new results

anova.py

@@ -56,6 +56,7 @@ def anova(lst_col_seg, n_level, oa_file, result_file):
         arr_f = df_s_non_error / ms_of_error
         do_print(arr_f.values.tolist()[0], f'{str_ind_name}\tF ratio')
 
+        # alpha = 0.05
         alpha = 0.1
         arr_p_value = f.sf(arr_f, n_level - 1, n_degree_error)
         do_print(arr_p_value.tolist()[0], f'{str_ind_name}\tP value')
@@ -71,7 +72,7 @@ def anova(lst_col_seg, n_level, oa_file, result_file):
 
 if __name__ == '__main__':
     # first test
-    the_lst_col_seg = [5, 1, 5]  # 11 factors, 2 for error, and 6 indicators
+    the_lst_col_seg = [5, 1, 6]  # 11 factors, 2 for error, and 6 indicators
     the_n_level = 5
     anova(the_lst_col_seg, the_n_level, "oa25.txt", "result/experiment_result_25.xlsx")
 

data_analysis.py

@@ -2,12 +2,12 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 
-num_time_step = 200
+num_time_step = 201
 num_iter = 10
 
 env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl', sheet_name=0))
 
-assert env_data.shape[0] == num_iter * (num_time_step + 1)
+assert env_data.shape[0] == num_iter * (num_time_step + 1), f"{env_data.shape[0]}, {num_iter * (num_time_step + 1)}"
 
 lst_df = []
 for i in range(num_iter):

experiment.py

@@ -4,7 +4,7 @@ from env import Env
 import datetime
 
 
-idx_start = 10  # first index is 1 !
+idx_start = 1  # first index is 1 !
 
 n_sample = 30
 

result/experiment_result_25.csv

@@ -1,26 +1,26 @@
-,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
-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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-2,0.5,0.2,1.0,0.75,4,1,2179839879.06716,0.09840417843256091,14870250970.957703,0.09615406368119288,0.9886333333333329
-3,0.5,0.3,0.75,0.25,1,2,3294909225.056388,0.07458035794885493,27529372104.086937,0.027549550048933245,0.9904999999999996
-4,0.5,0.6,0.25,0.0,2,3,3716906250.022679,0.08529021609092702,30937889107.04533,0.025723945674350865,0.9942999999999997
-5,0.5,0.8,0.0,1.0,1,1,3869912345.136378,0.09546657392456424,32080217202.03705,0.02533111889073941,1.0
-6,0.7,0.1,0.75,0.75,0,3,3323780234.219529,0.07324360974831064,27481709495.610508,0.02827211821311022,0.9934666666666666
-7,0.7,0.2,0.0,0.25,3,2,3890360391.8347297,0.09312614979722612,32025419370.64081,0.027331743681722087,0.9998666666666668
-8,0.7,0.3,0.25,1.0,2,4,3724730633.529055,0.08585608644546336,30966818797.24322,0.02556013680036385,0.9997333333333333
-9,0.7,0.6,1.0,0.5,4,0,2059080141.3863091,0.13011386291850205,11919309572.784859,0.17383571880300247,0.9850999999999996
-10,0.7,0.8,0.5,0.0,2,2,3558046321.01991,0.07852769877145456,29326434059.91672,0.02645991757420794,0.9920333333333334
-11,0.9,0.1,1.0,1.0,1,0,2217056469.2871513,0.09085566892663295,14019663732.042246,0.10640474650259805,0.9943333333333331
-12,0.9,0.2,0.75,0.0,0,4,3288880578.2219296,0.07786092688764147,27583988860.902683,0.028509652392809604,0.9885666666666665
-13,0.9,0.3,0.0,0.5,4,3,3879623567.573062,0.09407352725749332,32151544223.643482,0.026003986467554037,0.9999666666666667
-14,0.9,0.6,0.5,0.25,3,1,3577219455.771972,0.0785081831642713,29903884548.088444,0.02491509062583037,0.9924
-15,0.9,0.8,0.25,0.75,3,3,3710277766.924487,0.08632310052044662,30554824167.139378,0.026183286845803396,0.9967333333333335
-16,,,,,4,2,,,,,
-17,,,,,2,0,,,,,
-18,,,,,0,1,,,,,
-19,,,,,1,4,,,,,
-20,,,,,4,4,,,,,
-21,,,,,2,1,,,,,
-22,,,,,1,3,,,,,
-23,,,,,3,0,,,,,
-24,,,,,0,2,,,,,
+,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
+0,0.1,0.1,0.0,0.0,0,0,3964054687.4268475,0.08720663778245505,32888833432.274235,0.7063055988364291,0.02573588115431101,0.9999666666666668
+1,0.1,0.2,0.25,0.5,3,4,3771613705.088724,0.07954486986586938,31868416681.904884,0.7486264770120516,0.024921222378351093,0.9960666666666663
+2,0.1,0.3,0.5,0.75,4,1,3584820695.7431035,0.07319267750272562,30385929071.529438,0.7768847955950473,0.024373996254554064,0.9958666666666665
+3,0.1,0.6,0.75,1.0,1,2,3408013297.120691,0.08699814562875076,27485267714.345665,0.7779103018269108,0.04309196337844635,0.9709000000000001
+4,0.1,0.8,1.0,0.25,2,3,2540634743.6358457,0.05369244039646588,19646121099.463802,0.7791622239510231,0.041099371568935936,0.9961666666666666
+5,0.3,0.1,0.25,0.25,1,1,3822529021.562453,0.0761493282211473,32764211314.472507,0.73924191031523,0.02429009889320572,0.9944666666666666
+6,0.3,0.2,0.5,1.0,0,3,3607254045.656348,0.07610097626526957,30641750291.150898,0.7749833336482536,0.023537073789590485,0.9998333333333334
+7,0.3,0.3,1.0,0.0,3,2,2326166598.3574433,0.07193946514093447,17174550354.996449,0.7498407860386497,0.056183971569819934,0.9906666666666665
+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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