def cath_ccdf():
try:
plt.show()
except:
pass
# These are already computed values. Please look at the notes
# on discussion as to why and how they were chosen. The CDF
# and CCDF values for the fitted distributions are different.
# The reasoning for this is also in the notes.
m2 = ml.ModLav(0.7438, 657.1194, 0.0191)
m3 = ml.ModLav(0.7294, 644.6747, 0.0104)
x = util.read_data("/home/gautam/research/modlav-plots/seslen/cath_ses")
cc = util.read_data("/home/gautam/research/modlav-plots/seslen/cath_ccdf")
plt.loglog(cc[:,0],cc[:,1],'k-',label='Data',linestyle='steps')
lx=util.gen_points(math.log10(x.min()),math.log10(x.max()),2000)
ex=np.power(lx,10)
m2cc=m2.ccdf(ex)
m3cc=m3.ccdf(ex)
plt.loglog(ex,m3cc,'k--',label='MLE,MT, No OPT')
plt.loglog(ex,m2cc,'k-.',label='MLE, MT, OPT')
plt.grid()
plt.xlim((1.0,1e5))
plt.ylim((1e-4,1.1))
plt.ylabel("P(X > x)")
plt.xlabel("Session length [seconds]")
plt.legend(loc=3)
def catm_ccdf():
try:
plt.show()
except:
pass
# These are already computed values.
# The script is only to plot the data.
# Please refer to the notes on how to re-compute.
# xmax is the value from the optimization run for mme
x = util.read_data("/home/gautam/research/modlav-plots/seslen/catm_ses")
mle=ml.ModLav(0.9398,3003.4797,0.1488)
mme=ml.ModLav(1.0,3243.2566,0.1894)
xmax = 17123.2972
cc = util.ccdf(x)
lx=util.gen_points(math.log10(x.min()),math.log10(x.max()),2000)
ex=np.power(lx,10)
mlecc = mle.ccdf(ex)
mme_lx=util.gen_points(math.log10(x.min()),math.log10(xmax),2000)
mme_ex=np.power(mme_lx,10)
mmecc = mme.ccdf(mme_ex)
plt.loglog(cc[:,0],cc[:,1],'k-',label='Data',linestyle='steps')
plt.loglog(ex,mlecc,'k--',label='MLE fit')
plt.loglog(mme_ex,mmecc,'k-.',label='MME fit')
plt.xlim((x.min(),x.max()*10))
plt.ylim((1e-4,1.1))
plt.grid()
plt.ylabel("P(X > x)")
plt.xlabel("Session length [seconds]")
plt.legend(loc=3)
def catm_cdf():
try:
plt.show()
except:
pass
# These are already computed values.
# The script is only to plot the data.
# Please refer to the notes on how to re-compute.
# xmax is the value from the optimization run for mme
x = util.read_data("/home/gautam/research/modlav-plots/seslen/catm_ses")
mle=ml.ModLav(0.9398,3003.4797,0.1488)
mme=ml.ModLav(1.0,3243.2566,0.1894)
xmax = 17123.2972
ec = util.ecdf(x)
mleec = mle.cdf(ec[:,0])
mmeec = mme.cdf(ec[:,0])
plt.plot(ec[:,0],ec[:,1],'k-',label='Data',linestyle='steps')
plt.plot(ec[:,0],mleec,'k--',label='MLE fit')
plt.plot(ec[:,0],mmeec,'k-.',label='MME fit')
plt.grid()
plt.xlabel("Session length [seconds]")
plt.ylabel("P(X <= x)")
plt.ylim((0.0,1.0))
plt.legend(loc=4)
def all_ccdf():
try:
plt.show()
except:
pass
m1 = ml.ModLav(1.0186, 1534.7651, 0.0892)
m2 = ml.ModLav(1.0, 1539.8953, 0.0855)
x = util.read_data("/home/gautam/research/modlav-plots/seslen/all_ses")
cc = util.read_data("/home/gautam/research/modlav-plots/seslen/all_ccdf")
m1cc = m1.ccdf(cc[:,0])
m2cc = m2.ccdf(cc[:,1])
plt.loglog(cc[:,0],cc[:,1],'k-',label='Data',linestyle='steps')
plt.loglog(cc[:,0],m1cc,'k--',label='MLE fit')
plt.loglog(cc[:,0],m2cc,'k-.',label='MME fit')
plt.grid()
plt.xlabel("Session length [seconds]")
plt.ylabel("P(X > x)")
plt.ylim((1e-5,1.1))
plt.legend(loc=3)
def cath_ecdf():
try:
plt.show()
except:
pass
m1 = ml.ModLav(0.9147, 659.8731, 0.0493)
m3 = ml.ModLav(0.7294, 644.6747, 0.0104)
x = util.read_data("/home/gautam/research/modlav-plots/seslen/cath_ses")
ec = util.read_data("/home/gautam/research/modlav-plots/seslen/cath_ecdf")
m1ec=m1.cdf(ec[:,0])
m3ec=m3.cdf(ec[:,0])
plt.plot(ec[:,0],ec[:,1],'k-',label='Data',linestyle='steps')
plt.plot(ec[:,0],m3ec,'k--',label='MLE, MT, No OPT')
plt.plot(ec[:,0],m1ec,'k-.',label='MLE, No MT, OPT')
plt.ylim((0.0,1.0))
plt.xlim((1.0,10000))
plt.xlabel("Session length [seconds]")
plt.ylabel("P(X <= x)")
plt.grid()
plt.legend(loc=4)
def optfit(mx):
global x,fit,mt, c
dummy_m = ml.ModLav(0.0, 0.0, 0.0)
dummy_mx = float('NaN')
dummy_ks = float('Inf')
dummy_fit = float('Inf')
dummy_dif = float('Inf')
try:
xfit=fit
xmt=mt
xmx=mx
m = ml.ModLav.fromFit(x, xmax=xmx, fit=xfit, mt=xmt)
fm = m.fitmetric(cdf=c)
return (m, xmx, fm)
#except ml.ModLavConvergenceError, mlce:
# return (dummy_m, dummy_mx, dummy_ks, dummy_fit)
except BaseException, be:
print "Exception -> ", be
return (dummy_m, dummy_mx, dummy_fit)
def tlladmin_solver(pts):
ec = util.ecdf(pts)
x = ec[:, 1]
xrev = util.reverse(x)
i = np.array(range(len(x)), dtype=float)
n = float(len(x))
ib = 1.0
ic = float(np.median(x))
id = ic / float(x.max())
ivs = [ib, ic, id]
ovs = (i, x, xrev, n)
(fvals, infodict, ier, mesg) = opt.fsolve(tll_admin, ivs, ovs, None, 1, 0)
f_b = fvals[0]
f_c = fvals[1]
f_d = fvals[2]
if ier != 1:
raise ml.ModLavConvergenceError(mesg, (f_b, f_c, f_d))
return ml.ModLav(f_b, f_c, f_d)