def svr(C, gamma, eps):
    
    #initialization of data wmproxy
    traininput, traintarget, testinput, testtarget = initialize_wmproxy()
    #training of the SVR
    
    #scaling values in training and test targets
    
    for i in range(len(traintarget)):
        if(traintarget[i] != 0):
            traintarget[i] = log(traintarget[i])
        if(traininput[i] != 0):
            traininput[i] = log(traininput[i])
            
    
    for i in range(len(testtarget)):
        if(testtarget[i] != 0):
            testtarget[i] = log(testtarget[i])
        if(testinput[i] != 0):
            testinput[i] = log(testinput[i])
    
    avg = mean(traintarget)
    sigma = std(traintarget)
    maxtrain = len(traintarget)
    C = max([abs(avg + sigma), abs(avg - sigma)])
    print "C is equal to %f" % C

    svr = SVR(traininput[maxtrain-1440:maxtrain], testinput, traintarget[maxtrain-1440:maxtrain],gamma,C,eps,eps)
    
    
    out = svr.svr_req(testinput[0:30])
    
    error = 0
    for i in range(len(out)):
        error += (out[i] - testtarget[i])
    
    mean_error = error / len(out)
    variance = 0
    for i in range(len(out)):
        variance = abs(out[i] - mean_error)
    
    variance /= len(out)
    
    print "Variance = %f" % variance
    
    epsilon = 3*variance*sqrt(log(len(out))/len(out))
    
    print "Epsilon = %f" % epsilon
    #calculation of the metrics
    sme = svr.calc_sme(testtarget[0:30], out)
    mape = svr.calc_mape(out, testtarget[0:30])
    predx = svr.calc_pred(out, testtarget[0:30], 25)
    rsq = svr.calc_rsqr(out, testtarget[0:30])
    print out
    print testtarget[0:30]
    # print model results!
    x = array(testinput[0:30], dtype=int32)
    y = array(testtarget[0:30], dtype=int32)
    xp = array(testinput[0:30], dtype=int32)
    yp = array(out, dtype=int32)
    fig = figure()
    ax1 = fig.add_subplot(1,1,1)
    ax1.title.set_text("Predizioni modello SVR con C= %f, Gamma = %f, Eps = %f" % (C, gamma, eps))
    realvalues = ax1.plot(x, y)
    predictedvalues = ax1.plot(xp,yp,"r")
    ax1.axis([8.9,max(xp)+0.5,0,max(y)+10])
    ax1.set_xlabel('minutes of the week')
    ax1.set_ylabel('number of requests')
    legend([realvalues,predictedvalues], ["Real Values","Predicted Values"])
    
    fig.savefig("svr_model_%f" % time(), format='png')
    
    print "SME = %f" % sme
    print "MAPE = %f" % mape
    print "R^2 = %f" % rsq
    print "PREDX = %f" % predx