Commit 8528b855 by Kazu Otani

### 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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