def main():
a = 0
b = 5
x = np.linspace(a-1, b+1, 100)
dist = uniform(loc=a, scale=abs(a - b))
figure, ax = plt.subplots(figsize=(9, 9))
plt.subplot(221)
plt.title("Функция распределения")
plt.plot(x, dist.cdf(x), color='r', label=r'F({0}, {1})'.format(a, b))
plt.legend()
plt.subplot(222)
plt.title("Функция плотности распределения")
plt.plot(x, dist.pdf(x), color='b', label=r'f({0}, {1})'.format(a, b))
plt.legend()
dist = erlang(a=2)
plt.subplot(223)
plt.title('Функция распределения')
cdf = [erlang_cdf(4, 4, i) for i in x]
plt.plot(x, cdf, color='r', label=r'F({0}, {1})'.format(a, b))
plt.legend()
plt.subplot(224)
plt.title('Функция плотности распределения')
pdf = [erlang_pdf(4, 4,i) for i in x]
plt.plot(x, pdf , color='b', label=r'f({0}, {1})'.format(a, b))
plt.legend()
plt.show()
def _get_bg_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'), period=0):
"""Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
"""
bg_rate = d.bg_from(ph_sel=ph_sel)[ich][period]
#bg_rate_kcps = bg_rate*1e-3
bg_dist = erlang(a=m, scale=1./bg_rate)
return bg_dist
def _get_bg_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'), period=0):
"""Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
"""
bg_rate = d.bg_from(ph_sel=ph_sel)[ich][period]
# bg_rate_kcps = bg_rate*1e-3
bg_dist = erlang(a=m, scale=1./bg_rate)
return bg_dist
def runLenFees(fullSize=975000):
ds = generate_dataset()
grouped, not_full = list(), list()
r = False
total_tx = list()
for b in ds.interarrival:
total_tx.extend([b['satoshi_fee'] // b['num_tx']] * b['num_tx'])
if b['block_size'] <= fullSize:
not_full.append(b)
r = False
elif b['block_size'] > fullSize:
if not r:
grouped.append([b])
r = True
else:
grouped[-1].append(b)
mean = sum(total_tx) / len(total_tx)
print 'mean tx fee: {}'.format(mean)
fig = plt.figure()
fig.suptitle('Transaction Fee PDF/Histogram Fit')
plt.hist(sorted(total_tx)[:-len(total_tx) // 100], 300, normed=True)
plt.xlabel('Satoshi Tx Fee')
plt.ylabel('Probability')
plt.axvline(x=mean, color='red')
rv = sp.erlang(7, loc=11500, scale=1200)
x = np.linspace(0, 50000)
#plt.plot(x, rv.pdf(x))
#plt.show()
l1 = [
i / len(total_tx)
for i in range(len(total_tx) - (len(total_tx) // 50) - 1)
]
l2 = [e for e in sorted(total_tx)[:-len(total_tx) // 50]]
print len(l1), len(l2)
fig.suptitle('Sorted Fees vs. Erlang CDF')
plt.xlabel('Satoshi Tx Fee')
plt.ylabel('Probablility')
plt.plot(x, rv.cdf(x), color='red')
plt.plot(l2, l1, color='blue')
plt.show()
dic = defaultdict(list)
for blocklist in grouped:
dic[len(blocklist)].extend(blocklist)
avgFeeDic = dict()
for key in dic:
avgFeeDic[key] = calcAvgFee(dic[key])
avgFeeDic[0] = calcAvgFee(not_full)
return avgFeeDic
def plotDist(control, trial, cont_pr, parameters, label):
titles = ['G1', 'S-G2']
plt.figure(figsize=(12,6), dpi=200)
for ind, x in enumerate(trial):
plt.subplot(1,2,(ind+1))
plt.hist(control[ind], alpha=0.4, density=True, label="control", color="slategrey")
plt.hist(x, alpha=0.4, density=True, label=label, color="orchid")
plt.title(titles[ind])
rv = sp.erlang(parameters[ind][0],0,parameters[ind][1])
rv2 = sp.erlang(cont_pr[ind][0],0,cont_pr[ind][1])
xx = np.linspace(0, max(x))
plt.plot(xx, rv2.pdf(xx), lw=2, color="slategrey")
plt.plot(xx, rv.pdf(xx), lw=2, color="orchid")
plt.text(50, 0.07, 'shape: %d \n scale: %.3f' % (parameters[ind][0],parameters[ind][1]), fontsize=14)
