def main():
PRINT_TITLE = False
nowdate = datetime.datetime.now()
if not os.path.exists("plots"):
os.makedirs("plots")
if not os.path.exists("tables"):
os.makedirs("tables")
lines = ["-k", "--k", "-.k"]
linecycler = cycle(lines)
test_data_Poisson_initial = poisson.Bmmap5PoissonPhPh()
test_data_Poisson = poisson.Bmmap5PoissonPhPh()
test_data_02_initial = cor02.Bmmap502PhPh()
test_data_02 = cor02.Bmmap502PhPh()
test_data_04_initial = cor04.Bmmap504PhPh()
test_data_04 = cor04.Bmmap504PhPh()
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson_initial)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02_initial)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04_initial)
lambdas = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
Ls = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
LsPrior = {"poisson": [], "cor02": [], "cor04": []}
sigmas = {"poisson": [], "cor02": [], "cor04": []}
sigmas_prior = {"poisson": [], "cor02": [], "cor04": []}
Ps = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
Ps_alg = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
P1s = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
P2s = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
Ps_imp = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
w1s = {"poisson": [], "cor02": [], "cor04": []}
w2s = {"poisson": [], "cor02": [], "cor04": []}
table_poisson = []
table_cor_02 = []
table_cor_04 = []
for coef in tqdm(np.linspace(1, 150, 150)):
test_data_Poisson.test_matrD = test_data_Poisson_initial.test_matrD * coef
test_data_Poisson.test_matrD_0 = test_data_Poisson_initial.test_matrD_0 * coef
test_data_02.test_matrD = test_data_02_initial.test_matrD * coef
test_data_02.test_matrD_0 = test_data_02_initial.test_matrD_0 * coef
test_data_04.test_matrD = test_data_04_initial.test_matrD * coef
test_data_04.test_matrD_0 = test_data_04_initial.test_matrD_0 * coef
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson)
stationary_probas1 = qs1.calc_stationary_probas(False)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02)
stationary_probas2 = qs2.calc_stationary_probas(False)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04)
stationary_probas3 = qs3.calc_stationary_probas(False)
lambdas["poisson"].append(qs1.queries_stream.avg_intensity)
Ls["poisson"].append(qs1.calc_avg_buffer_queries_num(stationary_probas1))
LsPrior["poisson"].append(qs1.calc_avg_buffer_prior_queries_num(stationary_probas1))
sigmas["poisson"].append(qs1.calc_sd_buffer_queries_num(stationary_probas1))
sigmas_prior["poisson"].append(qs1.calc_sd_buffer_prior_queries_num(stationary_probas1))
Ps["poisson"].append(qs1.calc_query_lost_p(stationary_probas1))
Ps_alg["poisson"].append(qs1.calc_query_lost_ps_buffer_full(stationary_probas1)[1])
P1s["poisson"].append(qs1.calc_query_lost_ps_buffer_full(stationary_probas1)[0][0])
P2s["poisson"].append(qs1.calc_query_lost_ps_buffer_full(stationary_probas1)[0][1])
Ps_imp["poisson"].append(qs1.calc_nonprior_query_lost_timer(stationary_probas1))
w1s["poisson"].append(qs1.avg_wait_time_1(stationary_probas1))
w2s["poisson"].append(qs1.avg_wait_time_2(stationary_probas1))
lambdas["cor02"].append(qs2.queries_stream.avg_intensity)
Ls["cor02"].append(qs2.calc_avg_buffer_queries_num(stationary_probas2))
LsPrior["cor02"].append(qs2.calc_avg_buffer_prior_queries_num(stationary_probas2))
sigmas["cor02"].append(qs2.calc_sd_buffer_queries_num(stationary_probas2))
sigmas_prior["cor02"].append(qs2.calc_sd_buffer_prior_queries_num(stationary_probas2))
Ps["cor02"].append(qs2.calc_query_lost_p(stationary_probas2))
