Testing class CompositeDistribution
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=CompositeDistribution name=CompositeDistribution function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] antecedent=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] bounds=class=Point name=Unnamed dimension=3 values=[-7.65063,-0.739085,7.65063] values=class=Point name=Unnamed dimension=3 values=[56.5733,-0.800977,60.4909] probabilities=class=Point name=Unnamed dimension=3 values=[1e-14,0.229928,1] increasing=[0,1]
streamObject(const T & anObject)
class=CompositeDistribution name=CompositeDistribution function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] antecedent=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] bounds=class=Point name=Unnamed dimension=3 values=[-7.65063,-0.739085,7.65063] values=class=Point name=Unnamed dimension=3 values=[56.5733,-0.800977,60.4909] probabilities=class=Point name=Unnamed dimension=3 values=[1e-14,0.229928,1] increasing=[0,1]
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=CompositeDistribution name=CompositeDistribution function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] antecedent=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] bounds=class=Point name=Unnamed dimension=3 values=[-7.65063,-0.739085,7.65063] values=class=Point name=Unnamed dimension=3 values=[56.5733,-0.800977,60.4909] probabilities=class=Point name=Unnamed dimension=3 values=[1e-14,0.229928,1] increasing=[0,1]
Distribution CompositeDistribution=f(Normal(mu = 0, sigma = 1)) with f=[x]->[x^2 + 2 * sin(x)]
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[1.51269]
oneSample first=class=Point name=Unnamed dimension=1 values=[-0.304726] last=class=Point name=Unnamed dimension=1 values=[4.32165]
mean=class=Point name=Unnamed dimension=1 values=[0.981731]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[3.68811]
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=[-0.0620066]
log pdf=-1.82877
pdf     =0.160612
pdf (FD)=0.160612
cdf=0.621614
ccdf=0.378386
survival=0.378386
Inverse survival=class=Point name=Unnamed dimension=1 values=[-0.78963]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=1 values=[4.91251]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=1 values=[-0.78963]
cdf (tail)=0.95
characteristic function=(0.421744,0.00166811)
log characteristic function=(-0.863349,0.00395524)
pdf gradient     =class=Point name=Unnamed dimension=2 values=[0.0164482,-0.0648158]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[-0.274716,-0.309724]
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.800977] upper bound=class=Point name=Unnamed dimension=1 values=[4.91251] 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(CompositeDistribution=f(Normal(mu = 0, sigma = 1)) with f=[x]->[x^2 + 2 * sin(x)]) gradientImplementation=MinimumVolumeLevelSetGradient(CompositeDistribution=f(Normal(mu = 0, sigma = 1)) with f=[x]->[x^2 + 2 * sin(x)]) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(CompositeDistribution=f(Normal(mu = 0, sigma = 1)) with f=[x]->[x^2 + 2 * sin(x)]) level=3.43805
beta=0.0321272
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.79814] upper bound=class=Point name=Unnamed dimension=1 values=[5.94149] 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=[-0.800977] upper bound=class=Point name=Unnamed dimension=1 values=[4.91251] 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=[-0.78963] upper bound=class=Point name=Unnamed dimension=1 values=[60.4909] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[1]
standard deviation=class=Point name=Unnamed dimension=1 values=[1.93115]
skewness=class=Point name=Unnamed dimension=1 values=[1.56182]
kurtosis=class=Point name=Unnamed dimension=1 values=[6.15981]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[3.72933]
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=[[mean_0 : 0, standard_deviation_0 : 1]]
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]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[4.72933]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[23.436]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[154.038]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[1261.42]
Standard representative=CompositeDistribution=f(Normal(mu = 0, sigma = 1)) with f=[x]->[x^2 + 2 * sin(x)]
antecedent=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)]
newDistribution=class=CompositeDistribution name=CompositeDistribution function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[x^2 + 2 * sin(x)] antecedent=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] bounds=class=Point name=Unnamed dimension=3 values=[-7.65063,-0.739085,7.65063] values=class=Point name=Unnamed dimension=3 values=[56.5733,-0.800977,60.4909] probabilities=class=Point name=Unnamed dimension=3 values=[1e-14,0.229928,1] increasing=[0,1]
