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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=[1,0.5,0,0,0.5,1,0.5,0,0,0.5,1,0.5,0,0,0.5,1]
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=[1,0.5,0,0,0.5,1,0.5,0,0,0.5,1,0.5,0,0,0.5,1]
is composite? False
X dimension= 4
X realization (first )= class=Point name=Unnamed dimension=4 values=[2.2164,-0.584874,-1.17774,2.36926]
X realization (second)= class=Point name=Unnamed dimension=4 values=[-3.36277,-0.575095,0.82447,2.8377]
X realization (third )= class=Point name=Unnamed dimension=4 values=[2.62134,3.18445,1.14749,0.836432]
X sample = class=Sample name=Normal implementation=class=SampleImplementation name=Normal size=5 dimension=4 description=[X0,X1,X2,X3] data=[[-3.58012,-3.51208,-2.62348,-0.750139],[2.99159,1.75425,-0.0758382,1.01827],[1.64585,2.09505,-0.180423,-1.62596],[1.94723,1.25625,1.42895,4.24855],[1.14041,-0.283159,-1.0805,-0.265102]]
Y= class=RandomVector implementation=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x1,x2,x3,x4,y0,y1] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y0,y1] 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=[y0,y1] 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=[y0,y1] 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=[1,0.5,0,0,0.5,1,0.5,0,0,0.5,1,0.5,0,0,0.5,1]
is composite? True
Y dimension= 2
Y realization (first )= class=Point name=Unnamed dimension=2 values=[0.0185054,-0.00219281]
Y realization (second)= class=Point name=Unnamed dimension=2 values=[0.619855,0.0106974]
Y realization (third )= class=Point name=Unnamed dimension=2 values=[2.8838,0.219604]
Y sample = class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=5 dimension=2 description=[y0,y1] data=[[1.50051,0.0377816],[0.834465,0.0103864],[-0.048251,0.0320782],[7.45941,-0.0328878],[0.53899,-0.0490936]]
Y parameter = class=Point name=Unnamed dimension=14 values=[1,2,1,2,1,2,1,2,0.5,0,0.5,0,0,0.5]
Y parameter desc = [mu_0,sigma_0,mu_1,sigma_1,mu_2,sigma_2,mu_3,sigma_3,R_1_0,R_2_0,R_2_1,R_3_0,R_3_1,R_3_2]
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