Testing class VonMises
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=VonMises name=VonMises dimension=1 mu=-0.5 kappa=1.5
streamObject(const T & anObject)
class=VonMises name=VonMises dimension=1 mu=-0.5 kappa=1.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=VonMises name=VonMises dimension=1 mu=-0.5 kappa=1.5
Distribution VonMises(mu = -0.5, kappa=1.5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.492446]
oneSample first=class=Point name=Unnamed dimension=1 values=[-0.494608] last=class=Point name=Unnamed dimension=1 values=[0.388969]
mean=class=Point name=Unnamed dimension=1 values=[-0.474891]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.986948]
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=[-0.160799]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.160799]
log pdf=-2.23056
pdf     =0.107468
pdf (FD)=0.107468
cdf=0.915928
ccdf=0.0840718
survival=0.0840718
Inverse survival=class=Point name=Unnamed dimension=1 values=[-2.1122]
Survival(inverse survival)=0.95
characteristic function=(0.523156,-0.285801)
log characteristic function=(-0.517291,-0.5)
pdf gradient     =class=Point name=Unnamed dimension=2 values=[0.160799,-0.0564635]
pdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[0.160799,-0.0564635]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[-0.0815561,0.0925495]
cdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[-0.0815561,0.0925495]
quantile=class=Point name=Unnamed dimension=1 values=[1.42908]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-2.71399] upper bound=class=Point name=Unnamed dimension=1 values=[1.71399] 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(VonMises(mu = -0.5, kappa=1.5)) gradientImplementation=MinimumVolumeLevelSetGradient(VonMises(mu = -0.5, kappa=1.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(VonMises(mu = -0.5, kappa=1.5)) level=3.2363
beta=0.0393091
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-2.46547] upper bound=class=Point name=Unnamed dimension=1 values=[2.0656] 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=[-3.14159] upper bound=class=Point name=Unnamed dimension=1 values=[1.42908] 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=[-2.1122] upper bound=class=Point name=Unnamed dimension=1 values=[3.14159] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[-0.427827]
standard deviation=class=Point name=Unnamed dimension=1 values=[1.05959]
skewness=class=Point name=Unnamed dimension=1 values=[0.410037]
kurtosis=class=Point name=Unnamed dimension=1 values=[3.70254]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.12273]
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=[[mu : -0.5, kappa : 1.5]]
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=[-0.427827]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[1.30577]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[-1.03152]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[5.0989]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[-4.03982]
Standard representative=VonMises(mu = -0.5, kappa=1.5)
Circular mean=-0.5
Circular variance=0.403867
