R/sll_opts.R

In MehreenRuhi/newfusedlasso: fused lasso for least squares and logistic regression

sllOpts <- function(opts = list()) {
  # Options for Sparse Learning Library
  #
  # Notice:
  # If one or several (even all) fields are empty, sll_opts shall assign the
  # default settings.
  #
  # If some fields of opts have been defined, sll_opts shall check the fields
  # for possible errors.
  #
  #
  # Table of Options.  * * indicates default value.
  #
  ## FIELD            DESCRIPTION
  ## Starting point
  #
  # .x0               Starting point of x. 
  #                   Initialized according to .init.
  #
  # .c0               Starting point for the intercept c (for Logistic Loss)
  #                   Initialized according to .init.
  #
  # .init             .init specifies how to initialize x.  
  #                       * 0 => .x0 is set by the function initFactor *
  #                         1 => .x0 and .c0 are defined
  #                         2 => .x0= zeros(n,1), .c0=0
  #
  ## Termination
  #
  # .maxIter          Maximum number of iterations.
  #                       *1e4*
  #
  # .tol              Tolerance parameter.
  #                       *1e-4*
  #
  # .tFlag            Flag for termination.
  #                       * 0 => abs( funVal(i)- funVal(i-1) ) <= .tol *
  #                         1 => abs( funVal(i)- funVal(i-1) ) 
  #                              <= .tol max( funVal(i-1), 1)
  #                         2 => funVal(i) <= .tol
  #                         3 => norm( x_i - x_{i-1}, 2) <= .tol
  #                         4 => norm( x_i - x_{i-1}, 2) <= 
  #                              <= .tol max( norm( x_{i-1}, 2), 1 )
  #                         5 => Run the code for .maxIter iterations
  #
  ## Normalization
  #
  # .nFlag            Flag for implicit normalization of A.
  #                       * 0 => Do not normalize A *
  #                         1 => A=(A-repmat(mu, m, 1))*diag(nu)^{-1}
  #                         2 => A=diag(nu)^{-1}*(A-repmat(mu,m,1)
  #
  # .mu               Row vector to be substracted from each sample.
  #                           (.mu is used when .nFlag=1 or 2)
  #                       If .mu is not specified, then
  #                            * .mu=mean(A,1) *
  #
  # .nu               Weight (column) vector for normalization
  #                           (.mu is used when .nFlag=1 or 2)
  #                       If .nu is not specified, then
  #                       * .nFlag=1 => .nu=(sum(A.^2, 1)'/m.^{0.5} *
  #                       * .nFlag=2 => .nu=(sum(A.^2, 2)/n.^{0.5} *
  #
  ## Regularization
  #
  # .rFlag            Flag for regularization
  #                           (.rFlag is used for the functions with "R")
  #                        * 0 => lambda is the regularization parameter *
  #                          1 => lambda = lambda * lambda_{max}
  #                               where lambda_{max} is the maximum lambda
  #                               that yields the zero solution
  # .rsL2              Regularization parameter value of the squared L2 norm
  #                           (.rsL2 is used only for l1 regularization)
  #                        *.rsL2=0*
  #                    If .rFlag=0, .rsL2 is used without scaling
  #                       .rFlag=1, .rsL2=.rsL2 * lambda_{max}
  #
  ## Method & Line Search
  # .lFlag
  #
  ## Grooup & Others
  #
  # .ind              Indices for k groups (a k+1 row vector)
  #                   For group lasso only
  #                   Indices for the i-th group are (ind(i)+1):ind(i+1)
  #
  # .q                Value of q in L1/Lq regularization
  #                      *.q=2*
  #
  # .sWeight          The sample (positive and negative) weight
  #                   For the Logistic Loss only
  #                   Positive sample: .sWeight(1)
  #                   Negative sample: sWeight(2)
  #                   *1/m for both positive and negative samples*
  #
  # .gWeight          The weight for different groups
  #                      *.gWeight=1*
  #
  # .fName            The name of the function
  #
  ## Copyright (C) 2009-2010 Jun Liu, and Jieping Ye
  #
  # You are suggested to first read the Manual.
  #
  # For any problem, please contact with Jun Liu via j.liu@asu.edu
  #
  # Last modified 7 August 2009.
  
  if (is.null(opts)) {
    opts <- list()
  }
  
  ## Starting point
  if (!is.null(opts$init)) {
    if ( (opts$init != 0) && (opts$init != 1) && (opts$init != 2) ) {
      opts$init <- 0
    }
    
    if (is.null(opts$x0) && (opts$init == 1)) {
      opts$init <- 0 #.x0 is not defined and .init=1, set .init=0
    }
  } else {
    opts$init <- 0
  }
  
  ## Termination
  if (!is.null(opts$maxIter)) {
    if (opts$maxIter < 1) {
      opts$maxIter <- 10000
    }
  } else {
    opts$maxIter <- 10000
  }
  
  if (!is.null(opts$tol)) {
    if (opts$tol > 1e-1) {
      opts$tol <- 1e-1
    }
    if (opts$tol < 0) {
      opts$tol <- 1e-15
    }
  } else {
    opts$tol <- 1e-12
  }
  
  if (!is.null(opts$tFlag)) {
    if (opts$tFlag < 0) {
      opts$tFlag <- 0
    } else if (opts$tFlag > 5) {
      opts$tFlag <- 5
    } else {
      opts$tFlag <- as.integer(opts$tFlag)
    }
  } else {
    opts$tFlag <- 0
  }
  
  ## Normalization
  if (!is.null(opts$nFlag)) {
    if ( (opts$nFlag != 1) && (opts$nFlag != 2) ) {
      opts$nFlag <- 0
    }
  } else {
    opts$nFlag <- 0
  }
  
  ## Regularization
  if (!is.null(opts$rFlag)) {
    if (opts$rFlag != 1) {
      opts$rFlag <- 0
    }
  } else {
    opts$rFlag <- 0
  }
  
  ## Method (line search)
  if (!is.null(opts$lFlag)) {
    if (opts$lFlag != 1) {
      opts$lFlag <- 0
    }
  } else {
    opts$lFlag <- 0
  }
  
  if (!is.null(opts$mFlag)) {
    if (opts$mFlag != 1) {
      opts$mFlag <- 0
    }
  } else {
    opts$mFlag <- 0
  }
  
  opts
}