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)