Ps_alg["cor02"].append(qs2.calc_query_lost_ps_buffer_full(stationary_probas2)[1])
P1s["cor02"].append(qs2.calc_query_lost_ps_buffer_full(stationary_probas2)[0][0])
P2s["cor02"].append(qs2.calc_query_lost_ps_buffer_full(stationary_probas2)[0][1])
Ps_imp["cor02"].append(qs2.calc_nonprior_query_lost_timer(stationary_probas2))
w1s["cor02"].append(qs2.avg_wait_time_1(stationary_probas2))
w2s["cor02"].append(qs2.avg_wait_time_2(stationary_probas2))
lambdas["cor04"].append(qs3.queries_stream.avg_intensity)
Ls["cor04"].append(qs3.calc_avg_buffer_queries_num(stationary_probas3))
LsPrior["cor04"].append(qs3.calc_avg_buffer_prior_queries_num(stationary_probas3))
sigmas["cor04"].append(qs3.calc_sd_buffer_queries_num(stationary_probas3))
sigmas_prior["cor04"].append(qs3.calc_sd_buffer_prior_queries_num(stationary_probas3))
Ps["cor04"].append(qs3.calc_query_lost_p(stationary_probas3))
Ps_alg["cor04"].append(qs3.calc_query_lost_ps_buffer_full(stationary_probas3)[1])
P1s["cor04"].append(qs3.calc_query_lost_ps_buffer_full(stationary_probas3)[0][0])
P2s["cor04"].append(qs3.calc_query_lost_ps_buffer_full(stationary_probas3)[0][1])
Ps_imp["cor04"].append(qs3.calc_nonprior_query_lost_timer(stationary_probas3))
w1s["cor04"].append(qs3.avg_wait_time_1(stationary_probas3))
w2s["cor04"].append(qs3.avg_wait_time_2(stationary_probas3))
table_poisson.append(
[lambdas["poisson"][-1], P1s["poisson"][-1], P2s["poisson"][-1], Ps_alg["poisson"][-1],
Ps_imp["poisson"][-1], w1s["poisson"][-1], w2s["poisson"][-1]])
table_cor_02.append(
[lambdas["cor02"][-1], P1s["cor02"][-1], P2s["cor02"][-1], Ps_alg["cor02"][-1], Ps_imp["cor02"][-1],
w1s["cor02"][-1], w2s["cor02"][-1]])
table_cor_04.append(
[lambdas["cor04"][-1], P1s["cor04"][-1], P2s["cor04"][-1], Ps_alg["cor04"][-1], Ps_imp["cor04"][-1],
w1s["cor04"][-1], w2s["cor04"][-1]])
plt.plot(lambdas["poisson"], Ls["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], Ls["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], Ls["cor04"], next(linecycler))
plt.ylabel(r'$L$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость L от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_L_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], LsPrior["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], LsPrior["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], LsPrior["cor04"], next(linecycler))
plt.ylabel(r'$L^{(prior)}$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость L_prior от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_L_prior_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], sigmas["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], sigmas["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], sigmas["cor04"], next(linecycler))
plt.ylabel(r'$\sigma$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость sigma от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_sigma_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], sigmas_prior["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], sigmas_prior["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], sigmas_prior["cor04"], next(linecycler))
plt.ylabel(r'$\sigma^{(prior})$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость sigma^{(prior}) от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_sigma_prior_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
