Unverified Commit 5129f50b authored by JB Mouret's avatar JB Mouret Committed by GitHub
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Merge pull request #268 from kazuotani14/master

Minor typo fixes in comments and docs
parents fe23a48f fc3b4f7f
......@@ -6,7 +6,7 @@ Using Limbo as an environment for scientific experiments
The typical use case of Limbo for research in Bayesian Optimization is:
- we design an experiment that uses some components of Limbo
- we want to konw whether variant X of the experiment (e.g. with kernel XXX) is better than variant Y (e.g. with kernel YYY)
- we want to know whether variant X of the experiment (e.g. with kernel XXX) is better than variant Y (e.g. with kernel YYY)
- because the algorithms that we use have some stochastic components (initialization, inner optimization, ...), we usually need to replicate each experiment (typically, we use 30 replicates) in order to do some statistics (see `Matplotlib for Papers <http://www.github.com/jbmouret/matplotlib_for_papers>`_ for a tutorial about how to draw nice box plots with these statistics).
Limbo provides basics tools to make these steps easier. They are mostly additions to ``waf`` (see our :ref:`FAQ about waf <faq-waf>`). For users who are used to ROS, you can see these additions as our 'catkin for Bayesian optimization'.
......
......@@ -6,7 +6,7 @@ Basic Example
Let's say we want to create an experiment called "myExp". The first thing to do is to create the folder ``exp/myExp`` under the limbo root. Then add two files:
* the ``main.cpp`` file
* a pyhton file called ``wscript``, which will be used by ``waf`` to register the executable for building
* a python file called ``wscript``, which will be used by ``waf`` to register the executable for building
The file structure should look like this: ::
......
......@@ -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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