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statistics.py
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import model
import numpy
import math
def get_statistics(graph, reward_model, initial_sale_price):
client_sales = model.get_sales_per_client(graph, reward_model)
total_earned = 0
users_reductions = []
for sale in client_sales:
first_grade_reduction = initial_sale_price * reward_model.discounts[0] * sale[0]
second_grade_reduction = initial_sale_price * reward_model.discounts[1] * sale[1]
user_original_reduction = (first_grade_reduction, second_grade_reduction)
if reward_model.limited:
if len(reward_model.discount_limits) > 0:
if first_grade_reduction > initial_sale_price * reward_model.discount_limits[0]:
first_grade_reduction = initial_sale_price * reward_model.discount_limits[0]
if second_grade_reduction > initial_sale_price * reward_model.discount_limits[1]:
second_grade_reduction = initial_sale_price * reward_model.discount_limits[1]
if first_grade_reduction + second_grade_reduction > initial_sale_price * reward_model.total_discount_limit:
first_grade_reduction = initial_sale_price * reward_model.total_discount_limit
second_grade_reduction = 0
user_final_reduction = (first_grade_reduction, second_grade_reduction)
total_earned += initial_sale_price - first_grade_reduction - second_grade_reduction
users_reductions.append((user_original_reduction, user_final_reduction))
return (total_earned, users_reductions)
def get_statistics_vs_coop(coop_samples, stock, market_price, initial_sale_price, reward_model):
print "Total stock market price: " + str(stock * market_price)
print "Initial stock value price: " + str(stock * initial_sale_price)
cooperations = []
earnings = []
users_reductions_per_coop = []
for i in range(0, coop_samples + 1):
cooperation = i * float(1)/coop_samples
g = model.generateRandomGraph(cooperation, stock, reward_model)
total_earned, users_reductions = get_statistics(g, reward_model, initial_sale_price)
print "Cooperation: " + str(cooperation) + " => " + str(total_earned)
cooperations.append(cooperation)
earnings.append(total_earned)
users_reductions_per_coop.append(users_reductions)
return (cooperations, earnings, users_reductions_per_coop)
def get_stats_original_discount(users_reductions):
# Sum first and second grade discounts
original_reductions = [reductions[0][0] + reductions[0][1] for reductions in users_reductions]
return {'mean': numpy.mean(original_reductions),
'sd': numpy.std(original_reductions)}
def get_benefits_vs_numsales(sales_samples, stock, model, initial_price, market_price_zones):
def get_market_price_for_sales(num_sales, market_price_zones):
"""
Gets market price for a number of sales given intervals of sales and their
corresponding market prices
"""
for (sales_interval, market_price) in market_price_zones:
if sales_interval[0] <= num_sales <= sales_interval[1]:
return market_price
return 0
sales_list = []
worst_case_results = []
best_case_results = []
for i in range(0, sales_samples + 1):
num_sales = math.floor(i * float(stock)/sales_samples)
market_price = get_market_price_for_sales(num_sales, market_price_zones)
cost = market_price * num_sales
best_case = num_sales * initial_price - cost
worst_case = num_sales * initial_price - cost - model.maximum_discount(num_sales, initial_price)
sales_list.append(num_sales)
worst_case_results.append(worst_case)
best_case_results.append(best_case)
return (sales_list, worst_case_results, best_case_results)