# DEPRECATED
# plt.plot(lambdas["poisson"], Ps["poisson"])
# plt.plot(lambdas["cor02"], Ps["cor02"])
# plt.plot(lambdas["cor04"], Ps["cor04"])
# plt.ylabel(r'$P_{loss}$')
# plt.xlabel(r'$\lambda$')
# plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
# abs(round(qs1.queries_stream.c_cor[1], 3))),
# r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
# round(qs2.queries_stream.c_cor[1], 3)),
# r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
# round(qs3.queries_stream.c_cor[1], 3))
# ),
# loc=0)
# plt.title('Зависимость P_loss от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
#
# plt.savefig(str.format("plots/BMMAP5_P_loss_lambda_cor_0_02_04_{}.png", nowdate),
# bbox_inches='tight')
# plt.close()
plt.plot(lambdas["poisson"], Ps_alg["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], Ps_alg["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], Ps_alg["cor04"], next(linecycler))
plt.ylabel(r'$P_{loss}$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость P_loss_alg от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_P_loss_alg_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], P1s["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], P1s["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], P1s["cor04"], next(linecycler))
plt.ylabel(r'$P_{loss}^{(1)}$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость P1_loss от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_P1_loss_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], P2s["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], P2s["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], P2s["cor04"], next(linecycler))
plt.ylabel(r'$P_{loss}^{(2)}$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость P2_loss от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_P2_loss_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], Ps_imp["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], Ps_imp["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], Ps_imp["cor04"], next(linecycler))
plt.ylabel(r'$P_{loss}^{(imp)}$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title('Зависимость P_loss_imp от λ при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_P_loss_imp_lambda_cor_0_02_04_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], w1s["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], w1s["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], w1s["cor04"], next(linecycler))
plt.ylabel(r'$\bar{w}_1$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title(
'Зависимость $bar{w}_1$ от $lambda$ при различных \n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_w1_lambda_cor_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], w2s["poisson"], next(linecycler))
plt.plot(lambdas["cor02"], w2s["cor02"], next(linecycler))
plt.plot(lambdas["cor04"], w2s["cor04"], next(linecycler))
plt.ylabel(r'$\bar{w}_2$')
plt.xlabel(r'$\lambda$')
plt.legend((r'$c_{cor}^{(1)}$ =' + str(abs(round(qs1.queries_stream.c_cor[0], 3))) + r', $c_{cor}^{(2)}$=' + str(
abs(round(qs1.queries_stream.c_cor[1], 3))),
r'$c_{cor}^{(1)}$ =' + str(round(qs2.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs2.queries_stream.c_cor[1], 3)),
r'$c_{cor}^{(1)}$ =' + str(round(qs3.queries_stream.c_cor[0], 3)) + r', $c_{cor}^{(2)}$=' + str(
round(qs3.queries_stream.c_cor[1], 3))
),
loc=0)
if PRINT_TITLE:
plt.title(
'Зависимость $bar{w}_2$ от $lambda$ при различных \n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/BMMAP5_w2_lambda_cor_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
table_poisson = np.asarray(table_poisson)
table_cor_02 = np.asarray(table_cor_02)
table_cor_04 = np.asarray(table_cor_04)
np.savetxt(str.format("tables/BMMAP5_P_losses_lambda_cor_poisson_{}.csv", nowdate), table_poisson,
header=r'$\lambda$, $P_{loss}^{(1)}$, $P_{loss}^{(2)}$, $P_{loss}, P_{loss}^{(imp)}$, $\bar{w}_1$, $\bar{w}_2$',