plt.text(50, 0.09, 'control shape: %d \n scale: %.3f' % (cont_pr[ind][0],cont_pr[ind][1]), fontsize=14)
plt.ylim([0.0, 0.12])
plt.xlim([0.0, 90.0])
plt.xlabel("phase durations [hrs]")
plt.ylabel("probability")
plt.legend()
def runLenFees(fullSize=975000):
ds = generate_dataset()
grouped, not_full = list(), list()
r = False
total_tx = list()
for b in ds.interarrival:
total_tx.extend([b['satoshi_fee']//b['num_tx']]*b['num_tx'])
if b['block_size'] <= fullSize:
not_full.append(b)
r = False
elif b['block_size'] > fullSize:
if not r:
grouped.append([b])
r = True
else:
grouped[-1].append(b)
mean = sum(total_tx)/len(total_tx)
print 'mean tx fee: {}'.format(mean)
fig = plt.figure()
fig.suptitle('Transaction Fee PDF/Histogram Fit')
plt.hist(sorted(total_tx)[:-len(total_tx)//100],300, normed=True)
plt.xlabel('Satoshi Tx Fee')
plt.ylabel('Probability')
plt.axvline(x=mean, color='red')
rv = sp.erlang(7, loc=11500, scale=1200)
x = np.linspace(0,50000)
#plt.plot(x, rv.pdf(x))
#plt.show()
l1 = [i/len(total_tx) for i in range(len(total_tx)-(len(total_tx)//50)-1)]
l2 = [e for e in sorted(total_tx)[:-len(total_tx)//50]]
print len(l1), len(l2)
fig.suptitle('Sorted Fees vs. Erlang CDF')
plt.xlabel('Satoshi Tx Fee')
plt.ylabel('Probablility')
plt.plot(x, rv.cdf(x), color='red')
plt.plot(l2, l1, color='blue')
plt.show()
dic = defaultdict(list)
for blocklist in grouped:
dic[len(blocklist)].extend(blocklist)
avgFeeDic = dict()
for key in dic:
avgFeeDic[key] = calcAvgFee(dic[key])
avgFeeDic[0] = calcAvgFee(not_full)
return avgFeeDic
def __init__(self, start, end):
assert end <= RANGE_MAX
self.arguments = (start, end)
self.bitwise = np.array(
[1 if (start <= i <= end) else 0 for i in range(1, RANGE_MAX + 1)],
dtype=int)
self.numeric = np.nonzero(self.bitwise)[0] + 1
erlang_rv = erlang(2, loc=0, scale=10)
size = end - start + 1
size_prob = erlang_rv.pdf(size)
start_prob = 1. / (RANGE_MAX + 1 - size)
self.probability = size_prob / float(5050)
def _get_bg_distrib_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'), bp=(0, -1)):
"""Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
"""
assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
if np.size(bp) == 1: bp = (bp, bp)
periods = slice(d.Lim[ich][bp[0]][0], d.Lim[ich][bp[1]][1] + 1)
# Compute the BG distribution
if ph_sel == Ph_sel('all'):
bg_ph = d.bg_dd[ich] + d.bg_ad[ich]
elif ph_sel == Ph_sel(Dex='Dem'):
bg_ph = d.bg_dd[ich]
elif ph_sel == Ph_sel(Dex='Aem'):
bg_ph = d.bg_ad[ich]
rate_ch_kcps = bg_ph[periods].mean()/1e3 # bg rate in kcps
bg_dist = erlang(a=m, scale=1./rate_ch_kcps)
return bg_dist
def _get_bg_distrib_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'),
period=(0, -1)):
"""Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
"""
assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
# Compute the BG distribution
if ph_sel == Ph_sel('all'):
bg_ph = d.bg_dd[ich] + d.bg_ad[ich]
elif ph_sel == Ph_sel(Dex='Dem'):
bg_ph = d.bg_dd[ich]
elif ph_sel == Ph_sel(Dex='Aem'):
bg_ph = d.bg_ad[ich]
rate_ch_kcps = bg_ph[period[0]:period[1]+1].mean()/1e3 # bg rate in kcps
bg_dist = erlang(a=m, scale=1./rate_ch_kcps)
return bg_dist
def _get_bg_distrib_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'),
period=(0, -1)):
"""Return a frozen (scipy) erlang distrib. with rate equal to the bg rate.
