Commit 8528b855 authored by Kazu Otani's avatar Kazu Otani
Browse files

typo fixes and clarification

parent fe23a48f
......@@ -115,7 +115,7 @@ namespace limbo {
this->_compute_full_kernel();
}
/// Do not forget to call this if you use hyper-prameters optimization!!
/// Do not forget to call this if you use hyper-parameters optimization!!
void optimize_hyperparams()
{
_hp_optimize(*this);
......@@ -271,12 +271,12 @@ namespace limbo {
// --- cholesky ---
// see:
// http://xcorr.net/2008/06/11/log-determinant-of-positive-definite-matrices-in-matlab/
long double det = 2 * _matrixL.diagonal().array().log().sum();
long double logdet = 2 * _matrixL.diagonal().array().log().sum();
double a = (_obs_mean.transpose() * _alpha)
.trace(); // generalization for multi dimensional observation
_log_lik = -0.5 * a - 0.5 * det - 0.5 * n * std::log(2 * M_PI);
_log_lik = -0.5 * a - 0.5 * logdet - 0.5 * n * std::log(2 * M_PI);
return _log_lik;
}
......@@ -556,8 +556,8 @@ namespace limbo {
void _compute_incremental_kernel()
{
// Incremental LLT
// This part of the code is inpired from the Bayesopt Library (cholesky_add_row function).
// However, the mathematical fundations can be easily retrieved by detailling the equations of the
// This part of the code is inspired from the Bayesopt Library (cholesky_add_row function).
// However, the mathematical foundations can be easily retrieved by detailing the equations of the
// extended L matrix that produces the desired kernel.
size_t n = _samples.size();
......
......@@ -76,7 +76,7 @@ namespace limbo {
/// useful because the model might be created before knowing anything about the process
SparsifiedGP() : base_gp_t() {}
/// useful because the model might be created before having samples
/// useful because the model might be created before having samples
SparsifiedGP(int dim_in, int dim_out)
: base_gp_t(dim_in, dim_out) {}
......
......@@ -99,7 +99,7 @@ struct Params {
struct Eval {
// number of input dimension (x.size())
BO_PARAM(size_t, dim_in, 1);
// number of dimenions of the result (res.size())
// number of dimensions of the result (res.size())
BO_PARAM(size_t, dim_out, 1);
// the function to be optimized
......
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