delimiter=',',
fmt='%.5f', comments='')
np.savetxt(str.format("tables/BMMAP5_P_losses_lambda_cor_02_{}.csv", nowdate), table_cor_02,
header=r'$\lambda$, $P_{loss}^{(1)}$, $P_{loss}^{(2)}$, $P_{loss}, P_{loss}^{(imp)}$, $\bar{w}_1$, $\bar{w}_2$',
delimiter=',',
fmt='%.5f', comments='')
np.savetxt(str.format("tables/BMMAP5_P_losses_lambda_cor_04_{}.csv", nowdate), table_cor_04,
header=r'$\lambda$, $P_{loss}^{(1)}$, $P_{loss}^{(2)}$, $P_{loss}, P_{loss}^{(imp)}$, $\bar{w}_1$, $\bar{w}_2$',
delimiter=',',
fmt='%.5f', comments='')
def main():
test_data_Poisson_initial = poisson.MmapPoissonPhPh()
test_data_Poisson = poisson.MmapPoissonPhPh()
test_data_02_initial = cor02.Mmap02PhPh()
test_data_02 = cor02.Mmap02PhPh()
test_data_04_initial = cor04.Mmap04PhPh()
test_data_04 = cor04.Mmap04PhPh()
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson_initial)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02_initial)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04_initial)
lambdas = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
Ls = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
Ps = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
Ps_alg = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
P1s = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
P2s = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
Ps_imp = {
"poisson": [],
"cor02": [],
"cor04": [],
"poisson_PH_poisson_NoRemoval": [],
"poisson_NoRemoval": []
}
for coef in np.linspace(0.01, 3, 100):
test_data_Poisson.test_matrD = test_data_Poisson_initial.test_matrD * coef
test_data_Poisson.test_matrD_0 = test_data_Poisson_initial.test_matrD_0 * coef
test_data_02.test_matrD = test_data_02_initial.test_matrD * coef
test_data_02.test_matrD_0 = test_data_02_initial.test_matrD_0 * coef
test_data_04.test_matrD = test_data_04_initial.test_matrD * coef
test_data_04.test_matrD_0 = test_data_04_initial.test_matrD_0 * coef
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson)
stationary_probas1 = qs1.calc_stationary_probas(False)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02)
stationary_probas2 = qs2.calc_stationary_probas(False)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04)
stationary_probas3 = qs3.calc_stationary_probas(False)
lambdas["poisson"].append(qs1.queries_stream.avg_intensity)
Ls["poisson"].append(
qs1.calc_avg_buffer_queries_num(stationary_probas1))
Ps["poisson"].append(qs1.calc_query_lost_p(stationary_probas1))
Ps_alg["poisson"].append(
qs1.calc_query_lost_ps_buffer_full(stationary_probas1)[1])
P1s["poisson"].append(
qs1.calc_query_lost_ps_buffer_full(stationary_probas1)[0][0])
P2s["poisson"].append(
qs1.calc_query_lost_ps_buffer_full(stationary_probas1)[0][1])
Ps_imp["poisson"].append(
qs1.calc_nonprior_query_lost_timer(stationary_probas1))
lambdas["cor02"].append(qs2.queries_stream.avg_intensity)
Ls["cor02"].append(qs2.calc_avg_buffer_queries_num(stationary_probas2))
Ps["cor02"].append(qs2.calc_query_lost_p(stationary_probas2))
Ps_alg["cor02"].append(
qs2.calc_query_lost_ps_buffer_full(stationary_probas2)[1])
P1s["cor02"].append(
qs2.calc_query_lost_ps_buffer_full(stationary_probas2)[0][0])
P2s["cor02"].append(
qs2.calc_query_lost_ps_buffer_full(stationary_probas2)[0][1])
Ps_imp["cor02"].append(
qs2.calc_nonprior_query_lost_timer(stationary_probas2))
lambdas["cor04"].append(qs3.queries_stream.avg_intensity)
Ls["cor04"].append(qs3.calc_avg_buffer_queries_num(stationary_probas3))
Ps["cor04"].append(qs3.calc_query_lost_p(stationary_probas3))
Ps_alg["cor04"].append(
qs3.calc_query_lost_ps_buffer_full(stationary_probas3)[1])