"""
assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')]
# Compute the BG distribution
if ph_sel == Ph_sel('all'):
bg_ph = d.bg_dd[ich] + d.bg_ad[ich]
elif ph_sel == Ph_sel(Dex='Dem'):
bg_ph = d.bg_dd[ich]
elif ph_sel == Ph_sel(Dex='Aem'):
bg_ph = d.bg_ad[ich]
rate_ch_kcps = bg_ph[period[0]:period[1]+1].mean()/1e3 # bg rate in kcps
bg_dist = erlang(a=m, scale=1./rate_ch_kcps)
return bg_dist
def test_erlang_dist(input_list, list_name):
# erlang is a special dist of gamma with int shape alpha
fit_alpha, fit_loc, fit_beta = ss.erlang.fit(input_list, floc=0)
rv = ss.erlang(int(fit_alpha), fit_loc, fit_beta)
print 'fit alpha is %.4f and fit beta is %.4f' % (int(fit_alpha), fit_beta)
# chose the number of bins
n = count_bin(input_list)
print 'number of bins %i' % n
name = 'test Erlang on ' + list_name
draw_hist(input_list, n, rv, name, 'Chartreuse')
# set the adjustment to dof (degree of freedom) = to the number of parameters estimated
dof = 1
## experiment--------------------------------------------------
result = model(input_list, n, dof, rv)
t_value, p_value = result[0], result[1]
print "The chi_sq test value of erlang dist is %5.6f and the p-value is %5.10f" % (
t_value, p_value)
dat = [x[0] for x in res]
print dat
plt.plot([CRIT]*len(dat), 'r--', label='KS CRITICAL VALUE')
plt.plot(dat, 'bH:', label='Erlang Shape Parameter D values')
plt.show()
ds = generate_dataset()
times = sorted([block['time'] for block in ds.interarrival])
#times = [x/max(times) for x in times]
#trimmed_times = times[::len(times)//100]
#test_erlang(trimmed_times)
plt.hist(times,100)
plt.show()
tx_rate = sorted([block['num_tx']/block['time'] for block in ds.interarrival if block['time'] != 0 and block['num_tx'] != 0])
print 'mean: {}'.format(sum(tx_rate)/len(tx_rate))
print 'max: {}'.format(max(tx_rate))
print 'min: {}'.format(min(tx_rate))
trimmed_tx_rate = tx_rate[len(tx_rate)//25::len(tx_rate)//100]
test_erlang(trimmed_tx_rate)
rv = sp.erlang(3)
x = np.linspace(0,1)
#plt.plot(x, rv.pdf(x))
#plt.show()
plt.hist(tx_rate[:-len(tx_rate)//25], 100)
plt.show()
def Consumed_SKU(self):
print erlang(200, scale=0.002778).rvs(200)
import numpy as np
import random
import scipy.stats as ss
import matplotlib.pyplot as plt
import chiSquare
f = file('AllServiceTimes2.txt', 'r+')
service_times = [float(x) for x in f.read().split(', ')]
fit_alpha,fit_beta=ss.expon.fit(service_times, floc=0)
rv1 = ss.expon(fit_alpha,fit_beta)
print 'Exponential parameters: loc, mu: ',fit_alpha,fit_beta
fit_alpha,fit_loc,fit_beta=ss.erlang.fit(service_times, floc=0)
fit_alpha = int(round(fit_alpha))
rv2 = ss.erlang(fit_alpha,fit_loc,fit_beta)
print 'Erlang parameters: f, loc, mu: ',fit_alpha,fit_loc,fit_beta
fit_alpha,fit_loc,fit_beta=ss.gamma.fit(service_times, floc=0)
rv3 = ss.gamma(fit_alpha,fit_loc,fit_beta)
print 'Gamma parameters: alpha, loc, beta: ',fit_alpha,fit_loc,fit_beta
fig = plt.figure()
myHist = plt.hist(service_times, 60, normed=True)
x = np.linspace(0.001,500)
ex = plt.plot(x, rv1.pdf(x), lw=2, label="Exponential")
e = plt.plot(x, rv2.pdf(x), lw=2, label="Erlang")
g = plt.plot(x, rv3.pdf(x), lw=2, label="Gamma")
plt.legend(loc='upper right')
fig.suptitle('Service times')
# Simple Synapse
#
# The simple synapse does not simulate the axon, synaptic cleft, or dendrite.
# Instead, it simply takes a voltage from the presynaptic neuron and provides
# a means of activating the postsynaptic neuron.
# It shares an interface with ChemicalSynapse, and is thus interchangeable
# with it.
from molecule import Receptors
from collections import deque
from sys import maxint
from scipy.stats import erlang
er = erlang(2)
def erlang_generator():
"""
Creates an erlang generator.
