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#-------------------------------------------------------------

# The lm-function solves linear regression using either the direct solve method or the conjugate gradient
# algorithm depending on the input size of the matrices (See lmDS-function and lmCG-function respectively).
#
# INPUT:
# --------------------------------------------------------------------------------------
# X        Matrix of feature vectors.
# y        1-column matrix of response values.
# icpt     Intercept presence, shifting and rescaling the columns of X
# reg      Regularization constant (lambda) for L2-regularization. set to nonzero
#          for highly dependant/sparse/numerous features
# tol      Tolerance (epsilon); conjugate gradient procedure terminates early if L2
#          norm of the beta-residual is less than tolerance * its initial norm
# maxi     Maximum number of conjugate gradient iterations. 0 = no maximum
# verbose  If TRUE print messages are activated
# --------------------------------------------------------------------------------------
#
# OUTPUT:
# --------------------------------------------------------------------------------------------
# B     The model fit
# --------------------------------------------------------------------------------------------

m_lm = function(Matrix[Double] X, Matrix[Double] y, Integer icpt = 0, Double reg = 1e-7, Double tol = 1e-7, Integer maxi = 0, Boolean verbose = TRUE)
    return (Matrix[Double] B) {
  if( ncol(X) <= 1024 )
    B = lmDS(X, y, icpt, reg, verbose)
  else
    B = lmCG(X, y, icpt, reg, tol, maxi, verbose)
}