P1s["cor04"].append(
qs3.calc_query_lost_ps_buffer_full(stationary_probas3)[0][0])
P2s["cor04"].append(
qs3.calc_query_lost_ps_buffer_full(stationary_probas3)[0][1])
Ps_imp["cor04"].append(
qs3.calc_nonprior_query_lost_timer(stationary_probas3))
plt.plot(lambdas["poisson"], Ls["poisson"])
plt.plot(lambdas["cor02"], Ls["cor02"])
plt.plot(lambdas["cor04"], Ls["cor04"])
plt.ylabel('L')
plt.xlabel('λ')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость L от λ при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/MMAP_L_lambda_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], Ps["poisson"])
plt.plot(lambdas["cor02"], Ps["cor02"])
plt.plot(lambdas["cor04"], Ps["cor04"])
plt.ylabel('P_loss')
plt.xlabel('λ')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость P_loss от λ при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/MMAP_P_loss_lambda_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], Ps_alg["poisson"])
plt.plot(lambdas["cor02"], Ps_alg["cor02"])
plt.plot(lambdas["cor04"], Ps_alg["cor04"])
plt.ylabel('P_loss_alg')
plt.xlabel('λ')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость P_loss_alg от λ при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/MMAP_P_loss_alg_lambda_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], P1s["poisson"])
plt.plot(lambdas["cor02"], P1s["cor02"])
plt.plot(lambdas["cor04"], P1s["cor04"])
plt.ylabel('P1_loss')
plt.xlabel('λ')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость P1_loss от λ при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/MMAP_P1_loss_lambda_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], P2s["poisson"])
plt.plot(lambdas["cor02"], P2s["cor02"])
plt.plot(lambdas["cor04"], P2s["cor04"])
plt.ylabel('P2_loss')
plt.xlabel('λ')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость P2_loss от λ при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/MMAP_P2_loss_lambda_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(lambdas["poisson"], Ps_imp["poisson"])
plt.plot(lambdas["cor02"], Ps_imp["cor02"])
plt.plot(lambdas["cor04"], Ps_imp["cor04"])
plt.ylabel('P_loss_imp')
plt.xlabel('λ')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость P_loss_imp от λ при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/MMAP_P_loss_imp_lambda_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
def main():
nowdate = datetime.datetime.now()
if not os.path.exists("plots"):
os.makedirs("plots")
if not os.path.exists("tables"):
os.makedirs("tables")
lines = ["-k", "--k", "-.k"]
linecycler = cycle(lines)
PRINT_TITLE = False
test_data_less_mu_initial = cor02.Bmmap502PhPh(matrS_elem=8.)
test_data_less_mu = cor02.Bmmap502PhPh(matrS_elem=8.)
test_data_equal_mu_initial = cor02.Bmmap502PhPh(matrS_elem=16.)
test_data_equal_mu = cor02.Bmmap502PhPh(matrS_elem=16.)
test_data_larger_mu_initial = cor02.Bmmap502PhPh(matrS_elem=32.)
test_data_larger_mu = cor02.Bmmap502PhPh(matrS_elem=32.)
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_less_mu_initial)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_equal_mu_initial)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_larger_mu_initial)
W1s = {"less_mu": [], "equal_mu": [], "larger_mu": []}
W2s = {"less_mu": [], "equal_mu": [], "larger_mu": []}
table_less_mu = []
table_equal_mu = []
table_larger_mu = []
ts = []
for t in tqdm(np.linspace(0.01, 4., 40)):
test_data_less_mu.test_matrD = test_data_less_mu_initial.test_matrD
test_data_less_mu.test_matrD_0 = test_data_less_mu_initial.test_matrD_0
test_data_equal_mu.test_matrD = test_data_equal_mu_initial.test_matrD
