 6 indicators
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the_n_level = 5
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the_n_level = 5
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anova(the_lst_col_seg, the_n_level, "oa25.txt", "result/experiment_result_25.xlsx")
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anova(the_lst_col_seg, the_n_level, "oa25.txt", "result/experiment_result_25.xlsx")
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@ -2,12 +2,12 @@ import numpy as np
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import pandas as pd
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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num_time_step = 200
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num_time_step = 201
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num_iter = 10
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num_iter = 10
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env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl', sheet_name=0))
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env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl', sheet_name=0))
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assert env_data.shape[0] == num_iter * (num_time_step + 1)
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assert env_data.shape[0] == num_iter * (num_time_step + 1), f"{env_data.shape[0]}, {num_iter * (num_time_step + 1)}"
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lst_df = []
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lst_df = []
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for i in range(num_iter):
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for i in range(num_iter):
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@ -4,7 +4,7 @@ from env import Env
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import datetime
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import datetime
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idx_start = 10 # first index is 1 !
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idx_start = 1 # first index is 1 !
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n_sample = 30
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n_sample = 30
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@ -1,26 +1,26 @@
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,alpha,percent_search,is_RH_ratio,is_FH_ratio,4,5,out_w_avg_salary,out_w_gini_salary,out_f_avg_profit,out_f_gini_profit,out_w_percent_hired
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,alpha,percent_search,is_RH_ratio,is_FH_ratio,4,5,out_w_avg_salary,out_w_gini_salary,out_f_avg_profit,out_f_avg_yield,out_f_gini_profit,out_w_percent_hired
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0,0.3,0.8,0.75,0.5,0,0,3264853080.7861195,0.07448611065757892,27068180827.948566,0.028471074467998493,0.9926999999999998
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0,0.1,0.1,0.0,0.0,0,0,3964054687.4268475,0.08720663778245505,32888833432.274235,0.7063055988364291,0.02573588115431101,0.9999666666666668
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1,0.5,0.1,0.5,0.5,3,4,3637864305.7383804,0.07297697986009385,30978395677.329544,0.025035570583021458,0.9932
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1,0.1,0.2,0.25,0.5,3,4,3771613705.088724,0.07954486986586938,31868416681.904884,0.7486264770120516,0.024921222378351093,0.9960666666666663
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2,0.5,0.2,1.0,0.75,4,1,2179839879.06716,0.09840417843256091,14870250970.957703,0.09615406368119288,0.9886333333333329
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2,0.1,0.3,0.5,0.75,4,1,3584820695.7431035,0.07319267750272562,30385929071.529438,0.7768847955950473,0.024373996254554064,0.9958666666666665
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3,0.5,0.3,0.75,0.25,1,2,3294909225.056388,0.07458035794885493,27529372104.086937,0.027549550048933245,0.9904999999999996
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3,0.1,0.6,0.75,1.0,1,2,3408013297.120691,0.08699814562875076,27485267714.345665,0.7779103018269108,0.04309196337844635,0.9709000000000001
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4,0.5,0.6,0.25,0.0,2,3,3716906250.022679,0.08529021609092702,30937889107.04533,0.025723945674350865,0.9942999999999997
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4,0.1,0.8,1.0,0.25,2,3,2540634743.6358457,0.05369244039646588,19646121099.463802,0.7791622239510231,0.041099371568935936,0.9961666666666666
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5,0.5,0.8,0.0,1.0,1,1,3869912345.136378,0.09546657392456424,32080217202.03705,0.02533111889073941,1.0
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5,0.3,0.1,0.25,0.25,1,1,3822529021.562453,0.0761493282211473,32764211314.472507,0.73924191031523,0.02429009889320572,0.9944666666666666
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6,0.7,0.1,0.75,0.75,0,3,3323780234.219529,0.07324360974831064,27481709495.610508,0.02827211821311022,0.9934666666666666
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6,0.3,0.2,0.5,1.0,0,3,3607254045.656348,0.07610097626526957,30641750291.150898,0.7749833336482536,0.023537073789590485,0.9998333333333334
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7,0.7,0.2,0.0,0.25,3,2,3890360391.8347297,0.09312614979722612,32025419370.64081,0.027331743681722087,0.9998666666666668
