<jupyter_start><jupyter_text>MCDM TutorialBy: <NAME><jupyter_code>import pandas as pd
import scipy.stats as sps
import numpy as np
import decipy.executors as exe
        
matrix = exe.DataMatrix('D:\Gudang_Garam_salesman_data_2019.csv')
beneficial = [True, True, True, True, True, True, True, True, True, True, True, True, True, True]
weights = [0.05, 0.08, 0.08, 0.08, 0.09, 0.08, 0.08, 0.08, 0.06, 0.05, 0.05, 0.05, 0.08, 0.09]

simulator = exe.RankSimulator(matrix, beneficial, weights, [exe.Topsis, exe.WSM, exe.WPM], events=100, trials=10)
simulator.run()
simulator.get_top_rank_probabilities().round(3)
help(exe.RankSimulator)<jupyter_output>Help on class RankSimulator in module decipy.executors:

class RankSimulator(builtins.object)
 |  RankSimulator(matrix, beneficial, weights, executor_classes, events=1000, trials=100, rank_reverse=True, rank_method='ordinal')
 |  
 |  Methods defined here:
 |  
 |  __init__(self, matrix, beneficial, weights, executor_classes, events=1000, trials=100, rank_reverse=True, rank_method='ordinal')
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  get_event_index(self)
 |  
 |  get_event_ranks(self)
 |  
 |  get_executors(self)
 |  
 |  get_global_similarity_index(self)
 |  
 |  get_steps(self)
 |  
 |  get_top_rank_probabilities(self)
 |  
 |  get_top_ranks(self)
 |  
 |  get_top_similarity_index(self)
 |  
 |  run(self)
 |  
 |  run_analizer(self)
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      [...]
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