test_data_equal_mu.test_matrD_0 = test_data_equal_mu_initial.test_matrD_0
test_data_larger_mu.test_matrD = test_data_larger_mu_initial.test_matrD
test_data_larger_mu.test_matrD_0 = test_data_larger_mu_initial.test_matrD_0
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_less_mu)
stationary_probas1 = qs1.calc_stationary_probas(False)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_equal_mu)
stationary_probas2 = qs2.calc_stationary_probas(False)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_larger_mu)
stationary_probas3 = qs3.calc_stationary_probas(False)
ts.append(t)
W1s["less_mu"].append(qs1.func_W_1(stationary_probas1, t))
W2s["less_mu"].append(qs1.func_W_2(stationary_probas1, t))
table_less_mu.append([t, W1s["less_mu"][-1], W2s["less_mu"][-1]])
W1s["equal_mu"].append(qs2.func_W_1(stationary_probas2, t))
W2s["equal_mu"].append(qs2.func_W_2(stationary_probas2, t))
table_equal_mu.append([t, W1s["equal_mu"][-1], W2s["equal_mu"][-1]])
W1s["larger_mu"].append(qs3.func_W_1(stationary_probas3, t))
W2s["larger_mu"].append(qs3.func_W_2(stationary_probas3, t))
table_larger_mu.append([t, W1s["larger_mu"][-1], W2s["larger_mu"][-1]])
plt.plot(ts, W1s["less_mu"], next(linecycler))
plt.plot(ts, W1s["equal_mu"], next(linecycler))
plt.plot(ts, W1s["larger_mu"], next(linecycler))
plt.ylabel(r'$W_1(t)$')
plt.xlabel(r'$t$')
plt.legend((
str.format("µ={0}", round(qs1.serv_stream.avg_intensity, 3)),
str.format("µ={0}", round(qs2.serv_stream.avg_intensity, 3)),
str.format("µ={0}", round(qs3.serv_stream.avg_intensity, 3)),
),
loc=0)
if PRINT_TITLE:
plt.title(
'Зависимость W_1 от t при различных\n средних интенсивностях обслуживания'
)
plt.savefig(str.format("plots/BMMAP5_W_1_t_mu_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
plt.plot(ts, W2s["less_mu"], next(linecycler))
plt.plot(ts, W2s["equal_mu"], next(linecycler))
plt.plot(ts, W2s["larger_mu"], next(linecycler))
plt.ylabel(r'$W_2(t)$')
plt.xlabel(r'$t$')
plt.legend((
str.format("µ={0}", round(qs1.serv_stream.avg_intensity, 3)),
str.format("µ={0}", round(qs2.serv_stream.avg_intensity, 3)),
str.format("µ={0}", round(qs3.serv_stream.avg_intensity, 3)),
),
loc=0)
if PRINT_TITLE:
plt.title(
'Зависимость W_2 от t при различных\n средних интенсивностях обслуживания'
)
plt.savefig(str.format("plots/BMMAP5_W_2_t_mu_{}.png", nowdate),
dpi=400,
bbox_inches='tight')
plt.close()
f = open(str.format("tables/tables_{}.txt", nowdate), "w+")
print(str.format("µ={0}", round(qs1.serv_stream.avg_intensity, 3)), file=f)
print(table_less_mu, file=f)
print(str.format("µ={0}", round(qs2.serv_stream.avg_intensity, 3)), file=f)
print(table_equal_mu, file=f)
print(str.format("µ={0}", round(qs3.serv_stream.avg_intensity, 3)), file=f)
print(table_larger_mu, file=f)
f.close()
table_less_mu = np.asarray(table_less_mu)
table_equal_mu = np.asarray(table_equal_mu)
table_larger_mu = np.asarray(table_larger_mu)
np.savetxt(str.format("tables/BMMAP5_W_t_mu_4_{}.csv", nowdate),
table_less_mu,
header='t, $W_1(t)$, $W_2(t)$',
delimiter=',',
fmt='%.5f',
comments='')
np.savetxt(str.format("tables/BMMAP5_W_t_mu_8_{}.csv", nowdate),
table_equal_mu,
header='t, $W_1(t)$, $W_2(t)$',
delimiter=',',
fmt='%.5f',
comments='')
np.savetxt(str.format("tables/BMMAP5_W_t_mu_16_{}.csv", nowdate),
table_larger_mu,
header='t, $W_1(t)$, $W_2(t)$',
delimiter=',',
fmt='%.5f',
comments='')
import debugging.MMAP_cor04 as cor04
import two_priorities_qs as qs4
if __name__ == '__main__':
test_data = cor04.Mmap04PhPh()
# test_data = cor02.Mmap02PhPh()
# test_data = cor0.MmapPoissonPh()
qs1 = qs4.TwoPrioritiesQueueingSystem(test_data, verbose=True)
