#!/usr/bin/env python2

# -*- coding: utf-8 -*-

import numpy as np

import math

from Campaign import *

from Environment import *

from Auction import *

from Agent import *

from AgentPrior import *

from AgentUCB import *

from AgentFactored import *

from Core import *

from matplotlib import pyplot as plt

from Plotter import *

from joblib import Parallel, delayed

def experiment(k):

return chosenValuesThomp,chosenValuesUCB

convparams=np.array([0.4,100,200])

lambdas = np.array([0.9, 0.8, 0.7, 0.6, 0.5])

a1= Auction(nBidders=5, nslots=5, mu=0.59, sigma=0.2, lambdas=lambdas)

a2= Auction(nBidders=6, nslots=5, mu=0.67, sigma=0.4, lambdas=lambdas)

a3= Auction(nBidders=6, nslots=5, mu=0.47, sigma=0.25, lambdas=lambdas)

a4= Auction(nBidders=5, nslots=5, mu=0.57, sigma=0.39, lambdas=lambdas)

ncampaigns=3

c1 = Campaign(a1, nUsers=1000.0, probClick=0.5, convParams= convparams)

c2 = Campaign(a2, nUsers=1500.0, probClick=0.6, convParams= convparams)

c3 = Campaign(a3, nUsers=1500.0, probClick=0.6, convParams= convparams)

c4 = Campaign(a2, nUsers=1000.0, probClick=0.5, convParams= convparams)

c5 = Campaign(a4, nUsers=1250.0, probClick=0.4, convParams= convparams)

env = Environment([c1,c2,c3])

nBids=10

nIntervals=10

deadline = 2

maxBudget = 100

agent = Agent(1000, deadline, ncampaigns,nIntervals,nBids,maxBudget)

agent.initGPs()

plotter = Plotter(agent=agent,env=env)

# mi creo una lista con tutte le matrici dell'oracolo di ogni campagna

listMatrices = list()

for i in range(0,ncampaigns):

matrix = plotter.oracleMatrix(indexCamp=i,nsimul=10)

listMatrices.append(matrix)

if i==0:

optMatrix = np.array([matrix.max(axis=1)])

else:

maxrow = np.array([matrix.max(axis=1)])

optMatrix = np.concatenate((optMatrix,maxrow))

[newBudgets,newCampaigns] = agent.optimize(optMatrix)

# ora ricerco nelle matrici originali il numero di click nell'allocazione ottima

optValue = 0

for i in range(0,ncampaigns):

print i

index = np.argwhere(np.isclose(agent.budgets,newBudgets[i]))

tempValue = listMatrices[i][index,:].max()

optValue += tempValue

optValue = optValue * convparams[0] #converto i click in conversioni

## questo è il valore dell'oracolo per il plot ora devo simulare i valori del thompson!

nexperiments = 3

# mi salvo le tre realizzazioni degli esperimenti e poi alla fine le medio!

matrixValuesThomp = np.zeros((nexperiments,deadline))

matrixValuesUCB = np.zeros((nexperiments,deadline))

out = Parallel(n_jobs=3)(

delayed(experiment)(k) for k in xrange(nexperiments))

for i in range(nexperiments):

matrixValuesThomp[i,:] = out[i][0]

matrixValuesUCB[i,:] = out[i][1]

print "opt value:", optValue

#np.save("/home/alessandro/Dropbox/thesis_agos/plot/dati_plot_alessandro/valore_ottimo_3c",optValue)

#np.save("/home/alessandro/Dropbox/thesis_agos/plot/dati_plot_alessandro/matrice_thompson_3c",matrixValuesThomp)

#np.save("/home/alessandro/Dropbox/thesis_agos/plot/dati_plot_alessandro/matrice_UCB_3c",matrixValuesUCB)

finalValuesThomp = matrixValuesThomp.mean(axis=0)

finalValuesUCB = matrixValuesUCB.mean(axis=0)

plotter.performancePlotComparison(optValue,finalValuesThomp,finalValuesUCB,"/home/gugohb/Dropbox/thesis_agos/plot/thompson_vs_marc_3c.pdf")