"""
prev = 0.0
for x in xrange(1, maxint):
curr = er.cdf(x)
diff = curr - prev
if diff < 0.001: break
prev = curr
yield diff
class SimpleSynapse:
def __init__(self, postsynaptic_id=None, receptor=Receptors.AMPA,
spiking=True, delay=0, strength=1, environment=None,
# DISTRIBUCION ERLANG
elif optionSelected == 4:
os.system("cls")
nombreDistribucion = "Distribución Erlang"
print("\t::", nombreDistribucion, "::")
# Parametros necesarios para la generación
forma = float(input("->Ingrese la forma :"))
valorEsperado = float(input("->Ingrese el valor esperado :"))
numeroDatos = int(input("->Ingrese el número de variables aleatorias a generar :"))
#nivelDeSignificacia = float(input("->Ingrese el nivel de significancia para la prueba chi cuadrado :"))
nivelDeSignificacia = 0.05
tipoDistribucion = st.erlang(forma, 0, valorEsperado)
nombreDistribucion = "Distribución Erlang"
objErlang = erlangDistribution.Erlang(numeroDatos, tipoDistribucion, nivelDeSignificacia, nombreDistribucion,
forma, valorEsperado)
legendHistogram = r'$\kappa$' + "=" + str("{0:.2f}".format(objErlang.formGenerated)) + " ; " + r'$\theta$' + "=" + str("{0:.2f}".format(objErlang.expectedValueGenerated))
legendDensity = r'$\kappa$' + "=" + str(forma) + " ; " + r'$\theta$' + "=" + str(valorEsperado)
axisX = np.linspace(0, objErlang.median + 6 * objErlang.iqr, numeroDatos)
os.system('cls')
objErlang.chiSquareTest()
objErlang.graph(legendHistogram, legendDensity, axisX)
# DISTRIBUCION UNIFORME CONTINUA
elif optionSelected == 5:
def all_dists():
# dists param were taken from scipy.stats official
# documentaion examples
# Total - 89
return {
"alpha":
stats.alpha(a=3.57, loc=0.0, scale=1.0),
"anglit":
stats.anglit(loc=0.0, scale=1.0),
"arcsine":
stats.arcsine(loc=0.0, scale=1.0),
"beta":
stats.beta(a=2.31, b=0.627, loc=0.0, scale=1.0),
"betaprime":
stats.betaprime(a=5, b=6, loc=0.0, scale=1.0),
"bradford":
stats.bradford(c=0.299, loc=0.0, scale=1.0),
"burr":
stats.burr(c=10.5, d=4.3, loc=0.0, scale=1.0),
"cauchy":
stats.cauchy(loc=0.0, scale=1.0),
"chi":
stats.chi(df=78, loc=0.0, scale=1.0),
"chi2":
stats.chi2(df=55, loc=0.0, scale=1.0),
"cosine":
stats.cosine(loc=0.0, scale=1.0),
"dgamma":
stats.dgamma(a=1.1, loc=0.0, scale=1.0),
"dweibull":
stats.dweibull(c=2.07, loc=0.0, scale=1.0),
"erlang":
stats.erlang(a=2, loc=0.0, scale=1.0),
"expon":
stats.expon(loc=0.0, scale=1.0),
"exponnorm":
stats.exponnorm(K=1.5, loc=0.0, scale=1.0),
"exponweib":
stats.exponweib(a=2.89, c=1.95, loc=0.0, scale=1.0),
"exponpow":
stats.exponpow(b=2.7, loc=0.0, scale=1.0),
"f":
stats.f(dfn=29, dfd=18, loc=0.0, scale=1.0),
"fatiguelife":
stats.fatiguelife(c=29, loc=0.0, scale=1.0),
"fisk":
stats.fisk(c=3.09, loc=0.0, scale=1.0),
"foldcauchy":
stats.foldcauchy(c=4.72, loc=0.0, scale=1.0),
"foldnorm":
stats.foldnorm(c=1.95, loc=0.0, scale=1.0),
# "frechet_r": stats.frechet_r(c=1.89, loc=0.0, scale=1.0),
# "frechet_l": stats.frechet_l(c=3.63, loc=0.0, scale=1.0),
"genlogistic":
stats.genlogistic(c=0.412, loc=0.0, scale=1.0),
"genpareto":
stats.genpareto(c=0.1, loc=0.0, scale=1.0),
"gennorm":
stats.gennorm(beta=1.3, loc=0.0, scale=1.0),
"genexpon":
stats.genexpon(a=9.13, b=16.2, c=3.28, loc=0.0, scale=1.0),
"genextreme":
stats.genextreme(c=-0.1, loc=0.0, scale=1.0),
"gausshyper":
stats.gausshyper(a=13.8, b=3.12, c=2.51, z=5.18, loc=0.0, scale=1.0),
"gamma":
stats.gamma(a=1.99, loc=0.0, scale=1.0),
