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ProjectedGD( Ini_list, cmax = 1, eta_outer = 0.001, tmax_outer = 10, p_type = "logit", rd = "Non", show = TRUE, sgma = 1, sample_size = 500 )
the embedding results of nodes and layers
the output of function InitializationLSM
the upper limits for adding the coefficient constraint
the learning rate in gradient descent
the number of iterations in gradient descent
the type of link function (‘logit’, ‘probit’ or ‘poisson’ )
whether to use stochastic sampling (‘rand’ or ‘Non’ )
if print the ietation process
the link function parameter
the size of sampling
gen_list = GenerateMMLSM(200,3,5,2,d=NULL) Ini_list = InitializationLSM(gen_list,200,3,2)
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