another small adj

global_func/predict_dupes.py

@@ -193,8 +193,8 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
         # Calculate dupes formula (in progress still)
         portfolio['dupes_calc'] = ((portfolio['own_product'] + ((portfolio['CPT_Own_percent_rank'] - .50) / 1000) + ((portfolio['Own'] / 6) / (max_salary / 2))) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
         portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
-        portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.96)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
-        portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / (portfolio['Own'] / 90))
+        portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.98)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
+        portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / (portfolio['Own'] / 110))
         
         # Round and handle negative values
         portfolio['Dupes'] = np.where(