"gengamma":
stats.gengamma(a=4.42, c=-3.12, loc=0.0, scale=1.0),
"genhalflogistic":
stats.genhalflogistic(c=0.773, loc=0.0, scale=1.0),
"gilbrat":
stats.gilbrat(loc=0.0, scale=1.0),
"gompertz":
stats.gompertz(c=0.947, loc=0.0, scale=1.0),
"gumbel_r":
stats.gumbel_r(loc=0.0, scale=1.0),
"gumbel_l":
stats.gumbel_l(loc=0.0, scale=1.0),
"halfcauchy":
stats.halfcauchy(loc=0.0, scale=1.0),
"halflogistic":
stats.halflogistic(loc=0.0, scale=1.0),
"halfnorm":
stats.halfnorm(loc=0.0, scale=1.0),
"halfgennorm":
stats.halfgennorm(beta=0.675, loc=0.0, scale=1.0),
"hypsecant":
stats.hypsecant(loc=0.0, scale=1.0),
"invgamma":
stats.invgamma(a=4.07, loc=0.0, scale=1.0),
"invgauss":
stats.invgauss(mu=0.145, loc=0.0, scale=1.0),
"invweibull":
stats.invweibull(c=10.6, loc=0.0, scale=1.0),
"johnsonsb":
stats.johnsonsb(a=4.32, b=3.18, loc=0.0, scale=1.0),
"johnsonsu":
stats.johnsonsu(a=2.55, b=2.25, loc=0.0, scale=1.0),
"ksone":
stats.ksone(n=1e03, loc=0.0, scale=1.0),
"kstwobign":
stats.kstwobign(loc=0.0, scale=1.0),
"laplace":
stats.laplace(loc=0.0, scale=1.0),
"levy":
stats.levy(loc=0.0, scale=1.0),
"levy_l":
stats.levy_l(loc=0.0, scale=1.0),
"levy_stable":
stats.levy_stable(alpha=0.357, beta=-0.675, loc=0.0, scale=1.0),
"logistic":
stats.logistic(loc=0.0, scale=1.0),
"loggamma":
stats.loggamma(c=0.414, loc=0.0, scale=1.0),
"loglaplace":
stats.loglaplace(c=3.25, loc=0.0, scale=1.0),
"lognorm":
stats.lognorm(s=0.954, loc=0.0, scale=1.0),
"lomax":
stats.lomax(c=1.88, loc=0.0, scale=1.0),
"maxwell":
stats.maxwell(loc=0.0, scale=1.0),
"mielke":
stats.mielke(k=10.4, s=3.6, loc=0.0, scale=1.0),
"nakagami":
stats.nakagami(nu=4.97, loc=0.0, scale=1.0),
"ncx2":
stats.ncx2(df=21, nc=1.06, loc=0.0, scale=1.0),
"ncf":
stats.ncf(dfn=27, dfd=27, nc=0.416, loc=0.0, scale=1.0),
"nct":
stats.nct(df=14, nc=0.24, loc=0.0, scale=1.0),
"norm":
stats.norm(loc=0.0, scale=1.0),
"pareto":
stats.pareto(b=2.62, loc=0.0, scale=1.0),
"pearson3":
stats.pearson3(skew=0.1, loc=0.0, scale=1.0),
"powerlaw":
stats.powerlaw(a=1.66, loc=0.0, scale=1.0),
"powerlognorm":
stats.powerlognorm(c=2.14, s=0.446, loc=0.0, scale=1.0),
"powernorm":
stats.powernorm(c=4.45, loc=0.0, scale=1.0),
"rdist":
stats.rdist(c=0.9, loc=0.0, scale=1.0),
"reciprocal":
stats.reciprocal(a=0.00623, b=1.01, loc=0.0, scale=1.0),
"rayleigh":
stats.rayleigh(loc=0.0, scale=1.0),
"rice":
stats.rice(b=0.775, loc=0.0, scale=1.0),
"recipinvgauss":
stats.recipinvgauss(mu=0.63, loc=0.0, scale=1.0),
"semicircular":
stats.semicircular(loc=0.0, scale=1.0),
"t":
stats.t(df=2.74, loc=0.0, scale=1.0),
"triang":
stats.triang(c=0.158, loc=0.0, scale=1.0),
"truncexpon":
stats.truncexpon(b=4.69, loc=0.0, scale=1.0),
"truncnorm":
stats.truncnorm(a=0.1, b=2, loc=0.0, scale=1.0),
"tukeylambda":
stats.tukeylambda(lam=3.13, loc=0.0, scale=1.0),
"uniform":
stats.uniform(loc=0.0, scale=1.0),
"vonmises":
stats.vonmises(kappa=3.99, loc=0.0, scale=1.0),
"vonmises_line":
stats.vonmises_line(kappa=3.99, loc=0.0, scale=1.0),
"wald":
stats.wald(loc=0.0, scale=1.0),
"weibull_min":
stats.weibull_min(c=1.79, loc=0.0, scale=1.0),
"weibull_max":
stats.weibull_max(c=2.87, loc=0.0, scale=1.0),
"wrapcauchy":
stats.wrapcauchy(c=0.0311, loc=0.0, scale=1.0),
}
"""
percentiles = np.array([0.25, 0.5, 0.75])
time_values = np.array([iqr[0], median, iqr[1]])
mean = median
x0 = [mean / n] * n
return fit_chain_by_cdf(n, time_values, percentiles, lower, upper, x0=x0)
