bpr_optimize {BPRMeth} | R Documentation |
(DEPRECATED) The function bpr_optimize minimizes the negative
log likelihood of the BPR function. Since it cannot be evaluated
analytically, an optimization procedure is used. The
optim
packages is used for performing optimization.
bpr_optim(x, ...) ## S3 method for class 'list' bpr_optim( x, w = NULL, basis = NULL, lambda = 1/2, opt_method = "CG", opt_itnmax = 100, is_parallel = TRUE, no_cores = NULL, ... ) ## S3 method for class 'matrix' bpr_optim( x, w = NULL, basis = NULL, lambda = 1/2, opt_method = "CG", opt_itnmax = 100, ... )
x |
|
... |
Additional parameters. |
w |
A vector of parameters (i.e. coefficients of the basis functions) |
basis |
A 'basis' object. E.g. see |
lambda |
The complexity penalty coefficient for ridge regression. |
opt_method |
The optimization method to be used. See
|
opt_itnmax |
Optional argument giving the maximum number of iterations
for the corresponding method. See |
is_parallel |
Logical, indicating if code should be run in parallel. |
no_cores |
Number of cores to be used, default is max_no_cores - 2. |
Depending on the input object x
:
If x
is
a list
: An object containing the following elements:
W_opt
: An Nx(M+1) matrix with the optimized
parameter values. Each row of the matrix corresponds to each element of the
list x. The columns are of the same length as the parameter vector w (i.e.
number of basis functions).
Mus
: An N x M matrix with the
RBF centers if basis object is create_rbf_object
, otherwise
NULL.
basis
: The basis object.
w
: The
initial values of the parameters w.
If x
is a
matrix
: An object containing the following elements:
w_opt
: Optimized values for the coefficient vector
w. The length of the result is the same as the length of the vector w.
basis
: The basis object.
If calling
bpr_optim_fast
just the optimal weight matrix W_opt.
C.A.Kapourani C.A.Kapourani@ed.ac.uk
# Example of optimizing parameters for synthetic data using default values data <- meth_data out_opt <- bpr_optim(x = data, is_parallel = FALSE, opt_itnmax = 3) #------------------------------------- # Example of optimizing parameters for synthetic data using 3 RBFs ex_data <- meth_data basis <- create_rbf_object(M=3) out_opt <- bpr_optim(x = ex_data, is_parallel = FALSE, basis = basis, opt_itnmax = 3) #------------------------------------- # Example of of specific promoter region using 2 RBFs basis <- create_rbf_object(M=2) w <- c(0.1, 0.1, 0.1) data <- meth_data[[1]] out_opt <- bpr_optim(x = data, w = w, basis = basis, fit_feature = "NLL", opt_itnmax = 3)