# 需要导入模块: from apgl.util.Util import Util [as 别名]# 或者: from apgl.util.Util.Util importfitPowerLaw[as 别名]deftestFitPowerLaw(self):alpha =2.7xmin =1.0exponent = (1/(alpha)) numPoints =15000x = numpy.random.rand(numPoints)**-exponent x = x[x>=1] alpha2 = Util.fitPo...
y = 12.56 * x ** 0.25 + np.random.normal(0, 2, 100) # Defining fitting function for curve_fit def power_fit(x,a,b): return a * x ** b # Calling the curve_fit function params, covariance = curve_fit(f = power_fit, xdata = x, ydata = y) print('a is ', params[0])...
import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # Define the power law function def power_law(x, factor, exponent): ''' x: x axis data factor: y axis intersection exponent: slope ''' return factor * x ** exponent # Define ...
import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # generate some data with noise xData = np.linspace(0.01, 100., 50) aOrg = 0.08 Norg = 10.5 yData = Norg * xData ** (-aOrg) + np.random.normal(0, 0.5, len(xData)) # get logarithmic data...
Goodness-of-fit tests for the power-law process based on the TTT-plot The total-time-on-test plot (TTT-plot) and its theoretical counterpart, the scaled TTT-transform, are reviewed. It is shown how the TTT-plot can be used as... B Klefsjo,U Kumar - 《IEEE Transactions on Reliability...
Power law fit of conspiracy users attention patterns.Alessandro BessiFabiana ZolloMichela Del VicarioAntonio ScalaGuido CaldarelliWalter Quattrociocchi
Gaudoin. "U-plot for testing the goodness-of-fit of the power-law process," Communications in Statistics: Theory and Methods, vol. 28, pp. 1731-1747, 1999.Cretois E, Aroui MAE, Gaudoin O. U-plot for testing the goodness-of-fit of the power-law process. Communications in Statistics: ...
logfit(X,Y), will search through all the possible axis scalings and finish with the one that incurs the least error (with error measured as least squares on the linear-linear data.) Notes: A power law relationship [slope, intercept] = logfit(x,y,'loglog'); ...
This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data. Additionally, a goodness-of-fit based approach is used to estimate the lower cutoff for the scaling region. - csgillesp
Power-law distributionComplexityGoodness of fitEDF-based testsAnderson–DarlingAsymptotic distributionsType II censoringMaximum likelihood estimationa b s t r a c tMaximumlikelihoodestimationandatestoffitbasedontheAnderson–Darlingstatisticarepresented for the case of the power-law distribution when the ...