if __name__ == "__main__":
import matplotlib.pyplot as pl
from scipy.stats import erlang
n = 10
mean = 10
C = ExpChain([mean / n] * n)
E = erlang(a=n, scale=mean / n)
t, y = C.get_cdf()
pl.plot(t, y)
pl.plot(t, E.cdf(t))
print(C.get_median_and_iqr())
print(E.median(), E.interval(0.5))
pl.show()
# =========
times = [0.3, 6., 9, 0.4]
n = len(times)
C = ExpChain(times)
fit_C = fit_chain_by_median_and_iqr(3, *C.get_median_and_iqr())
ag, bg, thetaGamma = stats.gamma.fit(data)
pdf_gamma = stats.gamma.pdf(lnspc, ag, bg, thetaGamma)
plt.plot(lnspc, pdf_gamma, label="Gamma")
plt.legend(loc='upper right')
plt.savefig("fitting.png")
print "[*] Fitting Paramaters"
print "[#] Exponential: ", aexpon, '???, mu = ', muExp, '\n',
print "[#] Erlang: ", ae, '???, k = ', be, ', mu = ', muErl
print "[#] Gamma: ", ag, '??? k = ', bg, ', Theta = ', thetaGamma, '\n'
print "[*] Computing Chi Square Test"
# Apply Chi-Square test to the three fittings
dof = 2
n = 30
rv = stats.expon(aexpon, muExp)
exponFit = chisquare.model(data, n, dof, rv)
print "[#] Exponential: The chi_sq test value is %10.6f and the p-value is %10.6f" % (
exponFit[0], exponFit[1])
rv = stats.erlang(ae, be, muErl)
erlangFit = chisquare.model(data, n, dof, rv)
print "[#] Erlang: The chi_sq test value is %10.6f and the p-value is %10.6f" % (
erlangFit[0], erlangFit[1])
rv = stats.gamma(ag, bg, thetaGamma)
gammaFit = chisquare.model(data, n, dof, rv)
print "[#] Gamma: The chi_sq test value is %10.6f and the p-value is %10.6f" % (
gammaFit[0], gammaFit[1])
def __init__(self, a=1, loc=0, scale=1):
self.g = erlang(a=a, loc=loc, scale=scale)
self.init_stats(mean=self.g.mean(), var=self.g.var(), running=False)
self.init_buffer()
#k=(T_l/sigma)^2, mu=sigma^2/T_l
#1- constant variance sigma_0^2 across different T_l's
#2- constant k (not 1, because with k=1 we have an exponential distribution),
#sigma increases with T_l
erlang_type = 2
sigma_0 = 2 # will use only in case erlang_type=1 (constant variance)
k = 10 # will use only in case erlang_type=2 (constant k)
if erlang_type == 1:
a = T_l / sigma_0
mu = sigma_0 / a
k = a * a
elif erlang_type == 2:
mu = T_l / k
rv = erlang(k, scale=mu)
## give locations to all agents
Nruns = 10
peak_height = list()
peak_time = list()
start_time = list()
inf_per_day = list()
day_50 = list()
day_100 = list()
day_150 = list()
day_200 = list()
for irun in range(Nruns):
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--stages', type=int, required=False,
help='Etapas de la distribución')
parser.add_argument('-l', '--lambdap', type=float, required=True,
nargs='+',
help='Parámetro lambda de cada distribución')
parser.add_argument('-r', '--runs', type=int, required=True,
help='Ejecuciones a realizar por cada simulación')
parser.add_argument('-o', '--output', type=str, required=False,
help='Archivo de salida para la grafica')
parser.add_argument('-d', '--dist', type=str, required=True,
choices=['erlang', 'expon', 'hyperexp'],
help='Distribución a emplear para la simulación')
parser.add_argument('--no-graph', required=False,
help='Suprime la salida como gráfica',
dest='graph', action='store_false')
parser.add_argument('--graph', required=False,
help='Habilita la salida como gráfica (usar con [-o])',
dest='graph', action='store_true')
parser.add_argument('-p', '--probability', required=False, type=float,
help='Probabilidad para la distribución Hiperexp.')
