# In[5]:
'''Binomial randomness --- Binomial distribution'''
total_event = int(100)
gInput = np.arange(total_event)
output_level = total_event
probability_peak = 0.5
randomSeed = np.random.binomial(output_level, probability_peak, total_event)
sumP = 0
for i in range(total_event):
sumP = sumP + randomSeed[i]
meanP = sumP / (total_event)
totalLevel = int(total_event / 1)
category = alva.AlvaLevel(randomSeed, totalLevel, False)
gLevel = category[0]
numberLevel = category[1]
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)
plt.savefig(save_figure, dpi = 300)
plt.show()
# In[2]:
'''Logistic randomness --- Logistic distribution'''
totalPoint_Input = int(100 + 1)
gInput = np.arange(totalPoint_Input)
meanL = totalPoint_Input/2
randomSeed_normal = np.random.standard_normal(totalPoint_Input)
randomSeed = np.random.logistic(0, 3, totalPoint_Input)
totalLevel = int(totalPoint_Input/1)
category = alva.AlvaLevel(randomSeed, totalLevel, False)
gLevel = category[0]
numberLevel = category[1]
print category[2].shape
# calculating the mean
sumL = 0
for i in range(totalPoint_Input):
sumL = sumL + randomSeed[i]
current_mean = sumL/(totalPoint_Input)
print ('current mean', current_mean)
totalLevel = int(totalPoint_Input/1)
category_normal = alva.AlvaLevel(randomSeed_normal, totalLevel, False)
gLevel_normal = category_normal[0]
numberLevel_normal = category_normal[1]