qs1.calc_characteristics(True)
import debugging.MMAP_Poisson_PH_Poisson_NoRemoval as poisson_PH_noremoval
import two_priorities_qs as qs
if __name__ == '__main__':
test_data_Poisson_PH_noRemoval = poisson_PH_noremoval.MmapPoissonPhPoissonNoRemoval(
)
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson_PH_noRemoval,
verbose=True)
qs1.calc_characteristics(True)
# test_data_Poisson_noRemoval = poisson_noremoval.MmapPoissonNoRemoval()
#
# print("\nPoisson system:\n")
#
# qs2 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson_noRemoval, verbose=True)
#
# qs2.calc_characteristics(True)
def main():
test_data_Poisson_initial = poisson.Bmmap3PoissonPhPh()
test_data_Poisson = poisson.Bmmap3PoissonPhPh()
test_data_02_initial = cor02.Bmmap302PhPh()
test_data_02 = cor02.Bmmap302PhPh()
test_data_04_initial = cor04.Bmmap304PhPh()
test_data_04 = cor04.Bmmap304PhPh()
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson_initial)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02_initial)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04_initial)
W1s = {"poisson": [], "cor02": [], "cor04": []}
W2s = {"poisson": [], "cor02": [], "cor04": []}
coef = 0.25 # as we have \lambda = 32 and want it to be 8
ts = []
for t in np.linspace(0.0001, 1., 200):
test_data_Poisson.test_matrD = test_data_Poisson_initial.test_matrD * coef
test_data_Poisson.test_matrD_0 = test_data_Poisson_initial.test_matrD_0 * coef
test_data_02.test_matrD = test_data_02_initial.test_matrD * coef
test_data_02.test_matrD_0 = test_data_02_initial.test_matrD_0 * coef
test_data_04.test_matrD = test_data_04_initial.test_matrD * coef
test_data_04.test_matrD_0 = test_data_04_initial.test_matrD_0 * coef
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson)
stationary_probas1 = qs1.calc_stationary_probas(False)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02)
stationary_probas2 = qs2.calc_stationary_probas(False)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04)
stationary_probas3 = qs3.calc_stationary_probas(False)
ts.append(t)
W1s["poisson"].append(qs1.func_W_1(stationary_probas1, t))
W2s["poisson"].append(qs1.func_W_2(stationary_probas1, t))
W1s["cor02"].append(qs2.func_W_1(stationary_probas2, t))
W2s["cor02"].append(qs2.func_W_2(stationary_probas2, t))
W1s["cor04"].append(qs3.func_W_1(stationary_probas3, t))
W2s["cor04"].append(qs3.func_W_2(stationary_probas3, t))
plt.plot(ts, W1s["poisson"])
plt.plot(ts, W1s["cor02"])
plt.plot(ts, W1s["cor04"])
plt.ylabel('W_1')
plt.xlabel('t')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость W_1 от t при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/BMMAP3_W_1_t_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(ts, W2s["poisson"])
plt.plot(ts, W2s["cor02"])
plt.plot(ts, W2s["cor04"])
plt.ylabel('W_2')
plt.xlabel('t')
plt.legend((
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title(
'Зависимость W_2 от t при различных\n коэффициентах корреляции длин двух соседних интервалов'
)
plt.savefig(str.format("plots/BMMAP3_W_2_t_cor_0_02_04_{}.png",
datetime.datetime.now()),
bbox_inches='tight')
plt.close()
def main():
test_data_Poisson_initial = poisson.MmapPoissonPhPh()
test_data_Poisson = poisson.MmapPoissonPhPh()
test_data_02_initial = cor02.Mmap02PhPh()
test_data_02 = cor02.Mmap02PhPh()
test_data_04_initial = cor04.Mmap04PhPh()
test_data_04 = cor04.Mmap04PhPh()
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson_initial)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02_initial)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04_initial)
Ns = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