parser.set_defaults(graph=True)
args = parser.parse_args()
# msg = 'Distribución {3} con {0} etapas (lambda={1}) en {2} ejecuciones'
# print msg.format(args.stages, args.lambdap, args.runs, args.dist)
fig, ax = plt.subplots(1, 1)
if args.dist in 'erlang':
if args.stages <= 0:
print 'Error: se necesita un número válido de etapas'
sys.exit(1)
lambdap = args.lambdap[0]
mean, var, skew, kurt = erlang.stats(args.stages, scale=lambdap,
moments='mvsk')
x = np.linspace(erlang.ppf(0.00001, args.stages, scale=lambdap),
erlang.ppf(0.99999, args.stages, scale=lambdap),
num=1000)
rv = erlang(args.stages, scale=lambdap)
ax.plot(x, rv.pdf(x), 'r-', lw=5, alpha=0.6, label='Erlang PDF')
# Generate random numbers with this distribution
r = erlang.rvs(args.stages, scale=lambdap, size=args.runs)
ax.hist(r, bins=20, normed=True, histtype='stepfilled', alpha=0.4,
label='Experimental values')
meanexp = np.mean(r)
varexp = np.var(r)
print 'Mediaexperimental: {0} MediaAnalitica: {1}'.format(meanexp,
mean)
print 'Sigma2_exp: {0} Sigma2_a: {1}'.format(varexp, var)
print 'CoV_exp: {0} CoV_a: {1}'.format(np.sqrt(varexp)/meanexp,
np.sqrt(var)/mean)
elif args.dist in 'expon':
lambdap = args.lambdap[0]
mean, var, skew, kurt = expon.stats(scale=lambdap, moments='mvsk')
x = np.linspace(expon.ppf(0.00001, scale=lambdap),
expon.ppf(0.99999, scale=lambdap),
num=1000)
rv = expon(scale=lambdap)
ax.plot(x, rv.pdf(x), 'r-', lw=5, alpha=0.6, label='Exponential PDF')
# Generate random numbers with this distribution
r = expon.rvs(scale=lambdap, size=args.runs)
ax.hist(r, bins=20, normed=True, histtype='stepfilled', alpha=0.4,
label='Experimental values')
meanexp = np.mean(r)
varexp = np.var(r)
print 'Mediaexperimental: {0} MediaAnalitica: {1}'.format(meanexp,
mean)
print 'Sigma2_exp: {0} Sigma2_a: {1}'.format(varexp, var)
print 'CoV_exp: {0} CoV_a: {1}'.format(np.sqrt(varexp)/meanexp,
np.sqrt(var)/mean)
elif args.dist in 'hyperexp':
rv = hyperexp(args.probability, args.lambdap[0], args.lambdap[1])
x = np.linspace(0.00000001, 10.99999, num=1000)
ax.plot(x, rv.pdf(x), 'r-', lw=5, alpha=0.6, label='HyperExp PDF')
# ax.plot(x, rv.cdf(x), 'b-', lw=2, alpha=0.6, label='HyperExp CDF')
r = rv.rvs(size=args.runs)
ax.hist(r, normed=True, bins=100, range=(0, 11),
histtype='stepfilled', alpha=0.4, label='Experimental values')
meanexp = np.mean(r)
varexp = np.var(r)
mean = rv.mean()
var = rv.standard_dev()**2
print 'Mediaexperimental: {0} MediaAnalitica: {1}'.format(meanexp,
mean)
print 'Sigma2_exp: {0} Sigma2_a: {1}'.format(varexp, var)
print 'CoV_exp: {0} CoV_a: {1}'.format(np.sqrt(varexp)/meanexp,
rv.CoV())
if args.graph:
ax.legend(loc='best', frameon=False)
plt.show()
plt.show()
ds = generate_dataset()
times = sorted([block['time'] for block in ds.interarrival])
#times = [x/max(times) for x in times]
#trimmed_times = times[::len(times)//100]
#test_erlang(trimmed_times)
plt.hist(times, 100)
plt.show()
tx_rate = sorted([
block['num_tx'] / block['time'] for block in ds.interarrival
if block['time'] != 0 and block['num_tx'] != 0
])
print 'mean: {}'.format(sum(tx_rate) / len(tx_rate))
print 'max: {}'.format(max(tx_rate))
print 'min: {}'.format(min(tx_rate))
trimmed_tx_rate = tx_rate[len(tx_rate) // 25::len(tx_rate) // 100]
test_erlang(trimmed_tx_rate)
rv = sp.erlang(3)
x = np.linspace(0, 1)
#plt.plot(x, rv.pdf(x))
#plt.show()
plt.hist(tx_rate[:-len(tx_rate) // 25], 100)
plt.show()
def Consumed_SKU(self):
