Testing class Gamma
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=Gamma name=Gamma dimension=1 k=1.5 lambda=2.5 gamma=-0.5
streamObject(const T & anObject)
class=Gamma name=Gamma dimension=1 k=1.5 lambda=2.5 gamma=-0.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=Gamma name=Gamma dimension=1 k=1.5 lambda=2.5 gamma=-0.5
Distribution Gamma(k = 1.5, lambda = 2.5, gamma = -0.5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.281847]
oneSample first=class=Point name=Unnamed dimension=1 values=[-0.263977] last=class=Point name=Unnamed dimension=1 values=[-0.375541]
mean=class=Point name=Unnamed dimension=1 values=[0.101621]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.247946]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf     =class=Point name=Unnamed dimension=1 values=[-0.278355]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.278355]
log pdf=-2.05205
pdf     =0.128471
pdf (FD)=0.128471
cdf=0.942442
ccdf=0.0575585
survival=0.0575585
Inverse survival=class=Point name=Unnamed dimension=1 values=[-0.429631]
Survival(inverse survival)=0.95
characteristic function=(0.892418,0.0632527)
log characteristic function=(-0.111315,0.0707596)
pdf gradient     =class=Point name=Unnamed dimension=3 values=[0.16512,-0.115624,0.278355]
pdf gradient (FD)=class=Point name=Unnamed dimension=3 values=[0.16512,-0.115624,0.278355]
cdf gradient     =class=Point name=Unnamed dimension=3 values=[-0.0875625,0.0770828,-0.128471]
cdf gradient (FD)=class=Point name=Unnamed dimension=3 values=[-0.0875625,0.0770828,-0.128471]
quantile=class=Point name=Unnamed dimension=1 values=[1.06295]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.499368] upper bound=class=Point name=Unnamed dimension=1 values=[1.06337] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(Gamma(k = 1.5, lambda = 2.5, gamma = -0.5)) gradientImplementation=MinimumVolumeLevelSetGradient(Gamma(k = 1.5, lambda = 2.5, gamma = -0.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(Gamma(k = 1.5, lambda = 2.5, gamma = -0.5)) level=2.18978
beta=0.111942
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.456841] upper bound=class=Point name=Unnamed dimension=1 values=[1.36968] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.5] upper bound=class=Point name=Unnamed dimension=1 values=[1.06295] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.429631] upper bound=class=Point name=Unnamed dimension=1 values=[13.1546] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[0.1]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.24]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
parameters=[[k : 1.5, lambda : 2.5, gamma : -0.5]]
standard moment n=0, value=class=Point name=Unnamed dimension=1 values=[1]
standard moment n=1, value=class=Point name=Unnamed dimension=1 values=[1.5]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[3.75]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[13.125]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[59.0625]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[324.844]
Standard representative=Gamma(k = 1.5, lambda = 1, gamma = 0)
standard deviation=class=Point name=Unnamed dimension=1 values=[0.489898]
skewness=class=Point name=Unnamed dimension=1 values=[1.63299]
kurtosis=class=Point name=Unnamed dimension=1 values=[7]
Distribution class=Gamma name=Gamma dimension=1 k=15 lambda=2.5 gamma=0
Distribution Gamma(k = 15, lambda = 2.5, gamma = 0)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[6.03324]
oneSample first=class=Point name=Unnamed dimension=1 values=[4.73401] last=class=Point name=Unnamed dimension=1 values=[3.93099]
mean=class=Point name=Unnamed dimension=1 values=[6.02305]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[2.44857]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf     =class=Point name=Unnamed dimension=1 values=[1.00845e-05]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[1.00845e-05]
log pdf=-13.9469
pdf     =8.76911e-07
pdf (FD)=8.76911e-07
cdf=6.91531e-08
ccdf=1
survival=1
Inverse survival=class=Point name=Unnamed dimension=1 values=[3.69853]
Survival(inverse survival)=0.95
characteristic function=(0.275588,-0.178824)
log characteristic function=(-1.11315,5.7076)
pdf gradient     =class=Point name=Unnamed dimension=3 values=[-1.54166e-06,4.38456e-06,-1.00845e-05]
pdf gradient (FD)=class=Point name=Unnamed dimension=3 values=[-1.54166e-06,4.38456e-06,-1.00845e-05]
cdf gradient     =class=Point name=Unnamed dimension=3 values=[-1.26959e-07,3.50764e-07,-8.76911e-07]
cdf gradient (FD)=class=Point name=Unnamed dimension=3 values=[-1.26959e-07,3.50764e-07,-8.76911e-07]
quantile=class=Point name=Unnamed dimension=1 values=[8.75459]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[3.1431] upper bound=class=Point name=Unnamed dimension=1 values=[9.09027] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(Gamma(k = 15, lambda = 2.5, gamma = 0)) gradientImplementation=MinimumVolumeLevelSetGradient(Gamma(k = 15, lambda = 2.5, gamma = 0)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(Gamma(k = 15, lambda = 2.5, gamma = 0)) level=3.27167
beta=0.037943
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[3.35815] upper bound=class=Point name=Unnamed dimension=1 values=[9.39585] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0] upper bound=class=Point name=Unnamed dimension=1 values=[8.75459] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[3.69853] upper bound=class=Point name=Unnamed dimension=1 values=[26.3665] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[6]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[2.4]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
parameters=[[k : 15, lambda : 2.5, gamma : 0]]
standard moment n=0, value=class=Point name=Unnamed dimension=1 values=[1]
standard moment n=1, value=class=Point name=Unnamed dimension=1 values=[15]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[240]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[4080]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[73440]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[1.39536e+06]
Standard representative=Gamma(k = 15, lambda = 1, gamma = 0)
standard deviation=class=Point name=Unnamed dimension=1 values=[1.54919]
skewness=class=Point name=Unnamed dimension=1 values=[0.516398]
kurtosis=class=Point name=Unnamed dimension=1 values=[3.4]
