distribution = class=Normal name=Normal dimension=4 mean=class=Point name=Unnamed dimension=4 values=[1,1,1,1] sigma=class=Point name=Unnamed dimension=4 values=[2,2,2,2] correlationMatrix=class=CorrelationMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[1,0.25,0.25,0.25,0.25,1,0.5,0.5,0.25,0.5,1,0.75,0.25,0.5,0.75,1]
X=class=RandomVector implementation=class=UsualRandomVector distribution=class=Normal name=Normal dimension=4 mean=class=Point name=Unnamed dimension=4 values=[1,1,1,1] sigma=class=Point name=Unnamed dimension=4 values=[2,2,2,2] correlationMatrix=class=CorrelationMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[1,0.25,0.25,0.25,0.25,1,0.5,0.5,0.25,0.5,1,0.75,0.25,0.5,0.75,1]
is composite? false
X dimension=4
X realization (first )=class=Point name=Unnamed dimension=4 values=[2.2164,-1.14783,-0.590751,1.21787]
X realization (second)=class=Point name=Unnamed dimension=4 values=[-3.36277,0.587161,-0.382381,1.67616]
X realization (third )=class=Point name=Unnamed dimension=4 values=[2.62134,2.94127,1.31624,1.92717]
X sample =class=Sample name=Normal implementation=class=SampleImplementation name=Normal size=5 dimension=4 description=[X0,X1,X2,X3] data=[[-3.58012,-2.62933,-3.55105,-2.90207],[2.99159,1.22785,0.412411,1.31332],[1.64585,2.02472,-0.213594,-0.710983],[1.94723,0.993783,1.72527,3.81586],[1.14041,-0.478006,-0.906783,-0.796208]]
Y=class=RandomVector implementation=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x1,x2,x3,x4,y1,y2] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y1,y2] formulas=[(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1),cos(x2*x2+x4)/(x1*x1+1+x3^4)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y1,y2] formulas=[(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1),cos(x2*x2+x4)/(x1*x1+1+x3^4)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y1,y2] formulas=[(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1),cos(x2*x2+x4)/(x1*x1+1+x3^4)] antecedent=class=UsualRandomVector distribution=class=Normal name=Normal dimension=4 mean=class=Point name=Unnamed dimension=4 values=[1,1,1,1] sigma=class=Point name=Unnamed dimension=4 values=[2,2,2,2] correlationMatrix=class=CorrelationMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[1,0.25,0.25,0.25,0.25,1,0.5,0.5,0.25,0.5,1,0.75,0.25,0.5,0.75,1]
is composite? true
Y dimension=2
Y realization (first )=class=Point name=Unnamed dimension=2 values=[0.0356538,-0.00835553]
Y realization (second)=class=Point name=Unnamed dimension=2 values=[0.282949,-0.020219]
Y realization (third )=class=Point name=Unnamed dimension=2 values=[2.80213,0.196401]
Y sample =class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=5 dimension=2 description=[y1,y2] data=[[0.428415,-0.0602613],[0.786432,-0.00339685],[-1.74309,-0.0198032],[2.35963,0.0128567],[0.980094,-0.02506]]