print erlang(200, scale=0.002778).rvs(200)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--stages", type=int, required=False, help="Etapas de la distribución")
parser.add_argument(
"-l", "--lambdap", type=float, required=True, nargs="+", help="Parámetro lambda de cada distribución"
)
parser.add_argument("-r", "--runs", type=int, required=True, help="Ejecuciones a realizar por cada simulación")
parser.add_argument("-o", "--output", type=str, required=False, help="Archivo de salida para la grafica")
parser.add_argument(
"-d",
"--dist",
type=str,
required=True,
choices=["erlang", "expon", "hyperexp"],
help="Distribución a emplear para la simulación",
)
parser.add_argument(
"--no-graph", required=False, help="Suprime la salida como gráfica", dest="graph", action="store_false"
)
parser.add_argument(
"--graph",
required=False,
help="Habilita la salida como gráfica (usar con [-o])",
dest="graph",
action="store_true",
)
parser.set_defaults(graph=True)
args = parser.parse_args()
msg = "Distribución {3} con {0} etapas (lambda={1}) en {2} ejecuciones"
print msg.format(args.stages, args.lambdap, args.runs, args.dist)
fig, ax = plt.subplots(1, 1)
if args.dist in "erlang":
if args.stages <= 0:
print "Error: se necesita un número válido de etapas"
sys.exit(1)
lambdap = args.lambdap[0]
mean, var, skew, kurt = erlang.stats(args.stages, scale=lambdap, moments="mvsk")
print "E[X]={0}, var(X)={1}".format(mean, var)
x = np.linspace(
erlang.ppf(0.00001, args.stages, scale=lambdap), erlang.ppf(0.99999, args.stages, scale=lambdap), num=1000
)
rv = erlang(args.stages, scale=lambdap)
ax.plot(x, rv.pdf(x), "r-", lw=5, alpha=0.6, label="Erlang PDF")
# Generate random numbers with this distribution
r = erlang.rvs(args.stages, scale=lambdap, size=args.runs)
ax.hist(r, bins=20, normed=True, histtype="stepfilled", alpha=0.2)
meanexp = np.mean(r)
varexp = np.var(r)
print "Mediaexperimental: {0} MediaAnalitica: {1}".format(meanexp, mean)
print "Sigma2_exp: {0} Sigma2_a: {1}".format(varexp, var)
print "CoV_exp: {0} CoV_a: {1}".format(np.sqrt(varexp) / meanexp, np.sqrt(var) / mean)
elif args.dist in "expon":
lambdap = args.lambdap[0]
mean, var, skew, kurt = expon.stats(scale=lambdap, moments="mvsk")
print "E[X]={0}, var(X)={1}".format(mean, var)
x = np.linspace(expon.ppf(0.00001, scale=lambdap), expon.ppf(0.99999, scale=lambdap), num=1000)
rv = expon(scale=lambdap)
ax.plot(x, rv.pdf(x), "r-", lw=5, alpha=0.6, label="Exponential PDF")
# Generate random numbers with this distribution
r = expon.rvs(scale=lambdap, size=args.runs)
ax.hist(r, bins=20, normed=True, histtype="stepfilled", alpha=0.2)
meanexp = np.mean(r)
varexp = np.var(r)
print "Mediaexperimental: {0} MediaAnalitica: {1}".format(meanexp, mean)
print "Sigma2_exp: {0} Sigma2_a: {1}".format(varexp, var)
print "CoV_exp: {0} CoV_a: {1}".format(np.sqrt(varexp) / meanexp, np.sqrt(var) / mean)
elif args.dist in "hyperexp":
print "HyperExponential RV"
rv = hyperexp(0.1, args.lambdap[0], args.lambdap[1])
x = np.linspace(0.00000001, 10.99999, num=1000)
ax.plot(x, rv.pdf(x), "r-", lw=5, alpha=0.6, label="HyperExp PDF")
# ax.plot(x, rv.cdf(x), 'b-', lw=2, alpha=0.6, label='HyperExp CDF')
r = rv.rvs(size=args.runs)
ax.hist(r, normed=True, bins=100, range=(0, 11), histtype="stepfilled", alpha=0.2)
meanexp = np.mean(r)
varexp = np.var(r)
print "Mediaexperimental: {0} MediaAnalitica: {1}".format(meanexp, mean)
print "Sigma2_exp: {0} Sigma2_a: {1}".format(varexp, var)
print "CoV_exp: {0} CoV_a: {1}".format(np.sqrt(varexp) / meanexp, np.sqrt(var) / mean)
if args.graph:
plt.show()
