Create decisionSelector.py

strategy/decisionSelector.py

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+import pandas as pd
+import datetime
+import numpy as np
+import plotly as pl
+import plotly.graph_objs as go
+import matplotlib.pyplot as plt
+import math
+import scipy
+import random
+
+class decisionMaking():
+    
+    def __init__ (self, name=''):
+        
+        self.name=name
+        self.mop={
+            'up': pd.DataFrame(),
+            'down':pd.DataFrame(),
+            'none':pd.DataFrame()
+        
+        }
+        self.value={}
+        self.decision=''
+        self.real_decision=''
+        self.keys=[]
+        self.slice_dict={}
+        
+    def addValue(self, dic_value):
+        self.value=dic_value
+        self.checkForNew()
+        self.setSlice()
+        self.getDecision()
+        
+
+        
+    def checkForNew(self):
+        
+        if not (list(self.value.keys()) == self.keys):
+            self.createNewMop(list(self.value.keys()))
+
+    
+    def createNewMop(self,missing_indicators):
+        print('reassembly mop')
+        new_columns= (missing_indicators)
+        
+        
+        #new_columns=new_columns.append(['value','p'])
+        
+        
+        
+        n=len(new_columns)
+        start_value=-1
+        variator=3
+        new_lst=[]
+        buf_lst=[]
+        for i in range(n):
+            buf_lst.append(start_value)
+
+
+        for i in range(pow(variator,n)):
+            new_lst.append(buf_lst.copy())
+
+        for j in range(n):
+            for i in range(len(new_lst)):
+                dob_iterator=(i // pow(variator,j)) % variator
+                new_lst[i][j]=new_lst[i][j] + dob_iterator
+                
+        
+        #print (new_columns)
+        self.keys=new_columns
+        new_columns = new_columns+['amount']+['percentage']
+                
+        for i in new_lst:
+            i = i.extend([0,0])
+            #i = i.append(0)
+        
+        #print(new_lst)
+        #print(new_columns)
+        new_df=pd.DataFrame(new_lst,columns=new_columns)
+        
+        self.mop['up']=pd.DataFrame.from_dict(new_df.to_dict())
+        self.mop['down']=pd.DataFrame.from_dict(new_df.to_dict())
+        self.mop['none']=pd.DataFrame.from_dict(new_df.to_dict())
+        
+        
+    def setSlice(self):
+        
+        row_flg=True
+        self.slice_dict={}
+        for j in self.mop.keys():
+            for index, row in self.mop[j].iterrows():
+                for key, value in self.value.items():
+                    if value != row[key]:
+                        #print('fasle ',key,value,row[key])
+                        row_flg=False
+                        break
+                if row_flg:
+                    self.slice_dict[j]=dict(row)
+                    #print(j,dict(row))
+                row_flg=True
+
+    def getDecision (self):
+        
+        max_value=0
+        for key, value in self.slice_dict.items():
+            if value['amount'] >= max_value:
+                max_value = value['amount']
+                self.decision = key
+        return self.decision
+    
+    def setDecision (self,real_decision):
+        self.real_decision=real_decision
+        self.updMop()
+        self.slice_dict[real_decision]['amount']+=1
+
+
+    def updMop(self):
+        
+        row_flg=True
+        for index, row in self.mop[self.real_decision].iterrows():
+            for key, value in self.value.items():
+                if value != row[key]:
+                    row_flg=False
+                    break
+            if row_flg:
+                #self.slice_dict[j]=dict(row)
+                row['amount']=row['amount']+1
+            row_flg=True 
+        
+        
+        
+    
+
+        
+        
+        
+       
+    
+    
+        
+