Ls = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
Ps = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
p_0s = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
p_1s = {"poisson": [], "cor02": [], "cor04": [], "poisson_PH_poisson_NoRemoval": [], "poisson_NoRemoval": []}
for newN in range(1, 25):
test_data_Poisson.test_N = newN
test_data_02.test_N = newN
test_data_04.test_N = newN
qs1 = qs.TwoPrioritiesQueueingSystem(test_data_Poisson)
stationary_probas1 = qs1.calc_stationary_probas(False)
qs2 = qs.TwoPrioritiesQueueingSystem(test_data_02)
stationary_probas2 = qs2.calc_stationary_probas(False)
qs3 = qs.TwoPrioritiesQueueingSystem(test_data_04)
stationary_probas3 = qs3.calc_stationary_probas(False)
Ns["poisson"].append(newN)
Ls["poisson"].append(qs1.calc_avg_buffer_queries_num(stationary_probas1))
Ps["poisson"].append(qs1.calc_query_lost_p(stationary_probas1))
p_0s["poisson"].append(qs1.calc_system_empty_proba(stationary_probas1))
p_1s["poisson"].append(qs1.calc_system_single_query_proba(stationary_probas1))
Ns["cor02"].append(newN)
Ls["cor02"].append(qs2.calc_avg_buffer_queries_num(stationary_probas2))
Ps["cor02"].append(qs2.calc_query_lost_p(stationary_probas2))
p_0s["cor02"].append(qs2.calc_system_empty_proba(stationary_probas2))
p_1s["cor02"].append(qs2.calc_system_single_query_proba(stationary_probas2))
Ns["cor04"].append(newN)
Ls["cor04"].append(qs3.calc_avg_buffer_queries_num(stationary_probas3))
Ps["cor04"].append(qs3.calc_query_lost_p(stationary_probas3))
p_0s["cor04"].append(qs3.calc_system_empty_proba(stationary_probas3))
p_1s["cor04"].append(qs3.calc_system_single_query_proba(stationary_probas3))
plt.plot(Ns["poisson"], Ls["poisson"])
plt.plot(Ns["cor02"], Ls["cor02"])
plt.plot(Ns["cor04"], Ls["cor04"])
plt.ylabel('L')
plt.xlabel('N')
plt.legend((str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title('Зависимость L от N при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/MMAP_L_N_cor_0_02_04_NoRemoval_{}.png", datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(Ns["poisson"], Ps["poisson"])
plt.plot(Ns["cor02"], Ps["cor02"])
plt.plot(Ns["cor04"], Ps["cor04"])
plt.ylabel('P_loss')
plt.xlabel('N')
plt.legend((str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title('Зависимость P_loss от N при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/MMAP_P_loss_N_cor_0_02_04_NoRemoval_{}.png", datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(Ns["poisson"], p_0s["poisson"])
plt.plot(Ns["cor02"], p_0s["cor02"])
plt.plot(Ns["cor04"], p_0s["cor04"])
plt.ylabel('p_0')
plt.xlabel('N')
plt.legend((str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title('Зависимость p_0 от N при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/MMAP_p_0_N_cor_0_02_04_NoRemoval_{}.png", datetime.datetime.now()),
bbox_inches='tight')
plt.close()
plt.plot(Ns["poisson"], p_1s["poisson"])
plt.plot(Ns["cor02"], p_1s["cor02"])
plt.plot(Ns["cor04"], p_1s["cor04"])
plt.ylabel('p_1')
plt.xlabel('N')
plt.legend((str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs1.queries_stream.c_cor[0], 3),
round(qs1.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs2.queries_stream.c_cor[0], 3),
round(qs2.queries_stream.c_cor[1], 3)),
str.format("c_cor^(1)={0}, c_cor^(2)={1}",
round(qs3.queries_stream.c_cor[0], 3),
round(qs3.queries_stream.c_cor[1], 3)),
),
loc=0)
plt.title('Зависимость p_1 от N при различных\n коэффициентах корреляции длин двух соседних интервалов')
plt.savefig(str.format("plots/MMAP_p_1_N_cor_0_02_04_NoRemoval_{}.png", datetime.datetime.now()),
bbox_inches='tight')
plt.close()
