binomial_D = total_event * AlvaBinomialD(np.arange(totalLevel), totalLevel,
probability_peak)
# plotting
figure_name = ''
file_suffix = '.png'
save_figure = os.path.join(saving_dir_path,
file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
figure = plt.figure(numberingFig, figsize=AlvaFigSize)
plot1 = figure.add_subplot(1, 2, 1)
plot1.plot(gInput, randomSeed, color='gray', marker='o', label='data')
plot1.plot(gInput,
alva.AlvaMinMax(randomSeed),
color='red',
marker='o',
label='minMaxSorting')
if total_event < 100:
plot1.set_xticks(gInput, minor=True)
plot1.set_yticks(randomSeed, minor=True)
plot1.grid(True, which='minor')
else:
plot1.grid(True, which='major')
plt.title(r'$ Binomial \ randomness \ (mean = {:1.6f}) $'.format(meanP),
fontsize=AlvaFontSize)
plt.xlabel(r'$ event-input $', fontsize=AlvaFontSize)
plt.ylabel(r'$ output $', fontsize=AlvaFontSize)
plt.xticks(fontsize=AlvaFontSize * 0.6)
plt.yticks(fontsize=AlvaFontSize * 0.6)
sumP = 0
for i in range(total_event):
sumP = sumP + randomSeed[i]
meanP = sumP/(total_event)
sumP = 0
for i in range(total_event):
sumP = sumP + (meanP - randomSeed[i])**2
deviationP = (sumP/total_event)**(1.0/2)
totalLevel = int(total_event/10)
category = alva.AlvaLevel(randomSeed, totalLevel, False)
gLevel = category[0]
numberLevel = category[1]
maxEvent_per_level = alva.AlvaMinMax(numberLevel)[-1]
print ('max-events/level = {:}'.format(maxEvent_per_level))
gaussian_D = maxEvent_per_level * gaussianPMF(len(gLevel), meanP, deviationP)[1]
# plotting
figure_name = ''
file_suffix = '.png'
save_figure = os.path.join(saving_dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
figure = plt.figure(numberingFig, figsize = AlvaFigSize)
plot1 = figure.add_subplot(1, 2, 1)
plot1.plot(gInput, randomSeed, color = 'gray', marker = 'o', label = 'data')
plot1.plot(gInput, alva.AlvaMinMax(randomSeed), color = 'red', marker = 'o', label = 'minMaxListing')
if total_event < 100:
plot1.set_xticks(gInput, minor = True)
category = alva.AlvaLevel(randomSeed, totalLevel, False)
gLevel = category[0]
numberLevel = category[1]
print category[2].shape
# plotting
figure_name = ''
file_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
figure = plt.figure(numberingFig, figsize = AlvaFigSize)
plot1 = figure.add_subplot(1, 2, 1)
plot1.plot(gInput, randomSeed, color = 'gray', marker = 'o', label = 'data')
plot1.plot(gInput, alva.AlvaMinMax(randomSeed), color = 'red', marker = 'o', label = 'minMaxListing')
plot1.plot(gInput, alva.AlvaMinMax(randomSeed_normal), label = 'minMax_normal')
if totalPoint_Input < 100:
plot1.set_xticks(gInput, minor = True)
plot1.set_yticks(randomSeed, minor = True)
plot1.grid(True, which = 'minor')
else:
plot1.grid(True, which = 'major')
plt.title(r'$ Logistic (total-input = %i,\ mean = %f) $'%(totalPoint_Input, meanL)
, fontsize = AlvaFontSize)
plt.xlabel(r'$ input-time $', fontsize = AlvaFontSize)
plt.ylabel(r'$ output $', fontsize = AlvaFontSize)
plt.legend(loc = (0, -0.2))
plot2 = figure.add_subplot(1, 2, 2)
plot2.plot(numberLevel_normal, gLevel_normal, label = 'category_normal')
gLevel = category[0]
numberLevel = category[1]
gInput = np.arange(total_event)
# plotting
figure_name = '-random_seed_base{:}'.format(aMD.base)
file_suffix = '.png'
save_figure = os.path.join(saving_dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
figure = plt.figure(numberingFig, figsize = AlvaFigSize)
plot1 = figure.add_subplot(1, 2, 1)
plot1.plot(gInput, randomSeed, color = 'gray', marker = 'o', label = 'data')
plot1.plot(gInput, alva.AlvaMinMax(randomSeed), color = 'red', marker = 'o', label = 'minMaxSorting')
plot1.grid(True)
plt.title(r'$ Multinomial \ randomness \ (base-b = {:}) $'.format(aMD.base), fontsize = AlvaFontSize)
plt.xlabel(r'$ event-input $', fontsize = AlvaFontSize)
plt.ylabel(r'$ output $', fontsize = AlvaFontSize)
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.legend(loc = (0, -0.2))
plot2 = figure.add_subplot(1, 2, 2)
plot2.plot(numberLevel, gLevel, color = 'red', marker = 'o', label = 'category')
plot2.grid(True)
plt.title(r'$ Multinomial \ distribution \ (events = {ev:},\ levels = {le:}) $'.format(ev = total_event, le = totalLevel)
, fontsize = AlvaFontSize)
plt.xlabel(r'$ event/level $', fontsize = AlvaFontSize)