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7,0.3,0.3,1.0,0.0,3,2,2326166598.3574433,0.07193946514093447,17174550354.996449,0.7498407860386497,0.056183971569819934,0.9906666666666665
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8,0.7,0.3,0.25,1.0,2,4,3724730633.529055,0.08585608644546336,30966818797.24322,0.02556013680036385,0.9997333333333333
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8,0.3,0.6,0.0,0.75,2,4,3866291501.614393,0.09685969668328769,30553633014.9875,0.706168605244585,0.02919183959836508,0.9999666666666668
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9,0.7,0.6,1.0,0.5,4,0,2059080141.3863091,0.13011386291850205,11919309572.784859,0.17383571880300247,0.9850999999999996
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9,0.3,0.8,0.75,0.5,4,0,3302765119.010196,0.08739879310155402,28973869596.09232,0.7727757803240185,0.03792936672857634,0.9674666666666665
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10,0.7,0.8,0.5,0.0,2,2,3558046321.01991,0.07852769877145456,29326434059.91672,0.02645991757420794,0.9920333333333334
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10,0.5,0.1,0.5,0.5,2,2,3635164347.9166718,0.07223238071369359,29826975750.47325,0.7640343077593824,0.025579797905849556,0.9932666666666666
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11,0.9,0.1,1.0,1.0,1,0,2217056469.2871513,0.09085566892663295,14019663732.042246,0.10640474650259805,0.9943333333333331
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11,0.5,0.2,1.0,0.75,1,0,2145814519.6934304,0.10630648602237686,15121756598.665697,0.7429493087241388,0.10024898712596307,0.9883333333333331
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12,0.9,0.2,0.75,0.0,0,4,3288880578.2219296,0.07786092688764147,27583988860.902683,0.028509652392809604,0.9885666666666665
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12,0.5,0.3,0.75,0.25,0,4,3280215776.8779473,0.07392344601920621,27535874923.18503,0.7742386824858348,0.02778184966327841,0.9903666666666666
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13,0.9,0.3,0.0,0.5,4,3,3879623567.573062,0.09407352725749332,32151544223.643482,0.026003986467554037,0.9999666666666667
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13,0.5,0.6,0.25,0.0,4,3,3733053371.543826,0.08542184529182892,30294457718.652958,0.739994385787595,0.026143624669807606,0.9939666666666667
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14,0.9,0.6,0.5,0.25,3,1,3577219455.771972,0.0785081831642713,29903884548.088444,0.02491509062583037,0.9924
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14,0.5,0.8,0.0,1.0,3,1,3868081665.235581,0.09524632405793981,31123061915.61634,0.70811665076265,0.028232860276024917,1.0
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15,0.9,0.8,0.25,0.75,3,3,3710277766.924487,0.08632310052044662,30554824167.139378,0.026183286845803396,0.9967333333333335
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15,0.7,0.1,0.75,0.75,3,3,3330590377.077554,0.07324286087806994,28009354984.178905,0.77101870101639,0.026522250255100542,0.9938333333333332
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16,,,,,4,2,,,,,
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16,0.7,0.2,0.0,0.25,4,2,3879923892.405111,0.09287772214905302,32077456261.107548,0.7092648986079924,0.026124849236665932,0.9997666666666666
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17,,,,,2,0,,,,,
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17,0.7,0.3,0.25,1.0,2,0,3729559649.969672,0.08472110828756059,30574439942.61938,0.7366900948997877,0.025480143774951766,0.9997333333333331
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18,,,,,0,1,,,,,
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18,0.7,0.6,1.0,0.5,0,1,2019860002.646692,0.14496429252217075,12716899877.15417,0.754715476782345,0.16283168991147254,0.9836666666666667
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19,,,,,1,4,,,,,
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19,0.7,0.8,0.5,0.0,1,4,3568806095.650769,0.07871915191895583,30032167448.892323,0.7629522316567776,0.025065573066454436,0.9920666666666663
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20,,,,,4,4,,,,,
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20,0.9,0.1,1.0,1.0,4,4,2246819785.3982553,0.08289906596938025,16391553824.467003,0.7373396218956161,0.07195343604986927,0.9945999999999997
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21,,,,,2,1,,,,,
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21,0.9,0.2,0.75,0.0,2,1,3330876703.8792706,0.07610629773949494,27674320366.79667,0.7742527429660275,0.028118070033153513,0.9895999999999999
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22,,,,,1,3,,,,,
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22,0.9,0.3,0.0,0.5,1,3,3880659664.8644805,0.0940375231764736,31799708717.90585,0.701975186568906,0.02633369365903764,0.9999333333333333
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23,,,,,3,0,,,,,
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23,0.9,0.6,0.5,0.25,3,0,3572889817.877089,0.0792317259747448,28817388267.385258,0.7624308348187311,0.027316029066635297,0.9926333333333331
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24,,,,,0,2,,,,,
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24,0.9,0.8,0.25,0.75,0,2,3702065223.334019,0.08670772960451446,30971934313.33295,0.7391414945664546,0.02459908627439125,0.9962999999999997
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