mCurrentIter=30
mCounterStuck=0
mYPrev=0
mParameters.n_iterations=30
mParameters.n_inner_iterations=500
mParameters.n_init_samples=5
mParameters.n_iter_relearn=1
mParameters.init_method=1
mParameters.surr_name=sGaussianProcess
mParameters.sigma_s=1
mParameters.noise=0.5
mParameters.alpha=1
mParameters.beta=1
mParameters.sc_type=SC_ML
mParameters.l_type=L_EMPIRICAL
mParameters.l_all=1
mParameters.epsilon=0
mParameters.force_jump=0
mParameters.kernel.name=kHamming
mParameters.kernel.hp_mean=[1](1)
mParameters.kernel.hp_std=[1](10)
mParameters.mean.name=mZero
mParameters.mean.coef_mean=[1](1)
mParameters.mean.coef_std=[1](1000)
mParameters.crit_name=cPOI
mParameters.crit_params=[0]()
mY=[35](0.0127,0.0229,0.0214,0.0098,0.0217,0.0147,0.0112,0.0247,0.0102,0.0074,0.0196,0.0036,0.0013,0.0159,0,0.0121,0.0042,0.0066,0.0058,0.0199,0.0037,0.0076,0.0021,0.0126,0.019,0.0076,0.0046,0.0065,0.0125,0.0048,0.0155,0.0096,0.0181,0.008,0.0201)
mX=[35,8](1,1,1,1,0,1,1,1,1,0,0,0,1,0,1,0,1,1,0,1,0,0,1,1,1,0,1,1,1,0,1,0,1,1,0,1,1,0,1,0,1,0,1,1,0,1,1,1,0,0,1,1,0,1,1,1,0,0,1,1,0,1,0,1,1,0,1,1,0,1,1,0,0,0,1,1,0,1,1,0,0,0,1,1,0,1,0,0,0,0,1,1,1,1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,0,1,1,0,1,1,1,0,0,1,1,0,1,1,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,1,0,0,0,1,0,1,1,1,1,0,0,1,0,1,1,0,1,1,1,1,0,1,1,1,0,1,1,1,0,1,0,1,0,0,1,1,0,0,1,1,0,0,1,0,0,1,1,1,0,0,1,0,0,1,0,1,0,0,1,1,0,0,1,1,1,0,0,1,0,0,1,1,0,1,1,1,0,0,1,1,0,0,1,1,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,1,1,0,0,0,1,0,0,1,1,1,0,0,1,0,0,1,0,0,1,1,1,0,1,1,1,1,1,1,0,0,1,0,1,0)